Improving the outbound logistics

process at Euro Pool System Case study to reduce the costs made by Euro Pool System

in their connection with fresh produce producers

R.M. van der Sande

Improving the outbound logistics process

at Euro Pool System

Case study to reduce the costs made by Euro Pool System in

their connection with fresh produce producers

By

R. M. van der Sande

in partial fulfilment of the requirements for the degree of

Master of Science

in Transport, Infrastructure and Logistics

at Delft University of Technology,

to be defended publicly on Tuesday May 9, 2017 at 13:00

2017.TIL.8119

Supervisor: Dr. R.R. Negenborn TU Delft Thesis committee: Dr. J.M. Vleugel TU Delft

Dr. ir. H.P.M. Veeke TU Delft Drs. ing. N. de Goede Euro Pool System

Executive Summary Euro Pool System (EPS) is European market leader in the field of reusable packaging for the fresh produce

supply chain. Their core business is the reverse logistics of trays which are used to transport and sell all

kinds of fresh produce all over Europe. EPS delivers trays to producers and collects trays from major

retailers. When taking a closer look at the producer tray stock, strange behavior was found. Some tray

deliveries seemed rather random and unnecessary. Since approximately **% of all EPS costs (€** million)

are related to the usage and delivery of trays to producers, inefficient behavior can cost a lot of money. This

has resulted in the following research question:

How can the producer tray ordering behavior be used or influenced by EPS to reduce the tray delivery related costs while

maintaining 100% tray availability for producers?

The research starts by analyzing the current system and the involved actors. It was thereby found that EPS

charges producers a fixed renting fee. This fee is independent of order size, distance from depot and renting

period. EPS hardly interferes with producer ordering patterns and are basically working according to the

principle of: customer is always right. Since producers can do whatever they want without being punished, it is

quite likely that for EPS undesirable behavior occurs at producers.

To answer the research question and reduce the tray delivery related costs, the financial value of all separate

components should be known. During this process it was found that EPS has no idea of the tray value of

a tray per day. A model was made to calculate daily tray costs considering seasonal demand changes,

standstill time and storage costs. Thereby it was assumed that a tray in the absolute peak month would in

theory only be used for one cycle per year. All costs made for this tray over the entire year should be

allocated on this single cycle. This includes the total yearly tray depreciation costs and the storage costs for

all days a tray stands still. The developed method provided a way how investment costs of reusable products

in a heavy fluctuating environment could be allocated on a daily basis. This was therefore used to calculate

the costs currently spend on 23 producers. The calculated current costs have been compared with a

minimum cost situation. The minimum cost situation is calculated with a minimization model calculating

the delivery pattern with the least combined transport and tray costs. According to this analysis, in a perfect

situation with no uncertainties it would be possible to reduce the total required trays with 35% without

increasing transport costs.

Due to uncertainties and fluctuations there will never be a perfect situation. For this reason the 35% tray

reduction will not be possible. The model has been redesigned slightly to calculate two alternatives that

could reduce the delivery related costs. The first alternative is a daily renting fee for producers whereby

producers are charged for tray use on a daily basis. The second alternative is a vendor managed inventory

(VMI) system whereby EPS manages the tray stock of producers with producer tray demand forecast. Both

alternatives have pros and cons that are thoroughly discussed in the study. A VMI system is preferred due

to extensive possible indirect savings, easy possibility to partially implement, resistance from shareholders

for a daily renting fee alternative and a preferred equality between producers. The indirect savings that can

be created in a VMI system are relocation cost savings by planning in advanced and transport cost savings

by increasing truck utilization. These additional savings add up to approximately €** million on yearly basis.

A VMI system cannot yet be implemented for all producers for several reasons. With a questionnaire

producer information was retrieved. This information is used to estimated that a VMI system could be

implemented for approximately half of the producers, accountable for approximately 450 million tray

cycles. It is estimated that when a VMI system is introduced for these producers, the working tray capital

of EPS could be reduced from *** million trays in the current situation to *** million trays. This can be

managed by frequently delivering small amount of trays in peak period when there is high tray demand.

During low season more trays will be delivered to producers. Thereby the producer tray dwell time will

differ along the year. This will reduce the amount of trays in EPS stock and the related seasonal storage

costs. Besides a reduction of required trays and storage costs, there will be a slight increase in transport

costs. This is due to the smaller transport sizes in peak months.

The reduction of required trays will lead to more intensive tray use which reduces the life span. Trays have

to be replaced sooner compared to the current alternative. For approximately **% of all trays this will not

be an issue since these trays can last hundreds of cycles. Especially in a growing market it is for EPS

profitable to run the system with less trays. Tray investment costs can thereby be postponed. Together with

the additional savings on storage and relocation costs the total net present value savings could add up to

€***** million for the next ten years. This is a saving of 16% compared to the expected costs for the

upcoming ten years if nothing will change.

The results are based upon a study with 23 Spanish producers accountable for 3,5 million tray cycles. This

is 1,8% of the Spanish tray demand and 0,4% of the total demand. Since this is only a small part of EPS, it

is recommended to extend the study to more producers and especially other countries. Thereby the

alternative should also be tested by the actual producers to see if the theoretical savings meet the possible

actual savings.

In conclusion it can be stated that the tray delivery related costs of EPS can be reduced when a VMI system

is implemented. Thereby this study was the first known study that tested a VMI system for reusable

products with seasonal demand patterns.

Preface Since October I have been working on this master thesis as final project of the master Transport

Infrastructure and Logistics at the technical university of Delft. During the interdisciplinary design project

last year I got in touch with EPS. After finishing this product successfully my time at EPS was not finished.

I started my career at EPS by doing small modelling projects two days a week besides my regular courses.

After a few months all courses at the TU were completed except my master thesis. EPS provided me the

opportunity to stay connected to the company and go deeper into some matter that had not been studied

before. I really enjoyed studying this unknown part of the company. After spending effort to convince EPS

employees of the added value of the study I got full support from all separate departments. I want to thank

these departments for helping me to gather all data and by checking parts of the model related to their

expertise. Therefore I want to thank Jeroen Willems for everything related to tray value. Even though in

the beginning it was a complete new calculation approach I think we have managed to come with a new

way of internal tray value calculation. I want to thank Fred Lessing for the information on transport costs.

This information contributed a lot to the credibility of the model.

Special thanks will go to my supervisor within EPS, Niels de Goede. Thank you for all you have taught me

this period. Not only by teaching a lot of Excel skills but also by linking me to the right colleagues. Thereby

of course also thanks for all the time you have spent on this project.

From the university I want to thank Jaap Vleugel and Hans Veeke for the supervision. By criticizing my

work and especially giving a lot of positive feedback and support I managed to bring this project to a

successful end. This would not be possible without your help. Thereby especially thanks for the time you

always seemed to find. Whenever I mailed it was always possible to meet within a week which was really

useful to stay on schedule. During the meetings you always took a lot of time which I really appreciate.

Most meetings were at least one hour in which we covered all my questions and your feedback. Even though

you both have a lot of students to supervise I did not for a moment feel like ‘a number’. I also want to

thank Rudy Negenborn for being my chairman. Especially since you found the time to take over from

Gabriel Lodewijks after he left the university. Taking over an already scoped project does require some

extra effort for which gratitude.

At last I am glad the project is successfully finished and I am looking forward to continue my career at EPS.

Robin van der Sande

Delft, April 2017

Table of content

Executive Summary .................................................................................................................................................... iii

Preface .......................................................................................................................................................................... vi

Table of content ......................................................................................................................................................... vii

List of Figures .............................................................................................................................................................. ix

List of Tables ................................................................................................................................................................ x

Terminology ................................................................................................................................................................. xi

1 Introduction ......................................................................................................................................................... 1

1.1 Euro Pool Systems core business ........................................................................................................... 1

1.2 Problem statement..................................................................................................................................... 3

1.3 Project scope .............................................................................................................................................. 7

1.4 Theoretical framework .............................................................................................................................. 8

1.5 Report structure ......................................................................................................................................... 9

2 Literature study ................................................................................................................................................. 10

2.1 Cooperation in the fresh produce supply chain ................................................................................. 10

2.2 Inefficiencies in supply chains ............................................................................................................... 10

2.3 Information sharing between supply chain partners ......................................................................... 13

3 EPS in the fresh produce supply chain ......................................................................................................... 16

3.1 Actor involved in reusable packaging ................................................................................................... 16

3.2 The business of EPS ............................................................................................................................... 19

3.3 Connections producers and retailers .................................................................................................... 25

3.4 Factors influencing producer tray ordering behavior ........................................................................ 26

3.5 Producer field research ........................................................................................................................... 31

3.6 Conclusion ................................................................................................................................................ 33

4 Cost minimization model ................................................................................................................................ 34

4.1 Requirements and constrains ................................................................................................................. 34

4.2 Theoretical minimization model ........................................................................................................... 36

4.3 Daily tray cost ........................................................................................................................................... 39

4.4 Reorder point ........................................................................................................................................... 46

4.5 Overview of theoretical model .............................................................................................................. 47

4.6 Model implementation ............................................................................................................................ 48

4.7 Verification & validation ........................................................................................................................ 53

4.8 Alternatives ............................................................................................................................................... 58

4.9 Analysis method ....................................................................................................................................... 60

4.10 Conclusion ................................................................................................................................................ 64

5 Results ................................................................................................................................................................. 65

5.1 Minimum cost situation (min) ............................................................................................................... 65

5.2 Daily tray renting price (A1) .................................................................................................................. 68

5.3 VMI (A2) ................................................................................................................................................... 69

5.4 Future state ............................................................................................................................................... 73

5.5 Net Present Value calculation ................................................................................................................ 74

5.6 Conclusion ................................................................................................................................................ 77

6 Conclusion ......................................................................................................................................................... 79

7 Recommendations ............................................................................................................................................ 81

7.1 Recommendations for further research ............................................................................................... 81

7.2 Recommendations for Euro Pool System ........................................................................................... 81

Reflection .................................................................................................................................................................... 83

Bibliography ................................................................................................................................................................ 85

Appendix A Tray characteristics ......................................................................................................................... 87

Appendix B Dwell time analysis ......................................................................................................................... 89

Appendix C Causal relationship graph .............................................................................................................. 96

Appendix D Producer questionnaire ................................................................................................................ 102

Appendix E Transport costs ............................................................................................................................. 108

Appendix F List of producers for case study ................................................................................................. 110

List of Figures

Figure 1: The Rich Picture (van der Sande, 2016) ................................................................................................. 1

Figure 2 Schematic overview of a standard tray cycle (van der Sande, 2016) .................................................... 2

Figure 3 Cost per cost category ................................................................................................................................. 3

Figure 4 Standard tray cycle ....................................................................................................................................... 4

Figure 5 Stock of producers ST_307157_provedisVAL - 2015 ........................................................................... 5

Figure 6 Stock of producer ST_304080_llorensCAT - 2015 ................................................................................ 6

Figure 7 Stock of 136-cont in EPS depots - 2015 .................................................................................................. 6

Figure 8 Theoretical framework ................................................................................................................................ 9

Figure 9 Power-interest-diagram ............................................................................................................................. 17

Figure 10 Simplest scheme of a time-dependent system ..................................................................................... 19

Figure 11 Proper model ............................................................................................................................................ 20

Figure 12 Proper model of the fresh supply chain ............................................................................................... 20

Figure 13 Proper model of produce transformation ............................................................................................ 21

Figure 14 Proper model of tray delivery procedure 1 .......................................................................................... 23

Figure 15 Proper model of tray delivery procedure 2 .......................................................................................... 24

Figure 16 Producer - retailer connections .............................................................................................................. 26

Figure 17 Causal relation diagram ........................................................................................................................... 30

Figure 18 Model requirements on input, calculation model and output........................................................... 35

Figure 19 Ordering pattern used for determining tray costs .............................................................................. 38

Figure 20 Different parts of tray costs that have to be divided ........................................................................ 39

Figure 21 Rented 136 trays in 2014 and 2015 ....................................................................................................... 40

Figure 22 Example of SRT per month ................................................................................................................... 42

Figure 23 Daily tray price of 136-cont – 2015 ...................................................................................................... 44

Figure 24 Segments of tray cost calculation .......................................................................................................... 45

Figure 25 Summary theoretical model .................................................................................................................... 48

Figure 26 Outcome example of the minimization excel model ......................................................................... 50

Figure 27 Example of a current delivery route and a possible delivery route when transports can be

planned by EPS .......................................................................................................................................................... 53

Figure 28 Model behavior on direct extreme condition test – graphs provide stock information at a

producer ...................................................................................................................................................................... 56

Figure 29 Sensitivity analysis model current situation - producer 305114 ........................................................ 57

Figure 30 Sensitivity analysis model minimized cost situation - producer 305114 ......................................... 57

Figure 31 Sensitivity analysis forecast error average of ten runs - producer 305114 ..................................... 58

Figure 32 Sensitivity analysis total costs with different forecast uncertainties - producer 305114 ............... 58

Figure 33 Total costs and standard deviation for number of simulations ........................................................ 61

Figure 34 distribution of minimum tray costs for producer 304080 ................................................................. 62

Figure 35 Tray stock at producer 301383 in minimized cost situation compared to tray value ................... 65

Figure 36 Possible tray savings in percentage for individual tray types at producers for minumum cost

situation ....................................................................................................................................................................... 66

Figure 37 Current and new producer stock pattern ............................................................................................. 67

Figure 38 Shortcuts created by VMI alternative ................................................................................................... 71

Figure 39 Proper model of future state design ..................................................................................................... 74

Figure 40 Expected mutation of tray cycles for the foldable trays .................................................................... 75

Figure 41 NPV per year and cumulative NPV for base scenario and alternatives .......................................... 76

Figure 42 required trays for base scenario and alternatives ................................................................................ 76

List of Tables Table 1 Transport characteristics of producer 103277 in 2015 .......................................................................... 24

Table 2 Checks performed by EPS experts ........................................................................................................... 54

Table 3 Expected behavior on direct extreme condition test ............................................................................. 55

Table 4 Overview of alternatives ............................................................................................................................. 60

Table 5 Minimum costs results ................................................................................................................................ 66

Table 6 Daily tray renting price alternative (A1) results ...................................................................................... 68

Table 7 VMI alternative (A2) results ...................................................................................................................... 70

Table 8 Cumulative NPV for base scenario and 50% VMI alternative ............................................................ 77

Terminology Collection | Transportation of used trays from retailer to EPS depot.

Delivery | Transportation of clean, empty trays from an EPS depot to a Departure Point.

Departure Point (DPP) | The customer to which EPS delivers trays. Most DPPs are fresh produce

producers

Depot | Location that is managed by EPS or a third party where collection, relocation, distribution and

storage takes place. Washing and sorting also takes place at depots, but not all depots have washing and

sorting facilities.

Depreciation costs| All long term investments are depreciated over a certain period. For EPS the

depreciation costs are sorely the tray depreciation costs. Depreciation costs for buildings and supporting

systems are not including in depreciation costs as defined by EPS. Therefore, if stated depreciation costs,

this has to be read as tray depreciation costs.

Distribution Center (DC) | Central location of a retailer where products are collected before they are

send to individual shops.

Dwell time (DT) | ‘The period of time that a system or element of a system remains in a given state’.

Time an element (tray) remains in a state (one complete cycle).

Finished renting trip (FRT) | The moment a tray is collected by EPS

Full concept | The service of all required steps in return logistics including handling, sorting, washing

and transporting trays.

Full mix | A pallet of trays with different bottom dimensions (30x40cm and 40x60cm) and different

height dimensions (10 till 24 cm).

Intermediate Point (IMP) | Point in a tray cycle between DPP and retailer. Traders belong to the group

of intermediate points.

Packaging solution | Way of protecting products for distribution. This can be anything from standardized

trays to cardboard or wood.

Producer | Producer of (fresh) products, usually a (group of) grower(s) in horticulture. Empty trays from

EPS are delivered directly to the producer.

Relocation | Transporting empty trays from one depot to another. Relocation is needed to balance the

difference between incoming and outgoing trays per depot or due to sorting/washing limitations of certain

depots.

Retailer | Seller of (fresh) produce to the final consumer. Empty trays are collected directly from the

retailer, usually from Recollection Centers.

RTI | Tray type.

RTI-family | Grouping of tray types based on e.g. color or type of produce.

Sorted | A pallet of trays with all the same bottom and height dimensions

Service center | see definition depot.

Started renting trip (SRT) | Start of a tray cycle. Most SRTs start by delivering trays to a producer.

Unsorted | A pallet of trays with the same bottom dimensions but with different height dimensions.

Vendor managed inventory | ‘Business model where the buyer of a product provides information to a

vendor of that product and the vendor takes full responsibility for maintaining an agreed inventory of the

material, usually at the buyer's consumption location.’

Abbreviations

DC | Distribution Center

DPP | Departure Point

EPS | Euro Pool System

FRT | Finished renting trip

FSC | Fresh supply chain

FTL | Full truck load

IMP | Intermediate Point / Trader

IS | Information sharing

LFTL | Less than full truck load

RC | Recollection Center

RS | Revenue sharing

SRT | Started renting trip

VMI | Vendor managed inventory

1 Introduction Euro Pool System (EPS) is a company that provides standardized, reusable handling- and packaging

solutions and value-added reverse logistics to the retail market. EPS is a company sorely specialized in

providing services to support the fresh supply chain and is European market leader in the field of reusable

packaging. With a vast network of 45 service centers in 12 European countries and service provision in 27

countries, EPS ensures tray availability and flexibility in the entire chain. Growing extensively since the start

in 1992, EPS was in 2015 responsible for 887 million rotations with their fleet of approximately *** million

trays (Euro Pool System, 2016).

1.1 Euro Pool Systems core business

The service of EPS is taking care of the full concept of fresh produce packaging support. An overview of

this full concept is shown in Figure 1. EPS has contracts with European retailers to provide an easy

handling, standardized, sustainable product to increase process efficiency and provide an orderly showcase

for fruits and vegetables. Retailers mostly obligate producers to deliver their products in the standardized

trays used in their stores. Producers become clients of EPS to meet retailer requirements. In the current

system, producers rent clean, empty trays for a fixed price. This is including delivery of clean trays and

collection of used, dirty trays. Producers will fill the trays with produce and delivers them to the retailer.

Once the retailer has received the trays and sold the content, the trays will be returned to EPS for cleaning.

EPS makes sure the trays are ready to start all over again. To ensure trays return, a deposit system is used.

A fixed amount of deposit must be paid per tray which will be returned once the tray is collected by EPS.

Transport is not included in the core business of EPS as can be seen in Figure 1. EPS is responsible for the

transport but does not have any trucks themselves. The transport department within EPS uses a vast

network of transport companies to manage all the trays. The service of transport, washing and handling

trays are called ‘full concept’ within EPS.

Figure 1: The Rich Picture (van der Sande, 2016)

To support standardization for retailers, reduce the volume of empty trays, optimize filling rate of full trays

and satisfy all customers, different tray types are created over the years. The most important distinction

made between the trays is the difference between rigid and the foldable trays, which both operate in

different markets. Other differences are the bottom size, height and tray color. The main characteristics of

these different tray types are shown in Appendix A. The codes used to describe the different trays are also

explained in this Appendix.

1.1.1 Standard tray cycle

EPS provides a service of delivering clean trays to producers and collecting dirty trays from retailers.

General steps to conduct the service are: handling in, count & identification, sorting, washing, storing,

handling out and transport. These steps can take place in multiple service centers all over Europe and do

not always follow the same procedure. In this section, the standard, most common cycle will be discussed.

A schematic overview of the process is shown in Figure 2.

A cycle at EPS starts the moment a tray is collected from a retailer. It is generally transported to a nearby

depot. In this depot the trays are handled before the different tray types on a pallet are identified and

counted. Once the different trays are identified they will be sorted and washed. Washing installations in

some depots can process up to 5 million trays a month. The clean trays are stored in the depot until they

are needed to serve another cycle. Due to different geographical locations between producers and retailers

there is an imbalance between different depots. This imbalance is overcome by relocating trays between

depots. These relocations mostly takes place on long distances of over 1000 km. From this depot the trays

are delivered upon order to a specific producer. Most trays are relocated instead of directly delivered since

the delivery lead time on long distances is longer than the promised delivery time. Once the trays are at a

producer, they will be filled with fresh produce. The full trays will be transported directly or via traders

(IMP – Intermediate point) towards retailers. Most of the trays will arrive at major distribution centers from

where the retailers schedule the supplies to individual stores. Once the produce is sold, trays will be stacked

onto a pallet and transported to a retailer return center. EPS collects the trays when there enough pallets to

fill a truck.

In the figure above it can be seen that the producer/retailer part of a tray cycle is inside a ‘Black Box’. This

is since for the vast majority of trays it is not known what exactly happens in this part of the cycle. Trays

are not monitored and producers and retailers do not communicate about tray flows. For this reason EPS

does not have a clear overview on the producer origin of trays collected at a retailer. Without end-to-end

information, accurate data of these processes is missing and only assumptions can be made.

Figure 2 Schematic overview of a standard tray cycle (van der Sande, 2016)

1.2 Problem statement

To remain competitive and stay European market leader, EPS is constantly looking to increase the

efficiency of their processes and reduce costs. The most important cost factors are: throughput costs (€**

million), transport costs (€** million), costs spend on new trays (€** million) and overhead costs (€**

million) (2015). The last four years, these costs have increased with the company increase of 30%. This

increase in company size enabled economy of scale efficiencies on especially throughput and overhead

costs. This reduced the net costs per tray cycle on both of these costs. However, economy of scale was not

reached with tray depreciation and transport costs. The transport costs per tray cycle has even increased by

**% due to an increase in long distance transports. The net tray depreciation costs have increased by *%

due to the investment in new trays to cover the growth and by introducing new tray types. With the net

increase of depreciation and transport costs and the decrease of remaining costs, the total average tray cycle

costs have remained the same over the last four year. An overview of the cost ratio in 2015 is shown in

Figure 3.

Figure 3 Cost per cost category

The four main cost categories as discussed above can be further divided to get insight in individual

processes. The throughput costs are costs concerning the transformation of dirty trays into reusable clean

trays. This includes handling (€**m), counting & identifying (€**m), washing (€**m), sorting (€*m) and

storage (€**m). These costs include the investment costs for big washing and sorting installations,

machinery to support all these handlings, personnel and large depots where millions of trays can be

processed each month and stored when not used. The transport costs are made by collecting trays from a

retailer to an EPS depot (€**m), transporting trays between depots (€**m) and delivering trays to a producer

(€**m).

Besides process costs, EPS buys new trays on yearly basis to fulfill all demand. These trays are the working

capital of the company and depreciated over multiple years. The total costs spend on trays is determined

by the number of trays, the cost of a tray and the average lifespan of a tray. In 2015 a total of €** million

has been depreciated on a fleet of *** million operating trays. On average, a tray cost EPS €0, ** in 2015.

With the completion of 887 million tray cycles in 2015 each tray made on average *** cycles. This is equal

to an average cycle time of approximately ** days. The depreciation costs per tray cycle can thereby be set

on: €** mil / 887 mil = €0,0**.

A lot of trays are bought to cover the company increase. However, a cycle time of ** days appears long for

the fresh supply chain market. By improving some processes within a tray cycle, the total cycle time may

reduce. With a lower tray cycle time trays can be used more intensively throughout the year which results

in less trays required to serve the system and to cover the growth. The ** days a tray currently spent in a

system can be roughly divided by time spent at the producer, time spent at the retailer, time spent at EPS

for processing, transportation time, and a standstill time at EPS as shown in Figure 4. The standstill time

at EPS is divided in EPS safety stock and EPS seasonality time and is required to assure year round 100%

availability. If a tray cycle is assumed to be the time between a tray leaves EPS till the tray is ready to go to

a producer again, the standstill part is not included. If the downtime of the trays is excluded an average tray

cycle takes ** days. Reducing this cycle time and thereby reducing the amount of required trays can be done

at three stages in the system: at EPS, at the producer and at the retailer.

EPS is constantly trying to make own processes more efficient. The time trays spent at EPS for processing

reasons has been reduced to approximately **** days. In these days collecting, sorting, washing, drying,

relocating all over Europe and delivering are included. Trays within a depot can be processed within one

day. Collecting, relocation and delivery are the most time consuming in this process. EPS is not only

constantly working on its own dwell time, it also tries to reduce the time trays spent at retailers. An example

to reduce the retailer dwell time is the tray collection. Since a couple of years, retailers can stack all different

EPS trays on a single pallet (full mix). Therefore, instead of filling eight different pallets with the same type

of trays, a single pallet can be filled at a supermarket with multiple different tray types. Once a pallet is full,

it is transported to a relocation center of the retailer from where it is collected by EPS. Recent innovations

in automated sorting processes made this new way of collection possible without increasing EPS process

time. One specific retailer that changed from sorting to full mix returns, saw his dwell time reduced from

**** to **** days. The average retailer dwell time for all retailers is currently *** days. This is still rather

high but with more and more retailers going from (un)sorted to full mix returns it is expected that the

retailer dwell time will decrease rapidly.

Figure 4 Standard tray cycle

At last there is a time trays spent at a producer. It is estimated by EPS experts that the average tray spent

approximately **** days at a producer. However, on this part of the tray cycle, very limited information

and date is available. The available data supports the assumption of a high producer dwell time as explained

in Appendix B and schematically shown in Figure 4. The producer dwell time is rather stable throughout

the year according to EPS employees and supported by the little data available. The total stock at producers

increase during peak season together with the average daily outflow. During low season the average outflow

decreases and producers adept their stock. If both the outflow as well as the stock changes with an equal

percentage, the dwell time remains the same. Individual cases have shown some differences between peak

and off peak dwell time but these are occasionally and only during low season. It occurs that producers

have a couple of pallets left which remains at stock even if they are not producing during off season. This

increases the producer dwell time of these individual producer. However, on larger scale this has no

substantial influence to take into account. The dwell time is therefore assumed to be ** days on average

throughout the year as discussed in Appendix B. The ** days dwell time is remarkable because EPS serves

the fresh supply chain. Once a tray is filled, the products are transported quickly and sold within a few days

to prevent rotting. This means that trays spent most days at a producer empty and waiting to be filled.

The dwell time is directly affected by the tray ordering behavior from a producer. By analyzing two producer

stock patterns as shown in Figure 5 and Figure 6, some remarkable observations are made. The figures

show the producer tray stock on a daily basis throughout the year. An increase in stock indicates a delivery

by EPS and a decrease in stock is an indication of trays filled with produce transported to a retailer. The

red and green circles in both figures show orders being delivered while there are still thousands of trays in

producers stock. The two green cycles show very small increases in stock which means only a few pallets

have been delivered. By seeing these patterns it is questionable how cost efficient these delivery patterns

are and why they occur. Having a lot of nonmoving trays at producers will require more trays in the system

to meet al demand and guaranty 100% availability. Extra trays means that EPS has to invest more money

into trays which is one of their largest cost components.

The ordering behavior does not only effect the dwell time and thereby the depreciation costs. The delivery

costs is another cost component that is influenced by the ordering behavior. The delivery costs per tray

differs for a FTL (Full truckload) and a LFTL (Less than full truckload). Since in LTFL the transport costs

have to be divided by a lower amount of trays, these transport are more expensive. Combining transports

for multiple producers is possible and frequently done by EPS. This still raises costs by making detours and

extra stops. By taking the higher transport costs for LFTL in mind the green circles in the figures above are

even more remarkable. EPS delivers just a few pallets while there are respectively 11.000 and almost 6.000

trays in stock at these producers and the trays are not used for quite some days.

Figure 5 Stock of producers ST_307157_provedisVAL - 2015

Figure 6 Stock of producer ST_304080_llorensCAT - 2015

Besides the delivery costs and the depreciation costs there is another, indirect, cost component in this

process: storage costs. The storage costs are costs EPS makes to be able to store all unused trays. This is a

combination of the safety stock and the seasonality stock. Seasonality stock costs are made for trays that

are only used in peak period and stand still the remaining part of the year. In Figure 7 the stock of trays for

tray type 136 in all EPS depots combined is shown. It can be seen that in June/July the stock decreases

which indicated a tray demand peak. The remaining part of the year millions of trays have to be stored to

cover this peak. For just the 136 tray type the seasonal storage costs is €**.000. If the dwell time during

peak periods can be reduced and during low season be extended this may reduce storage costs. Even though

the seasonality storage costs are, with just above €**.000.000, significantly lower than the depreciation and

delivery costs, it still may be an important factor.

Figure 7 Stock of 136-cont in EPS depots - 2015

The cost factors, related to delivering trays, were €** million over 2015. This is over **% of the total

costs made by EPS. By looking at the figures shown above and the costs related to these patterns the

following questions arise:

How are the ordering patterns determined?

How can the costs related to the ordering patterns be reduced for EPS?

Before these questions can be answered it has to be taken into account that trays are used to support the

fresh supply chain like fruit, vegetables, fish and meat. The trays are just an enabler, helping producers and

retailers to get efficiency savings by standardization. The tray ordering pattern is therefore directly linked at

the produce going from producers to retailers. Even if the tray is just an enabler, it is still very important

for the supply chain. Automated logistic chains in supermarkets are built around the packaging material

(trays from EPS, other trays or cardboard). If produce cannot be delivered in a certain packaging solution

retailers will give order produce from competing producers who can meet all their delivery standards. Tray

availability is therefore a very important factor in the fresh supply chain. EPS has convinced retailers to

change from cardboard to EPS trays by assuring 100% tray availability for their suppliers. Availability

assurance is one of the core values of EPS that should be taken in mind when reducing tray delivery related

cost.

In the current system, EPS delivers trays as ordered by producers. Therefore the tray delivery pattern is

directly determined by the producers. Since EPS currently has hardly any control over this ordering pattern,

it is impossible to reduce the costs related to this process without interfering somehow. It is highly unlikely

that the current ordering system is cost minimized for EPS since producers determine the ordering pattern

and they use different criteria and considerations in the process than EPS would do. However, interfering

with the producer ordering behavior requires knowledge on these ordering patterns. It has to be known

why producers have current ordering patterns since it can be imagined that producers try to minimize their

costs within the available constrains.

The current tray ordering system and producer ordering patterns raise some questions. It is expected that

the current system does not assure minimum tray delivery related costs for EPS. This study will get a closer

look in the delivery patterns and the related costs by answering the following research question:

How can the producer tray ordering behavior be used or influenced by EPS to reduce the tray delivery related costs while maintaining 100% tray availability for producers?

The research question will be answered with the use of the following sub questions:

1) How does the current tray ordering system work for both producer and EPS?

