using simulation to predict performance of salmon portioning …€¦ · · 2017-05-15predict...

USING SIMULATION TO

PREDICT PERFORMANCE OF

SALMON PORTIONING LINE

Jón Kolbeinn Guðjónsson

Thesis of 30 ECTS credits Master of Science in Engineering Management

June 2013

USING SIMULATION TO




Thesis of 30 ECTS credits submitted to the School of Science and Engineering at Reykjavík University in partial fulfillment

of the requirements for the degree of Master of Science in Engineering Management

June 2013

Supervisor:

Dr. Páll Jensson Professor, Reykjavík University, Iceland

Examiner:

Dr. Margrét Vilborg Bjarnardóttir Assistant professor, University of Maryland, USA

USING SIMULATION TO




30 ECTS thesis submitted to the School of Science and Engineering at Reykjavík University in partial fulfillment

of the requirements for the degree of Master of Science in Engineering Management

June 2013

Student: ___________________________________________


Supervisor(s): ___________________________________________

Dr. Páll Jensson

Examiner: ___________________________________________

Dr. Margrét Vilborg Bjarnadóttir

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Ágrip

Íbúafjöldi jarðarinnar er í örum vexti og um leið vex þörfin fyrir framboð á mat. Ekki

eingöngu er þörfin fyrir mat að aukast heldur eru neytendur sífellt að krefjast betri afurða og

lægra verðs. Þetta hefur aukið þrýstinginn á framleiðendur að framleiða hágæða vörur með

sem minnstum kostnaði. Þessi þróun hefur haft það í för með sér að þróun á tækjum til

matvælavinnslu hefur aukist og tæknin í þessum geira verður betri og betri með hverjum

deginum.

Í þessari rannsókn er stuðst við hermilíkan til þess að ákvarða bestu uppsetninguna á

sjálfvirkri laxaskurðarvél. Markmiðið er að komast að því hvaða laxastærð hentar best til

framleiðslu á mismunandi vörum og hvernig flæðinu inn í kerfið gæti breytt eftir hverju

tilfelli fyrir sig. Annað markmið er skoða hvernig nýtni kerfisins breytist með því að leyfa að

skera aukabita af afgangi hvers flaks.

Líkanið er hannað í Simul8 með því að nota gögn frá framleiðanda tækjanna og frá

nokkura daga laxaframleiðslu. Niðurstöður sýna að stærð laxins hefur mikil áhrif á

framleiðsluna og með því að greina hvaða afurðir og laxastærðir virka best saman er mögulegt

að auka framleiðni og minnka mannleg afskipti í framleiðslunni. Þetta hefur í för með sér að

framleiðslukostnaður minnkar án þess að gæðum vörunnar sé fórnað.

Lykilorð: Hermun, framleiðslukerfi, sjálfvirkur sku rður, laxa framleiðsla

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Abstract

The population of the world is growing rapidly and with it the demand of food supply. Not

only is the demand for food growing but consumers are also demanding better products at

reduced prices. This has caused an increased pressure on producers to decrease manufacturing

cost without sacrificing the quality of the product. This evolution has resulted in an increased

development of equipment and machinery for food production and the technology becomes

better and better every day.

In this study a simulation model is constructed to try to determine the optimum setup for an

automatic salmon portioning cutter. The objective is to determine what salmon sizes are best

suited for different products and how the input into the system could be altered based on

different scenarios. A second objective is to see how it affects the efficiency of the system to

put the offcut of the fillet into better use.

The model is constructed in Simul8 using data from the manufacturer of the system and

few days’ production of salmon. The results shows that the size of the salmon has a big

impact on the production and analyzing what products and salmon sizes work most efficiently

together, can increase the productivity and decrease manual handling. This results in

decreased production cost without sacrificing the quality of the product.

Keywords: Simulation, manufacturing system, automatic portioning, salmon

production

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Acknowledgments

I would like to thank everyone that has contributed in one way or another to this thesis.

Especially my research supervisor, Páll Jensson for his help and contribution throughout the

research. He has kept calm during the process, no matter what problem I came up with and

helped me to keep both feet on the ground.

Everyone at IC-fish in Marel for letting me into their workplace and my questions and

disturbance throughout the process. My cousins Benjamin Thor Waldmann and Guðrún

Lísbet Níelsdóttir for taking the time to proofread the thesis.

Finally I would like to thank my beautiful girlfriend Lilja Rut Traustadóttir for her support

and understanding throughout the process of this research.

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TABLE OF CONTENTS

1 INTRODUCTION .......................................................................................... 10

1.1 OBJECTIVE .................................................................................................................... 11

1.2 STRUCTURE .................................................................................................................. 12

2 SIMULATION OVERVIEW ........................................................................ 13

2.1 SIMULATING MANUFACTURING PROCESS ............................................................................ 13

2.2 STATISTICAL ISSUES ........................................................................................................ 14

2.2.1 System randomness ...................................................................................................... 14

2.2.2 Design of experiment and analysis ................................................................................ 15

2.3 PROBABILITY DISTRIBUTIONS ............................................................................................ 15

2.3.1 Continuous distributions ............................................................................................... 15

2.3.2 Discrete distributions ..................................................................................................... 16

2.4 HISTORY OF SIMULATION IN THE ICELANDIC FISHING INDUSTRY ................................................ 16

3 THE COMPANIES ........................................................................................ 18

3.1 MAREL ........................................................................................................................ 18

3.2 NORDLAKS.................................................................................................................... 19

4 THE RESEARCH .......................................................................................... 20

4.1 PLANNING AND PROBLEM FORMULATION OF THE STUDY ........................................................ 20

4.2 FORMULATION OF THE SIMULATION MODEL ........................................................................ 21

4.2.1 The physical system ....................................................................................................... 21

4.2.2 Simulation model .......................................................................................................... 23

4.3 DATA COLLECTION .......................................................................................................... 28

4.3.1 Arrival of fillets .............................................................................................................. 28

4.3.2 Amount of portion and excess cuts per fillet ................................................................. 29

4.3.3 Breakdowns and stops .................................................................................................. 32

4.4 TESTING AND VALIDATION................................................................................................ 32

4.5 PLANNING OF SIMULATION .............................................................................................. 32

5 RESULTS ........................................................................................................ 33

5.1 OPTIMUM ARRIVAL RATE OF FILLETS................................................................................... 33

5.1.1 100g portions ................................................................................................................ 34

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5.1.2 125g portions ................................................................................................................ 38

5.1.3 150g portions ................................................................................................................ 42

5.1.4 175g portion .................................................................................................................. 47

5.1.5 200g portions ................................................................................................................ 52

5.1.6 Summary ....................................................................................................................... 57

5.2 PRODUCTIVITY ............................................................................................................... 58

5.2.1 100g portions ................................................................................................................ 58

5.2.2 125g portions ................................................................................................................ 62

5.2.3 150g portion .................................................................................................................. 64

5.2.4 175g portion .................................................................................................................. 67

5.2.5 200g portion .................................................................................................................. 70

5.2.6 Summary ....................................................................................................................... 72

6 CONCLUSION ............................................................................................... 74

7 FUTURE STEPS ............................................................................................ 76

8 BIBLIOGRAPHY .......................................................................................... 77

8.1 VERBAL REFERENCE ........................................................................................................ 79

9 APPENDIX ONE ............................................................................................ 80

10 APPENDIX TWO......................................................................................... 87

11 APPENDIX THREE .................................................................................. 112

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List of Tables

Table 1:The different weight categories in the simulation .................................................................................... 20

Table 2: The different portion sizes used in the simulation ................................................................................... 21

Table 3: The mean and standard deviation for the arrival rate ............................................................................ 29

Table 4: Results for different arrival rates for weight category one producing 100g portions. ........................ 35

Table 5: Results for different arrival rates for weight category two producing 100g portions. ........................ 36

Table 6: Results for different arrival rates for weight category three producing 100g portions. ...................... 37

Table 7: Results for different arrival rates for weight category one producing 125g portions. ........................ 39

Table 8: Results for different arrival rates for weight category two producing 125g portions. ........................ 40

Table 9: Results for different arrival rates of for weight category three producing 125g portions. ................. 41

Table 10: Results for different arrival rates of for weight category four producing 125g portions. ................. 42

Table 11: Results for different arrival rates for weight category two producing 150g portions. ...................... 43

Table 12: Results for different arrival rates for weight category three producing 150g portions. .................... 44

Table 13: Results for different arrival rates for weight category four producing 150g portions. ...................... 45

Table 14: Results for different arrival rates for weight category five producing 150g portions. ...................... 46

Table 15: Results for different arrival rates for weight category three producing 175g portions. .................... 48

Table 16: Results for different arrival rates for weight category four producing 175g portions. ..................... 49

Table 17: Results for different arrival rates for weight category five ................................................................... 50

Table 18: Results for different arrival rates for weight category six producing 175g portions ......................... 51

Table 19: Results for different arrival rates for weight category three producing 200g portions ..................... 53

Table 20: Results for different arrival rates for weight category four producing 200g portions ....................... 54

Table 21: Results for different arrival rates for weight category five producing 200g portions ....................... 55

Table 22: Results for different arrival rates for weight category six producing 200g portions ......................... 56

Table 23: The performance of the optimum weight categories for each portions size ......................................... 57

Table 24: The average weight of offcuts for 100g portions ................................................................................ 59

Table 25: The amount processed of different items for 100g portions ............................................................... 59

Table 26: The total weight of items processed per hour for 100g portions ........................................................ 60

Table 27: The percentage of usable and not usable products from 1 hour of processing of 100g portions ...... 61




Table 31: The percentage of usable and not usable products from one hour of processing of 125g portions.. 63







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viii

List of Figures

Figure 1: Outside the head office of Nordlaks ....................................................................................................... 19

Figure 2: Fillets arriving for filleting machine ........................................................................................................ 21

Figure 3: Workers valuing the quality of the fillets ............................................................................................... 21

Figure 4:Fillets arriving at the PortionCutter ......................................................................................................... 22

Figure 5: The packing robot .................................................................................................................................. 22

Figure 6: 3D drawing of the physical system ......................................................................................................... 23

Figure 7: The setup of the simulation model ......................................................................................................... 24

Figure 8: An example on the cutting pattern of the PortionCutter ....................................................................... 25

Figure 9: An overview of the first part of the simulation model............................................................................ 26

Figure 10: An overview of the second part of the simulation model ..................................................................... 26

Figure 11: A histogram of fillets weights ............................................................................................................... 30

Figure 12: Results for all weight categories and 100g portions with mean of arrival at 4,2 ................................ 34

Figure 13: Results from different arrival rates for 100g portions with weight category one ................................ 35

Figure 14: Results from different arrival rates for 100g portions with weight category two................................ 36

Figure 15: Results from different arrival rates for 100g portions with weight category three ............................. 37


Figure 17: Results from different arrival rates for 125g portions with weight category one ................................ 39



Figure 20: Results from different arrival rates for 125g portions with weight category four ............................... 42



Figure 23: Results from different arrival rates for 150g portions with weight category three. ............................ 45


Figure 25: Results from different arrival rates for 150g portions with weight category five ................................ 47





Figure 30: Results from different arrival rates for 175g portions with weight category six .................................. 52


Figure 32: Results from different arrival ra tes for 200g portions with weight category three ........................... 53



Figure 35: Results from different arrival rates for 200g portions with weight category six .................................. 56

Figure 36: Difference between allowing for an extra portion or not when producing 100g portions ................... 61

Figure 37: Difference between allowing for an extra portion or not when producing 125g portions .................. 64

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Figure 41: The increase in production for all portion sizes when allowing an extra portion to be collected ........ 73

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1 Introduction The population of the world is growing rapidly, and so is the demand for food. According

to FAO’s (Food and Agriculture Organization of the United Nations) annual report on

fisheries and aquaculture, the world’s demand for fish products has increased with a growth

rate of 3.2 percent per year between 1961 and 2009. Meanwhile, the world’s population has

only grown at a rate of 1.7 percent. This has led to an increase in aquaculture production,

which has grown dramatically from 1.6 million tons in 1970 to 55.7 million tons in 2009, and

in the last three decades food production from farmed fish has grown with an annual rate of

8.8 percent [1].

Not only has demand for food increased but the market for the industry is also changing

rapidly. As stated in report by S. Arason et al [2] retailers have been merging, creating multi-

national companies selling products under their own brands, with low prices. This is reflected

in pressure on the next link in the chain, the producers, to produce high quality products with

low cost. The pressure is so high that producers are finding it harder to adjust their processes

to the increasing demands of their customers. This was evident in Finnmark, Norway where

one third of the fish processing companies went bankrupt although previous years reflected

record annual sales for cod products [3]. Such factors lead producers to cut processing costs.

Some factors contributing to producing cost are labor and transportation and this goes for both

fisheries and fish farms. Therefore, maximizing the efficiency of a production chain is one

way to make the process more affordable.

The fish processing industry in Iceland has been striving extensively to find new methods

to process fish with the aim of increasing the quality and the yield of the production. As early

as 1929 Icelanders started to sell frozen fish to Great Britain even though the technology was

relatively new. Before this, fishermen tried to transport both live fish and iced fish to Europe

with the objective to fulfill new demand for customers who wanted fresh fish rather than

baccalao [4]. Globalization has affected several markets including the fish industry. To cope

with the fast changing atmosphere, the fishing industry in Iceland has been increasing the

efficiency of its workforce in the past few decades. This is shown in an Icelandic paper by

H.L. Haraldsson [5] from 2001. The paper stated that while the percentage for the cost of raw

materials in the production cost of fish products has increased, the overall labor cost has

decreased. The trend line for labor cost is on a downward slope while the cost of raw

materials has an upwards-going trend line. The data reaches from 1984-1996. Another report

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made for the Icelandic Ministry of Fishery and Agriculture also shows that the number of

people working in fish processing is decreasing [6]. The revolution in the processing of fish in

Iceland is in large parts due to strong Icelandic companies who produce machines and flow

lines for the fishing industry. The biggest of these companies is Marel, which is a global

provider of products and systems in the food industry [7].