2) Which factors influence producer tray ordering behavior?

3) What changes are needed in the current situation to reduce the combination of transport, depreciation and storage

costs?

4) How much money can be saved on the tray delivery process with producers?

5) What are the consequences of these changes for other partners in the supply chain?

These questions are a guidance through the thesis in finding out how processes are currently managed and

how inefficiencies can occur. It covers EPS as well as other supply chain partners involved in the tray

delivery process. By answering the five sub questions, a solution on how to reduce the tray delivery related

costs should be found.

1.3 Project scope The fresh produce supply chain consist of many links between partners. The scope of this paper will be the

link between retailers and producers and the link between producers and EPS. Connections with final

customer will just briefly named but not analyzed in detail. This also accounts for intermediate traders

(IMPs) operating between retailers and producers.

The geographical scope of the project will be limited to Spain. This partly chosen since the analysis phase

of this thesis took place in winter. Producers in north western Europa hardly grow any crop in winter period

while southern Europe is in peak season. A more producer active southern Europe makes it more attractive

for a detailed study. Spanish producers do however deliver produce to retailers all over Europe which has

to be taken in mind when reading the thesis. Besides the seasonality, Spain is also chosen for its willingness

to cooperate with EPS. The relationship between EPS and Spanish producers is very good and it is expected

that required information could be most easily be extracted from these producers. A final reason to limit

the initial scope to Spanish producers is the availability of information. In Spain the most producers can be

found that declare their flows. This means that for these producers there is information available on tray

flows between producers and retailers. This will be very useful for detailed analysis. In a later stadium of

the thesis the Spanish results will be extrapolated companywide. Thereby it is assumed that producers in

other countries have rather similar ordering patterns as Spanish producers. This is assumed since in

appendix B was already discussed that the dwell time of Spanish producers do not differ a lot from

producers in other countries.

This study will only take Spanish producers in account to which EPS directly delivers trays. There are also

some major producer organizations like auctions that manage part of the tray distribution for their

associated members. EPS delivers full trucks of empty trays on a daily basis to these organizations. The

organizations handle the distribution to their partners with all required type of packaging material (EPS,

IFCO, retailers specific trays, wood, cardboard etc.). Since these organizations have no depreciation costs

nor daily tray costs there are different considerations for these parties. Therefore, for this study the producer

organizations are left out of scope. This group is a very interesting group to study for another research. It

is assumed that producers doing more than one million SRTs a year are producer organizations. There are

nineteen of these parties in Spain doing twenty percent of the total SRTs.

For the financial minimization side of the study, the scope will be limited to tray depreciation, delivery and

storage costs. Other important cost components for EPS like: washing, sorting, relocation and collection

costs will not be considered. These processes are performed each tray cycle and are independent on order

size or time. It can therefore be stated that these processes are fixed costs from the studies point of view.

The project scope has been limited to the connection between EPS and Spanish producers doing less than

one million SRT a year. Hereby only the tray depreciation, storage and delivery costs will be taken into

account. The study is however built in a way that it can be easily extended to other counties. Therefore in

the analysis stage of the study, the scope will be on producers in general and general EPS processes.

1.4 Theoretical framework To answer the research question and related sub questions as stated in paragraph 1.2, a theoretical

framework is developed. The structure as shown in Figure 8 will be used as a guideline in this study. The

framework is split into four different activities: Analysis of current systems, Literature on related topics and

the fresh supply chain, Data provided by EPS and Product of the study.

An actor and system analysis will provide insight in the fresh supply chain and how EPS operates within

this chain. This will provide a global overview of the system and in what part of the system the problem is

located. Producers are the main actor involved in tray ordering behavior and they will be analyzed separately.

This is done by a field research which is based on literature and available producer data at EPS. This field

research should provide clear answers to why producers have current tray ordering behavior. The producer

field research, situation overview and a literature study will provide a definite, more detail problem

description and design requirements for a new, improved operating system. Thereby the initial analysis

phase of the study has finished.

Figure 8 Theoretical framework

The design requirements are used to build a theoretical model that calculates possible cost savings in the

tray delivery stage of a tray cycle. The requirements are not only used to build a theoretical model but also

to set its boundaries with market realistic constrains. These constrains are required to make sure an

improved situation is not only a theoretical improvement but can be implemented as well. The theoretical

model will be translated to an operational model. This model should analyze and provide indications on

how to improve the current situation.

Verification and validation will take place when the model building stage is finished. This part of the study

analyzes how the model reacts and how it can be used to improve the system. If the model is checked and

responds within accepted limits it can be used for testing. A case study will test the current situation and

multiple alternatives that possibly improve the system. At the end, based on all steps, conclusions on the

study will be drawn and recommendations for EPS will be given.

1.5 Report structure The research and sub questions as stated before will be answered in this study. This will be done by first

studying literature on how savings along the supply chain can be managed. The literature study is discussed

in chapter 2. Hereby the focus is mainly on the interaction between supply chain partners, extra costs caused

by interaction between supply chain partners and how these costs can be reduced. This will be followed by

a chapter analysing the fresh produce supply chain and how EPS and producers operates within this chain.

This is done by an actor analysis, analysis on how EPS operates within the system and an analysis that

identifies all possible factors that currently influences producer ordering behavior. In the remaining part of

chapter 3, the importance of each factor is checked by producers in a questionnaire. Hereby the focus is on

the level of detail of expected producer tray demand and how these expectations are transformed into

placed orders.

Chapter 4 discusses a minimization model which provides information on how to calculate the cost

inefficiencies in the current system. This chapter uses outcomes from chapter 3 as input. Chapter 4 starts

with an explanation of a theoretical minimization model. Later in the chapter the model is implemented

with EPS data. Chapter 4 also discusses alternatives that will be tested to improve the current system.

In chapter 5 the results of the analysis will be discussed. The results include expected short and long term

savings, changes that has to be realized and the overall influence on EPS and producers. Thereby analysis

of individual producers will be used to estimate the companywide consequences of alternatives. At last, the

report will be concluded by a conclusion and finished with some recommendations.

2 Literature study

2.1 Cooperation in the fresh produce supply chain

When trying to safe costs, focus should not only be on internal processes as stated by van der Vorst et al,.

(van der Vorst, Beulens, de Wit, & van Beek, 1998). They state that companies attend to spent millions on

making internal processes more efficient and not paying attention to easy, cheaper savings that could be

managed when companies look just out of their internal process scope. **% of all costs made by EPS is

directly influenced by producers. Increasing efficiency of these processes may reduce the costs significantly.

For making these processes more (cost)efficient, cooperation between supply chain partner is somehow

required. Cooperation between supply chain partners is getting more and more important and enables

partners to reduce one another’s costs and increase efficiency (Fawcett, Magnan, & McCarter, 2008).

Some studies take cooperation between supply chain partners to the next level and state that companies are

not competing each other but supply chains are competing with other supply chains. They suggest that

partners in the supply chain should cooperate to stay competitive. Full cooperation is very limited in the

fresh produce supply chain, mainly caused by the big power imbalances of supply chain partners (Hingley,

2005). Studies have shown that supply chain partners hardly ever look to the big picture and optimizing a

supply chain but are mainly focused on minimizing their individual costs (McCarter & Northcraft, 2007).

Thereby partners do not pay attention to consequences for other supply chain partners. Especially retailers

try to use their power to make their own processes more efficient even if this reduces efficiencies for their

suppliers (Hingly, 2005).

2.2 Inefficiencies in supply chains

Lack of cooperation enables inefficiencies to enter the supply chain. One of the inefficiencies that occur in

a vast amount of supply chains is the bullwhip effect. The bullwhip effect magnifies fluctuations on demand

site along the supply chain. It has even been stated that an 11% customer buying variation can cause up to

115% demand fluctuations for the manufacturer (Taylor & Fearne, 2009). The small fluctuations are

magnified along the supply chain as mentioned by Mason-Jones and Towill (Mason-Jones & Towill, 2000):

‘the practical real world consensus is that if the demand can find a way to multiply then it will’. This magnification is

caused by (Lee., Padmanabhan, & Whang, 1997):

Lack of accurate forecasting

Order batching

Price fluctuation

Rational and shortage gaming

Lack of accurate forecasting causes inefficiencies due to misalignment of information. Production

inefficiencies will likely occur if there is not enough or inaccurate forecast information. Without proper

demand forecast information it is almost impossible to produce the exact demand. This will either result in

too much or not enough product.

Order batching is another factor causing the bullwhip effect that occurs in many supply chains. In

this phenomenon a customer places larger orders than necessary to lower for example order costs or due

to limited time for ordering. This can be applied to EPS when producers order a full truckload instead of

just a few pallets. Ordering a full truckload means more stock at producers.

Price fluctuation is the third factor causing the bullwhip effect. Price fluctuations would stimulate

forward buying and keeping a stock for when prices increase. However, for the fresh produce supply chain

this is not interesting since produce is perishable and can only be contained a couple of days. The tray

renting price is a constant so this cannot stimulate the bullwhip effect.

Rational and shortage gaming is a less straightforward concepts which causes large impacts on

inventory stock and orders (Lee., Padmanabhan, & Whang, 1997). Rational and shortage gaming is a

customer’s reaction on a stock shortage at a manufacturer. If a manufacturer cannot deliver an order,

customers will adept their ordering behavior to assure their stock. They will deliberately increase their order

sizes hoping they receive enough product. This will increase the outstanding orders at a manufacturer and

thereby increasing the shortage. At the moment the manufacturer has increased his production and

processed the shortage, the customers have increased their stock and will wait ordering till their stock is at

normal level again. The manufacturer is now facing decreased orders while he just increased his production.

As a reaction, the manufacturer decreases his production to compensate for the decrease in demand. At

the moment the customers start ordering again the production may be too low and the cycle starts again.

This causes great costs inefficiencies for especially upstream supply chain partners (Lee., Padmanabhan, &

Whang, 1997).

Another cause of inefficiencies in supply chains is uncertainties. Uncertainties are caused by unexpected

fluctuations in demand, supply or by late deliveries (Yu, Yan, & Cheng, 2001). These unexpected

fluctuations in especially demand may arise as a result of a lack of demand information on other members.

Not having the information does not mean that the information is not available at other supply chain

members (Lofti, Mukhtar, & Sahran, 2013). Uncertainties are usually buffered by increasing inventory (Yu,

Yan, & Cheng, 2001). This results in extra stock which, in the case of EPS and producers, will increase the

tray dwell time.

It can be seen that there are multiple possible factors that can be easily applied on the case of EPS. Lack of

forecasting information, order batching, rational and shortage gaming and uncertainties can cause big

inefficiencies. These factors are especially interesting for companies acting in the start of a supply chain

where inefficiencies are usually largest. Since this study is focused on the start of the fresh produce supply

chain, a theory like the bullwhip effect should be taken into close account. To increase supply chain

efficiency and especially reduce costs for supply chain partners in the start of the supply chain, inefficiencies

as discussed above should be tackled. Fortunate there are multiple studies on how to increase efficiencies

in supply chains.

Reducing inefficiencies in the fresh supply chain Several studies on supply chain inefficiencies come to a similar conclusion: supply chain partners should

cooperate to reduce inefficiencies. Cooperation can be done on several levels but most effective is providing

partners with available demand and supply information. By sharing information between supply chain

partners uncertainties can be reduced and planning efficiencies can be improved. This results in reduced

inventory costs while increasing availability. It has been studied that when cooperating in a supply chain up

to 60% of costs could be saved (Xie, Zhao, & Leung, 2002). However, other studies have shown less

promising results with savings between 5% and 35% by Lofti and an average of 2% cost savings by a study

of Fisher (Fisher & Cachon, 2000) (Lofti, Mukhtar, & Sahran, 2013). Fisher provided the following

explanation for the low revenues when cooperating on shared information:

‘When a customer is flush with inventory, its demand information provides little value to the manufacturer because the customer

has no short-term need for an additional batch. A customer’s demand information is most valuable when the customer’s

inventory approaches a level that should trigger the supplier to order additional inventory, but this is also precisely when the

customer is likely to submit an order. Hence, just as the customer’s demand information becomes most valuable to the

manufacturer, the customer is likely to submit an order, thereby conveying the necessary information without explicitly sharing

demand data.’

This implies that the lead time of the studied manufacturers is short enough to produce the product in the

time between an incoming order and the expected delivery date. This is confirmed by the study which states

that the lead time in the tested case study is only a couple of days. In examples of the study by Xie, the

production lead time is longer. The shared information thereby helped production planning. Ordering and

production lead time are therefore very important for the value of information sharing. In general, all studies

state that even with basic cooperation as simple forecast sharing, supply chains could become more

efficient. This is not only financially by reducing costs but also by increasing availability and reliability.

Barriers in supply chain cooperation

Even if supply chain cooperation looks so promising and rather obvious, there are multiple reasons why it

is currently not used in many supply chains. At first there are the power imbalances. Retailers use their

power to put pressure on their partners. By maximizing their own profits these powerful companies mostly

prevent other parties from getting benefits or may even result in losing money (Hingly, 2005). Small

companies like producers can hardly match billion euro retailers. Retailers can easily shift to other producers

whereby the producer may lose his biggest source of income.

The second and most important cause as mentioned by multiple studies is trust. This is the trust in supply

chain partners and their true willingness of cooperation and creating a supply chain optimum. Companies

are afraid that their willingness of cooperation will be misused by their partner. This is also stated by Xie et

al,.: ‘Many companies are reluctant to share information with their trading partners, afraid that the

information will be used unfairly to their disadvantage’ (Xie, Zhao, & Leung, 2002). It has even been

concluded in a study by Li that organizations who share information, deliberately distort information to

their partners to avoid misusing the information (Li & Lin, 2006). When cooperating, all partners somehow

have to benefit. In many cases companies should trust each other that the shared information does not only

help their partner but also themselves. Trust is a difficult factor to capture and can easily change over time

due to small market changes.

Parkhe has studied the prisoners dilemma theory on trust in supply chains. Thereby he assumed that it was

always beneficial not to act cooperatively if the other side is going to act cooperatively. In this study available

information was misused by supply chain partners to optimize their own processes instead of working to a

supply chain optimum (Parkhe, 1993). This is exactly what is supported by several studies: the supply chain

optimum is not equal to the summed optima of each individual partner (McCarter & Northcraft, 2007).

Since companies want to maximize their own profit, a trust barrier is formed.

Not only misusing information is an issue in how information is shared, also the willingness of partners to

change behavior in favor for the other party can be an issue. If the manufacturer shares information on

how customer ordering behavior could save the manufacturer costs, there is no direct reason for a customer

to actually change his behavior if he does not increase his own revenues. Therefore even with full

information sharing, it does not automatically means that the information is used by partners to increase

revenues besides their own (Pereira, 2009).

Getting to supply chain efficiency

Several studies have focused on how to solve the trust issue between supply chain partners. Multiple types

of contracts and dependencies have been studied. The studies have a similar outcome: create a benefit for

the other supply chain partner. This can be either financial or by increasing availability or reliability.

Revenue sharing (RS) is a theory that should encourage cooperation and trying to get maximum supply

chain benefit. This theory should, if all parties commit to the agreement, counteract the individual

maximization and the possible lack of trust as stated above (Cachon & Lariviere, 2005). Partners are more

willing to find the supply chain optimum instead of an individual optimum when profits are shared. The

statement of Cachon that revenue sharing can reduce the lack of trust is not as simple as it sounds.

Companies do not trust other companies to simply give them money. Companies will be even more

suspicious with sharing information when money is directly involved. For this reason revenue sharing is

based on contractual agreements to assure cooperation (Cachon & Lariviere, 2005).

There are many ways of revenue sharing. The most extreme form of revenue sharing is that a customer

pays a manufacturer a wholesale price for each purchased unit, plus a percentage of the revenue the

customer generates (Cachon & Lariviere, 2005). This makes the manufacturers profit completely depended

on the customers profit. However, there are also less rigorous RS contracts. A very easy form of revenue

sharing is providing customers a discount. In this case revenue is directly transferred by reducing (or

increasing) the product price for a supply chain partner. The general concept behind RS remains the same:

partners share profits gained by collaborating in a supply chain to maximize the total profit (Kouvelis,

Chambers, & Wang, 2006). This means that if customers profit would decrease due to the supply chain

profit maximization, he will be compensated somehow by the manufacturer who have gained a larger profit.

The theory of revenue sharing can be useful for this study. Especially since EPS charge a full concept price.

It may be very well possible that producers will have to give up some of their revenue so EPS can increase

theirs. If this is the case, producers have to be compensated either financially or with an increase availability

or reliability to assure cooperation.

Revenue sharing is just an example of a theory for supply chain optimization. The basic principle is that

supply chain partners are aligned and cooperating together to assure maximum efficiency (Yu, Yan, &

Cheng, 2001). To make this possible, companies should not only look to their own processes but also to

the consequences and possibilities of supply chain partners. Therefore it is not only important that partners

do not counteract each other but actively make sure the other party can create as much benefits as possible

for the supply chain. This can be done best by making sure partners get all available useful information on

time for planning and decision making. Not only financial motives are important for increasing supply chain

efficiency. An increased availability, reliability or a decreased lead time may be factors that are, for some

partners, even more important than a slight increase in profit. These benefits may all be achieved when

demand/supply information is shared between supply chain partners. Studies on sharing information and

how to deal with this will be discussed in the next paragraph.

2.3 Information sharing between supply chain partners

Several studies have focused on sharing formation. Thereby the type of information, the accuracy of

information, delays in information and what has been done with the shared information were the main

topics (Fisher & Cachon, 2000). The available information and how is coped with this information on delay

and process level differs per study. This is a cause of the difference in results between the studies (Fisher

& Cachon, 2000) (Xie, Zhao, & Leung, 2002). As mentioned before, in some studies a 60% cost reduction

could be managed while in other studies the costs reduction was only a few percent.

How to cope with available information is extensively studied. The basic principle behind most studies is

that demand forecast information of other members is used to optimize own (production) processes.

However, there are theories who take this even further and try to get the most efficient supply chain. One

of these theories is vendor managed inventory (VMI).

Vendor managed inventory systems

VMI is an extensive integration of supply chain partnership. In this theory the manufacturer does not only

get forecasting and stock information of the customer, but he is also responsible for maintaining the

customers stock and supply (Yu, Yan, & Cheng, 2001). The manufacturer has the same information as the

customer and uses that to influence the customer’s order behavior to increase supply chain profits (Yao,

Leung, & Lai, 2008). This theory should eliminate the rational and shortage gaming, reduce the order

batching, lack of accurate forecasting and other uncertainties and get to a Seamless supply chain (SSC)

(Disney & Towill, 2003) (Mason-Jones & Towill, 2000). Even when all information is shared and decisions

are made by one party, the system cannot be completely optimized. Uncertainties can never be eliminated

completely and therefore an optimized situation is impossible. But, by reducing all these known

inefficiencies, the revenues could be increased by as much as 30 to 40% according to Trunick (Trunick,

2011). A study by Yu et al., shows that in a VMI system the revenues are generally not equally divided (Yu,

Yan, & Cheng, 2001). Manufacturers obtain more benefits than the retailers. Since the bullwhip effect

magnifies along the supply chain it is no surprise that at the start of the supply chain more benefits are

realizable. This will be taken into account in this study.

By placing the decisions and considerations at the manufacturer, trust can also be an issue in VMI.

Customers may fear that information is misused and manufacturers will try to maximize their own profit

instead of maximizing market profit. This possible lack in trust is a serious barrier for VMI. Kouvelis even

stated that VMI could lead to increasing channel inefficiencies if manufacturers try to increase their own

benefits or if there is inaccurate information (Kouvelis, Chambers, & Wang, 2006).

VMI seems as a very promising solution to increase supply chain efficiencies and especially parties at the

start of the supply chain. However, implementing VMI and getting cooperation from customers requires

more than just the VMI theory. Combining VMI and a way of revenue sharing should, according to studies

increase the supply chain efficiency (Yu, Yan, & Cheng, 2001). Kouvelis states that a VMI with revenue

sharing system dominates the wholesale-price-based. With a combination of VMI and RS a pareto-optimum

can be created (Kouvelis, Chambers, & Wang, 2006). A pareto-optimum implies that all members in a

system are at least as well off, and some members are better off (Yu, Yan, & Cheng, 2001). Most studies

state the theoretical benefit of a combination of VMI and RS but there is limited case study evidence.

Therefore the generated profits are hypothetical and it has to be seen what the actual revenues will be when

the theory is extensively applied in practice.

Integration in comparable supply chains

Even though the theory seems promising it is not known how it would affect EPS and producers. Studies

as discussed above have all focused on increasing efficiency with non-reusable products. There are no

studies on the delivery/order part of reusable products. There are several studies on how to get reusable

products like beer crates back but these are only focused on the return logistic side. In the case of reusable

products with a long production time, the system has to run with a given amount of products that cannot

be extended easily over time.

Elements of previous studies on optimizing order patterns for non-reusable goods can be used for this

study. The sharing of demand information, sharing of profits and barriers like trust remains the same for a

non-reusable and a reusable supply chain. Another similarity is the consideration between order/transport

costs and product costs to minimize total supply chain costs. However, for this study the product costs are

less straightforward. Trays are used with a changing demand pattern multiple times a year for several years.

This provides challenges on how to consider the best possible delivery pattern. No previous study has been

found on this topic so this study will have to provide an answer on how to divide and allocate tray costs.

Conclusion

There are many different methods that could increase the efficiency of manufacturers or the entire supply

chain. Previous studies shows that VMI could really smoothen inefficiencies in the supply chain and is,

when all information is shared and used correctly, the best solution in smoothening inefficiencies between

supply chain partners. By making sure all partners share in possible revenues, support can be created among

all partners. However, it is not sure if the same revenues can be achieved in a situation with reusable

products as in non-reusable products.

This study will focus on how product costs of a reusable product can be calculated and how cost inefficient

the current delivery pattern is for EPS and the supply chain. This is done by calculating a minimum cost

delivery pattern from a VMI point of view since this is theoretically the perfect theory for minimizing costs.

Placing all information and decisions to one party, a minimum costs scenario can be calculated. When the

inefficiency is known, this can be used to calculate alternative, cost saving strategies. This can be either VMI

or other strategies as will be discussed in this study.

3 EPS in the fresh produce supply chain EPS operates as a supporting partner in the fresh produce supply chain. To get better insight in producer

tray ordering behavior, EPS’ business policies and how delivery related costs can be reduced, the fresh

supply chain first needs to be examined. This will be done by answering the following questions:

What actors are involved in the fresh produce supply chain and how do they operate in the supply

chain?

How are actors involved with EPS and what is their perception on tray ordering behavior

inefficiencies?

How does EPS operate in the fresh supply chain?

How does EPS process tray orders?

How do producers determine their tray ordering behavior?

Together these questions will provide more insight and answer the first sub question:

How does the current tray ordering system work for both producer and EPS?

3.1 Actor involved in reusable packaging The key actors involved in the logistical processes of the fresh supply chain are analysed by an actor analysis.

This is done in two steps: At first by using a power-interest diagram followed by an involvement analysis.

The power-interest method, described by Eden and Ackermann (Eden & Ackermann, 1998) is a two-by-

two matrix showing the stakeholders’ interest and their power to achieve changes. It will be discussed how

stakeholders are positioned in this matrix and how they are generally involved in the process. The

involvement analysis discusses the problem perception of some of the most important actors and their

willingness to get involved in EPS’ problem. In this analysis, the problem is analysed from the perspective

of the individual actor instead of a more general overview perspective.

3.1.1 Power and interest of actors involved in the reusable packaging business

The power-interest matrix consists of four different categories of stakeholders. The most important

stakeholders are the main players. These have high power and are also highly interested in the process. It is

important to manage these stakeholders closely. Another powerful category of stakeholders are the context

setters. This powerful group is less interested but it is important to keep a powerful group satisfied. The third

group are the subjects. The subjects are highly interested in the process but have limited possibilities to

interfere. This group wants to stay informed. The last group is the crowd. The crowd is not very interested

nor powerful to interfere in the process. This group should just be monitored according to Bryson (Bryson,

2004). The results of the actor analysis are shown in the power-interest-diagram in Figure 9.

Auctions and retailers can be found in the first category of main players. Retailers can determine and

influence the fresh supply chain due to their bargaining power. There are only a limited amount of (large)

retailers in each country which determine a big part of the market. Due to their size they need large

quantities of fresh produce. This provides them with bargaining power and enables them to set hard

requirements to other partners in the fresh supply chain. Most retailers only accept fresh produce if it

satisfies all their requirements. This not only counts for price and quality but also how it is packed.

Requirements are set on wrappings and type of trays or boxes. These requirements are set to smoothen

their mostly fully automated internal processes. Retailers have sufficient power to set these demands and

other supply chain partners must agree to these terms to be able to sell their products.

The auctions have high interest and power in the fresh supply chain as well. For their core business they

enable the trade between producers and upstream partners like retailers and small sellers. They enable

producers to sell to other parties than the big retailers and therefore operating in actual market conditions.

However, as an enabler, an auction cannot influence prices or make big changes in the market. This makes

them less powerful than the retailers.

Auctions do not only act as a trade enabler. Three main auctions of Belgium, Germany and the Netherlands

are also the founders and only shareholders of EPS. Therefore these auctions have multiple interests. They

can interfere with the processes within EPS. It is however unlikely they will interfere with the EPS-producer

connection as long as they are kept satisfied. Auctions as shareholder will therefore not be taken into close

account in the analysis phase of this study.

The producers are another main actor in the process that will be analysed in more detail in this study. As

the actor that produces the actual product of the fresh supply chain it may be considered as one of the most

important actors. However, due to limited producer sizes, general overproduction and the power of

retailers, the producers have hardly any bargaining power themselves. If producers do not want to meet

retailer requirements they risk losing big orders. Selling to auctions is an option for producers to stay away

from the big retailers. Selling at auction brings certain risks. Since in auctions the price is determined by

bidding, producers cannot negotiate or influence the price during this process. For them it is just hoping

they get a good price.

Figure 9 Power-interest-diagram

Having all their business in the fresh supply chain, it is needless to say that EPS is highly interested in the

business. Their power is however limited. EPS can try to interfere in the fresh supply chain but has to take

into account that retailers are their prime customer determining the market. Once produce is packed in

trays, EPS cannot influence the process. The process before the produce is packed and after the produce

is sold can be influenced. This can only be done within the constrains set by producers and retailers. Since

there is competition on the packaging solution market (other tray pooling companies like IFCO, cardboard,

wood…), EPS has to pay attention and maintain a unique selling point. Retailers may easily go to a

competitor if interference by EPS causes disadvantages for producers and thereby indirect to retailers.

Other packaging solutions are in a similar position as EPS. Of course, due to company sizes there may be

a slight difference in power but in general they have the same interest and have less power than retailers in

the fresh supply chain.

Some less interested stakeholders are governmental and European stakeholders. With everlasting policy

changes on especially carbon footprint, sustainability and food regulations, the fresh supply chain constantly

has to pay attention. Regulations may have influence on all parts of the supply chain. For EPS it is very

interesting to follow changes since they mostly are in favour for the reusable tray handling business.

Reusable trays reduce waste and emissions. Therefore changes in regulations may create opportunities for

EPS in the fresh supply chain. However, for this study these actors will not have any influence.

Transport companies, traders and the end consumers are the crowd. The individual fresh produce

consumer has no power and hardly any interest in the chain. As long as there is produce that meets the

requirements they are satisfied. However, as a group they have more power. The total group of customers

determine the demand. Other partners in the supply chain will adept their demand, supply and prices to

meet the end consumer requirements in the best possible way. For simplification reasons this study will

assume that the retailers are well aware of and be able to meet customer demand. Therefore in the rest of

this study it is stated that the retailers determine the demand for the producers. The customer demand is

not directly related to the problem and will not be taken into account.

The transport companies are also not a key actor. There are a lot of different transport companies

competing for transport orders. The fresh produce supply chain is for a lot of transport companies an

important business since they have invested in reefer trucks. However, they can always transport other

products if the fresh produce market changes.

At last, there are traders in the fresh supply chain. They act as an intermediate person between retailers and

producers. The traders have different forms of power depending on the relationships. Traders are in general

more powerful than producers since they can use their large demand as bargaining power over the vast

amount of producers. On the other hand the traders still have to fulfil the retailers requirements. If traders

cannot convince retailers they add value compared to retailers buying their own produce they risk losing

the retailers. This means they have a certain power but, like other parties in the fresh supply chain, are

heavily dependent on the retailers.

The power-interest diagram provided an answer to the question: What actors are involved in the fresh handling

supply chain and how do they operate in the system? It is shown that not all actors are evenly important in the fresh

supply chain and connected to the problem of the study. Therefore the focus will be on the key fresh supply

chain actors involved in the problem of EPS: Retailers, Producers and EPS. Other actors will be mentioned

briefly in the study but have no direct relationship to the problem of EPS.

3.1.2 Problem perception and willingness to solving the problem

Identified as most important actors, retailers, producers and EPS all have a connection to the problem

stated by EPS. It has to be seen how these actors face this problem and their willingness to improve the

situation. This will be discussed in more detail in this section starting with retailers, followed by producers

and concluded by EPS.

For retailers, trays are a form of standardization and both improving the logistical process as well as the

experience customers have during shopping. A typical retailer is not committed to the logistical process

within EPS. The main things they care about is the availability, quality and the cost (or benefit) of the trays.

How EPS operates within the system is not an issue for retailers as long as the system works. For more

than 60 major retailers all over Europe the system of EPS apparently works since they have chosen to work

with EPS instead of IFCO, cardboard or another form of packaging. Because retailers are not at the delivery

side of trays they do not have any insights in the problem. They will probably not recognize the problem

since there are currently no direct consequences for them. It is also quite unlikely they will cooperate to

solve the problem of EPS without receiving direct benefits. Due to the power retailers have, EPS can also

not force retailers to collaborate by for example sharing forecast information. This means that if EPS want

retailers to collaborate, EPS should somehow make sure retailers will benefit.

Producers are, compared to retailers, directly involved with the problem. Since policies at EPS state that

they deliver orders as required by producers, producers are partly responsible for possible extra costs related

to tray delivery. However, it is unlikely producers are aware of the costs made by EPS on tray depreciation

and transport. As long as they receive the ordered trays they are satisfied. Even if producers do not directly

recognize the problem they may be willing to cooperate in solving the problem. With the direct involvement

in the problem there are definitely possibilities for EPS. This is of course only if the solution will not have

negative consequences for producers. If a solution for EPS will result in higher costs for producers and

thereby moving the problem along the supply chain, it is very unlikely that producers are willing to

cooperate. It is therefore important that a solution somehow creates benefits for producers if real

cooperation is necessary. It is still possible that producers cooperate if no direct benefits can be provided.

The big power difference between producers and EPS provides EPS bargaining power. However, as

mentioned in chapter 2, forcing partners to cooperate may result in deliberately distort information and

thereby losing even more money.

The stated problem is mainly an operational issue of EPS. It is likely that cooperation with producers is

necessary to solve the problem for EPS. A Pareto improvement is necessary to make sure that cooperation

is provided. If cooperation costs a producer time or requires them to share information they have to be

compensated somehow to be at least ‘as well off’. This is especially important since it is unlikely that other

actors in the fresh supply chain recognize the problem stated in this study or are able to solve the problem.

3.2 The business of EPS

The previous paragraph identified the actors and concluded that the problem is more an operational

problem for EPS than a combined supply chain problem. This paragraph will dive deeper in the operational

processes of EPS and how producers are involved in these processes. This step is taken to get a better

understanding of the system and to find opportunities for improvement. The analysis will be done by

answering the following question: How does EPS operate in the fresh supply chain? And How does EPS process tray

orders? These questions are answered with an analysis based on the Delft System Approach.

In the Delft System Approach a system is defined as:

“A collection of elements that is discernible within the total reality. These discernible elements have mutual relationships and

(eventually) relationships with other elements from the total reality”. (Veeke, Ottjes, & Lodewijks, 2008)

Figure 10 Simplest scheme of a time-dependent system

According to the Delft System Approach, the system

analysis starts by analysing the primary process on an

aggregated level. Once the general process is described,

a more detailed analysis of the main and detailed

elements of the system can be performed. This is done

with a PROPER model analysis. PROPER stands for

PROcess-PERformance and visualized the process of

input, transportation and output. An aggregated

PROPER model of a typical system is shown in Figure

10. In a more disaggregated proper model the

throughput is split into a perform, operate and use

aspect.

The perform aspect is a transformation of an order into

a handled order. The operate aspect is a product,

transforming into a delivered product. This operation is supported by the use aspect which transforms

resources into used resources. This transformation is executed according to certain requirements and can

be compared with the performance indicator. This basic proper model is shown in Figure 11.

3.2.1 Current situation fresh supply chain

For the fresh produce supply chain, the

transformation is fresh fruit, vegetables, fish or meat

at a producer to a retailer ready for customers to buy.

To enable this throughput, multiple enablers are

required. This is shown in the PROPER model of the

fresh supply chain in Figure 12. The orders for fresh

produce are the trigger for starting this process. These

orders are the base of the product transformation and

the resources required for this process. Resources are

at least trucks and packaging material. Most retailer

requirements are rather standard as: lead time, quality,

quantity and price. However, each retailer has also

requirements on the packing of their product. This can

be either wrappings with barcodes but also specific

handling materials like trays. These are required to be able

to have different suppliers but still assuring ease of

handling and a constant store appearance for the final customer.

The proper model as described in the picture above can be analyzed in a more disaggregate level. In Figure

13 this disaggregated level is shown. In this figure there are no intermediate traders and a few steps have

been simplified to keep overview. The process can start at two points: order arrival or picking produce,

depending on the order lead time. If final orders are made well in advanced, the process of tray and truck

planning can already start. If final orders arrive only a short time in advance it may occur that produce will

be picked with unknown destination, tray and truck characteristics. This is also the case for the auction

market and left-over produce. If the final orders are only received a short period in advanced or if the

orders do not cover all produce supply, there will be some buffer stocks to be able to meet the order. In

this case trays as well as picked produce will be stored in buffer till an order trigger is provided.

Figure 11 Proper model

Figure 12 Proper model of the fresh supply chain

A produce order contains, besides the amount of produce, also retailer characteristics like location and tray

requirements. With these characteristics, tray types can be chosen and truck companies can be contacted.

The produce is (picked and) loaded in assigned trays for the specific order. Once produce is all packed, it

can be loaded in the assigned truck from where it is transported to the retailer. In most cases this will either

go via a trader or directly to a retailer distribution center. For this study these steps are not interesting and

therefore not taken into this analysis.

Figure 13 Proper model of produce transformation

When the produce arrives at the shop, the truck is released and the produce can be sold. Once the produce

is sold, the trays are released and stocked at a shop before going to a retailer relocation center. The trays

will later be collected by EPS.

In this process, cost, lead times, reliability and waste are performance indicators important to satisfy the

parties in the supply chain. For retailers, reliability on quality and quantity are of course important

performance indicators. However, this also accounts for other requirements like packaging material (White,

2000). The produce should be of constant quality and easily processed to maintain customer satisfaction

and reduce costs by standardization. Lead times are also very important performance indicators in the fresh

produce supply chain. Lead times have to be short to prevent waste with especially perishable goods. Not

only production lead times have to be minimized, information lead times are also important. Having

accurate information well in advanced enables better planning and reduces uncertainties. If information

lead times are too long the uncertainties are frequently countered by creating buffers as mentioned in the

previous chapter.

In the figure can be seen that there is a stock of trays at the producer. This stock of trays is used to assign

to produce. Tray stock is depended on the demand of produce by specific retailers. How this tray stock at

the producer is exactly determined and replenished differs for each producer and is not shown in the figure.

It can just be seen that the stock is required to fulfill retailer produce demand. For this study it is important

to know how this stock is determined and replenished since it influences the costs made by EPS on trays.

Since the tray depreciation and delivery costs are related to the tray stock at producers this will be the focus

of the remaining part of the study. This will be done by answering the following two questions:

How is the tray stock at producers determined?

How is the tray stock at producer replenished?

The stock replenishment question will be answered in the next section by analyzing the tray delivery system

of EPS. The tray stock determining question will be answered in the remaining part of this chapter.

3.2.2 Current situation EPS

The tray replenishment system of EPS differs per country. There are two different procedures which are

shown in Figures 14 and 15. The processes are rather similar apart from one step. For this reason the

general procedure will first be explained followed by the difference. This analysis mainly focusses on how

orders are processed since the incoming orders are the first connection between EPS and the fresh supply

chain.

The process starts when a producer places an order for trays. Before the order arrives, EPS has no

information on expected individual incoming orders. On larger scale forecasts are made with historical

order date from previous years. However, there is no producer individual tray demand information available

by EPS. This means that the first connection with producers is made when an order arrives. From this

moment on EPS has two days for a FTL delivery and four days for a LFTL delivery. An incoming order is

shown in eWeb (order managing program used by EPS) before it is processed by a customer service

employee. This employee checks if the order matches the requirements or if information is missing. A

complete order is forwarded to the financial department where the order is financially checked. During this

step a financial employee checks the credit limit of a customer and the status of the customer’s account. If

the customer has too much debt or if the order exceeds the credibility, the employee will cancel the order.

Confirmed orders will be forwarded to a transport planner.

A transport planner first checks how many pallets of each tray type are required. He then checks if these

trays are available in the depot nearby the producer. If there are not enough trays in this depot he checks

in which other (nearby) depot the order is available. Once the transport planner knows from which depot

he will sent the trays, he starts planning the transport (Euro Pool System, 2016).

When planning the transport, a transport planner first checks the order size in pallets. If an order can fill

an entire truck (FTL) he can immediately start planning. If an order is less than full truckload (LFTL), the

planner checks if there are existing planned transports he can combine. If this is the case, transports are

batched to safe transport costs. The process of checking existing transports is done by heart since there is

no automated system that provides a clear overview of already planned transports. A new transport order

will be created if there is no existing transport that can be combined. A transport order is created in

Transporeon (market place where transport supply and demands meets). A transport company accepts this

order and the planned transport is confirmed. This process is not taken into further account since it has no

direct influence on the tray ordering process. A planned order is confirmed and the trays are prepared for

transport.

Pallets with trays are retrieved from the big stock in a depot and transported to a loading dock. Trays are

loaded in a truck and transported to the producer. At the producer, the trays are unloaded. If there are

multiple orders in one truck, the transporting and unloading will continue till the last order has been

delivered. At that moment the truck is released. Once all orders have been delivered, this is processed in

EPS administration. If the orders have been all correctly delivered the order is handled. If a mistake occur

the process will go back to the processing order part at which the process will repeat.

The difference between the two procedures is the ability to adjust orders. In several countries the orders

are delivered as requested by the producer. However, there are also some countries like Spain where EPS

can, with producer consultation, make slight adjustments. If orders can be batched but there is still space

for a few pallets, a transport planner will contact the involved producers to try to create a full truckload.

The same is done when a combination of orders is slightly bigger than the truck capacity. Producers will

then be asked if they agree when less pallets are delivered. This adjustment of orders increases the transport

utility and thereby saves transport costs. An example of losses by not being able to send FTL and adept

orders is shown in Table 1. It can be seen that different transports with the same amount of trays over the

same distance still vary a lot in costs. The difference between the cheapest (€64,-) and most expensive trip

(€145,-) is 127%. The expensive €145,- transport was most likely caused by a LFTL. The delivery costs for

the truck had to be divided over less trays which increased the delivery costs per tray. In 2016 LFTL loses

by not adjusting orders where monitored in Germany. These added up to €***.000 which is *** percent of

the total German delivery costs. EPS has proven that adjusting orders safes serious money. This is however

not companywide introduces partly due to local cultural considerations.

Figure 14 Proper model of tray delivery procedure 1

Figure 15 Proper model of tray delivery procedure 2

Date Transport type Transport cost

Distance (km)

Quantity Transport cost per tray

21-1-2015 Transport By EPS € 132 64 **** € 0,****

18-2-2015 Transport By EPS € 132 64 **** € 0,****

10-3-2015 Transport By EPS € 132 64 **** € 0,****

8-4-2015 Transport By EPS € 64 64 **** € 0,****

29-4-2015 Transport By EPS € 145 64 **** € 0,****

26-5-2015 Transport By EPS € 64 64 **** € 0,****

26-6-2015 Transport By EPS € 132 64 **** € 0,****

30-7-2015 Transport By EPS € 132 64 **** € 0,****

7-9-2015 Transport By EPS € 64 64 **** € 0,****

5-10-2015 Transport By EPS € 132 64 **** € 0,****

5-11-2015 Transport By EPS € 132 64 **** € 0,****

7-12-2015 Transport By EPS € 140 64 **** € 0,**** Table 1 Transport characteristics of producer 103277 in 2015

There are multiple requirements and performance indicators as can be seen in the figures before. These

performance indicators are the lead time (from received order till delivered trays), availability (percentage

of trays that can be delivered as ordered), internal tray stock (trays available for renting), and transport

indicators. This includes the transport utility and the transport costs. The transport costs in this case are

the only KPI’s on which transport planners are checked and reviewed (Euro Pool System, 2016). This is

quite a remarkable but also understandable observation in the current situation. In the introduction it has

been stated that the delivery, depreciation and storage costs are connected and affect each other. This makes

the observation of only transport costs as KPI remarkable. However, since orders are placed by customers

and can hardly be influenced by EPS employees, it can be understand that at least the transport costs are

minimized. With the current lack of tray cost information and order manipulation influence, transport

planners operate within their possible limits. Current policies do not only make it impossible to minimize

delivery related costs, it is also hard to minimize transport costs itself for countries like Germany.

Both these observations do not have to be a problem under different circumstances. However, due to a full

concept fee, producers do not get charged for cost inefficient behavior. Thereby it is not sure if changing

the pricing system would result in a minimize cost situation since individual considerations are unknown.

It is also unknown how retailers would respond to this change and if they still want to cooperate under

these conditions. In the current condition all prices are exactly known by the retailer which will never result

in unexpected situations.

It is observed that in the current system tray depreciation costs are not taken into account at all. The tray

depreciation costs are not even known by EPS and cannot be used for this reason. It is also observed that

EPS fulfills orders like requested by producers with hardly any changes. This is a rather remarkable finding

since tray depreciation costs is one of the biggest cost categories. It thereby raises the questions how much

extra costs are made at the tray delivery part of a cycle compared to a cost minimum situation. The next

chapters will therefore focus on the costs of the current delivery pattern, solutions to reduce the delivery

related costs and the consequences of these solutions. Thereby will be looked at all the related costs

components and especially to the costs of trays. The focus will also be on a possible change in order

processing to reduce the tray delivery related costs. The part that is mainly taken into account is marked in

red in Figures 13 till 15. In the remaining part of this chapter the focus will be on producers. Thereby it will

be studied how producers manage their tray stock and what factors are involved in this process.

3.3 Connections producers and retailers The previous paragraph provided a glance in how EPS operates. The upcoming paragraphs will take a closer

look at the producers. Thereby the following question will be answered: How do producers determine their tray

ordering behavior? This will be done by first looking at the connection between producers and retailers before

going deeper into producer tray ordering behavior.

Retailers have specific tray requirements as mentioned in the previous paragraph. Since the producers are

obligated to deliver produce in the requested trays, the connection between producers and retailers will

definitely influence the producer tray ordering behavior. A schematic overview of typical connections

between producers and retailers is shown in Figure 16. There are three retailers, two traders and six

producers in this overview. It can be seen that four producers deliver produce to multiple retailers/traders.

Producers delivering to multiple parties will have more requirements to take in mind. As can be seen in the

figure, producer D, E and F deliver (in)direct to retailer B and C. This means that the three producers

require both EPS trays as well as IFCO trays or cardboard. This makes it more complex for a producers to

manage these packaging materials. Even for producer B and C, both using EPS trays, it may still be equally

complex. It is possible that Retailer A uses two types of green foldable trays while retailer B uses two types

of black foldable trays. In this case, even when only dealing with EPS retailers, a producer needs four

different tray types. Having all these possible packaging materials makes tray inventory management a more

difficult business for producers compared to just a single tray type.

Figure 16 Producer - retailer connections

Producers and retailers can also do business via the spot market. The spot market can be divided in two

kind of connections. At first there is the original spot market where different customers bid for produce.

In this case produce is already packed and the tray type is less important. Tray ordering behavior is just

determined by producer supply since he will pack all his produce in the same packing. Some retailers do

not want to do the bidding but also want no contractual obligations. These retailers, like Lidl, scatter

between producers to get the lowest price. They are constantly changing producer or trader so there are no

real connections. These retailers do however generally ask for specific packaging material. However, these

retailers are also being fond of certainty and have a few preferred suppliers they will first contact. Thereby,

even without contractual obligations there may be still a connection.

There are many different kind of connections between producers and retailers as can be seen. How these

connections influence the ordering behavior of producers will be discussed in the next paragraph.

3.4 Factors influencing producer tray ordering behavior This chapter has briefly described the connections between producers, retailers and EPS to get an insight

in the supply chain. The goal of this part of the study is to identify factors that influence the producer tray

ordering pattern. This information is needed to find out within what constrains supply chain cost

minimization is possible. The following question is thereby asked in this section: Which factors influence producer

tray ordering behavior? To answer this question the producer-retailer and producer-EPS relationships as well

as producer characteristics are considered.

It is expected that the following factors influence the tray ordering behavior on aggregated level:

(Retailer) Demand fluctuations

(Produce) Supply fluctuations

Financial possibilities of producer

Information sharing and forecasts of retailer to producer

Producer characteristics

Trust in EPS

EPS transport policy

However, since producers have different characteristics and relationships with retailers, the influence of

factors may change per producer.

(Retailer) Demand fluctuations Demand fluctuations mainly occur due to retailer promotions, delivery contracts between producers and

retailers, produce quality fluctuations and produce market price. Several studies state that most weekly

demand fluctuations are caused by promotions (Taylor & Fearne, 2009) (Xu, Dong, & Evers, 2001). Another

important factor that changes per producer is the type of relationship with retailers. Producers dealing with

retailers as prime supplier have a more stable demand pattern than producers who fill multiple retailers

shortages. There are also producers delivering to the sport market. The sport market is for producers supply

driven. Producers determine the packaging material and the goods will be sold to the highest bidder. These

producers will not have any demand fluctuation to take care of.

(Producer) Supply fluctuations

Supply fluctuations are an important factor for fresh produce producers. There are several, mainly external,

factors influencing the supply or fresh produce. Most important factors are seasonal or weather related.

This makes the supply side not as stable as a factory line with a known constant production.

Financial possibilities of producers

Another factor that may influence producer tray ordering behavior is the financial capability of producers.

Producers need sufficient capital to be able to rent trays. Having insufficient capital will result in less tray

renting possibilities. The tray renting capabilities of a producer are influenced by: renting price, deposit

costs, producer capital and the terms of payment of both EPS as well as retailers. It is estimated that long

terms of payment by retailers cost supply chain partners in the Dutch fresh produce supply chain €700

million a year (Verheul, 2016). This may therefore be a serious barrier for producers with little cash funds.

Information sharing and forecasts of retailer to producer

Fluctuations in retailer demand should not be a very big issue for producers as long as they are known.

Known fluctuations can be taken into account when planning production and tray demand. Producers

make forecasts to make sure they have enough trays and other supporting materials. These forecasts can

be made by shared retailer forecasts, by retailer’s orders or with historical patterns. High quality information

will result in more accurate forecast. The lead time is also very important in making forecasts. Information

received only a few days in advanced may not be useful. More accurate forecast and planning can reduce

costs for the producer. For this study it will be very interesting to find out what information producers have

and how they use this information for tray management. If forecast information from the producer can be

shared with EPS inefficiencies caused by information delays may be reduced.

Producer characteristics

There are several producer characteristics unique for each producer that may influence the ordering

behavior. These factors are: Storage space, cost for storage, risk of tray theft, time for tray ordering,

flexibility of ordering trays and the overview on stock. Especially the overview of stock is a factor frequently

mentioned in literature. A study by Banomyong et al. (1999) shows that actors do not account for volumes

that are already in the pipeline. Other studies show that actors frequently do not have up-to-date

information on their stock. This raises the question to what level producers have knowledge of their tray stock.

This is even harder when producers have multiple packaging materials. Multiple different packaging solutions

means a more complicated inventory management with extra safety stocks. Lack of pipeline overview could

be an explanation to some of the stock patterns as discussed in the introduction. If producers have no clear

notice of their pipeline, they may order trays without realising they do not need new trays.

Order batching, a cause of the bullwhip effect, may happen due to producer characteristics. Periodic review

is one of the causes of order batching (Lee, Padmanabhan, & Wang, 1997) (Taylor & Fearne, 2009). Periodic

review can be caused by a lack of time or flexibility. Some producers may only have time to manage their

stock once a week on a fixed time period. This will support order batching and increase the bullwhip effect

further upstream in the supply chain (Taylor & Fearne, 2009).

Trust in EPS Another important factor that is related to the bullwhip effect is producer’s trust in EPS. Rationing and

shortage gaming can easily occur if producers do not completely trust EPS. Producers are likely to create

buffer stocks if they fear shortages or delivery delays. This may be a reason of the tray ordering behavior

as discussed in the introduction. It has occurred several times in the last couple of years that EPS could not

deliver specific tray types due to a shortage. If producers have experienced these problems before, it is likely

they will make sure there are enough trays in storage to overcome these shortages. When producers create

buffers, the trays are not rotating which may cause even bigger tray stock problems for other producers.

This can create a knock-on effect with big consequences. Rational and storage gaming may cause shortages

even if there are theoretically enough trays in circulations.

Delivery assurance is not the only type of trust important for the relationship between producers and EPS.

Producers also must trust EPS in not misusing information. It is unlikely that producers will ever share

information to increase supply chain efficiency if they do not trust EPS.

EPS transport policy

Order batching may not only be caused by producers but also by EPS. EPS has a general policy that they

only deliver full truck loads (FTL). This implies that only full trucks are sent to producers. Producers can make

LFTL orders but several small orders will be batched to get a FTL. However, the policy is not implemented

or executed strictly in all countries and depends on the distance between producer and EPS depot or the location of

the producer compared to frequent used transport routes. There are many exceptions of LFTL deliveries due to a lack

of producers on the transport route. Internal policies and the compliance are factors that may influence the

allowance of LFTL orders and thereby the producer ordering pattern. The FTL policy is just for transports

arranged by EPS. There is also a possibility for producers to collect their own trays. For this self-pick up the

producers get a discount on the renting price.

Producer tray ordering overview

All these main factors and corresponding sub factors somehow influence one of the three main ordering

pattern parts: Ordering size, ordering frequency and safety stock. The relationships of the factors to one of

the three ordering pattern parts is shown in the causal relation diagram in Figure 17 (explanation in

Appendix C). The relationships can be read as follow:

+ Is a positive relationship. If a factor increases the related factor increases as well. And of course, if the

factor decreases the related factor decreases.

- Is a negative relationship. If a factor increases the related factor decreases. This also works the other way

around: If a factor decreases the related factor increases.

As can be seen in the figure, there are a lot of factors involved in the process. Taylor & Fearne (2009)

support this finding and state that handling demand information is very complex for producers. It gets even

more complex since producers have multiple partners like EPS. This can be for wrappings, labels or

competing packaging companies. Therefore, Taylor and Fearne (2009) state, ‘the complexity of procedure

for handling demand information, the accuracy, availability and consistency of data, proliferation of

forecasts, problems with sharing consumer demand data, timeliness of orders, and disconnect between

primary product and final consumption’ emerged as problematic for producer demand management. This

complexity may result in inefficient ordering behavior since producers generally just want enough trays to

be able to continue their primary business.

The sub question staged in the beginning of this study: Which factors influence producer tray ordering behavior? has

been answered. Though, it is not yet known which factors are most important for the producers when

ordering trays. This will be studied in the next paragraph by doing a field research and asking producers.

The focus will be especially on the information sharing and forecasts as being vital information for

optimizing tray ordering behavior.

Figure 17 Causal relation diagram

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3.5 Producer field research A field analysis whereby producers are directly questioned makes it possible to directly verify the important

factors with the involved persons. This provides direct access to the rationale behind tray ordering. The

questioning can be done in person by interviewing, on paper with a questionnaire or a combination of both.

An interview will provide the possibility of asking additional questions on important subjects and

improvising to get the best possible answer. Interviewing takes a lot of time and therefore reduces the

number of respondents. Especially since there is a lot of variety between producers, a substantial amount

of respondents is desired. A questionnaire makes it possible to increase the amount of respondents within

a certain time period. Another, more practical argument to use a questionnaire is the language and

geographical barrier in this study. The producer study takes place in Spain where producers only speak

Spanish. I do not speak Spanish fluently which creates a language barrier. This barrier can be overcome by

making a questionnaire and translating it once. To increase the amount of respondents and overcome a

language barrier a questionnaire is chosen for this part of the study.

The main factors as discussed in the previous paragraph are translated into questions for the questionnaire.

These questions are complemented with general questions on the ordering behavior and if producers are

willing to change their ordering behavior. The questionnaire is mainly focused on the tray demand

predictability of producers. The questionnaire itself is shown and briefly discussed in Appendix D.

The questionnaire is taken by 50 producers in Spain. These producers are manually selected. This is mainly

done since EPS employees recommended to take the questionnaire in person instead of sending it by mail.

Several account managers took the questionnaire when visiting producers for their daily control/business.

Since this is rather time consuming a select number of producers have been manually selected to get the

best possible variation. Hereby is looked at the type of fresh produce and size of the producer. More on

the selection criteria and an overview of the producers can be seen in Appendix D.

3.5.1 Producer questionnaire results The results of the questionnaire are split into two separate parts. At first the questions related to the

relationship between EPS and producers are discussed followed by produce demand information and how

producers use this information for planning.

The producers unanimously state that EPS is a reliable partner and they are satisfied with the services

provided by EPS. Although they all state that EPS is a reliable partner some producers, mainly related to

fish trays, mentioned the shortage of trays in peak period. Supply shortages may result in rationing and

shortage gaming causing significant inefficiencies as stated in chapter 2. There were also some comments

to improve the service even more. Most comments where on the tray delivery and managing part. 14% of

the producers state they would appreciate assistance with managing their stock of tray. 28% of the

producers wants extended transportation possibilities. These producers have certain transport restriction

by EPS and can for example only order full truckloads. So 42% of the producers state that they can improve

at least their own tray managing process.

This is not the only outcome that indicates that there are points for improvement. 75% of the producers

state they only order full trucks of trays. **% of these producers are forced to do so by EPS. This increases

the producer dwell time and result in more trays required by EPS to fulfill all tray cycles.

Another rather remarkable outcome of the questionnaire is about the financial position of producers. It

was stated by other studies that producers have limited financial capabilities which might results in

difficulties when ordering trays. Renting a full tuck of trays costs can add up to €****1. This can be a big

burden for producers. However, none of the producers state that money is an issue when ordering trays.

The ordering size does not depend on their financial capabilities.

These observations are rather remarkable. However, this is just a small part of the tray ordering process.

The ordering process all starts with the information a producer has on expected demand and supply and

how he copes with this information. Producers state that retailers hardly share any demand forecast

information. Only one producer gets information from their retailer more than one week before final orders

are placed. Orders are on average not placed more than a week in advance. Lacking accurate demand

information, producers tent to estimate retailers demand by own experiences. Almost all producers make

their forecasts on expected retailer demand. They thereby take weather, seasonality, day of the week, special

holidays and promotions into account. Planning with no demand forecast from retailers seems rather

difficult. Even though, 70% of the producers state that retailers have a constant (15%) or a slightly

fluctuating (55%) demand. Demand estimations may be possible for constant or slightly fluctuating

demand. This is confirmed by over half of the producers (55%) stating that (fluctuations in) demand can

be predicted accurately. These producers are also able to predict their own supply rather accurate. With

these certainties it should be possible to get an accurate estimation on retailer demand of well over half the

producers.

The deviations and predictability of the remaining 45% are not exactly known. Even if it is for these

producers hard to predict retailer demand, it is not stated that this is impossible. These producers should

be studied in more detail to get answers to the matter of predictability and how they currently predict retailer

demand to assure enough tray stock. At least it can be concluded that these retailers deliberately take extra

stock of trays to cover fluctuations. Over 90% of all producers mention they have extra stock to assure tray

availability in case of fluctuation. This confirms the conclusion of several studies that safety stock is taken

to compensate for uncertainties. The results of the questionnaire did not provide clear answers on the

average size of safety stock. It can however be concluded that producers have extra stock standing still to

cover fluctuations. This is rather logical and also done by EPS. However, it can be asked if having two,

uncoordinated, safety stocks does increase costs for EPS.

Another cause of inefficiency could be the complexity as stated in the previous paragraph. Almost all

producers mention in the questionnaire they have more packaging solutions then just EPS. Most of them

also work with cardboard or with another tray company like IFCO. Even with all these different packaging

materials 76% of the producers still state they have a clear overview of the trays. They have an overview of

the amount of trays of each tray type on at least pallet level (300 trays on a pallet). Thereby 24% of the

producers only have a general overview of the amount of trays in stock. Not having a detailed tray overview

may easily result in having more stock than required.

There are several indications of inefficiencies when analyzing the results of the questionnaire. Part of these

findings can be used to explain producer ordering behavior as shown in the introduction. However, there

are also several findings that can be used to improve the system. It seems that there is enough and rather

accurate information on retailer demand. Thereby 35% of the producers would currently consider

outsourcing their tray management. With sufficient information and the willingness of producers, the

1 Depends on the amount of trays in a truck (between 5.000 and 20.000 for the most convenient tray types) and if a producer has to pay cash or a virtual deposit. Cash deposit is €*** per tray. The deposit can add up to €*** * 20.000 = €****. Including tray rent of approximately €**** per tray, the costs can add up to €**** costs for a producer.

inefficiencies as stated above can be significantly reduced. The possible reduction will be explained in the

remaining part of this study.

3.6 Conclusion This chapter provided answers to the first two sub questions:

How does the current tray ordering system work for both producer and EPS?

Which factors influence producer ordering behavior?

It has been found that producers are free to place orders and are, with almost no exception, excepted and

processed by EPS. In a few countries orders are slightly adjusted to utilize truckloads and some producers

are only allowed to order full trucks. These are however all active controls EPS currently uses. This is

remarkable since producers only pay a fixed renting fee per tray independent on order size or dwell time.

Thereby inefficiencies can easily occur as confirmed by the producer questionnaire. Another very

remarkable finding is the KPI’s EPS currently uses when processing tray orders. As mentioned in the

introduction, delivery costs, tray depreciation costs and storage costs are all related. In current processes

only transport costs is taken as a KPI. There is no consideration or even knowledge of the daily depreciation

costs of a tray. With only one major cost component taken into account is can be imagined that a minimized

cost equilibrium with the other components is very unlikely.

The producer questionnaire provides more insight in how the producer tray ordering behavior is affected

and how inefficiencies occur. Retailer demand is of course the main trigger. It has been found that retailers

have a more constant demand than expected and that fluctuations can be estimated rather accurately

according to producers. In more than half the cases a producer can make accurate demand forecasts well

in advance. Another finding from the questionnaire was rather remarkable. 75% of the producers only

orders full truckloads of empty trays. This may result in long dwell times at producers which increases the

amount of required trays and thereby the tray depreciation costs.

The remaining part of the study will therefore focus on two topics that are currently unknown within EPS

and from upmost importance to minimize the costs related to tray delivery:

How much is a tray worth per day for EPS?

What is the current cost imbalance between tray depreciation costs, delivery costs and storage costs

for EPS?

This information will then be used to simulate how producers can be influenced to reduce the combined

cost factors related to tray delivery.

4 Cost minimization model This chapter will go further on the second part of the research question: reducing the tray delivery related costs

while maintaining 100% tray availability for producers. The current situation will first be analyzed before cost

reduction steps can be made. In this chapter a model to calculate the current delivery related costs and a

theoretical cost minimization model will be described. With these models it is possible to compares the

current tray delivery related costs to a minimized cost situation whereby the current loses can be calculated.

This chapter will be the base to answer the fourth sub questions: How much money can be saved on the tray delivery

process with producers?

The basic requirements and constrains of the model will be discussed in paragraph 4.1. In paragraph 4.2

the theoretical cost minimization model will be designed. This will be followed by the supporting input for

the model in paragraph 4.3 and 4.4. Paragraph 4.5 concludes the theoretical minimization model followed

by the implementation model in paragraph 4.6. In paragraph 4.7 the verification and validation is discussed.

This will be followed by a paragraph mentioning the alternatives that will be tested to improve the system.

Before concluding the chapter the analysis method will be discussed in paragraph 4.9.

4.1 Requirements and constrains Some requirements have to be met to build a model realistic to the actual situation. These requirements are

set to make sure the model simulates a realistic order/delivery pattern. Due to producer characteristics,

external factors and company policies, the optimum situation will probably never be possible. The model

will therefore minimize the related costs within the given possibilities and uncertainties. A model should

take the uncertainties into account to get realistic output. The requirements are not only placed on input

variables and model characteristics but also on the output. A useful model should provide information that

can help a decision maker. The output should therefore be clearly stated to get a complete overview.