1.1 Objective This thesis focuses on a visual simulation of an integrated production chain, processing

farmed salmon. The system in hand is a flexible manufacturing system (FMS), which means

that it is computer controlled and can be reconfigured to produce different types of products

[8]. It consists of five different machines and is a product of Marel, previously mentioned as a

company specializing in making advanced equipment and systems for fish, meat and poultry

production. The initial inputs for the system are salmon fillets that have been trimmed,

cleaned and made ready for further processing. The production chain then processes fillets

for final output. Following this phase, consumer ready portions of salmon are packed before

being frozen. The user can choose which portion size to produce at a given time. The end

product of the machine is fixed to a given portion size decided by the user each time. Thus,

the bigger the salmon is, the bigger the fillet is and the more portions are generated from each

fillet.

The first objective of the study is to construct a simulation model that can efficiently

predict the performance of the system with respect to different scenarios based on different

categories of salmon sizes and portion size. The flow into the system will be optimized with

the aim to find the most suitable setup for each scenario and to find out if all salmon sizes

work for production of the different portion sizes. The most suitable setup is the one that has

high throughput with minimum manual handling.

A second objective of the study is to find out the efficiency of the current setup of the

system, i.e. how much of the total weight of raw material that goes into the system is turned

into value for the manufacturer, and also the efficiency change if a smaller extra portion is

allowed to be cut off the offcut of the fillet.

The production chain is already up and running in a Norwegian company called Nordlaks.

The company is both a farmer and producer of salmon. Data from Nordlaks is used to find out

how the system is performing and how the flow of fillets is to the PortionCutter. The real

flow will then be compared with how the system could be performing in theory. The results

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gathered will hopefully help to identify the true performance of the system which could

benefit Marel when it comes to selling the product to costumers.

The study can be divided into two parts, the first one being formulation, planning and

running of the simulation and the second part analyzes the results and identifies the next

possible steps.

1.2 Structure The introduction of the thesis is followed by a chapter on theoretical approach; it contains

an overview of literature relative to the methods used for conducting the survey and the

history of simulation research in Iceland. The literature review is followed by a brief

introduction to the two companies Marel and Nordlaks. The fourth chapter is about the

preparation of the research. When it comes to the preparation and implementation of the

research an eight step guide was used as a guideline for constructing and carrying out the

study. The guideline is from the textbook Introduction to Operations Research, 8th edition, by

Hillier and Lieberman [9]1. Chapter four starts with a short introduction to the research

method, it is then followed by a planning and problem formulation of the research. The setup

of the physical system, the current setup is for Nordlaks, is introduced, followed by ideas

behind the two different simulation systems. When referring to the current performance and

setup in Nordlaks, the terminology of physical system or performance is used. Once the

systems have been introduced, the relevant data can be defined and analyzed. The

performance of the system is validated and a plan for the simulation is introduced. Once the

system has been simulated and relevant data collected, the results are analyzed for the two

different objectives and findings presented. Towards the end of the thesis, results are

discussed and suggestions are made on how the performance of the system can be improved

as well as few ideas for possible next steps.

1 For further reading, the guide is listed in pages 954-959 in the textbook referred to

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2 Simulation overview Simulation is a relatively new field and the earliest work is from around 1930 involving

Monte Carlo methods. The introduction of computer programming opened the door to further

complex models. However, these models were hard to make in machine code, and manual

simulation was practiced in many places with the use of random number tables as a

foundation [11]. In 1960 K.D Tocher and D.G Owen published a description of what is today

known as the first simulator. The method is a three-phase method and was later known as the

GSP (General Simulation Program) [12]. Around the same time that Tocher and Owen were

working on their GSP method, Geoffrey Gordon, a member of IBM’s advanced system

development division, was also working on a system known as the GPSS (General Purpose

Simulation System). It was designed to help with simulating complex teleprocessing systems

and quickly became popular in the USA due to its easy use and IBM´s effective software

marketing strategy [13]. More programming languages followed after the GPSS. In 1963

Markowitz introduced Simscript through the RAND Corporation. The Simscript was not

designed only for a simulation purpose but also as a general programming language. The

General Activity Simulation Program (GASP) was developed in 1961 by Philp J. Kiviat and

was designed to bridge the gap between computer programmers and operating engineers.

These languages represent the backbone of most development in simulation languages and

computer simulation software’s from the 1950’s to the present time [12].

2.1 Simulating manufacturing process According to the APICS dictionary, manufacturing process is „The series of operations

performed upon material to convert it from the raw material or a semi-finished state to a state

of further completion“ [14]. The main purpose of simulating a manufacturing process is to

evaluate how small changes can affect the whole system, often with the objective to increase

overall throughput, utilization of resources, but also to reduce time in system for parts and

therefore capital requirements [10]. It has consistently been shown in various surveys on

application of simulation that a large proportion of simulation applications involve

manufacturing systems [9].

Averill M. Law and Michael G. McComas [15] divide the manufacturing issues addressed

by simulation into three categories. The first category is to determine the need for resources

and quantity needed of each resource. This addresses issues such as the type of equipment

needed, how they are arranged, labor requirement planning and investment issues as

evaluation of capital investment. The second issue is the evaluation of performance where

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throughput, time-in-system and bottlenecks are analyzed. The last category is the evaluation

of operational procedures, which focuses on the scheduling of production, inventory policies,

reliability analysis and quality control.

In industry, most systems can be described as discrete rather than continuous [16]. Discrete

systems change instantaneously with time while continuous systems change continuously

with time. It can often be hard to determine if a system should be modeled as continuous or

discrete and in practice, systems or models will rarely be fully continuous or discrete. It is

usually possible to determine the category of the system by identifying the category of the

predominant change of each system [10]. Arrival of new raw materials tends to have some

form of distribution and its arrival will change the overall state of the system. Therefore, in

general, manufacturing systems can be looked at as queuing systems and do not change

continuously over time. These kinds of systems can be categorized as discrete systems

because of their statistical features.

2.2 Statistical issues As mentioned above, simulation in manufacturing process is a method of trying to imitate

a type of physical system. The results are not 100% accurate but an implementation on the

system behavior. The simulation is based upon assumptions made by studying historical data

from previous performance or expected performance of the system. The simulation has

several sources, which are randomly analyzed with statistical methods. The output of the

simulated system is therefore also an incidental estimate of the performance. Therefore it is

important to sufficiently analyze such estimates, in order to plan both the design and analysis

accordingly [15].

2.2.1 System randomness System randomness will vary between different systems. It can involve arrivals (parts,

orders or raw material), loading, processing, assembly or inspection times. Some sources are

not related to the production itself like repair times and time to failure [15] but rather can be

treated in different forms. Such forms include using data values from historical data directly

in the simulation, using data value to determine an empirical distribution function or by the

use of statistical inference techniques to fit a theoretical distribution to the data [10]. The pool

of data used in the simulation has to be limitless. Using only historical data, also known as

trace-driven simulation, can therefore be problematic since the pool of data is not limitless

and can be expensive or difficult to gather [17]. The most effective solution is to use empirical

distribution from historical data or theoretical distribution if no data exists. In some cases it’s

15

not feasible to fit distribution to the data, therefore historical data can be used or some

simulation software has the option of using histograms of data to use as a distribution.

2.2.2 Design of experiment and analysis Because of the random nature of systems, the output of a simulation is based on random

numbers and is therefore a statistical representation of the physical system. So before

conducting the experiment it is important to evaluate how often the system is simulated and

the length of the warm up period. Instead of running the system once and using the output

data as results for the simulation, it is recommended to run the system at least three to five

times for each design. If the desired performance evaluation is based on the steady behavior

of the system, the system is then allowed to warm up for a certain amount of time before

starting to collect output data [15].