The requirements related to the theoretical model can be split in three parts:

Input

Calculation model

Output

Input

The input requirements are related to the characteristics of producers and EPS. The requirements are based

on capacity, demand and costs of different processes. The input variables should clearly distinguish:

Tray types used by producers

Distance from producer to closest EPS depot

Tray flows between producers and retailers

Current producer tray demand

Transport capacity of trucks

Transport costs factors to calculate transport costs

Storage capacity at producer

Daily tray value per tray type per month

Type of pallets trays are transported on

Forecast horizon

Producer tray demand forecast uncertainty

The type of trays, tray demand, storage capacity, forecasting horizon, distance to producer and producer

uncertainty are producer characteristics that are required as input data for the model. Transport and Tray

cost factors are, together with transport capacity and type of pallet, input variables not related to producers.

The pallets and some transport factors are the only factors that can be influenced by EPS. The other input

variables are external fixed data required to calculate the minimized cost delivery pattern.

Calculation Model

The model should calculate an ordering pattern with an approximation of the minimum possible costs

given the input variables and constrains. This should be done with different levels of uncertainties, delays

and constrains. The model should therefore be flexible enough to adept to producer characteristics and

market fluctuations. With the available input the model should calculate a minimum cost ordering pattern

taking all these characteristics into account.

Output

The output requirements are related to the results of the model. The model should provide a clear overview

of the transport and the tray costs. This should be shown of both the current situation as well as an

alternative (minimum cost) situation. The model should also provide some additional information. The

amount of transports and the transport utilization are other important indicators. This provides a transport

planner with vital information when planning transports.

It is also required that the tray amounts are monitored as outcomes. It is a requirement that producers will

always have enough trays for production. The minimum stock should be monitored to see if this will not

reach zero. Not only the minimum stocks is required as output but also the average stock per month at a

producer. With this information it can be determined how many trays are required to serve the entire

system.

All requirements as mentioned above are shown in the figure below.

Figure 18 Model requirements on input, calculation model and output

4.1.1 Modelling program considerations There are many different options suitable for calculating minimum costs with certain constrains. This can

be done by existing optimization programs like AIMMS, programming the situation or creating a model in

an existing tool like Excel. All these options have pros and cons for this study. Programs like AIMMS are

specially designed for optimization and provides a good overview of all considerations and the minimum

cost scenario. Thereby it is very easy to change input variables and constrains. Writing an own program is

also possible to minimize the related costs. This is however less interesting for this study because there are

already many existing programs suitable for the job. Writing a new program would be very time consuming

and will not provide any extra benefits since existing programs can deal with all requirements and constrains.

At last there is an option for making a model in Excel. Since Excel has some calculation limitations the

results will be less optimum compared to minimization programs. However, Excel offers a pro that all other

options cannot provide: easy integration in current database. All the required data is stored in a cube

accessible from excel and stored on an external server. In this cube all tray flow information between EPS

and producer including order size and delivery cost information of 10.000 producers since 2007 is

registered. In this cube is also flow information on 300 producers and their related retailers. Therefore it is

a consideration between a slightly better performing model or a model that can be integrated in the current

database and therefore easier extensively tested. Since there are so many different producers with different

characteristics, extensive testing is preferred to get a better overview. Getting a perfect model is also not

the goal of this study. The goal of the study is to get an overview of the financial inefficiencies at tray

delivering. It is a more exploratory study instead of an implementation study. The possibility of integrating

the model in the current data cube is for this study considered as most beneficial. Therefore it is chosen to

build a minimization model in Excel. This will however mean that when using a specialized minimization

program the costs might reduce slightly more. This cannot be confirmed since it is not yet known how both

models will deal with the given uncertainty as discussed in the previous chapters.

4.2 Theoretical minimization model This paragraph discusses a model to calculate the current and minimum costs. This model will be later

slightly adjusted to test alternatives. The models will look to individual producers and will therefore have

cost outcomes spent on individual producers. As discussed before, there are delivery, tray depreciation and

storage costs. The depreciation costs is an indication of the costs made by EPS for renting a tray for one

day. This can be explained best by the following example:

Let’s state that EPS is a company that leases the trays from another company that does not make any profit.

To use a tray that costs €3,65 and last for 10 years, a yearly rent of €0,365 has to be paid. EPS will therefore

pay €0,001 per day for renting the tray.

With the storage costs a same calculation can be made. It can be calculated how much a day of storage costs

and how many storage days are related to one tray cycle. By adding this to the tray depreciation costs, the

daily tray costs can be calculated. How this daily tray costs are calculated will be discussed in paragraph 4.3.

This simplification will reduce the model to just two cost components: Transport and tray costs. The total

costs spend on a tray can thereby be formulated as follows:

𝐶𝑇𝑜𝑡𝑎𝑙 = ∑ 𝐶𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦,𝑡𝑡 + ∑ 𝐶𝑇𝑟𝑎𝑦,𝑡𝑡 ( 1 )

With:

CTotal : Total costs

Cdelivery : Delivery costs

CTray : Tray costs (storage and depreciation)

The total tray costs per producer is calculated by taking the stock of trays at a producer and multiplying

this by costs for a tray per day as shown in the formula below. This will provide the daily total tray costs

per producer.

𝐶𝑇𝑟𝑎𝑦 = 𝑇𝑆𝑡𝑜𝑐𝑘 ∗ 𝐶𝑡𝑟𝑎𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦 ( 2 )

With: Tstock : Stock of trays Ctray per day : Daily costs for a single tray

Transport costs

The delivery costs are slightly less straight forward compared to the tray depreciation costs. The transport

costs are an important but also uncertain factor in the model since transport is outsourced and not all trucks

are fully loaded. A simplification is required to calculate the transport costs and be able to compare with

the current situation. This simplification will be used for both the current model as well as the minimization

model. For the model a fictive current transport costs will be calculated to filter LFTL and fluctuating

transport prices. Filtering LFTL transports and fluctuating prices is done since this is market depended and

depends on possible transport combinations. This is rather random and changes on daily basis. For the

most fair comparison these random transport cost and utilization changes are taken out. This is done by

assuming there are three cost components: starting fee, fee per km and an extra stop fee for combined

transports. This results in the following calculation:

𝐶𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦,𝑝,𝑐 = (𝐶𝑆𝑡𝑎𝑟𝑡𝑢𝑝,𝑐 + 𝐷𝑝 ∗ 𝐶𝑘𝑚,𝑐) ∗ 𝐿𝑝 + 𝐶𝑠𝑡𝑜𝑝,𝑐 + 𝜀 ( 3 )

With:

Lp : Loading degree per producer 0 < L < 1

CStartup,c : Fixed starting costs for a trip country specific

Dp : Distance between EPS and producer (in km)

Ckm,c : Price per km country specific

CStop,c : Extra stopping costs made to combine multiple LFTL transports

𝛆 : Error factor to compensate for distance differences between producers

The starting, km and extra stop fee differs per country since countries have different labor costs and taxing

systems. Therefore the model should take producer country into account as a variable for transport costs.

The km costs is not only influenced by the country but also by the covered distance. It is expected that the

km costs for a short trip are higher than the km costs for long trips. This will be taken into account in the

model as shown in the following example:

The total km costs (D * Ckm) and starting fee will be proportionally divided over the number of pallets

each producer has ordered. This can be done since it is assumed that for combined LFTL transports the

producers are located in the same region with only limited distance between each other. If the distance

between producers is not negligible, the error factor can be used to transfer transport costs between

producers.

The formula for a FTL order transport is more straightforward. Only a starting costs and km costs are

required as input variables in this case. The loading factor L is set on one which means that the truck is

completely full.

𝐶𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦,𝑝 = (𝐶𝑆𝑡𝑎𝑟𝑡𝑢𝑝 + 𝐷𝑝 ∗ 𝐶𝑘𝑚) ∗ 𝐿𝑝 ( 4 )

With: L : L = 1

Minimization formula

With the simplified formulas described above the current situation can be calculated. However, some extra

steps are required to calculate how much can be saved by EPS. To build a model which simulates the

minimization between transport and tray costs the following formula is used:

𝑀𝐼𝑁 ∑ (𝐶𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦,𝑘,𝑡 + 𝐶𝑇𝑟𝑎𝑦,𝑘,𝑡)𝑘𝑡=𝑖 ( 5 )

With:

Cdelivery : Transport delivery costs

Ctray : Tray costs

t = i : Forecasting horizon in days

k : Number of trays

With this calculation the minimum combination of transport and delivery costs over a certain time frame

can be calculated. The time frame will be set to the forecasting horizon of a producer. If a producer can

estimate the tray use over the next month: t = 30. In this case the minimum cost delivery pattern for the

next 30 days will be calculated. The sequential day the same formula is used to calculate the most optimum

delivery pattern for the next 30 days etc…

Tray costs

The forecasted data provided by the producer is probably not completely correct. If the estimation would

be accurate, figures as shown in the introduction would probably not occur. Since the forecast is an

estimation, a simplification on the tray cost calculation will be made for more easily and understandable

calculation. In the calculation model it will be assumed that producers have a linear tray use pattern. With

this assumption, the tray costs will be estimated by using the following formula:

𝐶𝑡𝑟𝑎𝑦 =#𝑡𝑟𝑎𝑦𝑠∗𝑇𝑑𝑎𝑦𝑠∗ 𝐶𝑡𝑟𝑎𝑦 𝑝𝑒𝑟 𝑑𝑎𝑦

2 ( 6 )

With:

# trays : 0 < # trays < Max # trays

# trays < (Smax – S) in which S is the stock at the producer and Smax is the maximum amount of

trays a producer can store

# trays = Ltray type * X in which Ltray type is the number of trays that fits on a pallet and X is a

rounded number. This is since only full pallets are delivered.

Tdays : Number of days till next transport

The formula for calculating the tray costs is graphically shown in Figure 19. This tray cost formula also

determines the required number of trays per transport to get a minimum cost delivery pattern. There are

two constrains which limits the possibility of the number of trays in a transport. At first the number of

trays cannot exceed the loading capacity of a truck. It is also not possible to exceed the maximum storage

capacity of a producer.

Figure 19 Ordering pattern used for determining tray costs

The maximum number of trays that can be loaded in a truck is the number of trays that can be loaded on

a pallet times the number of pallets that can be loaded in a truck. Since there are two conventional pallets

this must be taken in close account. The two types of pallets have different dimensions which results in a

different number of trays per pallet and pallets per truck.

𝑀𝑎𝑥 # 𝑡𝑟𝑎𝑦𝑠 = 𝐿𝑡𝑟𝑎𝑦 𝑡𝑦𝑝𝑒 ∗ 𝐿𝑚𝑎𝑥,𝑝𝑎𝑙𝑙𝑒𝑡 ( 7 )

With:

Ltray type : The number of trays of the specific tray type that can fit on a pallet. This differs for Europallets

and Poolpallets.

Lmax,pallet : Maximum amount of full pallets in a truck. This is 33 for Europallets and 26 for Poolpallets

The basic calculations for the model are now explained. However, two very important factors are not

covered: daily tray costs and the reordering point. How the tray costs per day is determined will be explained

in the next paragraph. This will be followed by determining the safety stock and thereby the corresponding

trigger when new trays must be ordered.

4.3 Daily tray cost The daily tray cost is a cost category of which is currently no calculation method within EPS. Also existing

research provides no satisfying solution how to divide investments costs related to renting products like

trays with heavy seasonality demand fluctuations. Therefore, a theoretical calculation method will be

discussed in this paragraph to calculate the daily value of trays for EPS. Before a theoretical calculation

method for the tray value is made, the trays are separated into different components: Stable demand of

trays, seasonal use of trays, storage for unused trays and safety stock to cover fluctuation. A graphical

overview of these separate parts is shown in Figure 20.

Trays used in seasonal peak (seasonality) have standstill time during off season (unused trays). During this

period the trays are not rented and therefore not paid for by customers. This seasonal use provides a

challenge when calculating tray costs. The costs can be divided in two ways: dividing all related tray costs

equally over all trays or allocating costs to each individual tray. This means that a tray that is theoretically

only used in the peak period will get all the related costs allocated in just one cycle per year. For this method

the second option is chosen. This option is a more realistic overview since in the current situation a tray is

specially bought just to serve the peak period. It can therefore be imagined that the daily tray value in peak

season is higher than during off season. This might even result in different delivery patterns during peak

and off season in a minimum cost model. This will be discussed later in this chapter.

Figure 20 Different parts of tray costs that have to be divided

The peak of tray use is just one single day per year. On this day the most trays of a specific tray type are

rented. However, it is impossible to determine this peak in advance. As can be seen in Figure 21, the peak

between 2014 and 2015 differs approximately 20 days. This peak in tray use is corresponding to the peak

in produce supply which is mainly depended on seasonal factors. Because of this uncertainty, tray value is

not calculated on a daily basis but on a monthly basis. The tray use is therefore divided in twelve segments.

Each segment will get cost allocated which are made to cover all tray movements in that specific month.

Since trays are used for multiple years, the investments costs are spread over a long period. The length of

this period is an important factor in determining daily tray costs. This will be further discussed in the next

section.

Figure 21 Rented 136 trays in 2014 and 2015

4.3.1 Depreciation time

To calculate the daily tray value, at first the yearly tray value should be calculated. Yearly tray value is

determined by purchase costs of a tray and the depreciation time. There are three different depreciation

times used by EPS: financial depreciation time, technological depreciation time and real depreciation time.

The financial depreciation time is the time in which the trays are financially depreciated. According to the

bookkeeping of EPS the trays only last for this financial depreciation period. The technical depreciation

time is the time a tray should be operating according to the specifications under normal use. At last there

is a real depreciation time. This is the depreciation time a tray actually last and differs per individual tray.

These times are set as followed:

Financial depreciation time

The financial depreciation time for EPS differs greatly from the technological depreciation time. For the

technological depreciation time the specifications of the trays are the most important while for the financial

depreciation time this is the minimum period of usage. The minimum usage period is determined by the

relationships with retailers. In general, the financial depreciation time is equal to the standard contract

period between a retailer and EPS: five years. By taking a financial depreciation time, it is assured that trays

are paid for when retailers decide to stop the arrangement with EPS. This also accounts for a change in tray

type. If a retailer decides they want another tray color, green instead of blue for example, he can make this

decision in the new contract negotiations. In the beginning of the contract period EPS makes an investment

in buying enough trays to serve the retailer’s demand. If after 5 years the contract is extended, the trays are

financially depreciated but are still usable. Currently EPS uses the financial depreciation time (and related

costs) in all situations. During all financial and planning calculations five year depreciation time is used.

In this case a tray with a new price of €3,65 will costs €0,73 per year in the books. This tray will cost €0,002

per day.

Technological depreciation time

The technological depreciation time is generally higher than the financial depreciation time. The technical

depreciation time is the time a tray should last under normal usage. There is no clear technological

depreciation time since the financial depreciation time is leading for most business components of EPS. In

consultations with EPS managers it is decided to set the technological depreciation time at ten years for all

tray types. It can be safely assumed that trays can last at least ten years. Even if contracts are not extended

after five years, the trays can still be used for other retailers.

In this case, a tray with a new price of €3,65 will costs €0,365 per year in the books. This tray will cost

€0,001 per day. It can be seen that if the technological depreciation time is taken, the tray depreciation costs

per day will be half of the financial depreciation time. Taking different depreciation times will strongly effect

the equilibrium between transport and depreciation costs.

Actual depreciation time

The financial and technological depreciation times are both fixed, preset periods used for calculating and

modeling. There is however a difference between theoretical depreciation times and the actual period a tray

can be used. The actual depreciation time is the time a tray last on average. Until now it seems that trays

can last longer than EPS initially thought (theoretical depreciation time). There is even one type of rigid

tray that has an average age of seventeen years. This actual depreciation time is constantly changing due to

new batches, new designs, slightly different materials and the intensity of tray usage. Even though it is the

best actual representation it is impossible to get the actual depreciation time for most tray types. The

majority of tray types are introduced in the last decade. Therefore, the actual depreciation time for these

trays is still unknown. Another disadvantage of the actual depreciation time is the actual usage of the tray.

Currently EPS is negotiating with retailers to start using foldable green trays instead of foldable blue. This

means that a lot of trays will be prematurely destroyed. The possible depreciation time of trays has shown

to be longer than predictable policies within EPS, retailers and the entire fresh supply chain.

For this study three different depreciation times, corresponding the depreciation costs, can be used. The

technological depreciation time is the most stable time to use for modeling. This period of ten years is more

veracious than the financial depreciation time. Setting the depreciation time at five years will result in

unrealistic high daily tray costs and considerations will be made on inaccurate information. The

technological depreciation time is within the time frame of predictable policy and contract changes.

Therefore, the depreciation time for all tray types in this study will be set on ten years.

4.3.2 Depreciation costs

After determining the depreciation time and the related yearly costs, a method to calculate the daily tray

costs will be explained. The depreciation costs will be discussed in three separate parts:

Stable use

Seasonal use

Safety stock

The use of trays in each category should be known before the size and related costs of each category can

be calculated. This first starts by calculating the required trays to fulfill monthly demand:

𝐷𝑚 =𝑆𝑅𝑇𝑚

𝐷𝑎𝑦𝑠𝑚∗ 𝑇𝑐 ( 8 )

With:

Dm : Required trays per month (demand)

SRTm : Number of starting renting trips in month m

Tc : Cycle time of one tray (excluding storage and safety stock days)

The stable and seasonal demand can be calculated with this formula. The remaining trays form the safety

stock. The safety stock of EPS is created by making sure that on absolute peak period there is still stock to

deliver producers for **** consecutive days. Thereby the stock of trays in this absolute peak should be

equal to **** times the average daily tray demand of the peak week. It can be checked if the size of the

safety stock is corresponding to these **** days.

Stable use

The costs of the stable use is the easiest part to calculate. It is thereby assumed that these trays never stand

still. Formula 8 is used to calculate the required amount of trays to serve the stable demand. This can then

be multiplied by the trays costs and divided by the depreciation time. This results in the total depreciation

costs for the stable demand. When this is divided by twelve the monthly stable depreciation costs is

calculated. This can be written as followed:

𝐶𝑡𝑟𝑎𝑦 𝑠𝑡𝑎𝑏𝑙𝑒,𝑚 = 𝐷𝑠𝑡𝑎𝑏𝑙𝑒,𝑦 ∗ 𝑐𝑡𝑟𝑎𝑦

𝑇𝑑𝑒𝑝,𝑦𝑒𝑎𝑟/12 ( 9 )

With:

Tdep,year : Depreciation time in years

Seasonal use

Allocating the depreciation costs related to the seasonal costs is a bit more complicated compared to the

stable use. In this case the depreciation costs should be paid for when the trays are moving. The moving

trays must compensate for the standstill time and therefore be more expensive when moving.

To calculate the seasonal use at first it has to be seen that there are twelve demand levels corresponding to

the number of months in a year as can be seen in Figure 22. These demand levels are first ordered from

low to high. The lowest month will have a demand level of zero since this is the stable use. The successive

months will each have a higher demand level. The difference between sequential months can be interpreted

as the extra amount of SRTs required for this month. When formula 8 is used, the required number of trays

to fulfill the demand can be calculated per month. When this is done, it is known how many extra trays are

needed to be able to meet demand of the next level.

Figure 22 Example of SRT per month

The trays required to cover the seasonal peak in each month have to be assigned to corresponding costs. It

is assumed that trays only used in one peak month have to be paid for in just that single month. One level

lower, the trays have to be paid for in two months. This will continue till the lowest seasonal level in which

the trays will be depreciated over eleven months. This is calculated with the following formula:

Dseasonal,m = Dseasonal,m−1 + ∑ΔTd

13−m𝑚=𝑖 ( 10 )

With:

𝜟𝑻𝒅 : Tray demand difference compared to one lower level

𝑫𝒔𝒆𝒂𝒔𝒐𝒂𝒏𝒍,𝒎 : The amount of allocated trays of seasonal usage for this month

𝑫𝒔𝒆𝒂𝒔𝒐𝒂𝒏𝒍,𝒎−𝟏 : The amount of allocated trays of seasonal usage for the previous month

m : lowest month K = 1 highest month K = 12

When all Dseasonal,m are summed, this should be equal to the amount of trays needed to cover the peak.

This formula assigns trays to specific months. When this is done the related costs can be assigned as follow:

Ctray seasonal,m = Dseasonal,m ∗ ctray

Tdep,year/12 ( 11 )

Safety stock

The costs related to the safety stock are calculated in a similar way as the seasonal use. However, in this

case the stable demand is also taken into account. In this case ΔTd of the lowest month is not 0 but the

amount of trays needed to fulfill the stable demand. This results in the following formulas:

Dsafety stock,m = Dsafety stock,m−1 + ∑ΔTd

13−m𝑚=𝑖 ( 12 )

CSafety stock,m = D𝑠𝑎𝑓𝑒𝑡𝑦 𝑠𝑡𝑜𝑐𝑘,m ∗ ctray

Tdep,year/12 ( 13 )

This formula can be best explained as followed: In January, the lowest month, there are 1.000.000 SRTs for

tray type X. in July, the peak month, there are 2.000.000 SRTs. To cover 1.000.000 SRTs in a month only

half the number of trays are needed compared to July. The safety stock is the number of trays which can

last **** more days when run out of normal stock. In January, there are half the number of trays going out

each day so only half the number of trays are required. In this case the formula is not about the net

difference between January and July but about the total demand.

By adding these three cost components the daily tray depreciation costs is calculated. However, as explained

before there are also storage costs. When trays are not used in low season they are stored by EPS. Storing

trays costs money by renting warehouse capacity. This has to be added to get the daily tray costs.

4.3.3 Storage costs The storage costs are calculated in a rather similar way as the seasonal use and the safety stock. However,

there are also a few differences. For the seasonal use there is a fixed tray cost which have to be paid for in

one year. In this case the storage costs will increase when the trays have a longer down time. Trays sorely

used in the peak month will be stored for most of the year and therefore have high absolute storage costs.

Trays that are used for multiple cycles and thereby have less down time will have lower storage costs.

It is known how many days a tray stands still on an average tray cycle due to seasonality. With this data, the

total amount of standing still tray days can be calculated. The storage costs related to this stand still time

should be allocated over the tray movements. Other storage costs related to the processes of EPS are not

considered since they are not directly related to the tray delivery procedure.

The storage costs are only made for trays in the seasonal peak. Trays in the stable demand section do not

have to be stored and therefore have no storage costs. It is therefore necessary to only take the peak tray

demand as discussed in the seasonal use section. The starting point for this calculation will be the number

of trays needed each month to cover the seasonality. This amount can be retrieved from the seasonal use.

A calculation factor will be used to divide the storage days and allocate the storage costs. This is done with

the following formulas:

F𝑠𝑡𝑜𝑟𝑎𝑔𝑒,m = F𝑠𝑡𝑜𝑟𝑎𝑔𝑒,m−1 +ΔTd∗(𝑚−1)

(13−𝑚) ( 14 )

𝐶𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑚 = 𝐶𝑆𝑡𝑜𝑟𝑎𝑔𝑒

𝐹𝑠𝑡𝑜𝑟𝑎𝑔𝑒∗ 𝐹𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑚 ( 15 )

With:

𝑭𝒔𝒕𝒐𝒓𝒂𝒈𝒆,𝒎 : Factor indicating the amount of storage days per month

𝑭𝒔𝒕𝒐𝒓𝒂𝒈𝒆 : Total storage factor days

The total storage days are divided over the months resulting in a storage costs per month with the use of

Fstorage,m.

4.3.4 Tray costs The total tray costs per month is a summation of the stable, seasonal, safety and storage costs.

𝐶𝑡𝑟𝑎𝑦,𝑚 = 𝐶𝑡𝑟𝑎𝑦 𝑠𝑡𝑎𝑏𝑙𝑒,𝑚 + 𝐶𝑡𝑟𝑎𝑦 𝑠𝑒𝑎𝑠𝑜𝑛𝑎𝑙,𝑚 + 𝐶𝑡𝑟𝑎𝑦 𝑠𝑎𝑓𝑒𝑡𝑦 𝑠𝑡𝑜𝑐𝑘,𝑚 + 𝐶𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑚 ( 16 )

With this summation, the total tray costs per month can be calculated. These total costs have to be translated

to a daily tray value. This is done as followed:

𝑐tray per day,m = 𝑆𝑅𝑇𝑚

𝐶𝑡𝑟𝑎𝑦,𝑚

𝑇𝑐 ( 17 )

This finally results in the value of a tray per day in each month. This data can be calculated for each

individual tray and used as input in the model. The results of one tray type are shown in Figure 23 (demand

shown in Figure 21). During the peak in May, June and July, the daily tray costs are higher compared to the

rest of the year. This corresponds to the tray demand as shown in Figure 21.

Figure 23 Daily tray price of 136-cont – 2015

4.3.5 Tray costs of part of SRTs The theory above discussed the tray costs if all SRTs are taken in mind. However, it should also be possible

to calculate the delivery cost loses of only a segment of SRTs. In this case, only the relevant SRTs and the

related required trays are considered. As an example, Figure 24 shows some layers which could be calculated

separately. If only the tray costs of the seasonal peak are desired, layer 3 and 4 should be taken. In this cases

only the SRTs and related trays required for these tray cycles are separated. Thereby all trays and SRTs in

layers 1 and 2 will not be taken into account.

When calculating only a part of the SRTs it has to be carefully chosen if the top segment of the lower

segment of tray cycles is taken. The top segment will always be more costly per tray day since the costs are

spread over less cycles and storage costs are added. The lower segment is the stable demand and will have

lower costs since these trays are assumed always moving. If cost minimization is the goal, the top part

should be taken. Since this segment is the most expensive, the most benefits can be achieved.

Calculating the daily tray costs of a specific segment of SRTs can be done exactly the same way as with the

total tray movements as discussed above. The most important part in this case is isolating the right amount

of SRTs and required trays to complete these cycles for each month. It is therefore possible that for different

months, other combination of layers will be chosen. This depends on the amount of SRTs per month that

is analyzed.

Figure 24 Segments of tray cost calculation

4.3.6 Producer tray costs The previous paragraphs provided a theoretical minimization model for EPS. However, a cost minimization

for EPS will have consequences for producers. This can be either a decrease or increase in costs. As

discussed in chapter 2, information may be deliberately distorted if partners expect misuse of information

or will not benefit themselves. The consequences for producers should therefore be calculated as well.

Producer costs can be separately calculated from EPS costs. Since producers pay a full concept price, they

have no transport or ordering costs. It is assumed that producers only have to pay for inventory. The

inventory costs are, in this case, set to be the interest costs for the money required to rent a tray for one

day. The daily interest costs per tray can be calculated by the following formula:

𝐶𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑟,𝑑 =𝐶𝑑𝑒𝑝𝑜𝑠𝑖𝑡+ 𝐶𝑟𝑒𝑛𝑡

365∗ 𝐶𝑖𝑛𝑡𝑒𝑟𝑒𝑠𝑡 ( 18 )

With:

𝐂𝐏𝐫𝐨𝐝𝐮𝐜𝐞𝐫,𝐝 : Actual costs a producer makes for renting trays for one day.

𝐂𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭 : Interest rate

The costs for producers can be calculated with formula 18. However, this formula could also be used to

calculate a supply chain optimum. Since 𝐶𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑟,𝑑 is the daily costs per tray, it has the same unit as the

daily tray costs of EPS (costs per tray per day). By adding these costs the total tray costs for EPS and

producers combined could be calculated. This will result in the following formula:

𝐶𝑡𝑟𝑎𝑦,𝑚 = 𝐶𝑡𝑟𝑎𝑦 𝑠𝑡𝑎𝑏𝑙𝑒,𝑚 + 𝐶𝑡𝑟𝑎𝑦 𝑠𝑒𝑎𝑠𝑜𝑛𝑎𝑙,𝑚 + 𝐶𝑡𝑟𝑎𝑦 𝑠𝑎𝑓𝑒𝑡𝑦 𝑠𝑡𝑜𝑐𝑘,𝑚 + 𝐶𝑠𝑡𝑜𝑟𝑎𝑔𝑒,𝑚 + 𝐶𝑃𝑟𝑜𝑑𝑢𝑐𝑒𝑟 ( 19 )

By adding producer tray costs, the equilibrium between tray and delivery costs may shift. This may result

in EPS losing money to get a supply chain optimum. Exact results on the impact on producers and a

possible supply chain optimum will be calculated in the next chapter.

4.4 Reorder point An approach to calculate the different cost components have now been explained. Thereby it will be

possible to calculate the minimum tray delivery related costs. However, it was a requirement to have 100%

tray availability. New trays should be delivered before a producer runs out of stock to make sure there will

be no shortage of trays. Since it takes a couple of days to deliver the trays, orders should be places in

advance. The moment when an order is placed is called the reorder point. The reorder point consist of the

normal consumption during the lead time and a buffer stock to cover fluctuations.

The normal consumption during the lead time is easy to calculate. This is the expected amount of required

trays from the moment of ordering till the expected moment of delivery. However, there are differences

between expectations and the actual situation. These differences are formed by uncertainties. As discussed

in the literature study and confirmed by the producer questionnaire, uncertainties are buffered by increasing

stock and maintaining a safety stock. This safety stock is used to assure enough trays with (unexpected)

fluctuations. A larger buffer size can withstand more fluctuations. Each producer will has his own safety

stock and method/rationale to determine this stock. It is impossible to simulate all these individual safety

stocks. Therefore, the safety stock for each producer will be determined in the same, rational way.

There are many methods to determine the safety stock as described by Schmidt et al,. (Schmidt, Hartmann,

& Nyhuis, 2012). Schmidt et al,. have extensively compared the safety stock methods on robustness and

inventory costs. Most of these methods consist of the following three factors or derivatives:

Demand size and fluctuations

(Fluctuations in) Lead time

Safety factor (allowable chance to run out of stock)

Thereby the safety factor can be adjusted to assure a certain level of availability. A low safety stock may

result in occasional insufficient stock while a high safety stock will be more costly. Therefore the value of

availability should be calculated. In this study there should always be enough trays since EPS assures

producers and retailers 100% tray availability. The value of availability is almost unlimited.

As mentioned before, the tray demand differs during the year. In peak season the tray demand will be higher

than during low season. The safety stock should change with this demand changes over the year. It can be

imagined that the safety stock for an average demand of 100 trays a day will most likely be lower compared

to a demand of 1.000 trays a day. The safety stock will therefore change on daily basis according to the

average expected tray demand for the forecast period.

Besides the demand size, the lead time is another important factor to take into account. In four days a lot

more can happen than in two. EPS has different transport time policies for LFTL (4 days) and FTL (2 days)

transports. The higher LFTL transport time is set to enable combined transports and thereby reducing

transport costs. This decision will probably result in more safety stock for producers since the unexpected

fluctuations of four days have to be encountered.

All three factors will be taken into account in this study since they are all important. Schmidt et al,. discussed

many safety stock methods with different pros and cons. Some methods did not take a safety factor or lead

time into account and are therefore excluded (Schmidt, Hartmann, & Nyhuis, 2012). Of all extensive

methods to calculate the safety stock, there are five methods tested who performed to expecting standards.

Two of those methods however had a very high stock level up to two times as high as the other three

methods with no extra certainties guaranteed. The three remaining methods have rather similar stock and

certainty levels. According to the study all three methods performed equally well. Of the three methods

one is shown below:

𝑆𝑎𝑓𝑒𝑡𝑦 𝑠𝑡𝑜𝑐𝑘 = 𝑍𝛼 ∗ √𝐸(𝐿) ∗ 𝜎𝐷2 + (𝐸(𝐷))2 ∗ 𝜎𝐿

2 + 𝜀 ( 20 )

With:

𝒁𝜶 : 𝛼 is the service level (safety factor), 𝑍𝛼 is the inverse distribution function of a standard

normal distribution with cumulative probability 𝛼.

E(L) : Mean lead time

E(D) : Mean demand in time period

𝝈𝑳 : Standard deviation of the lead time

𝝈𝑫 : Standard deviation of demand in time period

This formula is rather similar to the other formulas and have a con that increases the required safety stock

severely: standard deviation of demand. The standard deviation is for many producers rather high. On

Monday and Tuesday more produce is generally ready to be picked after a quite weekend. The high standard

deviation can also be caused by producing for one retailer on one day and for another retailer with another

tray type on another day. This results in a high standard deviation and thereby a high required safety stock.

Therefore the methods discussed by Schmidt et al,. are not directly useful for EPS. Since it is assumed and

supported by the producer questionnaire that most fluctuation can be predicted, a different approach will

be used for the safety stock. This approach will take in mind that a 100% availability is required and the

fluctuations and uncertainties are two separate parts.

For this model the safety stock will be the uncertainty factor (𝛆) provided by producer. This can be

determined by the maximum difference in expected demand and real demand during the transport lead

time. For example, if a producer has an uncertainty factor of 25% and an expected tray demand of 100 trays

per day, the actual tray usage will be between 75 and 125. The model will calculate with the biggest possible

demand which will be 125 trays per day. Besides the uncertainty factor, another 10% of the expected tray

demand (𝐃𝐞) is added for extra certainty for the producer. The safety factor can be set for each producer

individually depending their uncertainty factor.

With the safety stock as discussed above the reorder point (RP) can be set as followed:

𝑅𝑃 = 𝐷𝑒 ∗ (𝜀 + 10%) ( 21 )

This method will solve the problem with a high standard deviation and should assure a 100% availability as

long as the uncertainty factor is not exceeded. New trays will be ordered once the stock reaches the reorder

point. The reordering point will increase if the lead time increases. This to cover consecutive days of

possible maximum underestimation of demand. Thereby the longer the lead time the higher the safety stock

will be.

4.5 Overview of theoretical model The theoretical model as explained in this chapter can be summarized as shown in Figure 25. Each day is

checked if the tray stock is lower than the reordering point. If this is the case an order is places. The order

size is a minimization of the transport and tray costs over the forecast period within the given constrains.

With this model a more cost optimum delivery pattern can be calculated and therefore a basic analyzing

tool to answer sub question four.

Figure 25 Summary theoretical model

4.6 Model implementation The theoretical model as shown in Figure 25 can be implemented in Excel. By converting a theoretical

model into a calculation model some simplifications are required to reflect the actual market situation and

minimize within given constrains.

One of the difficulties between the theoretical model and the real model is the use of multiple tray types.

Most of the producers have multiple tray types in stock which creates challenges on finding the cheapest

possible delivery pattern. It was however required that the model should be able to calculate with multiple

tray types. A single fictive tray is created to solve this challenges. This is done by converting all trays to the

same size and get the related tray cost for that new tray. Within EPS the ‘standard fictive tray’ is defined as

SEU (Standard equivalent unit). This tray has a bottom dimension of 60*40 cm and is three cm high. In

the Excel model all trays are therefore calculated to these dimensions with the following two formulas a:

𝐿𝐹 =𝐿𝑇𝑟𝑎𝑦 𝑡𝑦𝑝𝑒

𝐿𝑆𝐸𝑈 ( 22 )

With: Ltray type : Number of trays on a pallet LF : Tray load factor LSEU : Europallets: 304, Poolpallets: 380

𝑀𝑎𝑥 # 𝑡𝑟𝑎𝑦𝑠 =𝑓𝑢𝑙𝑙 𝑡𝑢𝑐𝑘 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦

𝐿𝐹 ( 23 )

With: Full truck : Europallets: 10.032, Poolpallets: 9.880

For this new fictive tray 304 trays can be placed on a Europallet and 380 trays on a Poolpallet. For a full

truck this means 10.032 trays on Europallets and 9.880 on Poolpallets. If of tray type X 152 trays fit on a

pallet, this tray type will have a LF of 2. If the tray costs are normally €0,002 per day in this case the tray

will cost €0,001 per day. The trays going out at a producer have also be multiplied by LF. When this is done,

all trays are recalculated to a similar standard size with a corresponding cost. This fictive tray cost and

demand can be used to calculate a minimum cost pattern.

The minimum cost pattern is a consideration between delivery and tray costs as mentioned several times

before. The theoretical transport formula has already been discussed together with the statement that it will

differ per country. For Spain, the delivery costs are calculated with the following formula:

𝐶𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦,𝑝 = (∗∗∗ + 𝐷𝑝 ∗ ∗,∗∗) ∗ 𝐿𝑝 +#𝑠𝑡𝑜𝑝𝑠∗ ∗∗

#𝑠𝑡𝑜𝑝𝑠+1 ( 24 )

This formula has been determined by analyzing 17.747 deliveries in 2015. By comparing the delivery costs,

loading degree and distance, this formula could be distracted. A more detailed explanation on how the start,

extra stop and km costs are determined is discussed in Appendix E. According to the analysis the km costs

are independent of the distance. No matter the distance, the km costs will be €*,** on average.

A simplification in this formula is an assumption on truck utilization. It is assumed that if a single producer

has truck utilization of at least 80% he will not share the truck. There are not enough very small producers

to take only 2 or 3 pallets for one transport. It is therefore modeled that from 80% truck utilization a FTL

truck price is assigned to the transport. If the truck utilization for one producer is less than 80%, it is

assumed that there is a combined transport which results in a FTL. A transport of less than 40% truck

utilization is assumed to share the truck with two other deliveries. This means that there will be extra stop

costs assigned to a transport and the start and km costs can be divided over 10.032 or 9.880 trays.

An optimum is found between the simplified transport costs and the ‘standard fictive tray’ costs. Since the

model is built to calculate the current tray delivery related losses, ordering data from 2015 is taken. The

calculation factor is used to transfer the 2015 producer outgoing tray data into the SEU trays. The daily

2015 incoming, outgoing and related stock data is imported in the model for two different tray types per

producer. The distance and amount of trays per transport are also imported in the model. This data is used

to calculate the current transport costs and as input for the minimum cost calculation. All these input data

can be extracted from the EPS data query into the model by just selecting the producer.

The model will automatically calculate the current transport and tray costs when the data is imported. It

will also calculate these costs in a cost minimization situation. This is done with the theoretical model as

described in Figure 25. On a daily basis the Excel model checks if the producer stock (per tray type) is still

higher than the reordering point. If this is the case no actions are necessary. However, if the stock is lower,

trays will be ordered. From that moment on the model will calculate how to deliver the producer with

minimum costs within the forecasting horizon. The model will calculate the delivery size in ‘standard fictive

trays’. These trays are then divided over the different tray types. Thereby the forecast of each individual

tray is taken into account as well as the current stock. The ratio of delivered trays depends on the still

available producer stock to prevent building huge stocks.

An example of the model outcome is shown in Figure 26. The graph on the top right shows the 2015 tray

ordering pattern. The graph below shows the compared model results for the two tray types and the total

stock. The third graph indicated the daily tray costs over the year. This is shown as a reference to see if and

how the delivery pattern differs in peak period. The left top table shows the producers input characteristics

like tray types, distance to depot, forecast horizon, type of pallet and the maximum storage space at the

producer. In this table it is also possible to decide if a supply chain optimum or an EPS optimum should

be calculated. Thereby the interest rate can be selected as well as the type of deposit of a producer.

Calculations are made with this input information. The tables below show the results starting by the

information on the initial and minimum tray stocks. In the three consecutive tables the transport and tray

costs and related transport sizes are shown. This is done on aggregated as well as individual tray level. The

sixth table shows the consequences for the producers when the minimized cost delivery pattern is applied.

The last table show the potential savings or losses for producers and EPS combined.

Figure 26 Outcome example of the minimization excel model

4.6.1 Forecasting model

Until this moment the model is designed to calculate losses due to a potential cost inefficient delivery

pattern. However, as discussed in the previous chapter there are forecasting uncertainties. Even though

most producers are able to predict retailer demand (fluctuations) rather accurate, there will always be some

unforeseen changes. This should be taken into account to simulate a more realistic situation. When these

uncertainties are taken into account and retailer demand is known, the model could also be used as a

forecasting model. The used forecasting error is as follow:

𝐸𝑟𝑟𝑜𝑟 = 𝑅𝑎𝑛𝑑𝑜𝑚(± 𝜀 ) ( 25 )

With:

𝜺 : Error margin in percentage. If 𝜀 = 10% then the actual demand is between 90% and 110%

of the original forecast

The error helps making the simulation more realistic. 55% of the producers claim they can predict retailer

demand rather accurate as mentioned in paragraph 3.5. However, it must be taken into account that the

study is about the fresh produce supply chain. This is a supply chain whereby uncertainties are generally

rather big. Therefore, even a producer admitting to have an accurate forecast can easily be 10-20% off. For

producers that have more difficulties with retailer planning due to fluctuations the forecast error can be set

higher (20-40%). The producer forecasting error is not exactly known and will differ per producer. For this

study a fixed forecast error of 25% is assumed. Even when stated that a lot of producer are able to forecast

accurate, the error is set on 25% just to be conservative.

This study aims to calculate potential revenues by comparing model output with historical data. If the error

is placed on the real historical demand, there is no current situation to compare with. The producer outgoing

trays should in both the current situation as well as the new situation be the same to compare the results.

The error factor can therefore not influence the actual tray usage of a producer. The historical forecast is

assumed to be the actual outs times the forecast error. This results in the same outs but with a fictive historical

forecast.

4.6.2 Requirements check

By adding a forecasting error, all components of the model are discussed. After implementing the model it

is checked if all requirements are met. The requirements as set in paragraph 4.1 are shown in the figure

below. When this figure is compared to Figure 26, most input and output requirements can directly be seen.

Tray types, distance, producer tray storage (in pallets), forecast uncertainty and transport factors are all

shown in the left top part of the figure. Retailer demand flow and corresponding producer tray demand are

background input variables imported from the data warehouse and adjusted with the forecast error. At last

there are daily tray costs. The daily tray costs are calculated in a separate model and exported to the

calculation model. These can be checked in the last graph of the previous figure. The transport costs are

calculated depending on the tray demand in the model. Thereby all input requirements can also be found

in the model.

Most output requirements can all be found in the previous figure. The transport costs per tray type, tray

costs per tray type for both EPS as well as for producers, transport utilization, number of transports and

the minimum stock at producer can all be found in this overview. The (average) stock at producer and the

required trays to serve the system are calculated in a separate sheet in the same model. This is done to keep

the overview in the result sheet.

It can be concluded that all input and output requirements have been met in the model. The calculation

model itself is more difficult to check. The method describes a way to calculate the minimum given

certain constrains. However, it cannot be checked if this is actually the absolute minimum cost. Therefore

there are many uncertainties and maybe even better existing methods as described in paragraph 4.1. The

actual cost saving possibilities will be calculated and discussed in the next paragraph and chapter 5.

4.6.3 Considerations The model as discussed in the previous six paragraphs has some limitations that should be taken into

account when analyzing the results. First and most important this is a model calculating with historical data.

Producer forecasts are not known by EPS so they cannot be used. The model uses historical patterns and

adjusts these patterns to simulate a demand forecast. This is done with the forecast error percentage. It is

not exactly known how the forecast errors will influence the model but it is expected that with bigger

forecast errors less savings are possible. This will be tested in the sensitivity analysis. Actual producer

forecasting data is required to predict the possible savings more accurate. For now the forecast error has

been set on 25% in consultation with EPS employees.

Another limitation in the model is the calculation of the order sizes. Each single day the optimum transport

size is calculated. This is done with the tray costs for that day. It is not taken into account that some trays

may still be at the producer in the next month and therefore should get a different daily tray cost assigned.

This is done because Excel could not handle the formula’s calculating this tray costs over time due to model

size. For this to be calculated correctly, another simulation program is required. It is expected that this

simplification does not cause major disruptions since the average tray costs does not differ a lot for most

consecutive months. This is strengthened by the rounding up to whole pallets. For individual trays there

may be a difference but since the model rounds up to whole pallets, it is expected that the consequences

are limited. Only at tipping points between months where there is a big difference in tray price the model

may show slightly negative results.

The third consideration that has to be taken into account is the linear pattern of the outgoing trays. This

has already be discussed and is done due to the uncertainty of forecasts. Depending on how the forecasts

differ from the actual pattern this may have a positive or a negative effect. Since order behavior factors

fluctuates, the real demand pattern is never known in advance.

A last consideration that has to be taken into account is the vast network of producers. This model only

calculates the minimum cost delivery pattern of a single producer. However, there are a vast amount of

producers and they can never been seen as single, independent identities. This specially accounts for LFTL

orders which have to be combined. By combining LFTLs, it is quite probably that orders will be slightly

adjusted to get a FTL. It is also possible that transports are delivered a few days in advance which will

slightly reduce the savings. However, this will probably also create extra savings not taken in this model.

This is shown by the figure below. The upper figure is a current delivery pattern and the lower figure is a

possible delivery pattern with the minimization model. The routes can be optimized since it is known in

advance when producers require trays. Even if this means that an order is delivered two days in advance,

this may still result in overall cost savings. In the current model the km costs are deducted from the current

delivery costs. This means that the km costs are probably slightly too high since detours are not extracted

in this calculation. When the transports are optimized these detours could be reduced and therefore the

input variables for the model may be slightly lower. However, this is not taken into the model and will

require further research on network optimization.

Figure 27 Example of a current delivery route and a possible delivery route when transports can be planned by EPS

Identifying these main considerations helps by interpreting the results. Some considerations will have a

slightly positive effect while others will influence the results in a slightly negative way. In the end it is

expected that the positive and negative consequences will compensate each other. No major differences

between the model outcomes and real possible savings are expected. However, this can only be tested by

identifying the real forecast error and extending the model to all producers in the network. This will not be

done in this study. The model as described above will, with the discussed consideration, be used for testing

and analyzing.

4.7 Verification & validation Before the model can be used for analysis, it should be checked. This is done by verification and validation.

Verification of a model entails whether the model has been coded correctly and consistently. Validation

checks the model structure and behavior (Daalen & Pruyt, 2009). There are several methods to verify and

validate the model. This paragraph will briefly discuss two verification methods and four validation methods

used to check the model. These methods involve examining individual calculations, discussing with EPS

experts and comparing results with different input conditions.

4.7.1 Verification

The first check of the model is a dimension analysis which is already done during model building stage. A

dimension analysis is a check if the units of the model corresponds with the real variables. So should the

unit of tray costs per day be: Ctray/Tday. If the unit would be just tray costs or days there is a mistake in the

model. During the building stage on each calculation a dimension check was performed. This method was

especially useful when calculating the standard fictive tray and the daily tray costs. Small intermediate errors are

prevented from accidently sneaking in by checking the dimensions already in the model building stage.

Before and during the building stage EPS experts were consulted. Methods for calculating daily tray value,

producer costs and transport costs are discussed with department experts before modeling to assure a

realistic calculation model. Not only the methods themselves are discussed but also the input variables

required for calculation. This includes: depreciation time, interest level, km costs, initial transport costs,

extra stop costs etc… When they all agree on the methods and the input data is as accurate as possible, the

model outcomes will be more realistic. Therefore this verification step is extensively used. Experts

consulted for each topic are shown in the table below:

Topic Transport Tray costs + General model

General model

EPS employee Fred Lessing Jeroen Willems Niels de Goede

Title Manager International Flows

Business Controller Tray Division

Supply Chain Manager

Table 2 Checks performed by EPS experts

4.7.2 Validation The validation section checks the behavior and outcomes of the model. Therefore the model is tested with

actual producers to see how it behaves and responds to certain situations. This part is executed to get

knowledge of the model behavior and to identify deviations. Especially since some simplifications have

been made, the model may have outcomes that are not completely representative to the real world. By

identifying these deviations they can be accounted for during the analysis of the model outcomes. The

model is tested on four different validation methods:

Empirical parameter confirmation test

Face validation

Direct extreme conditions test

Sensitivity analysis

Empirical parameter confirmation test

The empirical parameter confirmation test does not validate the model itself but checks the model input

variables. For the minimization model the transport and tray cost parameters are the most important

parameters. These parameters have been numerical checked by analyzing existing data. The parameters

have also been conceptual checked by experts within EPS. They have checked whether the variables in the

calculated parameter corresponds to the real system. Hereby it is checked if no variables were missed.

Besides the conceptual check, there was also a numerical check done by EPS experts. The data retrieved

from several analysis have been checked with expert knowledge. Fred Lessing has approved the transport

cost parameters and Jeroen Willems has approved the tray cost parameters both conceptual as numerical.

The tray costs parameter has been checked by calculating how many days per month trays are out of EPS’

control and multiplying this by the daily tray costs for each month. Thereby it was seen that this value

corresponds to the costs made on that tray on both storage and depreciation costs combined. Thereby EPS

approves the input parameters and states that it represents the real system.

It has to be taken into account that the transport input parameters is an approximation of the real transport

costs. The transport costs have been averaged and estimated over Spain. The transport costs related to

individual producers may differ due to the geographical location. The model also calculates the new

transport costs for the current delivering situation. This means that the actual transport costs and the

transport costs as in the model will differ. In most situations the transport costs in the model will be lower

than the actual transport costs have been. This is due to the assumption that all transports are fully utilized.

When looking at the results and comparing the current situation with the minimum cost situation, it should

be taken into account that the transport costs will probably be slightly higher for the current situation.

Face validation The checked parameters are implemented in the model to run simulations. The results of these simulations

are checked to see if the model with initial parameters act like expected. It is thereby expected that during

peak season the delivery pattern will be different compared to low season. In the face validation part this

assumption has been verified. It has been seen that during peak season more transports with less utilization

are preferred. During low season the model suggest to have less but more utilized transports. It was also

expected that costs could be saved on especially trays. The provisional results confirm this hypothesis. This

indicates that the model operates like expected. The results will be discussed in more detail in the next

chapter but initial outcomes indicate a well operating model. Approving the initial outcomes is just part of

the validation analysis. The next part will go further in analyzing the behavior of the model when parameters

are changed and manipulated.

Direct extreme condition test

A direct extreme condition test tests if the model operates like expected by manipulating conditions. For

this analysis both the tray as well as the transport costs will be in- and decreased. High transport or low tray

costs should result in less, fully utilized transports. Low transport costs or high tray costs should increase

the amount of low utilized transports. The expected behavior is summarized in the table below.

Test # transport Transport utilization

High transport cost ↓ ↑

Low transport cost ↑ ↓

High tray cost ↓ ↑

Low tray cost ↑ ↓ Table 3 Expected behavior on direct extreme condition test

The results of this validation test shown in Figure 28. The results are like expected. However, there was

one observation which was not directly expected but it could be explained with the taken assumptions. For

the transport costs the initial and km costs did not matter for the model behavior. Only the extra stop costs

were important. This can be explained since it is assumed that LFTLs are combined to a FTL. It is also

assumed that these loads are quite close to each other so the costs can be equally divided over all trays.

Therefore it does not matter how high the km costs, initial costs or the distance of a producer is when the

amount of transports are determined. Since the transport costs are equally divided it does not differ if a

producer has two times half the initial costs or four times the quarter initial costs. Only the extra stop costs

makes a difference in the transport formula since they are fixed for a single producer independent on the

tray size. However, even if the delivery pattern does not change when initial and km costs are manipulated,

the total costs do. An adjustment of either one of the transport variables will have influence on the total

costs for that specific producer. In- and decreasing the extra stop costs will influence the number of

transports and the transport utilization as shown in the top figures of Figure 28. The left figure show just a

few highly utilized transport and the figure on the right show a lot of low utilized transports. The two lower

figures show exactly the same result when the tray costs are manipulated. These results are just like expected

when the input conditions are extremely manipulated.

Figure 28 Model behavior on direct extreme condition test – graphs provide stock information at a producer

Sensitivity analysis After concluding the model acts like expected and is of sufficient quality to analyze, further analysis can be

performed to really understand the model. This is done by a sensitivity analysis. ‘The sensitivity analysis is

used to determine the elements in the model to which the model is sensitive (i.e. that have major influence

on the behavior when they are (slightly) changed)’ (Daalen & Pruyt, 2009).

The most important input variables will be analyzed for the sensitivity analysis. This includes the following

variables:

Tray costs

Distance

Km costs

Initial transport costs

Extra stop costs

Forecast horizon

Safety stock size

Interest rate

Forecast error/uncertainty level

The variables will be adjusted by both in- and decreasing five, ten and twenty percent of its initial value.

The influence on the total costs will then be calculated. Hereby not only the minimum costs will be

compared but also the current costs. By assuming a producer is located twenty percent further away, this

will also influence the transport costs of the current situation. Producer 305114 with two tray types is taken

as example for this analysis. The results of the sensitivity analysis are shown in Figures 29 and 30. The costs

for EPS and producers combined in the current situation are €7.355,- and this could be reduced to €5.085,-

according to the model, a reduction of 31%. The sensitivity figures show hardly any outstanding results.

Changes in tray and initial delivery costs have the highest impact on the model. When these costs increase,

the total costs for EPS will increase as well and the other way around. It can however be seen that the

minimization model is able to absorb part of the tray costs. When the tray costs is increased a new

equilibrium is found with more, lower utilized transports. This results in a smaller increase of the total costs

and more net profit.

The increase in initial transport costs cannot be absorbed. This can be explained since the initial transport

costs are divided equally over all trays in a truck. Thereby the initial transport costs per tray are always the

same. This explains the linear connection between the costs and the deviation. The increase in the

minimized situation looks bigger compared to the current situation due to a higher increase in percentage.

However, the net difference is exactly the same. The difference in percentage can be explained since the

base situation of the minimum costs is €2.270,- lower. The same net change will have more percentage

impact.

The same explanation also counts for distance and km costs. These variables have however less impact on

the total costs compared to the initial transport costs. This can be explained by the distance between the

producers and EPS. If the initial distance would increase, a percentage change in distance or km costs would

have more impact and the initial transport costs will be less important. With initial transport costs of €***,-

and km costs on €*,** this equilibrium is at *** km. Since the producer is located just 102 km from the

depot, the total initial costs are higher than the total transport costs. However, all these costs have to be

made anyway and will not change behavior of the model. This is due to the assumption of the possibility

of combined transport to get 100% utilization in any situation. If this assumption cannot be accomplished

the model behavior would change when the transport related characteristics change.

Figure 29 Sensitivity analysis model current situation - producer 305114

The sensitivity analysis on the interest rate shows rather remarkable outcomes. For clearing, an increase in

rent results in less costs. For the cash deposit scenario both an increase as well as a decrease results in less

savings. The exact reason for this outcome is not known but it is very likely this happens due to rounding

off to pallet level. The cost minimization calculation is in first place on tray level. Since EPS only delivers

full pallets, the required trays are rounded to full pallet loads (304 SEU). It is thereby possible that a small

increase in interest rate still creates some profits and the other way around. This will however not be taken

into close account since the cost differences are very small.

Figure 30 Sensitivity analysis model minimized cost situation - producer 305114

Figure 31 shows the sensitivity analysis of the forecast error/uncertainty level. Per scenario ten simulations

were ran. Since the errors are randomly generated each simulation is different. The outcomes show that the

safety stock remains positive with even a 40% forecast error. This is due to the increase in safety stock with

higher uncertainty levels. It can be seen that with a higher uncertainty level the average minimum safety

stock increases. This will result in more trays at producers which increases the costs. It can be seen in Figure

32 that the total costs clearly increase when the forecast error increases. However, even with a 40% forecast

error an average profit of €1.921 (26%) could be generated. The connection between forecast uncertainty

and total costs seems linear for at least this producer with an r square of 0,99.

Figure 31 Sensitivity analysis forecast error average of ten runs - producer 305114

Figure 32 Sensitivity analysis total costs with different forecast uncertainties - producer 305114

The sensitivity tests validates the stability of the model. Even for planning with forecasting errors the model

seems to be stable. The most important constrain, always tray availability at the producer, could be managed for

producer 305114 in all situations. Not only the availability was assured, the costs could be decreased as well.

Even with high uncertainties the costs could be reduced significantly. Now the stability of the model is

tested and approved the model can be used for testing alternatives.

4.8 Alternatives In the previous seven paragraphs of this chapter a model to calculate and reduce the tray delivery related

costs has been introduced, implemented and validated. The model seems to be working as expected and

can be used to calculate the possible savings. However, calculating current delivery related losses does not

improve the system. Therefore the model will be slightly adjusted to be able to calculate alternatives.

The current tray delivery system of EPS is based on: Customer is always right. This is so advanced that

customers are not directly punished for extra costs they are accountable for. To reduce the tray delivery

related costs, some control needs to be shifted back from producers to EPS. There are a vast amount of

ways to do so with two different base principles: charge producers for extra costs made because of their

ordering pattern or assist/steer producers to reduce costs. Since there are a vast amount of slightly different

alternatives this study will just focus on the two main principles.

A1 daily renting fee The first alternative is: Charge producers for extra costs made because of their ordering pattern (A1). This

is done by simply charge a time related renting fee for the trays. A time related renting fee is already charged

for many rentable products in many sectors. One example is when a car is rented a daily fee has to be paid.

There are also products where renting fee starts only after a certain period. For example when books are

rented from a library this is free for the first couple of weeks with a subscription. When the book is not

returned within this free renting period a penalty has to be paid. This same principle can be applied on

producers: Make them pay from the start or only charge them after a certain time period. The principle

does not differ that much because the total depreciation costs should somehow be paid and thereby be

collected from producers. If the first days are time renting free this will result in a higher initial renting fee.

Implementing a time renting free period still does not punish all cost inefficient behavior. Maybe some

producers could plan more cost efficient within the time renting free period enabling even more savings.

Producers using trays less than the time renting free period will also pay too much renting fee compared to

a daily renting fee starting from day one. Thereby the best time free period may change for each producer

due to individual characteristics. This will make the system more complex. It is most transparent to calculate

with a daily renting fee starting from the first day. For this alternative the transport costs should be charged

to producers as well. If this is not done, producers will only consider the renting costs depended on time.

This will, in the most extreme situation, result in (daily) transports of one pallet just to minimize the tray

days. This will increase the transport costs for EPS significantly and not result in a minimized cost situation.

A minimization cannot be found if part of the costs are not taken into consideration during planning.

This results in the following charges to producers:

Initial renting fee (covering washing, sorting, handling and overhead costs)

Daily renting fee (covering tray depreciation and storage costs)

Transport costs

The variable tray value for EPS have already been discussed. In peak period trays are more valuable

compared to off season. This will be charged to producers. However, a monthly changing daily renting fee

will be complex for producers in making fair considerations and may even result in the bullwhip effect due

to price fluctuations. Therefore this is not a preferable option. It is chosen to have a peak period and an off

season daily renting fee. The six months with the higher demand will get the peak season charge and the

months with the lowest tray demand will get the off season charge. The initial renting fee is assumed to be

the daily renting fee minus ten days average tray costs for EPS. The daily renting fee for producers in low

season is the average tray costs for EPS in the lowest six months and the average tray costs in the peak

period is the average renting fee for the six months with highest demand. Trays with more seasonality will

therefore have bigger differences between low and peak season daily tray costs The model will use this

information to calculate expected producer behavior when these costs are directly charged.

The safety stock will in this case be calculated slightly different. In the model the lead time has been set on

two days. This can be easily done when all expected demand is known and EPS is able to plan transports

themselves. However, in the current transport policy a lead time of four days in used to enable combined

orders and thereby reduce transport costs. The lead time for this alternative will be extended to four days

and the safety stock will thereby increase to assure availability for the producer over this longer period. This

is well within the boundaries of the current safety stock as derived from producer stock patterns.

A2 Vendor managed inventory

The second chosen alternative is a vendor managed inventory system (A2). In this theory EPS helps

producers with tray management planning in the most extreme way. All information producers have will

be shared with EPS and EPS will make sure producers get trays as required. The model as discussed in this

chapter can be easily used to calculate the results of this alternative. Proper considerations can be made

since all available information will be at EPS. This is possible since over half of the producers mentioned

in the questionnaire that they can accurately predict retailer demand. With this information tray demand

can be predicted. The minimized cost situation can be calculated when all information is put in the model.

Since there are always some differences between forecasts and actual orders, there will be a forecast error

of 25% included in this alternative as discussed before.