2.3 Probability distributions As mentioned in chapter 2.2.1, the randomness of a system is most accurately simulated by

fitting empirical distribution to historical data. Each variable needed for the simulation of the

system is then randomly generated from the empirical distribution. Like systems, random

variables can be either discrete or continuous. In his book Introduction to Probability Models,

Sheldon M. Ross [18] describes discrete random variables as factors that can for example take

either a finite or countable possible values when throwing a dice. A further example would be

that a continuous random variable can take an infinite number of values e.g. a random

variable denoting the lifetime of a car.

2.3.1 Continuous distributions Continuous probability distributions can have various roles in simulation studies. The most

commonly recognized is the normal distribution, which can be helpful when determining

errors of various types as well as the sum of quantities which are the sum of other large

number quantities [10]. The exponential distribution is another important distribution, the

most important attribute of the exponential distribution is that it is ‘memory less’, meaning

that it does not contain memory of what has previously occurred. For example, the remaining

lifetime of an item has the same distribution at time 0 as it has at time 0+t [18]. The uniform

distribution is also important for simulation, since it is essential for drawing random numbers

that are used to form other distributions. Other discrete distributions commonly used in

simulation studies are lognormal which is commonly used in the absence of data and is good

for simulating time to perform a task. Weibull distribution is also successful in its duration of

16

equipment and time to complete a task and the beta distribution has proved to be useful for

distribution of random proportions such as defects in items of shipment or time to complete a

task[9].

2.3.2 Discrete distributions Discrete distributions are commonly used in simulation for finding the number of certain

events happening in any given number of Bernoulli trials. For an example a geometric

distribution can be used to find the number of failures before success with a given probability

[9]. The most common discrete distributions are binomial, geometric and Poisson.

2.4 History of simulation in the Icelandic fishing industry Much work focusing on simulation modeling has been made in the Icelandic fishing

industry. Some of the earliest work dates back to 1966 when a computer program simulating

the fishing and landing of herring. This was a collaborative project between Iceland and

Denmark. In the late 70’s, a different individual operational research study was done, looking

at the fishing fleet and the fish stocks instead of focusing on specific companies [19]. In 1981

Páll Jensson published a study containing a simulation model of capelin fishing in Iceland.

The main objective of this study was to figure out if it could be beneficial to sail longer

distances for unloading instead of waiting in close-by harbors to get service. Some of the

results of the study showed that the catch could be increased by maximum 10% by using the

model. The model was constructed in GPSS [20].

In 1986 Elías Jónatansson and Sabah U. Randhawa worked on a research project involving

a network-based simulation model to analyze a fish processing facility. The research provided

production mangers with a tool for developing short term policy for production [21].

Two Icelandic researchers Snjólfur Ólafsson and Þorkell Helgason developed a model for

the Ministry of Fishery in Iceland and published their findings in 1988. The idea behind the

model was to come up with a tool to aid decision making in fishery management. Parts of the

results showed that the size of the Icelandic fishing fleet was far too big, that is the vessels

were too many [22].

Randhawa introduced another research in 1995 this time accompanied with Einar T.

Bjarnason. The research was a mixture of both a simulation model and a linear programming

optimization model. The research was intended as guidance for coordination between fishing

and processing. It showed that heuristics and optimization models can function as an effective

aid in decision making for production planning [23].

17

Ingólfur Arnarson and Páll Jensson carried out a simulation study in 2006 where their

focus was on economic agents and their behavior with the value of time as a resource

contributing as a significant factor [24].

18

3 The companies As mentioned in the introduction, the research is done at the initiative of Marel. They hope

to gain a tool for predicting yield of their portion cutter flow line, helping their sales team to

introduce and evaluate the product to future customers. The line is already up and running in a

production company in Norway called Nordlaks. This chapter contains a brief introduction to

the history and operation of these two companies. It is important to the research, as it gives

insight into the structure and procedures of both companies. The history of Marel explains

how they originate and where they are going. It identifies why they have high expectations,

developed from both, researching and developing new products as well as improving older

models.

3.1 Marel In 1977 two engineers at the University of Iceland started working on the development of a

new type of scales. Their aim was to come up with a solution which could lead to an effective

control in the production of fish. This was to be the beginning of Marel as it is known today.

The company itself was founded in Reykjavík in 1983 and has since then developed into

one of the world’s leading companies in developing and manufacturing high-tech equipment

for processing in the food industry. Marel has throughout its lifespan always kept focus on

their vision of being an international leader, increasing their customers´ productivity by

developing and marketing high-tech processing instruments and machinery [25].

An important part of pursuing their vision is an effective focus on research and

development (RD). This is underlined with annual investment in RD of 6-7% of all revenue.

They also try to keep a strong connection to the scientific community by participating in

international research projects, welcoming research guests and supporting the teaching of

science and mathematics [26]. Throughout its lifetime, Marel has kept their competitive

advantage by constantly striving to improve their products for the benefits of their customers.

Marel focuses on four different fields of food production. The four fields are fish, poultry,

meat and further processing. Marel has the vision to become the customers’ choice when it

comes to supplying integrated systems and other services to these four fields [27].

Since 2008 the revenues of Marel have been increasing from being over 430.000 Euros to

almost 714.000 Euros in 2012. Earnings before income, tax depreciation and amortization

(EBITDA) was in 2012 almost 86.000 Euros [28].

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3.2 Nordlaks Nordlaks is a relatively young company and was founded in 1989. The whole operation is

placed around a small town in the northern parts of Norway called Stokmarknes. Nordlaks is

not only a production company producing various products from Atlantic salmon and rainbow

trout, but also a fish farm. The company owns all production facilities to produce fish from

roe to finished products and over 50% of their fish come from their own farms. Since

Nordlaks rely mostly on only themselves for producing their products they have a great

advantage when it comes to traceability, quality control and hygiene of the production [29].

Figure 1: Outside the head office of Nordlaks

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4 The Research As mentioned before, the 8-step guide from Hillier and Lieberman is used as a guideline

for the simulation study. The guide starts with a formulation and planning of the study. Once

this step is completed, data is collected and the simulation model is formed. The third step is

to check the accuracy of the simulation model and then to select the right software to simulate

the model. The fifth step is to validate the model which is followed by a plan of how the

simulation is performed and then the conduction of the simulation, where results are collected

and analyzed. The last step is to present the results to the management and make

recommendations regarding the performance of the system [9].

This guide is partially used in this study. The research starts with planning and problem

formulation where the problem at hand is identified and the leading variables of the system

are identified before the formulation of the simulation model. This chapter contains a

description of the physical model in Nordlaks and a description of the simulation model and

the computer program used. This is not the same as suggested by Hillier and Lieberman but to

help figure out what data is needed, it is good to have the setup of the simulation model. The

third step is then to find out what data is needed and collect it. Once the data is in place then

the model can be tested and the simulation planned, just as is done in the guide. The last two

steps of the guide are then carried out in chapter 5, Results.

4.1 Planning and problem formulation of the study This chapter deals with the preparation work of the simulation. As mentioned before, the

objective of the study is to determine the optimum yield and the efficiency of the system.

There are numerous different variables that can change the systems overall performance. The

biggest issues are the variable sizes of fish and portions. Not only do the fish sizes vary but

the producers use different values in categorizing the fish. In this study a choice was made to

divide the salmon into six weight categories, each category has the range of one kilogram.

The portion sizes used run from 100g to 200g and change every 25g. The different portion

sizes and weight categories can be seen in Table 1 and Table 2.

Table 1:The different weight categories in the simulation

Weight category 1 2 3 4 5 6

Fish sizes [kg] 2-3 3-4 4-5 5-6 6-7 7-8

21

Table 2: The different portion sizes used in the simulation

Portion size [g] 100 125 150 175 200

In all, there are thirty possible scenarios, five for each weight category. To determine the

efficiency and how it changes if the extra portion is removed, then each scenario has to be

simulated both with and without the extra portion. The extra portion is cut from the offcut of a

fillet, if the offcut exceeds the weight of the extra portion

4.2 Formulation of the simulation model To be able to setup the simulation model, it is important to first become familiar with the

physical system and how each component of the system impacts the overall performance.

When talking about the system, then the term upstream is used to describe something that

happens further up in the flow system and downstream to describe something that will happen

further down. When talking about processed portions, these are portions that have gone the

right way through the portioning line and have been packed by the robot.

4.2.1 The physical system

There are two sets of portioning lines in the production chain, line one and line two. A

single fillet from each salmon goes to each of the lines. The trimmed fillets arrive from the

filleting machines, onto a series of conveyors which take them to the two lines. The filleting

machine and some of the conveyors can be seen in Figure 2 and Figure 3 which also show

workers valuing fillets, looking at the figure there are some obvious gaps in flow. When the

fillets arrive at the first conveyor, a worker makes sure they are lined as close as possible from

head to tail before going to the I-Cut 3000 PortionCutter. In the next phase, the gap between

the fillets is increased to at least the minimum length needed for the portion cutter. This is

Figure 2: Fillets arriving for filleting machine Figure 3: Workers valuing the quality of the fillets

22

done with a small speed increase between each of the conveyors in the series of conveyors

from the filleting line to the PortionCutter. Workers then remove any fillets that are not up to

standard, which inevitably cause gaps to appear in the inflow of fillets. When the fillets arrive

at the PortionCutter, it intelligently scans the fillets and cuts them into desired portions, based

on the pre-defined settings in the active portioning program.

Figure 4:Fillets arriving at the PortionCutter

After the PortionCutter has portioned the fillets the tail, extra portion and offcuts are

removed to a special line where they get further processed or packed. The offcuts are the rest

of the fillet that are not big enough for a whole portion. They have to be cut before the tail or

at the start of the fillet. Sometimes it is allowed for an extra portion to be taken if the offcut is

big enough. Figure 4 shows an overview of the portioning line and Figure 5 shows the

packing robot.

The FlowEqualizer 15 then equally distributes the remaining portions, utilizing the space

created by the tail, extra portion and offcuts removed earlier. The evenly spaced portions are

delivered to the robot, which with its two gripping arms and computerized camera vision

gently picks up the portions and place them into the thermoformer pouches also known as

pockets. The robot communicates with the thermoformer which delivers further empty

pouches when all available pouches have been filled. The filled up pouches than get sealed

and sent to the freezer. Figure 6 shows the setup of the physical system.

Figure 5: The packing robot

23

Figure 6: 3D drawing of the physical system

4.2.2 Simulation model In this study a choice was made to use a computer program called simul8 to simulate the

physical system. Simul8 is a program specially designed for simulating discrete event

simulations. It allows for a visual recreation of systems and shows how items flow through it.

A typical model in Simul8 is made out of four types of main building blocks. The first is

arrival of work items where new work items are generated with a predefined rate. The arrival

of work items can follow a distribution and therefore be stochastic or follow a scheduled

pattern and therefore be deterministic. The second building blocks are queues. These serve as

storage for work items and have various available settings to help with simulating systems.

After each queue, there is usually a work center, this is where the work is carried out and the

main settings of the systems are defined. Work centers have various different settings for

probability distribution of work time and routing in and out of the work center. The last

building block of all systems is a work exit point and this is where the work items leave the

system [30, p. 8].

The model can be divided into two parts. The parts are marked with red and green squares

in Figure 7. The first part is inside the red square and the only function it has is to count how

many tails, offcuts and extra portions have been processed during the simulation. The second

part is the simulation of the flow line and lies inside the green square.

24

The main purpose of the first part of the system is to count the offcuts, tails and extra portions

being generated during the simulation. The physical system is setup to cut 100g of the tail of

each fillet and then cut the rest of the fillet into portions. The rest of the fillet that is left after

the fillet has been divided into portions is called offcut and is also removed. The system can

be set to cut an extra portion, smaller than the portions being produced at the time, off the

offcut given that it is big enough. So if there is 150g portions being produced and the offcut is

between 100g and 149g then the PortionCutter can be set to remove an extra 100g portion and

then the rest is offcut. The extra portion can of course be bigger or smaller than 100g. The

objective with the extra portion is to minimize the waste of the system. Figure 8 shows the

idea with a fillet cut into 150g portion with the extra portion being removed as well.

Figure 7: The setup of the simulation model

25

Figure 8: An example on the cutting pattern of the PortionCutter

Fillets arrive first into the counting part of the model, which can be seen in Figure 9. They

arrive from a station called Fillets arriving. This work stations simulates the arrival rate of

fillets. For this station to work accurately, a probability distribution needs to be fitted to the

data for the arrival of fillets. They then go to through the Q for Labeler which is a storage

point that makes sure that new fillets can always arrive even though there is blockage ahead.

From the storage point they go to the Labeler which sends them either to the Q for extra

portion or the Queue for offcuts the fillets get routed to the different queues based on the

likelihood of it having an extra portion or not. The ones that have extra portions go to the

workstation called Extra portion where they either end up at Extra and offcut or Extra only

based on if they have only an extra portion or if they have extra portion and offcut. Here again

they get routed out from the likelihood of having containing an extra portion and an offcut or

only an extra portion. The fillets that go to Extra and offcut then send one portion to each of

the three exit points connected to it, Offcut 2, extra processed and Tai processed. The fillets

that go to the extra only station they send one portion to Tail processed and one to Extra

processed. The fillets that don’t have enough offcut for an extra portion they go to No Extra

portion where they either go to Offcuts or No offcuts based on likelihood The ones that go to

Offcuts send one portion to Offcut 1 and one to Tail processed. The fillets that go to No

offcuts, send one portion to Tail processed. All of the four stations Offcuts, No offcuts, Extra

and offcut and Extra only send one portion to the next part of the model which then represents

the rest of the fillet. A closer look of the first part of the simulation model can be seen in

Figure 9.

26

Figure 9: An overview of the first part of the simulation model

The fillets now arrive at second part of the model which is meant to represent the

performance of the flow line and can be seen in Figure 10. All the process in the first part

happens in zero seconds, in order for the system to not get blocked then the fillets have to

begin the second part of their journey at a storage bin.