As mentioned before, not all producers are willing to cooperate in a VMI or are not able to predict retailer

demand accurate. For this group VMI might not be suitable. For that reason a VMI system for a limited

amount of producer will be taken into account as well. This will result in a different daily tray renting value

for the producers cooperating in the VMI system as explained in section 4.3.5. It is estimated that since

only the more expensive top part of the tray demand is used the savings per producer can be bigger. The

top part encounters moments of stand still which reduces the amount of days in which the tray investment

costs can be earned back. In the partial VMI alternatives 25%, 50% and 75% producer participation ratio

will be calculated. Thereby it can be seen how much extra profit can be gained when extra producers are

added in a VMI system. This can later be compared with the additional costs to add these producers. When

less producers are participating the tray costs of the base situation should be adjusted as well. This is since

the tray costs in both situations should be equal to make accurate considerations. All alternatives as

discussed in this paragraph are shown in the table below:

Alt Alternative Specifications

A0 Base Current situation

Min Full information Supply chain optimum

A1 Daily renting fee Producer optimum

A2 VMI with 25% forecast error Supply chain optimum

A2.1 VMI with forecast error - 75% producers Supply chain optimum – Adjusted daily tray costs – base scenario A0.1

A2.2 VMI with forecast error - 50% producers Supply chain optimum – Adjusted daily tray costs – base scenario A0.2

A2.3 VMI with forecast error - 25% producers Supply chain optimum – Adjusted daily tray costs – base scenario A0.3

Table 4 Overview of alternatives

4.9 Analysis method The alternatives will be tested to get better understanding of the possible savings and how they can be

managed. Each simulation will be different due to the forecast error on the expected demand. Therefore

multiple simulations are required to get an overview on the possible savings of each alternative and what

average savings can be expected. With insufficient simulations it is possible to get outliers that affect the

results and lead to wrong conclusions. For this reason it is important to have sufficient simulations.

However, running too many simulations requires a lot of time and will not result in more accurate results.

Finding a balance between not too little but also not too many simulations will be explained in this

paragraph.

At first several producers are selected for the case study. This is done by selecting Spanish producers that

declare all outgoing flows to EPS on a daily basis. Unfortunately there are also a lot of producer that only

declare once a week or once every two weeks. With this information it is impossible to determine producers

daily demand. There were also a lot of producers that did not declare all their flows. If producers do not

declare 100% of their flows, stock will steadily increase. This results in very high safety stocks, dwell times

and an unrealistic possible savings. For this reason only producers that declare all their flows on a regular

basis can be used. 23 of these producers were found in Spain. These are all selected to get as much

information as possible. Since the producers differ in size, distance, type of produce and tray type, it is

preferred to test at least the 23 available producers. The 23 producers combined are accountable for 3,5

million SRTs which is 1,8% of the Spanish SRTs and 0,4% of the total SRTs of 2015. The list of selected

producers is shown in Appendix F.

With the producers selected, the required amount of simulations have to be determined. This is done in

two ways. At first the t-test is used to check if the model outcomes are significant. It is thereby assumed

that the model outcomes are normal distributed. After approximately 10-20 simulations, it could be

concluded with more than 99% certainty that the minimum costs are lower than the current costs.

According to the t-test 20 simulations would be sufficient.

The required number of simulations is also checked with a slightly different method. This method checks

if the standard deviation changes when adding one more simulation. It is also checked if the result of this

extra simulation is within the boundaries of the previous simulations or if the simulation exceeds the

previous boundaries. This is tested by running 150 simulations as shown in the figure below:

Figure 33 Total costs and standard deviation for number of simulations

It can be seen that after approximately 30 simulations the standard deviation remains stable at

approximately 40. Besides the standard deviation, the model results have no major outliers after the first

ten simulations. At simulation 130 there are two outliers whereby the total costs are €10,- higher and lower

compared to the results in the first ten simulations. These outliers are however so small that the effort for

the additional 100 simulations per alternative per producer are not worth the slightly increasing bandwidth.

That these results are uncommon outliers are proven by Figure 34. It can be seen that the results are, like

assumed, normally distributed and the results at simulation 130 are extreme outliers. Therefore these two

outcomes can be placed outside the 95% confidence interval.

Figure 34 distribution of minimum tray costs for producer 304080

These two methods provide an idea about the required amount of simulations. 30 simulations should be

enough for this producer. However, for other producers this may slightly differ. Therefore each alternative

will be tested 50 times. This should be more than enough to get reliable outcomes and is still manageable.

In total this will result in 50 simulation of five scenarios for 23 producers which adds up to 5750 simulation

results. These will be analyzed in the next chapter.

4.9.1 Output expectations Before the analysis is performed, some expectations are made that are going to be tested. It has been

mentioned several times that the current delivery pattern is expected to be far from cost efficient for EPS.

It is therefore expected that with all alternatives EPS will be able to safe costs.

Hypothesis 1:

Supply chain cost savings on tray delivery are possible for all discussed alternatives

The current delivery patterns show long producer dwell times and large transport quantities. It is expected

that costs could be especially saved by delivering smaller amount of trays in mainly the peak season. This

may even cause the transport costs to increase slightly.

Hypothesis 2:

Supply chain cost savings on tray delivery will be mainly caused by a reduction of tray costs

The total tray costs only provides an indication of the costs related to a specific producer. However, it is

more interesting to see what will happen with the total amount of trays required to serve all producers. It

is expected that the alternatives will have consequences for the total required tray stock since the daily tray

value differs per month. Especially in the peak month, where the trays are currently more valuable than

during off season period, it is expected that producers will shift to smaller orders. This will decrease the

stock and cycle time at producers. It is therefore expected that during peak period EPS can manage with

less trays compared to the current situation. This will flatten the total required trays along the year.

Hypothesis 3:

EPS can manage with less trays during peak period which results in a lower tray working capital

To test the hypotheses some KPI’s are required. These will be discussed in the next section.

4.9.2 KPI’s The current KPI’s are not sufficient to calculate the possible savings. In the current system only the

transport costs are taken into account as KPI as discussed before. To be able to calculate the total savings,

some additional KPI’s are required. These will cover the tray related costs and tray stock levels. The KPI’s

that should be monitored in the future state are as follow:

Supply chain cost

Tray costs (EPS)

Transport costs

Producer costs

Minimum stock at producer

Required trays per month for EPS

The supply chain cost KPI is introduced to see the consequences for producers and EPS combined. The

supply chain costs should be minimized for all the alternatives. This KPI is a combination of the tray,

transport and producer costs. These individual cost components will be monitored as separate KPIs to get

insight in producer and EPS consequences. Especially the tray costs are interested for EPS since this has

direct influence on the amount of trays required to run the total system. Changing the amount of trays in

the system will have long term consequences due to the long depreciation times. The required tray capital

will be estimated with another introduced KPI: required trays per month. This KPI shows the average

amount of trays for each producer required to serve the system. Thereby it is taken into account that the

trays are **** days at the retailer before they can be used again. This KPI is a daily average amount of trays

on monthly level that are not in position of EPS. For this analysis the two peak months are most interested

for calculating the possible tray savings. The lowest months are interested to calculate the possible storage

savings. With this data it can be calculated how many trays will be in EPS storage in low season to assure

enough trays in peak season.

Producers will also be monitored. This is done for two reasons: assuring tray availability and providing a

possibility to share revenue. At first the minimum stock at producers is monitored as a KPI since there

should always be trays available for producers. This was a requirement as mentioned in the research

question. Secondly the producer costs are monitored. This is since the study will look to a supply chain

minimum cost situation. However, a supply chain minimum does not automatically result in both EPS as

well as producers profiting. The costs of both participants is monitored to enable a possible revenue sharing.

This may be necessary to convince producers to cooperate and not to deliberately distort information.

The six KPIs form the most important and interesting indicators for the analysis. However, there are some

secondary indicators that are interested to monitor and provide more information on possible changes in

(behavior of) the system. These interested indicators that will be monitored are:

Number of transports

(Extra) rent for producers

SRT

Tray type

The number of transports provide a good overview on the delivery differences between the current and

the proposed system. It will give a brief indication if it will be more profitable to increase the number for

transports and thereby reducing the load per transport or if sending full truckloads is cheaper. The (extra)

rent producers have to pay to EPS only counts for the daily renting fee alternative. In this case the rent

producers pay to EPS will in- or decrease. However, the rent is directly and only paid to EPS. Therefore, a

change in rent will be a change in money from producers to EPS. The costs of both producers as well as

EPS are combined in the total supply chain costs. Since a rent difference will only mean a difference in

costs and revenues for EPS and producers, the total supply chain costs will remain exactly the same. This

means that an in- or decrease in rent will not influence the supply chain costs. The tray rent interest costs

paid by producers is taken apart from the change in rent since this does not remain in the supply chain but

is paid to a bank. By monitoring the difference in rent, it can be seen what the financial consequences for

both EPS and producers will be. At last, some basic characteristics like SRT and tray type are monitored.

This is to scale the results for making general conclusions.

4.10 Conclusion This chapter discusses a method on how to calculate a more optimum delivery pattern to reduce costs for

EPS as well as producers. The model calculates a minimum between the delivery costs and the tray costs

within given constrains of both EPS and producers. It is calculated how delivery patterns should change to

minimize transport and tray related costs. The tray costs in the model consist of both depreciation costs as

well as storage costs. These costs are calculated with the following principle: In absolute peak period a tray has

to be purchased but will only be used once a year. The purchase costs of this tray will be divided by the days the tray is actually

used. The daily tray costs are calculated on a monthly basis with the seasonal usage taken into account.

During peak months the tray value is higher compared to off season period.

The theoretical model is implemented as discussed in paragraph 4.6 with a few simplifications on especially

forecasting. The model calculates both the costs made in the current situation as well as a cost minimum

situation to get an overview of the possible savings. Variables like: tray type, tray costs, producer distance,

costs per km, trip starting costs, extra stop costs, forecasting horizon, interest rate, pallet type and storage

space are all easily changeable to meet individual producer characteristics. By changing these variables a

minimum cost situation can be found for each producer.

The model will be used to calculate the current costs and loses as well as two alternatives to improve the

system. These alternatives include a daily tray renting price for producers and a vendor managed inventory

system. The VMI alternative will be calculated with different participation grate scenarios. The alternatives

will be tested on 23 Spanish producers with the following hypothesis:

1 Supply chain cost savings on tray delivery are possible for all discussed alternatives

2 Supply chain cost savings on tray delivery will be mainly caused by a reduction of tray costs

3 EPS can manage with less trays during peak period which results in a lower tray working capital

5 Results In this chapter the model outcomes of the alternatives will be discussed. This is done by extrapolating the

23 analyzed producers. It is thereby assumed that the 23 producers form a representative base for all

producers dealing with EPS trays. Appendix B showed that Spanish producers have a slightly lower

producer dwell time than other countries. Since there are no known reasons why producers in other

countries should have longer dwell time than Spanish producers, it is assumed that the savings for other

producers are in line with Spanish producers. The outcomes are therefore scaled to companywide level.

The results are discussed in six paragraphs in this chapter. In the first paragraph the minimum cost situation

on tray delivery will be discussed. This is followed by a paragraph on the daily tray renting price alternative

(A1). The thirds paragraph continues with the VMI alternative. In this paragraph all VMI participation

scenarios – 25%, 50%, 75% & 100% - are dealt with. The fourth paragraph will describe a future state of

EPS and which alternative is recommended. This is followed by a paragraph that discusses the long term

financial consequences for the chosen alternative. Thereby a Net Present Value is calculated. The chapter

will finish with a conclusion paragraph.

5.1 Minimum cost situation (min) In a situation with perfect information, no uncertainties and only rational supply chain minimum cost

decisions it is possible to create a lot of tray savings. The savings are especially gathered in peak season. It

can be seen in Figure 35 that the tray delivery sizes change with the tray value. In June/July the delivery

sizes are lower due to high tray value caused by companywide demand increase. In other months when

there is lower tray demand, the tray value decreases and the delivery sizes increase. In a perfect situation

this will results in a variable dwell time throughout the year. The dwell time decreases in peak season to

reduce the required trays. In low season the dwell time will increase slightly to reduce transport costs and

safe seasonal storage costs.

Figure 35 Tray stock at producer 301383 in minimized cost situation compared to tray value

The savings are not only gathered by a different delivery patterns but also from a reduction in safety stock

as can be seen in Figure 37. It is possible to manage the tray stock more accurately which can lower the

safety stock if there is complete information and orders are controlled by EPS. Reducing the safety stock

and changing the delivery pattern could safe up to 35% of the required tray fleet. This is a reduction of over

** million trays. Figure 36 shows the possible tray stock savings at each individual producer for the peak

month. It can be seen that for most tray types the possible reduction can be between 25% and 53%.

However, there is also one producer for which it would be more beneficial to higher his average tray stock

in peak month and saving transport costs. This possible tray reduction is calculated with the safety stock

calculation as explained in the previous chapter. This results in a rather low safety stock which may be

uncomfortable to producers. When the minimum safety stock is increased to the same level as the current

situation, a tray fleet reduction of 30% (** million trays) could still be managed. In this case the minimum

safety stock is of equal size compared to the current situation.

Figure 36 Possible tray savings in percentage for individual tray types at producers for minimum cost situation

There are hardly any delivery cost differences between the current situation and the minimum cost situation

for the 23 selected producers. During peak period the transport costs increase slightly due to smaller

delivery sizes and thereby extra stop costs. However, during low season the individual transports increase

in size whereby transport costs are reduced. The larger delivery batches in especially low season result in a

suggested reduction of transports from 643 to 598 as summarized in Table 5. This reduction is especially

caused by five producers who are responsible for a reduction of 93 deliveries in the new situation. This

means that for most other producers the amount of transports will slightly increase. In the end it can be

concluded that for the 23 producers the effect of another delivery pattern is very small on the transport

costs.

Current situation Minimum Minimum extra Safety stock

# Trays *** m *** m *** m

Trays percentage 100% 65% 70%

Producer costs € ** m € ** m € ** m

Transports 643 598 598

Transport costs € ** m € ** m € ** m

Storage costs € ** m € ** m € ** m Table 5 Minimum costs results

The different delivery pattern will not only influence the required amount of trays. With a reduction of trays

and a variable dwell time over the year, the trays in storage for seasonality will decrease as well. In low

season the trays will be longer at producers compared to high season. For this reason the required amount

of trays to serve all producers will fluctuate less over the year. This results in less trays in storage which

saves depot size and storage costs. Up to 66% of all storage costs can be saved by reducing the required

amount of trays in peak month by 35% and increasing the required trays in low season.

The change in delivery pattern will have consequences for producer costs. The overall producer tray dwell

time will decrease according to model results. Producers will therefore pay less interest and safe money. In

this situation not only EPS but also producers can reduce their costs. The costs made by producers are

however very low compared to EPS since it was assumed that producers only pay interest. Still, all producers

combined could reduce their costs with approximately €** m in a perfect information situation.

The results as shown above are only in a minimum cost situation. It can be seen that the required amount

of trays can be reduced by up to 35%. This change in delivery pattern will have hardly any influence on the

delivery costs but will reduce the producer costs as well as the storage costs. The financial consequences of

the tray reduction will be discussed in paragraph 5.5. This is since more intensive use of trays will reduce

the depreciation time and will therefore require different long term tray investments. For this reason the

tray depreciation costs will be discussed with a net present value (NPV) calculation in a separate paragraph.

The possible savings as discussed above are only manageable in a perfect world with full information, no

fluctuation or uncertainties and with all control at EPS. Since the world we live in is full of uncertainties

and complexities, the situation as described above will not be manageable. The next two paragraphs will

discuss the results of the two alternatives and their variations. It will also discuss how realistic the

alternatives are and how they can be managed.

Figure 37 Current and new producer stock pattern

5.2 Daily tray renting price (A1) Charging producers a daily renting fee and transport costs on top on a fixed initial renting fee will reduce

the required tray stock with an estimated 23% or ** million trays. This is an estimation based on possible

producer considerations when a standard renting period of ten days is chosen. If the standard renting period

is extended the savings will be less and a shortening of the period will increase the tray cost savings. This

will however have consequences for the other delivery related costs. The ten days seem a rather good

estimation of the average possible dwell time. The average producer tray costs only increases a little to

€0,****. This is mainly caused by a few producers who are charged significantly more rent as they increase

the ten day average dwell time.

This alternative is calculated with the assumption of deliberate producer consideration. If producers show

some irrational behavior, which is quite likely, the tray savings will reduce. This will be partly compensated

by the extra renting fee collected from producers. However, irrational behavior that only occurs in peak

period will not be solely paid for by producers. Thereby irrational behavior will reduce the supply chain

savings. In the end irrational behavior will lead to more required trays, less storage savings and producer

paying more rent to EPS.

Current situation Daily renting price

# Trays *** m *** m

Trays percentage 100% 77%

Producer costs € ** m € ** m

Producer costs per SRT € 0,*** € 0,***

Upper bound extra producer rent

- € 0,***

Lower bound extra producer rent

- - € 0,***

Table 6 Daily tray renting price alternative (A1) results

It can be seen that the producer costs per SRT are very low with just €0,***. This is since it is assumed that

producers only pay the interest rate over the renting fee. This is again a possible explanation why producers

have such high stocks. Keeping trays in stock cost hardly any money. It can be seen that when a daily tray

renting price is introduced inequality between producers is created. For producers that are able to lower

their tray dwell time the renting costs will decrease. Some producers will even earn money from renting

trays. This is since it was assumed producers get a fixed fee from retailers. Some producers on the other

hand where not able to reduce their dwell time below ten days. There was even one producer who saw his

costs increase ten times. This high increase is possible since the daily renting fee is a lot higher compared

to the daily interest costs a producer currently pays for a tray (€0,***). Thereby this alternative causes

inequalities between producers. Producers with more uncertainties or lower quantities will have a higher

dwell time which results in more rent. These producers will therefore be less competitive since retailers pay

a fix price per tray. Retailers will not pay the price of high uncertainty and low quantities and will go to

cheaper competitors. This may cause producers to lose customers or even stay operative. The inequalities

between producers may increase even further since producers will be accounted for transport costs.

Producers located near an EPS depot will have lower costs compared to producer located further away.

This is not necessarily a con in a normal market. However, EPS does not want to create inequalities between

producers based upon their depot locations or unique characteristics. EPS has started as a company close

to the producer with equality for everybody. This is still in their core values. Therefore inequality between

producers may be a good alternative in normal free market conditions but not preferable for EPS.

5.2.1 How to manage and possible setbacks

A daily tray renting price system is rather easy to implement. If all producers and retailers declare their

incoming and outgoing flows it is for EPS easy to calculate the rent. It is then known how long trays spent

at a producer so what daily renting fee should be charged. There are several existing administrational

programs that are capable of doing so and could be easily implemented within current EPS administration.

The cooperation of producers is not very difficult. Every day they do not declare tray flows more rent will

be charged. Thereby this solution could also be implemented by charging producers a higher renting fee

and paying compensations when the trays are returned. Staying up to date and declaring flows will prevent

unnecessary high bills. However, the retailer side is more difficult. Retailers or intermediate traders should

confirm flows to prevent producers from cheating with their renting days. This is already done by clearing

deposit flows. However, retailers cannot just be forced since they are not directly involved. By enforcing

retailers there is always a change to lose these big customers. It should therefore be taken into account that

retailers may not want to cooperating. In this case retailers may have to be convinced with incentives or the

retailer check can be skipped and the producers are fully trusted.

Another difficulty for implementing a daily renting fee is related to the auctions and producer organizations.

EPS delivers trays to these organizations and they distribute the trays to their associates. As can be imagined

this increases the dwell time between EPS and retailers and thereby the costs. Auctions will, as main

shareholders, not accept being charged for this inefficiency. It is highly unlikely that auctions will agree with

this alternative since it will increase the tray renting price for their customers. This will create a real challenge

when a daily renting fee is introduced companywide.

5.2.2 Summarizing daily renting price alternative (A1) The daily tray renting fee alternative is a rather easy implementable alternative that directly charges

producers a time based rent. It can safe up to ** million trays required to serve all producers. The alternative

will however create inequalities between producers which may result in some producers paying at least ten

times more compared to other producers. This is against initial EPS values but with these possible savings

it can always be considered. Thereby the savings are only possible if producers make rational tray ordering

decisions. All irrational behavior, which is quite likely to occur, will lead to less supply chain savings.

A serious setback in implementation could be the interference of auction. This alternative will have negative

influence on the renting price for these shareholders. It is therefore likely that these parties will not agree

with this alternative or want an exceptional position within the system. By not doing so, the auctions may

block the entire alternative and the possibility for any savings.

5.3 VMI (A2) The VMI alternative with 25% forecast error show promising results which are summarized in Table 7. It

can be seen that millions of trays can be saved on all participation levels of the VMI alternative. Besides

that, a couple of million euro’s storage costs can be saved. These savings are caused by two factors: reducing

safety stock and reducing dwell time. Since the lead time can be reduced, less safety stock is required which

reduces the required trays. This reduction is intensified by reducing the irrational ordering behavior of

producers. This is possible by calculating the safety stock in an uniform, automated way. At last, the dwell

time in especially peak period is reduced my sending more, low utilized transports. According to the 23

tested producers this will result in a possible reduction of 32% of the total tray stock.

As can be imagined, the supply chain costs per producer are higher for alternatives where less producers

are participating. This can be explained since these producers get a more expensive peak demand tray value

allocated. These higher tray costs result in another delivery pattern equilibrium. It can be seen that when

less producers are participating, transport costs increase. This is due to a lot of small deliveries. This slightly

higher transport costs result in proportionally more tray stock savings.

Current situation

VMI 100% VMI 75% VMI 50% VMI 25%

# Trays *** m *** m *** m *** m *** m

Trays percentage 100% 68% 75% 83% 91%

Transports 643 592 608 641 722

Transport costs € ** m € ** m € ** m € ** m € ** m

Storage costs € ** m € ** m € ** m € ** m € ** m

Supply chain costs per producer

€ 8.981,- € 8.981,- € 9.246,- € 9.801,- € 11.214,-

Producer costs per producer

€ 177,- € 99,- € 98,- € 95,- € 89,-

Table 7 VMI alternative (A2) results

As discussed before, the amount of transports increases as result of an increased tray value. When just 25%

of the producers is participating, the allocated tray costs is higher compared to a 100% participation rate.

This results in more, low utilized transports. The amount of deliveries between the 25% and 100% VMI

alternative differs with a little over 20%. This will not mean that the amount of driving trucks increases but

just more combined transports. Since it is assumed that all trucks are fully loaded and total tray demand

stays the same, the amount of trucks will remain the same as well. The indication of the amount of

transports can therefore also be red as the amount of orders. For the 100% VMI alternative this will mean

more FTL orders in low season and more LFTL orders in peak season. When the participating ratio

decreases, the amount of LFTL will increase even further.

In the last row the producer costs are shown. The net producer costs are relatively low since they only pay

interest rate as discussed before. The producer costs will, on average, reduce by almost half. This is however

still less than €100,- per producer. EPS savings are a lot higher but this was already expected. It has to be

taken into account that it is possible that some producers will not even receive any savings but will lose

money to get a supply chain optimum. This was not the case for the 23 selected producers but may still

arise occasionally.

5.3.1 Additional benefits

With a vendor managed inventory system all transport planning is done by EPS which provides a change

to plan well in advance. By planning transport in advance a very costly issue within EPS can be solved:

relocation. As mentioned in the introduction, € ** million is yearly spend on relocations. This money is spend

on almost **.000 transports between EPS depots to even out imbalances and assure enough stock in each

depot. EPS already takes shortcuts and tries to make direct deliveries when possible instead of first

relocating trays. This will safe startup costs for a trip, handling in and out costs as well as extra km costs

since no more detours are needed. However, direct deliveries are not always possible in the current situation.

More trays could be delivered directly in a VMI situation with full information. By assuming the starting

costs are €*** per trip as discussed in Appendix E, a total of €** million could be saved on a yearly basis if

all relocations would change into direct deliveries. The handling in and out costs of a transport are €** in

total made by a depot. This is another €** million on possible savings. If it is also assumed that 50 km could

be saved per trip, which is verified by EPS transport planners, another *** million km costs savings is

possible. Adding all these savings together adds up to €** million in potential savings when all relocations

are diverted in direct deliveries. However, some relocations will always be necessary due to forecast errors

or washing, sorting or storage capacity shortages in certain depots. According to EPS employees it is also

not possible to plan combined LFTL orders as relocation. Therefore only FTL orders can be directly

delivered. Especially in peak months when more combined LFTL deliveries are planned will lower the

possible extra savings. In consultation with EPS transport planners it is estimated that 60% of the

relocations will always be necessary which account to **.000 transports. The savings of €** per trip will add

to €** million if 40% of the relocations can be directly delivered. The savings will probably be a little less

since current relocations are strategically timed. This means that relocations are planned in periods when

transport is cheapest. This saves transport costs that cannot be saved with direct deliveries. These savings

are only possible on relocations to Spain because there is no cost difference over time in other countries.

EPS transport manager estimates this savings to be no more than €**. This reduces the possible cost savings

to €** million a year on shortcuts. The shortcut savings of €** million is a gross estimation. This is estimated

on the 100% VMI alternative. If only part of the producers work with a VMI system this will have

consequences for the possible extra benefits. The relocations that could be saved are estimated with

employees within EPS working with relocations for several years on a daily basis. There is however no

scientific support for this assumption. Extra study on individual relocation flows is required to get a more

accurate overview on the possible relocation savings. However, it can be imagined that a lot of relocation

costs benefits can be achieved by having more control over the transports and being able to plan in advance.

For this study the possible maximum savings of €** million as supported above is taken in further

calculations.

Figure 38 Shortcuts created by VMI alternative

Besides reducing the relocation costs, an extra delivery cost reduction is possible with a VMI alternative.

As discussed before there is a yearly €*** lost due to LFTL deliveries in Germany alone. By implementing

a VMI system this LFTL transport loss will be reduced to near zero. These extra delivery cost savings are

on top of the savings discussed in the start of this paragraph.

The direct reduction of relocation and delivery costs is not the only extra benefit of this alternative. Patterns

can be found with the forecasted and actual tray demand information. These patterns could be used to help

producers with their forecast and for better planning within EPS. This can be relocating more cost efficient

or even reduce the EPS safety stock. Especially information over several years could create additional

benefits for both producers and EPS. It is unknown what savings can be created with this additional

information. For this reason this extra potential benefit is not taken into account.

5.3.2 How to manage and possible setbacks The VMI alternative shows very promising results with reduced storage costs, required trays and millions

of additional savings. The alternative based upon perfect information and central based decision making is

the most beneficial alternative for the supply chain. However, implementing this alternative may have some

challenges. At first, it is not possible to implement this alternative for producers related to auctions or

producer organizations. Only the organizations themselves could be directly reached and served with a

VMI system. However, these big parties have already daily deliveries since they serve a vast amount of

producers. It is therefore unlikely that big savings can be managed in this part of the EPS clientele.

Another possible setback is the determination of the uncertainty level and the corresponding safety stock.

For this analysis a general assumption has been made. When VMI is implemented this has to be done for

each individual customer. It may for EPS not be interesting to implement a VMI system for customers with

a high uncertainty level. This would shift some of the responsibilities to EPS which is not preferable in a

highly uncertain situation. For these cases it may be more beneficial to keep the current system.

The willingness of producers to share information with EPS is also a possible setback. This has already be

discussed several times in this study. It is undesirable to force producers to cooperate. This may result in

distorting information and increased tray delivery related costs.

The three possible setbacks discussed above indicates that the VMI alternative is possibly not suitable for

all producers. Introducing VMI for only a limited amount of producers is still beneficial but does not result

in overall minimum cost equilibrium. When all producers participate in a VMI system is it easier to combine

transports than when only part of the producers are involved. Having less cooperating producers reduces

the change of combining transports. This may reduce the possible savings due to detours or extra loads.

Implementing a VMI alternative will be more challenging compared to a daily renting fee. In this case (part

of) the responsibility will be shifted to EPS. Determining the uncertainty level and the required safety stock

for thousands of producers will be a time consuming and labor intensive job. If an account manager visits

four producers a day for 200 days a year he can manage 800 producer visits. Visiting each of the 5.000

producers twice a year would require thirteen extra staff members. Besides extra personnel, a custom made

program is required which can handle all the demand forecast and calculate the best possible delivery pattern

for all these producers. This program should take many variables into account like mentioned in the model

described in the previous chapter. A model should also be able to calculate multiple producer delivery

patterns combined. Thereby loads are adjusted to get a FTL and to minimize the combined delivery related

costs. For this analysis it is assumed that the program will cost €500.000,- each year for the implementation

period of three years. This may seem a lot but is comparable to current big programs within EPS. Thereby

it is assumed that two years after implementation another €200.000,- will be spend on updates and

improving the model.

5.3.3 Summarizing VMI alternative (A2) VMI is a very promising alternative as already proven in other studies with non-reusable products. VMI is

not only interesting when all producers are participating but also with a lower producer participation degree.

Due to many, already discussed, reasons it is probably not possible to get all producers participating. Further

study is necessary but for now it is estimated with data received from the producer questionnaire that a

VMI strategy can be used for approximately 50% of the producers. This are 450 million tray movements

on yearly basis. The possible savings with 50% of the producers participating and an uncertainty ratio of

25% is still over *** million required trays and €** million storage costs. A different delivery pattern for

50% of the producers will result in slightly higher transport costs.

Besides the three main cost components there are some additional benefits for this alternative. Extra savings

could be generated by more shortcuts which could increase to €** million on yearly basis. However, with

only 50% producer participation this will be a little lower. Thereby other costs are saved by reducing the

amount of partly empty trucks. Current losses by less than full transports can be solved when EPS is control

of the orders. This could save at least €** on yearly basis in Germany alone. At last, it may be possible to

find patterns to help producers with their forecasts. Analyzing forecasting patterns and actual demand for

several years can help planning more (cost) efficient.

These results should be possible but are not as easy to manage. New minimization software has to be built

which monitors and interacts with all producers. The program will be a more elaborate way to calculate the

best possible delivery pattern for the entire system. Besides a new software product this alternative requires

close interaction with producers in especially the starting period. This will require more staff members

visiting the producers on regular basis. For 5.000 producers at least thirteen staff members are necessary

for two visits a year.

5.4 Future state A daily tray renting price is an easy, companywide implementable alternative that directly charges producers

for inefficiencies. Producers can make delicate considerations and thereby reducing the required amount of

trays and save money. However, irrational ordering behavior will most likely still occur. This alternative will

also result in inequalities between producers and will most likely evoke resistance by the shareholders.

A VMI alternative can in theory decrease the required amount of trays including other related costs even

more than a daily tray renting price alternative. This is done by reducing the delivery lead time and

implementing a rational ordering decision making system. VMI is a labor intensive alternative that requires

new software development programs. Thereby the success of this alternative depends on the cooperation

with producers. Producers have to share accurate tray demand forecast information to succeed. The

estimation is that a VMI alternative will only be possible for approximately 50% of the producers. This will

reduce the possible savings. But, besides direct savings, extra savings can be managed by taking relocation

shortcuts, increasing the truck utilization level and after analyzing producer patterns increasing the forecast

accuracy.

The additional savings, resistance from shareholders and a preferred equality between producer makes the

VMI alternative preferred over the daily tray renting alternative. A VMI system should be able to reduce

the producer related costs. This will be done by changing the current system as discussed in chapter 3 into

a system as shown in Figure 39.

As can be seen in the figure, part of the ordering system will be automated by a VMI program. Thereby an

extra loop is created between the confirmation of a transport and the minimum cost delivery pattern

calculation. The system will constantly recalculate the minimum cost delivery pattern when new information

arrives or is adjusted by a producer. Thereby new requirements and performance indicators are added.

These requirements and performance indicators are related to tray, producer and supply chain costs.

Compared to the current state which only takes transport costs into account this is a big extension. These

requirements will also change the standards. From minimum transport costs in the current system to

minimum supply chain costs in the future system. These changes are all needed to assure a successful

implementation of a vendor managed inventory system.

Figure 39 Proper model of future state design

5.5 Net Present Value calculation Till this moment the tray savings have been mentioned as the reduction of required amount of trays without

adding value to these savings. This is done since a reduction of required trays will result in more intensive

tray use. More intensive use of trays will reduce the technical depreciation time of a tray. This is since trays

with a height exceeding thirteen cm will almost solely break on the hinges. These trays break after a certain

amount of cycles/uses. It is thereby assumed that if the trays are used twice as much they will have to be

replaced in half the current time. Since in this period exactly the same amount of tray cycles have taken

place. Trays with a height of ten and thirteen cm will have significantly less force on the hinges when

opening. These trays can last hundreds of cycles according to EPS employees. It is therefore assumed that

even with more intensive use they will not be depreciated due to malfunction. It is way more likely that

these trays will be replaced for some other reason before they actually break. It is therefore assumed that

these trays do not have to be replaced. All small trays combined form **% of the total stock and the larger

trays are the remaining **% of the trays.

When this is taken into account it can be calculated what the long term financial consequences for EPS will

be. This is done for the foldable trays by using a Net Present Value calculation (NPV). In a couple of years

the rigid trays will be replaced by foldable trays which will be taken into account in the process. In this

calculation it is assumed that EPS will continue growing for the next decade. EPS has been growing

constantly since the founding 25 years ago. In the last four years the company had an average growth of

nine percent per year. It is expected that growth will continue in especially eastern Europa and Great Britain.

For the NPV calculation the internal expected growth rate is used. This is a more conservative rate with an

average yearly growth of slightly over ***** percent. With this growth rate EPS expects to manage almost

*** tray cycles of foldable trays in 2027 as can be seen in Figure 40.

Figure 40 Expected mutation of tray cycles for the foldable trays

The NPV calculation uses a discount rate of **%. This rate seems rather high but it is the current calculation

rate used by EPS for all their investments. With a higher discount rate the long term consequences will

have limited influence. The short term results are therefore more influential on the calculation results.

The results from the current situation, 100% and 50% VMI participation alternative are calculated. The

50% participation rate is calculated since this is the most realistic alternative given the questionnaire. The

100% alternative is added to provide an overview of the possibilities if all producers are somehow involved

in VMI.

5.5.1 Results NPV

In Figure 41 it can be seen that a VMI alternative will have most influence in the first five years. The first

three years this will be mostly due to the postponing of tray investments. The investments are not necessary

since less trays are required to fulfill all cycles. Trays that have to be replenished or are needed to cover

growth can be taken from the stock of trays that is leftover from the VMI implementation. In 2020 VMI

is fully active and there are no more trays within the system that can be saved. In 2021 and 2022 there is a

big growth expected for the foldable trays since they will replace the rigid trays. This will result in hundreds

of millions extra tray cycles for the foldable trays in just two years’ time. Since trays are used more

extensively in a VMI system less trays will be required to cover this growth. This will cause the savings in

2021 and 2022. Figure 42 shows the required trays over the years. In a growing market the VMI alternative

enables postponed investments.

If growth would stop after the forecast period of ten years there are still several cost factors creating

differences compared to the current situation. For the 50% VMI alternative there are extra delivery costs,

staff costs, lower storage costs and savings due to shortcuts. When the growth of tray cycles stop after the

forecast period of ten years this will end up to **** cycles per year. The net savings for these **** tray

cycles is estimated to be €** million for the 50% VMI alternative. This is since the savings on storage and

relocation costs outnumber the extra delivery and staff costs. In the calculation this is hardly visible since a

discount rate of ***% will lower the present value of those savings. However, this are sustainable long term

savings that should be taken into account.

Figure 42 required trays for base scenario and alternatives

When the current growth scenario of EPS is taken in account the total tray cycles for foldable trays will

more than double in the next ten years. With average tray purchase costs of € ** per tray this will cost

almost **** million euro’s on tray investments. Implementing a VMI alternative can postpone these

investments by several years. Together with extra investments on storage and shortcuts this will add up to

approximately *** million euro’s in present value savings. An overview on the net present value build up is

shown in Table 8. It can be seen that in the first year the alternative will cost money. This is of course due

to investments. In the years following there will be savings exceeding the (initial) investments. It can thereby

been seen that three quarter of the savings is made in the first five years. Savings are still made in the last

couple of years but not as extensive. This results in an estimated 16% tray delivery related cost savings for

the next ten years.

Figure 41 NPV per year and cumulative NPV for base scenario and alternatives

Year Base scenario 50% VMI alternative Savings € Savings %

2017 € ****** € ****** € ****** -4%

2018 € ****** € ****** € ****** 11%

2019 € ****** € ****** € ****** 20%

2020 € ****** € ****** € ****** 23%

2021 € ****** € ****** € ****** 23%

2022 € ****** € ****** € ****** 20%

2023 € ****** € ****** € ****** 19%

2024 € ****** € ****** € ****** 17%

2025 € ****** € ****** € ****** 17%

2026 € ****** € ****** € ****** 16% Table 8 Cumulative NPV for base scenario and 50% VMI alternative

5.6 Conclusion The results show improvement possibilities to save tray delivery related costs. In a theoretical situation with

full information and no uncertainties the system could be managed with 35% less trays. If uncertainties are

taken into account some extra trays are required but savings are still manageable for all alternatives. This

thereby confirms the first hypothesis made in the previous chapter: Supply chain cost savings on tray delivery are

possible for all discussed alternatives.

Costs are especially and almost sorely created by a reduction in required trays and thereby postponed

investment costs. Therefore the second hypothesis: ‘Supply chain cost savings on tray delivery will be mainly caused

by a reduction of tray costs’ is also confirmed. All alternatives show a reduction of tray costs and a reduction of

storage costs. This will increase the transport costs for most alternatives slightly. However, the increase in

transport costs is lower than the decrease in tray and storage costs.

The third hypothesis: ‘EPS can manage with less trays during peak period which results in a lower tray working capital’

is also confirmed. The savings are almost entirely created in the peak months. By using trays more

extensively the tray capital in peak period is reduced by up to 35% in a perfect situation and 17% in the

most realistic situation. This reduction of required trays in peak months will therefore lower the dwell time.

In low season relatively more trays are required to serve the system which increases the dwell time. On net

base this will result in less fluctuation in required trays throughout the year which decrease storage costs

significantly. Thereby all results are as expected.

The two selected alternatives both show million euros saving possibilities. However, both alternatives do

have challenges which have to be overcome before the savings can be created. There are cooperation,

supporting system, shareholder and demand uncertainty challenges which have to be taken into account.

By comparing the two alternatives, the VMI alternative is the most promising and therefore preferred

alternative. The 50% participation ratio is currently most realistic to start with. This could manage up to

€**** million net present value savings in ten years.

This chapter has thereby answered the third, fourth and fifth sub question:

(3) What changes are needed in the current situation to reduce the combination of transport, depreciation and storage

costs?

The required changes to achieve minimum delivery related costs is a vendor managed inventory system that

minimizes costs by using producer forecasts. The major change is that producers will not order trays but

share demand forecasts and EPS will not just deliver orders but create orders themselves. This can be done

since this study provides a method to calculate the tray value on a daily basis. With this new insight,

considerations on delivery related costs can be made to reduce costs.

(4) How much money can be saved on the tray delivery process with producers?

The most realistic alternative shows possible net present value savings up to €*** million for the next ten

years. This is mainly caused by postponed investment costs of new trays. Apart from the investment savings

structural yearly savings of €*** million can be achieved by the reduction of relocation and storage costs.

(5) What are the consequences of these changes for other partners in the supply chain?

The consequences for other supply chain partners are very limited. Financially, producers only safe a few

euros. Besides the financial side, producers will not have to think about ordering trays anymore. This part

will be a lot easier. In return producers will have to share their demand forecast information. Making

accurate demand forecast may cost some extra effort for producers. This should be taken into account

when producers are asked for cooperation. Increase assurance of tray availability or a financial benefit can

both be incentives to persuade producers to cooperate.

Thereby all sub questions have been answered. This will lead to the answer of the research question and

the general conclusion as will be discussed in the next chapter.

6 Conclusion In this chapter a conclusion is drawn by answering the research question and the related sub questions as

discussed in this thesis. The research started by composing the following question:

How can the producer tray ordering behavior be used or influenced by EPS to reduce the tray delivery related costs while

maintaining 100% tray availability for producers?

There are several solutions that can answer this question. These solutions show possibilities to reduce the

delivery related costs and still assure 100% tray availability. The basics of all the alternatives is getting insight

in all cost components related to tray delivery. This insight can be used to either directly influence delivery

patterns or indirect by charging producers for costs caused by their tray management. This study has shown

the added value of a direct influence approach by implementing a vendor managed inventory system. This

provides EPS with control on producer tray stock to achieve minimum supply chain costs. It is estimated

that in the next ten years the net present costs could be reduced by €*** million or 16%. How this is done

is explained by answering the following five sub questions:

1) How does the current tray ordering system work for both producer and EPS?

The current tray ordering system works quite differently compared to most industries. At first, trays are a

reusable product used multiple times a year as an enabler for the fresh supply chain. The tray demand differs

per tray type along the year due to seasonality. Some trays will therefore only be used once a year during

peak season and remains in storage for the rest of the year due to lower demand.

For producers the system works quite easily. They can order trays whenever they want with a lead time of

two to four days. Producers pay a fixed renting fee for the trays independent of distance to depot or renting

days. Producers manage their own stock and determine their own required safety stock. EPS hardly

interferes with this process. Some producers are only allowed to order full truckloads and in some countries

EPS can, in consultation with producers, make minor order changes to make FTLs. However, the current

tray ordering system can be simplified as a system where producers have almost unlimited freedom and are

not punished for extra costs caused by their decisions.

2) Which factors influence producer ordering behavior?

This sub question is answered by asking producers in a questionnaire. As expected, producer supply and

retailer demand are the leading factors for producers when ordering trays. Storage capacity or financial

ability appeared to form no limitation for producers. Almost all producers admit they have extra stock to

cover (unexpected) fluctuations. This does not automatically mean that there are a lot of unexpected

fluctuations. Approximately half the producers are able to forecast the expected tray use accurately. This

information was used in the remaining part of the study as guidelines to reduce the delivery related costs.

3) What changes are needed in the current situation to reduce the combination of transport, depreciation and storage

costs?

The first change required to get any savings at all is identifying the value of all cost components. This is

already done for delivery costs with standard delivery costs KPI on truck and tray level. However, in the

current situation no attention is paid to tray costs. In this study it is demonstrated that for EPS the daily

value of trays differ during the year. This should be taken into account to reduce the tray delivery related

costs. It is impossible to reduce the overall costs if the value of some of the cost components is unknown.

An overview on daily tray value is not sufficient to reduce the related costs. As answered in the first sub

question, producers pay a fixed tray renting fee. To reduce the costs EPS can either start charging producers

for what they use or interfere with the tray delivery pattern directly. This study has proven the benefits of

a vendor managed inventory alternative where EPS takes over the tray management of individual producers.

This will require producers to share their tray demand forecast information with EPS. EPS will have to

calculate the best possible delivery pattern taking tray cost, transport cost, producer cost and tray availability

into account. In general, this requires close cooperation between EPS and producers.

4) How much money can be saved on the tray delivery process with producers?

It is estimated that over the next ten years savings with a current value of €*** million, which is 16% of the

tray delivery related costs, can be achieved. This is in a situation where half the producers are participating

in a VMI system with a forecast uncertainty of 25%. In the first few years most savings are possible on

postponing investment costs due to more efficient use of current tray stock. In the years following, savings

are created by reducing storage costs and by transport shortcuts. Even with extra staff costs, a net saving

of €*** million can be achieved on yearly basis.

5) What are the consequences of these changes for other partners in the supply chain?

Partners in the supply chain will hardly feel any consequences. This is since the alternative will only influence

producers in a generally positive way. Most producers will financially benefit slightly from another delivery

method. However, producers will have to ‘manage’ their forecast more accurately compared to their current

way of working. This may require some extra time and effort. EPS can always choose to compensate for

this extra effort by using incentives in the form of extra availability assurance or with financial stimulations.

In the study a method to calculate the daily value of trays along the year was introduced. The method takes

seasonal demand fluctuations, storage costs, standstill time and different process times into account. This

method was used as input for a cost minimization model calculating several alternatives as discussed in the

study. The model can be easily converted to calculate alternatives for all declaring producers in each required

time period. The study concludes a vendor managed inventory system as the most promising alternative by

limiting information disruptions along the supply chain and making rational decisions.

7 Recommendations

7.1 Recommendations for further research In this study minimum delivery cost balance for reusable, seasonal products have been studied. This has

been done from a vendor managed inventory point of view. Such a study with reusable, seasonal products

is done before. It can now be answered that cooperation and sharing of demand forecast information

between supply chain partners with reusable and seasonal products can be beneficial. This is especially

beneficial if supply chain partners in the end of the supply chain actually change their behavior somehow.

The study discussed the possibilities for both daily renting price as well as a vendor managed inventory

system. Especially a vendor managed inventory system for reusable products had not been studied before.

Companies similar to EPS may therefore save substantial costs on their delivery related processes by

implementing a vendor managed inventory system with their customers. However, the study also raises

some questions for further research:

The first question is related to the uncertainty. In the study an uncertainty level of 25% has been

chosen. A brief sensitivity analysis showed a linear relationship between the uncertainty level and

the possible savings. However, the uncertainty has not been tested and studied severely. The

consequences of uncertainty for VMI has also not been studied thoroughly in other studies.

Therefore it would be of added value to study how uncertainties influence the possible savings for

a vendor managed inventory system. This can be done for either a conventional VMI or for a

market with reusable products. This may differ from each other.

A second recommendation for further study is related to the safety stock. For this study a very

conservative safety stock has been chosen to assure availability. However, just like the uncertainty

level there will be differences in the chosen value/approach. It would be of added value to test a

VMI implementation for reusable products with different availability assurance scenario’s.

Calculating safety stock/reordering point in a different way will probably influence the results.

Thereby a more intensive study on safety stock/reordering point and vendor managed inventory

for reusable and seasonal products in recommended

7.2 Recommendations for Euro Pool System There are also some recommendations for Euro Pool System besides the general research

recommendations as discussed above. These recommendations are related to the implementations of the

vendor managed inventory solution and related findings as discussed in this study.

The first and most important recommendation for EPS is to start taking the daily tray value into

account as a KPI in daily processes. A lot of money can be saved on producer side of the supply

chain. As long as EPS does not take the value of trays into account, accurate considerations can

never be made. A daily tray value may also help to make investment related considerations in other

parts of the tray cycle.

Concluded from the results of this study, a vendor managed inventory system is recommended to

reduce the tray delivery related costs. The model as discussed in this study is an exploratory model

that shows the huge potentials of a VMI system. This potential is however only based on 23 Spanish

producers. This is a very limited amount of producers only accountable of 1,8% of Spanish and

0,4% of total tray demand. Before further steps are taken it is recommended to extend the study

to more producers. Thereby producers from other countries should be studied as well. It is not

directly expected that producers in other countries show different results but this first has to be

studied. The extra stop costs in other countries may be different which may result in a changing

delivery equilibrium. It is therefore recommended to study the consequences of implementing a

VMI model in other countries.

Before a vendor managed inventory system can be implemented on a larger scale it is

recommended to test the system with a selected group of producers. This is not only necessary to

find out if the real savings meet the theoretical savings but also to get more insight in the

uncertainty and fluctuations of producer tray demand. The uncertainty is a very important factor

that has been simplified in this study. A higher uncertainty will cause an increased safety stock

which reduces the potential profit. The uncertainty is not only necessary to calculate the possible

profit but also to assure producer tray availability. With an unknown uncertainty it will be

impossible to assure 100% availability to producers. If this cannot be assured it is unlikely that

producers will cooperate. To test a VMI system it is recommended to select a group of producers

located close to each other. In this way transports can be shared and the interaction effect between

producers can be tested.

The fourth recommendation is about the cooperation of producers. The study as discussed above

only focusses on the possible savings. It is not studied how producer can be convinced to

cooperate. Producers have to share their demand forecast information and agree to outsource their

tray management. Further research has to be performed on how this can be managed best. There

are many different types of incentives that can be used. The effect of each of this incentives on

individual producers is unknown. Further study on how to convince producers to cooperate and

what this will cost EPS is therefore required before a VMI alternative can be a success.

Other research is recommended on the indirect savings. The yearly indirect savings on relocations

of €** million is a substantiated estimation made in consultation with a transport manager.

However, exact savings are unknown since the reasoning behind each relocation is different. Some

relocations are made due to capacity shortages. For these relocations a shortcut will never be

possible. To find out how much of the relocation costs could be saved extra study on this matter

is recommended.

At last, it is recommended to study extended use of a VMI system and the acquired data. The

model could be used to help producers predict their forecast, reduce their uncertainty and help

EPS with even better planning of tray purchases, relocations and processing steps. How this can

be managed and what value can be added is unknown. Since it can be imagined that more accurate

forecasting can be profitable for both producers and EPS further study is recommended.

This list of recommendations should help to answer current knowledge gaps and bring EPS and producers

towards a more integrated supply chain. Implementing a VMI system will require a lot of extra study on

especially customization for each individual producer. The possible savings are definitely worth taking a

closer look into the matter.

Reflection When starting this project at EPS there was an open problem description with a lot of possibilities. The

problem description was basically the following sentence: Trays are too long at producers, the producer

dwell time has to be reduced to safe tray investment costs.

This was a very interesting but also open problem description which at first raises the following questions

to me:

How long do trays stay at producers?

Why is this too long?

What is too long?

The first part of the study was spend on understanding the problem and get a well scoped thesis. This

started by identifying all available information, trying to figure out how to use this information and what

information was still missing. Getting this all together provided a good initial start for the project.

There is an enormous amount of data available at EPS which was very useful but created also challenges.

The first challenge in this project was to identify the right data and how this could be used in the thesis.

Fortunately the data is structured nicely and could be well used in the initial phase of the study. Thereby

the first few weeks were spend on trying to understand what was happening, why this was happening and

what could be improved. After a couple of weeks of identifying and analyzing the project was scoped.

Although the project was clearly scoped in my opining, I struggled with getting this on paper. My

supervisors assist me by restructuring the storyline of my thesis. This was a very useful and important step

in the study. From that moment on there was a storyline which helped me in all parts of the thesis.

However, this was still rather the beginning of the study. During the study itself it was a challenge for me

to find a balance between the practical side for EPS and the scientific added value which is of course the

bases for a thesis. I tend to go more to the practical side and sometimes pay a little less attention to the

scientific part. For a study, this is of course not the way it should go so I constantly tried to keep up with

the scientific research. Personally I think there is a good balance between the added value for EPS as well

as added scientific value. However, some parts of the study would have be scientifically stronger if there

was more data available. Especially since there are only 23 producers in the simulation part of the study

which have all different characteristics.

Thereby I also want to reflect on the questionnaire part of the study. This part was necessary to get more

information on the producer ordering behavior. The results have been very useful even though it was only

taken from 50 producers. However, when looking back I would have liked to get more useable information

on producer safety stock. A few questions have been spend on safety stock and reordering point. The

answers to these questions could unfortunately not be used for this study. I strongly think that I have not

asked these questions properly in the questionnaire and for that reason could not take that into account in

the study. Even though it does not directly influence the conclusions of the study, it would have been nice

to have some information on how safety stock is currently determined by producers.

My general opinion on the process it that the problem of EPS has been mapped and explained quite clearly.

Not only conceptual but also the financial consequences of the current situation have been explained. The

project is easily expandable and reproducible. However, due to the limited test cases I would not be

surprised if a new study with different producers would not exactly match the results of this study.

Therefore there is definitely added value for an expanded study.

Since the master thesis for TIL takes 30 ects/6 months it is very important to have a clear scope and make

choices in the process. Especially since a lot of time is spend on the exploratory phase and some

preconditions. Unfortunate I had to simplify and generalize some parts of the study to keep it manageable.

For an exploratory study it is impossible to keep all individual behaviors and exceptions into account.

Especially the uncertainty and transport cost parts of the study have been generalized in extreme forms.

This definitely influences the results. Therefore I would strongly recommend that more attention would be

paid to a less generalized study. Of course choices have to be made during the study and I am glad I have

taken these choices. When I look back and see how many exceptions there are at EPS it would be impossible

to finish the project without making these assumptions. This makes it in the end a successful project in my

opinion.

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Appendix A Tray characteristics Transport efficiency became more and more important when EPS started to operate all over Europe.

Therefore, EPS introduced the foldable trays besides the standardized blue rigid trays in 1997. Foldable

trays can reduce the transport volume of empty trays up to 86% (Euro Pool System, 2016). Nowadays, EPS

has multiple standardized trays with different colors and sizes, which are used for different purposes in

different countries. The most important distinction between the trays is the difference between the rigids

and the foldables, which both operates in different markets. First the blue rigids will be discussed, followed

by the foldable trays. (van der Sande, 2016)

The blue rigid trays, showed in Figure A 1, have been successfully

serving the Benelux since 1992 (Euro Pool System, 2016). The rigid

tray is easy to handle and perfectly stackable with other Euro Pool

trays. Without any moving parts the rigid tray is very robust and has

a long lifespan. Another benefit is the 100% recyclability of the tray,

so there will be no waste.

There are six different rigid trays with other dimensions. The four

most popular trays are: H, M, L and T, which characteristics can be

seen in Table A 1.

Figure A 1: A blue rigid tray

Tray type H M L T External [mm] 600x400x241 600x400x179 600x400x126 400x300x165 Internal [mm] 566x366x225 566x366x164 566x366x111 364x264x158 Maximum internal [mm]

566x355 566x366 576x382 376x276 Internal usable height [mm]

212 151 97 147 Max. capacity per crate [kg]

20 18 10 7 Nominal capacity [kg] 400 400 400 200 Tare weight [gram] 1950 1500 1300 542

Table A 1 Dimensions of rigid trays

The rigids are all blue while the foldable trays are available in three different colors to satisfy all customers:

black, blue and green. The green foldable trays are the most popular trays and is used for over 50% of all

cycles. A main advantage of the foldable tray is the empty volume reduction. This creates major storage

and transportation advantages. The trays are stackable, both in the folded (Figure A2c) and unfolded status

(Figure A2a). With a special mechanism the tray can be folded easily as shown in Figure A2b. Due to the

movable parts of the tray this tray is less robust as the rigid and especially vulnerable during opening.

a.) Stacked unfolded trays b.) How to fold a tray c.) Stacked folded trays

Figure A 2: Characteristics of the green foldable trays

There are 21 different foldable tray types. The dimensions of the eight most popular green foldable tray

types are shown in Table A 1.

Tray type 246 216 186 156 136 106 154 104

External length [mm] 600 600 600 600 600 600 400 400

External width [mm] 400 400 400 400 400 400 300 300

External height unfolded [mm] 238 211 176 153 123 101 153 101

External height folded [mm] 60 30 30 30 30 30 30 30

Internal length [mm] 577 577 577 590 577 577 389 377

Internal width [mm] 380 380 380 381 380 378 284 284

Internal height unfolded [mm] 226 202 167 144 114 92 143 92

Internal usable height unfolded [mm]

213 193 158 135 105 83 135 83

Volume [liter] 47.14 43.08 43.08 35.66 30.9 23.8 19.14 14.75

Max. capacity per tray [kg] 20 20 18 12 10 8 7 4

Weight [gram] 2,070 1,820 1,540 1,350 1,340 1,190 690 550 Table A 1 Dimensions of green foldable trays

As can be seen all tray types have a number. This number corresponds to the size of the trays. The digit (4

or 6) is the bottom size of the tray. 4 stands for 40x30 cm and 6 stands for 60x40 cm. Two smaller trays

can be stacked on one bigger tray as can be seen in the figure above. The first two digits provide the height

of the trays in cm. the 246 tray is therefore 60x40 cm and 24 cm high.

For the black foldable tray the dimensions can be determined with this exact same principle. In this case

there are two digits added (04) at the end that indicates the color (black). A black tray with a bottom size

of 60x40 and a height of 24 cm is therefore the 24604. The black version of the 136 is the 13604 etc…

Besides the rigid and standard foldable trays there are also some special trays. The special trays consist of

trays for a specific product (meat or fish) or trays for a specific retailers. These trays have different

characteristics and dimensions which will not be discussed during this study.

Appendix B Dwell time analysis The average time a tray has been at a producer can be calculated in multiple different ways. In this appendix

two different methods will be used. The first method is a detailed analyses per producer based on an

individual analysis. The second method is a top down overview in which the company average is analyzed.

For the first method inflow, outflow and a time indication (seconds, hours, days, weeks etc.) are needed.

For EPS to calculate the producer dwell time at first the inflow has to be determined. The inflow are the

trays delivered by EPS to producers. The outflow are the trays that go from the producer to a trader or

retailer. For logistical operations on this scale with daily ordering and delivering patterns the time is

calculated in days.

To get the correct data for analysis some steps have to be performed. All the trays leaving EPS are registered

in the database of EPS. Most of the trays are delivered to producers. However, a relatively small amount of

trays go to other parties like retailers and traders for repacking and ease in handling. For the producers

dwell time analysis only the trays delivered to producers are interesting. All other parties should therefore

be filtered. The producers are coded DPP (10.423), DPPIMP (245) and DPPTRE (7). All other codes will

be excluded in the further analysis. A next step that has to be taken is selecting the producers which register

outgoing tray movements.

Producers are not forced to register trays going to another party (trader or retailers). Because this is not

mandatory, a lot of producers do not declare the tray movements. However, some producers do declare

streams in consultation with the retailers. This group can be used to estimate the producer dwell time. The

producers which do not declare all of the outgoing tray movements provide incomplete information and

have therefore be excluded. The following steps are taken to exclude all non-declaring producers.

At first the total amount of incoming and outgoing trays per producer are calculated. As stated before the

incoming trays are all registered automatically by EPS and therefore reliable. The outgoing trays are declared

by producers and therefore less reliable. The difference between the incoming and outgoing trays is taken

in percentage. For the first selection 90% of the incoming trays have to be declared as outgoing trays.

Producers having register less than 90%of the incoming trays as outgoing are either very new and have not

a stable dwell time or are expected to not declare all flows. The stock of trays at these producers are

unrealistically high so it can be safely assumed that producers declaring less than 90% of the incoming trays

do not declare consistently. Producers who do not meet this first requirement are excluded in this analysis.

This also accounts for producers declaring more outgoing trays than incoming trays (100%+). These

producers have received part of their trays from another party and therefore the incoming flow is not

registered.

This selection reduces the initial 10.675 producers to 1.237 producers for further analysis. Of the 7

DPPTRE producers none meet the requirements. 28 DPPIMP producers meet the requirements and the

remaining 1209 producers are DPP coded. The 1.237 producers are all analyzed in more detail. This is done

by calculating the dwell time.

There are several formulas that can calculate the average time a tray has been in the system taking the three

basic parameters into account. For this analysis it is chosen to use the cumulative stock/cumulative in

method. This method has multiple advantages upon other methods. The first advantage is the detailed

information. While other methods only provide the average dwell time over a fixed period ,his method

provides the average dwell time every single day of the time span. With this detailed information patterns

can be distinguished easily. Analyzing the pattern is very important because the actual dwell time may be

reasonable but the pattern is highly unlikely. Figure B 1 shows an example of a producer with a dwell time

of 50 days at the recording date but a very unlikely pattern. The dwell time first increase to almost two

years. This dwell time calculation method is very useful because it easily shows the complete pattern with

just one simple formula.

Figure B 1: Graph of average dwell time per day – producer ST_302355_eurobanHER - Spain

Another main advantage is the use of this method within EPS. Because this method is widely used within

the company and existing models it has advantages for comparison and communication. Employees can

easily understand the analyzing models and can provide proper feedback.

The calculations are as followed:

𝑫𝒕𝒊𝒎𝒆 = ∑ 𝑺𝒕

𝒏

𝒕=𝟏

/ ∑ 𝑭𝒕

𝒏

𝒕=𝟏

With:

Dtime : Dwell time

S : Stock

F : Incoming flow

The stock at a producer can be calculated by measuring the inflow and the outflow. By summing the stocks

and the inflow the cumulative stock and cumulative inflow are calculated to determine the dwell time. Data

used for this analysis is from January 1st 2007 till September 30th 2016. An example of the formula and the

results are shown in figure B 2 and figure B 3.

Results from the graph show that during the first year (2011) the dwell time increases to approximately **

days and remain stable. Because the dwell time remain stable for the next two years it can be assumed that

for this producer the average dwell time was ** days. After day 2700 the dwell time increases slowly. This

may have multiple reasons that cannot be checked with this data. For the small increases as shown in this

figure it is probably an increase in the safety stock of a producer. A constant nonmoving amount of trays

to cover fluctuations will result in a higher dwell time. The producer may also have forgotten to declare the

output of a few trays. If the output is not registered the trays stay at the producer for this calculation and

the dwell time increases slowly. The data does not recognize the difference between a nonmoving safety

stock and not declared trays.

Figure B 2: Example calculation of dwell time – producer ST_56142_boboliBUN - Netherlands

Figure B 3: Graph of average dwell time per day – producer ST_56142_boboliBUN - Netherlands

The sample set of 1.237 producers is reduced further by only taking producers that have received trays in

2016. This still leaves a large sample group that has to be checked in more detail. The previous selections

could be easily made with selecting a few filters on highly aggregated level. However, this analyzing method

does not provide any information on the dwell time. To check the dwell time each producers has to be

extracted manually. For this analysis a few basic criteria are set:

1) Producers are customer of EPS for at least 6 months

2) Dwell time is between 1 and 100 days.

3) Minimum 5.000 SRT

These three criteria can be tested by just comparing numbers. However, as shown in figure B 1 a dwell time

of 50 days may still have a very unlikely pattern. For this reason the patterns of all producers are visually

checked. This method is rather subjective but results in a more likely average dwell time. Different patterns

are discussed with EPS experts at the start of this analysis. This provided more insights and a better notion

of (un)likely patterns.