Figure 10: An overview of the second part of the simulation model

27

The Queue for Portioning serves as storage for fillets before they go to one of the six

portioning stations called Portioning 1-6. These stations represent the process from the

PortionCutter to the SpeedSorter. The reason there are six of the stations is because the line

mentioned has a capacity around six fillets. These stations hold the fillets for 14,4 seconds

which is the travel time for the fillets. After the 14,4 seconds the stations release batches of

items that represent the portions generated by each fillet. The batches are decided by a

probability profile or a histogram and the probability and quantity of batches changes from

one weight category to another as well as with different portion sizes. The traveling time is

found by using equation (1).

( 1)

Where X is the traveling speed of the conveyor, Y is the time it takes to travel from one

end to the other and Z is the traveling distance. The conveyor that takes the fillets through the

PortionCutter to the FlowEqualizer is traveling at a speed of 15m/m and the distance it has to

travel is around 3,6m. Thus, the traveling time of fillets from arrival to the SpeedSorter is:

3,6*60 *secsec 14,4sec

15

mY

m= =

When the portions have left the Portioning they go to a storage bin called Queue for

Sorter. It makes sure the system does not get blocked if the Sorter is full. The Sorter´s

function is to send the portions to the Flow EQ if it is full. Then the portions go to Rejected

from Flow EQ and do not get packed by the robot. The Flow EQ holds the portions for 4,5

seconds which is the average travel time through the FlowEqualizer if it is working

efficiently. One side of it is traveling at the same speed as the upstream system or 15m/min

and the other side is traveling at the same speed as the downstream system or 25m/min. So the

average traveling time is found with equation (1):


(15 25) / 2

mY

m= =

+

The Flow EQ also has duplicates which work the same way as having many workstations

with same function. The amount of duplicates varies between scenarios. The physical

FlowEqualizer can take two fillets so the duplicates for each scenario are equal to two times

the maximum amount of batches available in each scenario. From the Flow EQ the portions

sec*min 60 sec 60 sec

*60 *secsec

m X m X mX Y Zm

Z mY

X m

= ⇒ =

⇒ =

28

now get sent to the Queue for Robot. The queue has a life time of 13,5 seconds based on the

calculations below:


25

mY

m= = (Time of traveling from FlowEQ to Robot)


25

mY

m= = (Time interval of picking window)

This queue holds the portions for at least 8,2 seconds and after 13 seconds (4,8+8,2) the

portions have gone past the robot so they get sent to the Total rejected from Robot. Otherwise

they will go to the Robot and from there to the Total Processed portions. Every time the

physical Robot fills up the available pouches it moves new pouches into place. This causes

the robot to stop for a short time while it gets empty slots to put the portions in. It is hard to

figure out precisely the duration of each stop is but it is evaluated to be around half a second.

This happens after every 24 portions so to simulate this; the robot has a probability profile

where 4% of the time it takes one second to pack a portion but 0,462 seconds otherwise. The

0,462 seconds comes from the physical capacity of the robot being 130 portions per minute

which means that it takes 0,462 second to pack each portion.

4.3 Data collection Now that the simulation model has been constructed, the next step is to analyze what data

is needed to carry out the simulation. The traveling time of different stations has been

identified but the rest of the setup has not. Much of the data varies between scenarios and

therefore much work has to be done to find the correct setup for each one. The main

categories are arrival of fillets, amount of portions and excess cuts per fillet (tail, extra

portions and offcuts) and then breakdowns and stops.

4.3.1 Arrival of fillets As mentioned in chapter 4.2.1 the fillets do not arrive at fixed rate but with a given

distribution function. While on the conveyor the fillets go through an inspection and all fillets

that are not up to standard are removed. This causes gaps in the arrivals. To find out the

distribution for arrival rate of fillets a measurement was made to monitor the physical arrival

of fillets. The measurements can be found in appendix one. The physical data was then fitted

for distribution using Crystalball and Easyfit.

29

The distribution had to be compatible with simul8 and both Crystalball and Easyfit came

up with lognormal as the best fit for the data. Easyfit gives µ as 1,3939 and σ as 0,26489.

Simul8 needs to know the mean value of the distribution and the standard deviation (std.dev)

of it. These are given by equations two and three:

( 2)

( 3)

Crystalball gives the mean and standard deviation directly so it is not necessary to

calculate it. As Table 3 shows, both methods give a relatively similar result, which

suggests that the fit is close to accurate.

Table 3: The mean and standard deviation for the arrival rate

Mean Std.dev

Easyfit 4,17 1,13

Crystalball 4,18 1,14

Average 4,175 1,135

4.3.2 Amount of portion and excess cuts per fillet The data for salmon sizes is from a couple of days of processing in Nordlaks and the data

is collected when the salmon is being sorted into categories. The list contains over 100.000

salmons with weight and length. The shape of the distribution of fillets can be seen in Figure

11, it seems to follow an almost normal distribution.

2

2

1(2 * )

2

1(2*1,3939 *0,2649 )

2 4,17

Mean e

Mean e

µ σ+

+

=

⇒ = =

22

22

1

2

11,3939 0,2649 0,26492

. * 1

. * 1 1,13

Std dev e e

Std dev e e

µ σ σ+

+

= −

⇒ = − =

30

Figure 11: A histogram of fillets weights

When fitted with Crystalball the actual distribution followed a lognormal distribution with

a mean of 1,42 and standard deviation of 0,34. So when the salmons have been divided into

the weight categories then they will only represent a small part of the whole distribution at a

time and each weight category will only generate up to maximum 5 different quantities of

portions. So instead of fitting an empirical distribution to each weight category, a probability

profile is used.

The amount of portions generated by each fillet is subject to the size of the fillets and

portions each time. According to Marel, their filleting machines gives a yield of 65% for fully

trimmed and skinless fillets [31]. So to find the size of each fillet the size of each salmon is

multiplied with the yield (0,65) and then divided by two because there are two fillets on each

salmon. Each fillet can contain four different cuts when extra portion is allowed and three cuts

when it is not allowed. All fillets have a tail cut which ways 100g [31], the rest of the fillet is

divided with the given portion sizes to find the amount of portions generated from each fillet.

The other two cuts are offcut one and offcut two. As mentioned in 4.2.1the offcuts are the

rest of the fillet after portions and tail. Offcut one is found by subtracting the total amount of

portions generated from the rounded down portions generated (portion(rd)). The result is then

multiplied with the given portion size each time to give the total weight of offcut for each

fillet. Offcut two is only when an extra portion is allowed, if offcut one weighs more than the

extra portion than the weight of the extra portion is subtracted from offcut one and the

difference is the weight of offcut two. It was assumed that only one fillet from each salmon

0

2000

4000

6000

8000

10000

12000

14000

Fil

lets

Weight of fillets[kg]

31

would go through this part of the line and the other fillet would go through an identical

production line as it is currently with the physical system. The following data was gathered

with this information

Portions generated for each size category

When the amount of portions for each fillet is found then a probability profile of portions

quantity was made for each size category and each portion size.

Capacity of flow equalizer

As previously mentioned the FlowEqualizer can hold two fillets before it starts to reject

fillets. Because it is receiving portions, it has to be able to hold the maximum amount of

portions possibly generated from two fillets in each scenario. Therefore, the capacity is set to

be double the maximum amount of portions possibly generated from a single fillet in each

scenario.