The analysis have reduced the sample set to 329 producers for which is expected they declare 100% of the

outgoing trays. All of these producers are DPP coded which completely excludes the DPPIMP and

DPPTRE producers. For finding patterns some basic information of the 329 selected producers is recorded:

1) Country of producer

2) Name/code of producer

3) Dwell time at September 30th 2016

4) Amount of SRT

5) Average yearly stock 2011-2016

6) Weekly average incoming trays over year 2011-2016

The collected data is exported in an Excel sheet and analyzed in more detail in a pivot table. Results of this

analysis are shown in the tables and figures below. Table B 1 shows the amount of selected producers on

country level and the average dwell time per country. It can be seen that especially in eastern Europe a lot

of producers are declaring their outgoing tray movements. Italy and Spain are also represented quite well

compared to Belgium, Germany and the Netherlands. Even though it cannot be proved if the data is

completely correct, some patterns and relationships can be observed. The average dwell time of countries

in eastern Europe (*** days) is lower than the other parts of Europe (*** days in southern Europe and ***

days in north/western Europe).

Country # Producers Dwell time % producers

Belgium 20 **** ****%

Czech republic 17 **** ****%

France 14 **** ****%

Germany 4 **** ****%

Hungary 3 **** ****%

Italy 75 **** ****%

Netherlands 3 **** ****%

Poland 42 **** ****%

Slovakia 5 **** ****%

Spain 146 **** ****%

Total 329 **** ****% Table B 1 Results producer dwell time analysis

Besides country statistics, information on size may also be interesting. As can be seen in figure B 4 there is

a pattern between the size of a producer and the dwell time. Size is given in the average amount of trays

ordered per week in 2016. Larger producers seem to have a lower dwell time on average. With the

observation on country and size it is not certain how important the country variable is on the dwell time.

For this reason the average producer size per country and the corresponding dwell time is shown in figure

B 5. It can be seen that the average dwell time seems independent of the average producer size if country

averages are taken. This means that countries with smaller producers have on average the same dwell time

as countries with bigger producers. This however is mainly caused by Spain, Italy and Poland. These three

countries contain 80% of the selected producers which have huge consequences on the average.

Figure B 4: producer dwell time given by size of producer (Average weekly ordered trays in 2016)

Figure B 5: producer dwell time given by average producer size per country (Average weekly ordered trays in 2016)

To get more insight the producer size and country are analyzed in one graph in figure B 6. As can be seen

in the figure, the most selected countries (Poland, Italy and Spain) have rather similar patterns. Especially

Poland and Italy have the same trend line with a different Y-axis interception. It seems that for the countries

with more producers it can be said that larger producers have smaller dwell times. However, there still

seems a difference between the countries. Because this difference may just be coincident, behavioral

variation between countries or variations with treating producers different per country a conclusion cannot

be drawn.

Figure B 6: producer dwell time given by size of producer per country (Average weekly ordered trays in 2016)

The analysis of the current dwell time shows that only a small amount of producers seems to declare all

outgoing trays. Within producers and countries there seems a difference in behavior. Although it seems

that larger producers have a lower dwell time. Conclusions cannot be drawn because the quality of the data

is not guaranteed. EPS experts have checked the data and approved the results. Still, the result may be

affected in multiple ways. Producers may have forgotten to declare some trays which will increase the dwell

time. They may also have declared the outgoing trays in advance or late which has effect on the average

dwell time. It is even possible that producers have received EPS trays from another producer in which case

the stock is higher and the calculated dwell time is too low. A last remark on the quality of this analysis the

overview/awareness. Producers that declare 100% of the trays have a clear online overview of the trays.

They are probably more aware of the stock and actively managing their tray inventory. With this many

uncertainties and data that cannot be checked conclusions cannot be drawn. This data of 329 producers

just provides information on the knowledge gap EPS has and the probable variety of dwell time within size

and country of producers.

Top down dwell time calculation

A second, more reliable method is a top down overview. In this case instead of looking to individual

producers a companywide average is taken. In this case it is known how many trays EPS manages and how

many cycles take place. It is therefore known how many days each cycle takes on average. For this

calculation the most popular RTI-family of EPS will take as example: foldable green. Foldable green trays

form over **% of the total tray cycles. On average a foldable green tray takes ** days to complete a cycle.

The days include process time at EPS, safety stock at EPS, seasonality stand still at EPS, time at retailer,

time at IMP, transport and time at producer. For this calculation reversed calculation will be used. It is

known that it takes EPS on average **** days to process a tray. The safety stock is also known and can be

shown as ***** days of dwell time. The seasonality can be calculated by a simplified formula and the retailer

dwell time has been examined by EPS employees and is estimated to be ** days. All transport together is

estimated to take ** days per cycle. At last the IMP time is set on *** days. This time at the IMP is an

average because many trays will not pass an IMP and other trays may spend more than *** days at an IMP.

The amount of days that are left is the time a tray spent at a producer.

Seasonality are fluctuations in demand over the year. This is caused mainly by geographical and climate

factors as well as production demand from retailers. In figure B 7 the fluctuations of the green foldable tray

are shown. To cover the fluctuations extra trays are needed. Only in the peak period all trays will be used

while during the rest of the year some trays will not be used due to lack of demand. The total standstill of

trays due to the peak differences can be calculated and are for this calculation called seasonality dwell time.

The average standstill of trays can be calculated by dividing the maximum tray use by the average tray use.

In the case of green foldable trays it is calculated that during peak month July there are **% more trays

needed than the monthly average. If tray demand would be divided completely equal this **% of trays

would not be needed. On average these **% extra trays will not move while they are upon average use. The

seasonality in days can be calculated by dividing ** by **%. In this case the seasonality is ** days. On

average a cycle without seasonality takes ** days.

Figure B 7: Use of green foldable trays over 2015

Now the seasonality is calculated, all parts of the chain are known or approximated except for the producer

dwell time. The producer dwell time can be calculated by taking the difference between the total dwell time

and the sum of all individual dwell times. This is shown in figure B 8.

According to this method the producer dwell time for the green foldable trays is ** days. This method is

more accurate than the previous method because all foldable green tray days are divided based upon

knowledge and reasoned approximations. However, this method does not provide detailed information on

single producers. Therefore it is not possible to find relationships between producer characteristics as size,

country, crop etc….

Figure B 8: Dwell time calculation cycle

Appendix C Causal relationship graph

Factor Explanation

Amount of packaging types How many different packaging types a producer uses. This includes packaging types of other companies than EPS

Availability of trays EPS If there are sufficient trays available at EPS to fulfill producers demand. An arrangement is made that EPS can deliver 100% of the demand within 2 days.

Capital/cash The amount of cash/capital/loan a producer has for renting trays.

Cash deposit Deposit paid to EPS according to the normal 30 days payment terms.

Clearance Deposit virtually paid. If the trays are transshipped to a retailer the virtual deposit will transfer. No party has to pay the actual deposit as long as the trays are declared to EPS.

Consistency EPS To what order EPS meets agreements with a producer

Cost of returning unused trays The costs of a producer for returning an overshoot of ordered trays.

Distance producer to EPS depot The distance between a producer and a nearby EPS depot.

Distance producer to frequent transportation routes

To what order a producer is located along frequently used transportation routes.

Direct compensations/discount EPS gives compensations to producers that have specific characteristics (if a producer supplies local retailers. In this case EPS does not have to relocate trays all over Europe which saves money. Part of this savings are returned to producers). This factor are the compensations directly subtracted from the renting price and therefore a discount.

Ease of ordering How easy a producer can order trays in time and complexity of the ordering system.

EPS lead time How long it takes EPS to deliver trays to a producer.

Final order time How long in advance a retailer places a final order in which noting will change

Fixed ordering costs The costs a producer makes for one order. If a producer collects trays himself he gets a discount but he makes transportations costs which are included in fixed ordering costs. For a normal producer there are no ordering costs since they pay a full concept price.

Flexibility for ordering trays How flexible a producer can order trays. If this is a specific moment in the week when doing administration or variable.

Historical demand information The amount of information a producer has about previous demand and ordering behavior of their retailers. Even if information is shared historical information can be very useful. If a retailer forecast is consequently to low or high historical patterns can help.

Information sharing To what order and accuracy retailers share (forecast) information with producers.

Inventory costs The costs of keeping inventory. This may be calculated by a discount rate or rent paid for a loan.

Irrationality To what extent producers react without a reason provided in the diagram. This may be due to an unknown factor or just human behavior which can be unpredictable

Lead time retailer information How long in advance retailers share information or provides an initial forecast

Market price fluctuations Fluctuations in the price producers get from a retailer or from the spot market.

Order frequency retailer How often a producer receives an order from a retailer.

Order size retailer The size of the orders a retailer places.

Overview stock To what degree producers have a clear view on its total empty tray stock.

Producer-retailer order batching To what extend producers batch produce before sending it to a retailers. From 1 pallet to a full truckload.

Renting price The gross price a producer pays for a tray. This is excluding deposit.

Retailer promotions To what extend retailers have promotions on fresh produce.

Risk of theft The risk of a producer that trays are stolen.

Seasonality of product To what extent the product of a producer has seasonal fluctuations. Some products like meat have a constant year round production and vegetables are generally only grown during a certain time period. The seasonality of products is a factor that implicates long term changes.

Self-pickup Producers picking up empty trays at an EPS depot.

Stability of product harvest The daily fluctuations in harvest. Some products are more dependent on external conditions (except weather) or will only be harvested once or twice a week while other products have a stable daily flow. The stability of products is a factor that implicates short term changes.

Stability retailer The fluctuations in retailer demand. Is there a daily/weekly stable demand or does the demand fluctuates a lot.

Stability quality product Fluctuations in product quality.

Storage cost The costs to maintain this storage space. This also includes costs to create more storage space.

Storage space The available space for tray storage

Strictness policy FTL To what extent producers can order LFTL. EPS has a policy to drive with full trucks. This policy however is not as strict in every country

Strictness retailer To what extend producers can change tray type if there is a shortage at the producer.

Terms of payment retailer How long a retailer takes to pay a producer after receiving the product.

Terms of payment to EPS How long a producer takes to pay EPS. This is in EPS wide policy 30 days but in many countries the actual period is longer.

Time for ordering trays The time a producer has to order trays. The time and flexibility seem similar but the flexibility is the easiness a producer can order each day or only on Saturday morning while the time is the amount of times (once a week, every day).

Trust retailers in producers The trust a retailer has in the capability of producers to deliver an order.

Weather fluctuations Daily fluctuations in weather conditions.

From factor To factor +/-

Explanation

Availability of trays EPS

Trust in EPS + If there are always trays available for producers, producers trust EPS on capability of delivering trays to meet demand. If EPS cannot provide trays at all times as promised, producers will lose trust in EPS.

Consistency EPS

Trust in EPS + If EPS meets all its agreements, producers will trust EPS as a supplier more than an inconsistent supplier. Consistency in lead time is an example. If EPS has a constant lead time of 1 day or 5 days the producers knows exactly when trays are coming and can count on EPS’ services. But if the lead time changes between 1 and 5 days a producers has no certainty and will calculate on worst case scenario.

Trust in EPS Safety stock - The more trust a producer has on EPS’ services the less safety stock he needs. If there are a lot of inconsistencies and uncertainties a producer will not trust EPS on its capability to deliver enough stock and will therefore keep extra stock just in case. This is rational and shortage gaming (bullwhip effect).

Risk of theft Safety stock - If there is a high risk of theft a producers want to minimize the possible damage. Especially in areas with a high change of stolen trays the safety stock will be lower.

Cost of returning unused trays

Safety stock - Lower costs of returning unused trays results in less incentive to accurate planning. If returning trays cost a lot of money producers will plan more accurate and reduce the safety stock, especially at the end of production season.

EPS lead time Safety stock + An higher lead time will result in a higher safety stock. If EPS delivers trays within 1 day a producer does not need a high safety stock. If there are sudden fluctuations he just orders new trays. If the safety stock is multiple days a producer needs a higher safety stock in case of a peak in demand/supply.

Strictness retailer

Safety stock + This relationship especially counts for producers delivering to multiple retailers. If retailers allow producers to deliver in another packaging type in case of shortage this will give the producer more action potential. If retailers insist on just one packaging type and refuse to take any other type this will force the producer to make sure he has enough trays of each packaging type which will increase the safety stock.

Information sharing

Forecast accuracy

+ The more information a retailer shares with a producer the more accurate a producer can plan.

Lead time retailer information

Forecast accuracy

- The longer in advance retailers provide forecasting information the more accurate producers can plan.

Historical demand information

Forecast accuracy

+ If more historical demand information is available forecast can be made more accurate. If no information is available it is uncertain in what pattern retailers will order, this makes it harder to forecast.

Final order time Forecast accuracy

+ If final orders are placed more in advanced producers can forecast more accurate. The later the final orders come in the less accurate the forecasts are.

Forecast accuracy

Safety stock - If there is accurate forecasting information producers will have a lower safety stock. The more accurate they know retailers demand the better they can plan. More forecasting uncertainty means a higher safety stock for ‘ just in case’.

Self-pickup Awareness of ordering and stock

+ Producers that collect trays themselves are more into tray management than producers getting trays delivered. This is because they have to schedule transportations and transport costs are

involved. These producers are assumed to manage the trays more accurate to safe money.

Overview stock Awareness of ordering and stock

+ If there is a clear overview of inventory/stock, producers will be more aware. This can be managed in simple excel sheets, in special ordering programs or just by visual monitoring.

Awareness of ordering and stock

Safety stock - Producers that are more aware of their tray stock and ordering pattern are likely to have a lower stock. This is because it seems that there is currently a quite high dwell time so a high safety stock. Because stock costs money it is expected that these producers have less (safety) stock.

Producer-retailer order batching

Supply fluctuations

+ If producers only delivers produce once a week there are more fluctuations in supply to a retailer. This order batching results in more trays at a producer.

Weather fluctuations

Supply fluctuations

+ Especially for fruits and vegetables weather is a very important growing factor. In some regions like the Netherlands there are more fluctuations in temperature and rain than in southern Spain. If there are more fluctuations in weather there will be more fluctuations in supply.

Seasonality of product

Supply fluctuations

+ A product that only grows in certain periods of the year creates more fluctuation in supply. These long term fluctuations are rather predictable.

Stability of product harvest

Supply fluctuations

- Daily fluctuations of product harvest due to e.g. external factors causes more fluctuations in supply. A stable productivity causes little supply fluctuations (meat industry).

Supply fluctuations

Safety stock + Fluctuations in supply causes uncertainty. To cover this uncertainty producers will keep more safety stock.

Financial capabilities

Safety stock + The more money a producer has to invest in trays the more likely he will have a significant safety stock. This relationship is even stronger in negative point of view. If a producer has limited financial means he will not have enough money for a large safety stock.

Stability quality product

Demand fluctuations

- Certain retailers only sell top quality products. If the quality fluctuates a lot this may influence the demand of a retailer. A retailer will probably buy higher quality products from a competitor.

Stability retailer Demand fluctuations

- The instability of retailers order patterns influence causes fluctuations in demand. The less stable a retailer order pattern is the more fluctuations in demand.

Market price fluctuations

Demand fluctuations

+ The price of products is set on a combinations of supply and demand. If supply or demand changes the price will change. Some products have highly fluctuating prices which results in demand fluctuations.

Retailer promotions

Demand fluctuations

+ As explained in paragraph 3.4, promotions is the most important factor in weekly demand fluctuation. More promotions means more fluctuations.

Trust retailers in producers

Demand fluctuations

- Trust retailers in producers with demand fluctuations is the same relationship as trust in EPS and safety stock. If there is no trust rational and shortage gaming may take place which results in more demand fluctuations.

Amount of packaging types

Demand fluctuations

+ If there are more packaging types the demand fluctuations per packaging type is likely to be higher than when there is only one packaging type.

Demand fluctuations

Safety stock + The demand fluctuations have the same relationship with safety stock as the supply fluctuations. Producers will keep a larger safety stock if demand changes frequently. This just to cover the fluctuations and be able to be able to sell their product at all time.

Storage space Safety stock + This relationship is similar to the financial capabilities and safety stock relationship. Especially in a negative point of view there is a strong relationship. If there is only limited storage space, the safety stock will be rather low as well.

Storage costs Safety stock - Keeping or increasing storage space costs money. The higher storage costs are the lower the safety stock will be.

Inventory costs Safety stock - The higher the inventory costs for a producer the less likely he will keep a high inventory (safety stock). Lower inventory costs may encourage safety stock.

Irrationality Safety stock + A personal factor is always an issue. Because it is highly unlikely that producers are going to gamble and risk a tray shortage it is more likely that producers will get an extra safety stock just in case without a direct reason.

Order frequency Safety stock - If a producer orders trays more frequently he needs less safety stock. Producers ordering only once every few weeks need a larger safety stock to cover the entire period till the next order/delivery

Storage space Ordering size + More storage space makes larger ordering size possible. This relations is similar to the safety stock. If there is just a storage space of 10 pallets an order will not be larger than 10 pallets.

Storage costs Ordering size - If there are higher storage costs it is likely that a producer will order smaller amounts at the time. Larger orders means more stock which causes extra costs

Inventory costs Ordering size - The inventory costs has the same relationship with the ordering size as the storage costs. Higher inventory costs will result in smaller order batches.

Risk of theft Ordering size - If there is a high risk of theft a producers want to minimize the possible damage. Especially in areas with a high change of stolen trays the order sizes will be lower.

Cost of returning unused trays

Ordering size - Lower costs of returning unused trays results in less incentive to accurate planning. If returning trays cost a lot of money producers will plan more accurate and reduce the order sizes, especially at the end of production season.

Amount of packaging types

Ordering size - More packaging types means dividing the product over multiple packages. Therefore less packages of each type are needed which results in smaller orders.

Fixed ordering costs

Ordering size + Higher fixed ordering costs will result in higher order sizes. Transportation costs or personnel costs may result in ordering more pallets to minimize the total costs.

Distance to frequent transportation routes

Transport utilization

+ Producers located far away from frequent used transportation routes cannot easily share a truck. Therefore transports towards these retailers will be higher utilized to reduce transport costs.

Distance producer to EPS depot

Transport utilization

+ The distance from a producer to an EPS depot is similar to the distance to frequent transportation routes. If a producer is less accessible, EPS will only sent high utilized trucks.

Strictness policy FTL

Transport utilization

+ This is EPS policy that differs per region. In some regions trucks are sent with just a few pallets while in other regions only FTLs are sent. This are either combined transports or a transport to a single producer. If the FTL policy is followed there will be a high transport utilization.

Transportation utilization

Ordering size + Higher transportation utilization of trucks means more trays in a truck. This will result in larger delivered orders.

Direct compensation/ discount

Fixed tray costs

- Discount reduces the costs or trays. More compensations/discount means a lower fixed tray costs (net rent).

Renting price Fixed tray costs

+ Higher renting price increases the fixed tray costs (net rent).

Fixed tray costs Tray costs + Higher net rent costs increases the total amount of money a producers has to pay to rent a tray.

Cash deposit Deposit + Producers paying a higher percentage of the deposit cash have higher deposit costs.

Clearance Deposit - Producers having a higher percentage of the deposit in the clearance system have lower deposit costs.

Deposit Tray costs + Higher deposit costs result in higher tray costs. A combination of renting fee and deposit gives the total tray costs for a producer.

Tray costs Ordering size - Higher costs per tray results in higher total costs. Especially with cash deposit a single pallet of 300 trays can easily cost ****. A full truckload of 33 pallets is can cost ****. For clearance deposit the costs for 33 pallets is just *****. The high costs may result in smaller order sizes.

Terms of payment retailer

Financial capabilities

- The period retailers take to pay producers influences the financial capabilities of the producers. Some retailers pay within 30 days but there are also retailers who take 90 or 120 days before they pay a producer. The long the payment period is the more producers need to pay in advance which reduces their financial capabilities.

Terms of payment to EPS

Financial capabilities

+ The longer producers can take to pay EPS for tray rent and deposit the higher their financial capabilities are. The normal term is 30 days but in Spain producers pay on average after 45 days.

Capital/cash Financial capabilities

+ The higher the initial amount of cash available for tray renting the higher the financial capabilities of a producer are.

Financial capabilities

Ordering size + The higher the financial capabilities of a producer, the more money he has for buying trays. If there are sufficient funds there is a higher change a producer will order more trays.

Order frequency retailer

Tray demand + The more often retailers order products the more trays are needed (if the order size does not decrease).

Order size retailer

Tray demand + Higher order sizes of a retailer results in a higher tray demand (if order frequency does not decrease).

Tray demand Ordering size + Higher tray demand results in higher tray ordering size

Ordering frequency

Ordering size - More frequent orders will result in smaller order as long as the demand keeps the same. Less frequent ordering means larger orders to get the same amount of trays.

Tray demand Ordering frequency

+ Higher tray demand results in more frequent tray ordering

Ease of ordering Order frequency

+ The easier it is for producers to order trays the more often they will order trays (this will result in lower ordering size). If it is very hard to order trays for a producer he will order less frequent but larger batches.

Time for ordering trays

Order frequency

+ The more time a producer has to order trays the more frequent he will order trays. This can be between once a day to just once a month. Of course, order frequency and order size are related as stated above.

Flexibility for ordering trays

Order frequency

+ The more flexible a producer can order trays the more often he will order trays. This is comparable with time for ordering trays.

Ordering size Ordering frequency

- Larger orders reduce the need to order more frequent. To get a certain amount of trays order batching results in less frequent ordering.

Appendix D Producer questionnaire The producer questionnaire exists of 48 questions divided over eight sections. Seven sections cover the

main factors of the causal relation diagram:

1) General characteristics

2) Supply

3) Demand

4) Forecast & information

5) Financial

6) Tray stock

7) EPS

This is complemented by a section that contains several overarching statements related to ordering

behavior. The main focus of the questionnaire is on demand/supply stability and the information producers

have. If it is clearly known what kind of information producers have, this information may be used to

increase ordering pattern efficiency. Receiving information well in advance reduces the uncertainty and

thereby created possibilities for EPS to interfere.

EPS can already answer some of the general characteristics questions with available information. These

questions are related to the tray type, number of SRT, season length and if there is a Cash or Clearing

deposit system. Especially for the amount of SRTs and the length of the season it is beneficial if EPS

answers these questions. Since the tray ordering pattern is clearly monitored for all trays this information is

available. Producers can easily make mistakes when answering these questions by heart. This may especially

occur when producers have multiple tray providers.

The questionnaire will be taken in Spain as has been mentioned before. In Spain, there are approximately

1.000 active DPPs. To get a statistic significant questionnaire with a 5 percent error possibility, 278

respondents are required according to the following rule of thumb:

n ≥N ∗ z2 ∗ 𝑝(1 − 𝑝)

z2 ∗ 𝑝(1 − 𝑝) + (𝑁 − 1) ∗ 𝐹2

With:

n : Required number of respondents

N : Population size (1.000 producers)

p : Change someone provides a certain answer (usually set on 50%)

z : standard deviation (95% = 1,96)

F : margin of error (5%)

1000 ∗ 1,962 ∗ 0,5(1 − 0,5)

1,962 ∗ 0,5(1 − 0,5) + (1000 − 1) ∗ 0,052= 278

For the study, statistic significant results are not required. It is more important to get a general overview of

the situation. For this reason, the study can be done with less producers. Together with EPS employees it

has been decided to question 50 producers. Having less respondents makes it more difficult to get a

representative sample group. By picking the respondents manually it is possible to find a balance between

different type of producers. Since basic producer information on tray type and ordering pattern is known,

a representative group can be selected. Producers with different sizes and operating in different sectors will

be selected. The questionnaire is taken from 50 producers in the sectors as shown in the figure below. For

the three largest sectors (fruit & vegetable, fish and meat) a variety in producer size has been selected. With

this selection a very complete sample is questioned.

Category Total % of total

Bread 3 ****%

Convenience 2 ****%

Fish 6 ****%

Fruit & Vegetable

31 ****%

Meat 8 ****%

Total 50 5% Table D 1 Type of producers selected for questionnaire

Since the questionnaire is taken in Spanish, translations are needed. This is not only to get the questionnaire

in Spanish but also to translate the results back to English. To overcome unnecessary work and speed up

the process, the questionnaire will be multiple choice as much as possible. This will not only reduce a lot

of workload of translators but also provides very clear answers that can be easily analyzed.

In a multiple choice questionnaire the answer possibilities are already given. This makes it important to

select a range of possible answers that is representative to the answers producers would give if an interview

was taken. This is tested on both EPS staff as well as two producers. For multiple choice questions with

unrelated answers an extra choice: ‘other…’ is introduced. This provides producers with the possibility to

add an option if that is more representative for their situation.

The questionnaire itself is shown below in English.

Appendix E Transport costs The theoretical costs formula has been formulated as discussed before:

𝐶𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦,𝑝 = (𝐶𝑆𝑡𝑎𝑟𝑡𝑢𝑝,𝑐 + 𝐷𝑝 ∗ 𝐶𝑘𝑚,𝑐) ∗ 𝐿𝑝 + 𝐶𝑠𝑡𝑜𝑝,𝑐 + 𝜀

When determining the actual transport costs all orders delivered in Spain by EPS in 2015 were analyzed.

The orders has been split into ten groups depended on size. This range is from orders with a 10% truck

utilization till orders that fill a complete truck. All LFTL orders have been recalculated to FTL prices. It is

thereby assumed that all LFTL could be combined to create FTL. Outliers have then be excluded since

especially for outliers with very high transport costs the assumption of combined orders was clearly wrong.

There are some cases, even in Spain, where transports could not be combined and FTL are not possible.

After excluding the outliers the transports are being analyzed by plotting them in a graph and insert a trend-

line. The results of these analysis are shown in Table E 1. It can be seen that the km costs are approximately

€** for each formula. It can also be seen that for a FTL the starting costs are €**,- and for orders with a

utilization between 0,5 and 0,8 (group 1) the standard costs are on average approximately €**,- higher. For

orders with a utilization of less than 0,4 (group 2) it can be seen that the average starting costs are on average

€**,- higher than for a FTL. The average extra stop costs needed to combine two orders is €**,- according

to transport planners at EPS. It can thereby be assumed that for orders within group 1 the order is combined

with one other order. For group 2 it can be assumed the order is combined with two other orders. To check

if this assumption is correct the group 1 orders and group 2 orders have been combined. When they are

analyzed they indeed have an initial starting costs of €***,- and €***,- higher than a FTL. This is quite close

to the estimated one and two extra stops. The initial costs for group 2 is slightly high but this is compensated

by a lower km costs. The km costs are higher for transports in group 1. Both these slightly higher costs for

LFTL orders are expected to occur due to the inability to combine transports to FTLs.

FTL Formula km costs standard costs Sample size r2

1 y = ****x + ***** € *** € *** ****** 0,72

0,9 y = ****x + ***** € *** € *** ****** 0,62

0,8 y = ****x + ***** € *** € *** ****** 0,5

0,7 y = ****x + ***** € *** € *** ****** 0,6

0,6 y = ****x + ***** € *** € *** ****** 0,63

0,5 y = ****x + ***** € *** € *** ****** 0,52

0,4 y = ****x + ***** € *** € *** ****** 0,46

0,3 y = ****x + ***** € *** € *** ****** 0,4

0,2 y = ****x + ***** € *** € *** ****** 0,33

0,1 y = ****x + ***** € *** € *** ****** 0,37

0,1-0,4 y = ****x + ***** € *** € *** ****** 0,38

0,5-0,8 y = ****x + ***** € *** € *** ****** 0,59 Table E 1 Transport cost analysis - Spain 2015

The transport cost calculation is on a highly aggregated level. When looked in detail the transport cost

would differ within a country and even within regions. This is due to the fact that EPS outsources its

transports. On frequently driven routes with a lot of competition the price will be a lot lower than transports

to remote locations. The difference is also high because EPS operates on the reverse logistics side of the

supply chain. On major routes there are a lot of empty trucks which are used by EPS. If producers are not

located nearby this major routes or they are located in the same direction as most full trucks, EPS cannot

uses the overcapacity. They will therefore pay a lot more for the same kind of transport. The formula shown

below does not take this into account and only calculates the nationwide average. For a more detailed

analysis the formula may be adjusted for each region or even producer.

By analyzing the 2015 delivery data the following generalized formula was constructed:

𝐶𝐷𝑒𝑙𝑖𝑣𝑒𝑟𝑦,𝑝 = (∗∗∗ + 𝐷𝑝 ∗ ∗∗∗) ∗ 𝐿𝑝 +#𝑠𝑡𝑜𝑝𝑠 ∗ ∗∗∗

#𝑠𝑡𝑜𝑝𝑠 + 1+ 𝜀

Appendix F List of producers for case study

Producer SRT Tray type 1 Tray type 2 Distance to EPS depot (km)

ST_304080_llorensCAT ****** F01 Fish-Cont 102

ST_304572_gesalagaZAR ****** 410-Cont 66

ST_301151_codesgrGRA ****** 46-Cont 54

ST_301383_huercasSAN ****** 154-Cont 127

ST_301640_solerXAT ****** 154-Cont 23

ST_307796_bimboGUA ****** 136-Cont 366

ST_305189_quinmarVIA ****** 46-Cont 159

ST_304249_freshALQ ****** 156-Cont 400

ST_307157_provedisVAL ****** 106-Cont 154-Cont 260

ST_305114_huevmonSIN ****** 186-Cont 246-Cont 100

ST_304278_gondamaVAL ****** F01 Fish-Cont F03 Fish-Cont 125

ST_304209_grobaVIG ****** 154-Cont 136-Cont 620

ST_303687_iscarVAL ****** 216-Cont 186-Cont 161

ST_301350_faccsaCAR ****** 186-Cont 216-Cont 270

ST_303504_delavegFUE ****** 154-Cont 216-Cont 136

ST_301585_camporiSAN ****** 104-Cont 154-Cont 470

ST_306973_mataleLLE ****** 186-Cont 216-Cont 343

ST_303733_pescadonaPON ****** 136-Cont 156-Cont 672

ST_302150_itsasbiMUN ****** 154-Cont 156-Cont 26

ST_305376_uvesaMAL ****** 610-Cont 216-Cont 401

ST_304310_agrosoROQ ****** 186-Cont 216-Cont 70

ST_300984_catalaBAR ****** 18-Cont 136-Cont 35

ST_351784_giannanGIO ****** 186-Cont 136-Cont 181

Table F 1 Participating producers including tray information and distance