Average weight of offcuts

The average weight of offcuts one is found by summing up all the instances where the

offcut is smaller than the extra portion and then counting all the instances. The average weight

is then found by dividing the sum with the total amount of instances. The average weight of

offcut two is found by summing up the instances where offcut is bigger than the extra portion

and then divide the sum with the count of instances. When no extra portion is allowed the

offcut is simply found by summing up the offcuts and dividing with the count of instances.

Likelihood of fillets containing extra portion and different offcuts

When extra portion is allowed then the likelihood of a fillet containing an extra portion is

found by counting how many fillets generate an extra portion. The count is then divided by

the total amount of fillets in each category. When this has been done, the likelihood of fillet

containing an extra portion and offcut (offcut two) is calculated it the same manner, the

instances with offcut two is counted and then divided with the total amount of fillets

containing an extra portion. The fillets that do not have an extra portion are still likely to have

an offcut (offcut one), The likelihood of this is found by counting how many instances of

offcut one there is and dividing the amount with the total amount of fillets with no extra

portion.

32

When extra portion is not allowed then the instances with offcuts are counted and divided

with the total amount of fillets in each size category to find the likelihood of fillet containing

an offcut.

4.3.3 Breakdowns and stops Breakdowns are very rare, stops are usually because of problems upstream in the system.

These problems are not because of the machinery involved in the process of cutting and

packing predefined portions. To find the true yield from the overall process, stops caused

from other equipment need to be overlooked. The only scheduled stop is when new pouches

are needed by the robot.

4.4 Testing and validation The model was validated by observing the running in slow tempo and watching the

running’s of the system. The setup was also viewed by a team of senior engineers at Marel,

who all have good knowledge of the system. A few changes were made from suggestions

after the test runs.

4.5 Planning of simulation The simulation will be made for all six weight categories and five portion sizes mentioned

in chapter 4.1. There are 30 different scenarios and each one is simulated for eight hours with

seconds as the time unit. The simulation is based on random variables. Therefore, if it only

runs once then the possibility exists for a deviation in the outcome. To minimize this risk,

each configuration will be simulated for five runs, which will now be referred to as a trial.

After each trial the mean value of results of the five runs will be extracted from the program.

Simul8 automatically calculates the average. For the first objective then the simulation will be

made at least seven times for each scenario. This is to find the optimal performance and to be

able to plot the results on a graph to see graphically if the results follow any recognized trend.

For the second objective two simulations are made for each setup, one where an extra portion

is allowed and one where it is not allowed.

33

5 Results This chapter is divided into two subchapters. Each subchapter is assigned to one of the

two research questions stated in the introduction. The first one tries to find an optimal arrival

rate of fillets for the different scenarios. No consideration is made to if the filleting machines,

upstream from the portioning line, can handle the increase. In the second sub chapter the

current performance of the production line is analyzed with respect to the six size categories

mentioned previously and different size of portions. Here a simulation with an extra portion is

compared to one without an extra portion, with respect to productivity of the system.

5.1 Optimum arrival rate of fillets The main objective of this chapter is to find optimal setup for the arrival of fillets for the

different size categories and portion sizes. The optimal setup is the one that has high amount

of portion processed and a low amount of portions rejected, which is high production with

low manual handling. Manual handling occurs when the system rejects portions which then

are packed manually. This can be either because the robot does not have time to pick the

portions or because the FlowEqualizer has reached it maximum capacity. A decision was

made to set the optimal setup to when the performance of the system is such that the robot is

not waiting more than 10% of the time and when the rejection rate is less than 5%. The

rejection rate is found by dividing rejected portions with the portion processed each time. The

numbers for arrival of fillets and amount of portions processed and rejected are given for each

minute of production. For results for hourly and eight hour rates with 95% confident intervals,

see appendix two. Each portion size is analyzed and simulated for the six different size

categories. Not all of the scenarios had an optimum point as large salmons will generate too

many of the smaller portion sizes and small salmons will generate too few portions of the

bigger portion sizes. Therefore, some size categories are not simulated for the smaller and

larger portion sizes.

Each analyzes of the different portion size starts with a general introduction, where the

results from running the simulation for the given portion size with the physical arrival rate for

all the six size categories is presented. All the settings for the simulation model where

introduced in chapters 4.2 and 4.3.

34

Figures (graphs)

Many of the figures in the chapter have two y-axis. In these cases, all the bars are plotted

on the right hand side but the lines on the left hand side. The numbers for waiting of the robot

and the rejection rate are in percentage while the numbers for fillets arriving and processed

portions are for pieces of fillets or portions.

5.1.1 100g portions This is the first and the smallest portion size and as can be imagined it seems to work best

for the smaller salmons as the big one generate too many portions for the system to handle.

When looking at the curve of the rejection rate in Figure 12, it becomes very steep after size

category two. This results in increased manual handling which will result in higher processing

cost. To force the rejection rate down the input of fillets has to be decreased for categories

three to six, while category one and two could be running with an increased arrival rate. Also,

when the system is processing 131 portions every minute, the system must be overloaded as

this is the maximum capacity of the robot and rejection rate has to be high. In size categories

five and six the system is in fact rejecting more than it is processing so the rejection rate is

higher than 100%.

Figure 12: Results for all weight categories and 100g portions with mean of arrival at 4,2

Weight category one

Table 4 shows the results from the simulations for weight category one and according to

them the optimum arrival rate is around 3.2 to 3.4. The system would be taking in 18,8 to 20,0

fillets per minute at these rates. The rejection rate would be around four to six percentage and

14,3 14,3 14,3 14,3 14,3 14,3

103,2

128,2 131,0 131,1 131,1 131,1

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0,0%

20,0%

40,0%

60,0%

80,0%

100,0%

120,0%

140,0%

160,0%

1 2 3 4 5 6

Po

rtio

ns

an

d f

ille

ts

Weight category

Fillets arriving Processed portions Waiting percentage[%] Rejection rate [%]

35

same for the waiting time of the robot. Figure 13 shows graphically how the arrival rate

affects the robot waiting time and the rejection rate. The rejection rate falls rapidly as the

mean of arrival increases but as it happens the waiting percentage also increases.

Table 4: Results for different arrival rates for weight category one producing 100g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

3,0 20,0 128,7 7,8 6% 4%

3,2 18,8 126,1 4,9 4% 6%

3,4 17,7 122,6 2,5 2% 8%

3,6 16,7 118,3 1,2 1% 12%

3,8 15,8 113,3 0,5 0% 15%

4,0 15,0 108,1 0,2 0% 19%

4,2 14,3 103,2 0,1 0% 23%

Figure 13: Results from different arrival rates for 100g portions with weight category one

Weight category two

For weight category two the optimum arrival rate has a mean around 4,8. This is shown in

Table 5 which shows the results from the simulating this scenario. The system would be

processing 12,5 fillets every minute with this setup while it would be having a rejection rate

of 4 percent and the robot would be waiting nine percent of the time This can also be seen

20,0

18,8

17,7 16,7 15,8 15,0 14,3

128,7 126,1 122,6 118,3113,3

108,1103,2

0

20

40

60

80

100

120

140

0%

5%

10%

15%

20%

25%

3,0 3,2 3,4 3,6 3,8 4,0 4,2

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals

Fillets arriving per min Portions processed per min

Waiting percentage[%] Rejection rate[%]

36

graphically in Figure 14. This weight category is not far from being optimal at the physical

arrival rate of 4,2 but the setup is rejecting too many portions.

Table 5: Results for different arrival rates for weight category two producing 100g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

4,2 14,3 128,2 15,3 12% 4%

4,4 13,6 126,3 10,6 8% 5%

4,6 13,1 124,0 7,2 6% 7%

4,8 12,5 121,1 4,6 4% 9%

5,0 12,0 117,9 2,9 2% 12%

5,2 11,5 114,4 1,7 1% 14%

5,4 11,1 110,8 1,0 1% 17%

Figure 14: Results from different arrival rates for 100g portions with weight category two

14,3 13,6 13,1 12,5 12,0 11,5 11,1

128,2 126,3 124,0 121,1 117,9114,4

110,8

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

4,2 4,4 4,6 4,8 5,0 5,2 5,4

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



37

Weight category three

Table 6 shows the results from the simulating for category three, According to it and

Figure 15 there is no arrival rate that fulfills both criteria’s of waiting percentage less than

10% and rejection rate of less than 5%.

Table 6: Results for different arrival rates for weight category three producing 100g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

5,6 10,7 123,1 16,0 13% 8%

5,8 10,4 121,0 13,2 11% 9%

6,0 10,0 118,7 11,1 9% 11%

6,2 9,7 116,1 9,4 8% 13%

6,4 9,4 113,5 8,1 7% 15%

6,6 9,1 110,8 7,2 6% 17%

6,8 8,8 108,1 6,4 6% 19%

Figure 15: Results from different arrival rates for 100g portions with weight category three

After running the first three categories it was obvious that salmons bigger than 3-4kg

where too big for this production. As the arrival of fillets decreased, so did the amount of

processed portions and each rejected portion had more leverage in the rejection rate which

caused it to increase. So when the rejection percentage was under 5% the robot was waiting

10,7 10,4 10,0 9,7 9,4 9,1 8,8

123,1 121,0 118,7 116,1 113,5110,8 108,1

0

20

40

60

80

100

120

140

0%

5%

10%

15%

20%

25%

5,6 5,8 6,0 6,2 6,4 6,6 6,8

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



38

for more than 19% of the time. Categories four to six were therefore not simulated for this

portion size.

5.1.2 125g portions This size is slightly better suited for the bigger weight categories than the previous portion

size. As can be seen in Figure 16 the steepness of the slope for rejected portion increases after

category two and then again after category three. It is noticeable how few portions go through

the system in category one. This is because so few portions are generated from each fillet. The

best performance seems to be for categories two and three.


Weight category one

Weight category one turned out to generate too few portions per fillet to make the

production meet the requirements of robots waiting percentage and the rejection rate. The

system never rejects any portions in this scenario but instead the amount of portions being

processed per minute is low and the robot is therefore starving for a considerable amount of

time. When the mean of the arrivals was changed to 2,4 from 2,6 the bottleneck in the system

shifted from the Robot to the PortionCutter. So the fillets would queue up for the

PortionCutter and it could not handle more arrivals. The maximum amount of fillets each time

in the PortionCutter is set to six and that is close to its true capacity. The results for category

one can be seen in Figure 17 and Table 7.

14,3 14,3 14,3 14,3 14,3 14,3

78,5

113,7

129,0 131,0 131,1 131,1

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

80,0%

90,0%

100,0%

1 2 3 4 5 6

Po

rtio

ns

an

d f

ille

ts

Weight category


39

Table 7: Results for different arrival rates for weight category one producing 125g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

2,6 23,2 105,9 0,0 0% 21%

2,8 21,5 102,6 0,0 0% 23%

3,0 20,0 99,6 0,0 0% 25%

3,2 18,8 96,8 0,0 0% 28%

3,4 17,7 93,6 0,0 0% 30%

3,6 16,7 90,1 0,0 0% 33%

3,8 15,8 86,3 0,0 0% 35%

Figure 17: Results from different arrival rates for 125g portions with weight category one

Weight category two

The performance of category two had more obvious results than for category one and

can be seen in Table 8 and Figure 18. The optimum performance according to the simulation

is with mean of arrival ranging from 3,6 to 3,8. There are 16,7 to 15,8 fillets arriving per

minute with this setup and rejection is quite low and falls quickly as the mean of arrival

rises. This setup works quite well with the physical arrival rate (4,2), although the robot is

waiting fifteen percentages of the time.

23,2 21,5 20,0 18,8 17,7 16,7 15,8

105,9 102,6 99,6 96,893,6

90,1 86,3

0

20

40

60

80

100

120

140

0%

5%

10%

15%

20%

25%

30%

35%

40%

2,6 2,8 3,0 3,2 3,4 3,6 3,8

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



40


Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

3,2 18,8 129,2 12,6 10% 3%

3,4 17,7 127,7 9,1 7% 4%

3,6 16,7 125,5 6,1 5% 6%

3,8 15,8 122,3 3,5 3% 8%

4,0 15,0 118,2 1,8 1% 12%

4,2 14,3 113,7 0,8 1% 15%

4,4 13,6 109,0 0,3 0% 18%

Figure 18: Results from different arrival rates for 125g portions with weight category two


Table 9 shows the results from the simulations for weight category three. According to it, the

optimum arrival rate is around 4,6 which gives rejection rate of seven percent and waiting

percentage of six. This can also be seen graphically in Figure 19. Like category two, category

three does perform well with the mean of arrival set at 4,2 but it is rejecting a lot of portions.

18,8 17,716,7 15,8 15,0 14,3 13,6

129,2 127,7 125,5 122,3 118,2113,7 109,0

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

3,2 3,4 3,6 3,8 4,0 4,2 4,4

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



41

Table 9: Results for different arrival rates of for weight category three producing 125g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

4,2 14,3 129,0 18,5 14% 3%

4,4 13,6 127,4 13,4 10% 5%

4,6 13,1 125,5 9,3 7% 6%

4,8 12,5 123,0 6,2 5% 8%

5,0 12,0 120,2 3,9 3% 10%

5,2 11,5 116,8 2,4 2% 13%

5,4 11,1 113,4 1,5 1% 15%


Weight category four

After looking at the fourth category it was obvious that categories four to six are not ideal

for production of 125g portions. Table 10 shows the results from the simulations of category

four, according to it and Figure 20 there is no arrival rate that fulfills both criteria’s of waiting

percentage less than 10% and rejection rate of less than 5%.

14,3 13,6 13,1 12,5 12,0 11,5 11,1

129,0 127,4 125,5 123,0 120,2116,8 113,4

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

4,2 4,4 4,6 4,8 5,0 5,2 5,4

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



42

Table 10: Results for different arrival rates of for weight category four producing 125g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

5,4 11,1 124,8 17,4 14% 7%

5,6 10,7 122,9 14,2 12% 8%

5,8 10,4 120,8 11,6 10% 10%

6,0 10,0 118,4 9,6 8% 11%

6,2 9,7 115,9 7,9 7% 13%

6,4 9,4 113,2 6,7 6% 15%

6,6 9,1 110,5 5,8 5% 17%

Figure 20: Results from different arrival rates for 125g portions with weight category four

5.1.3 150g portions The change in the steepness of the curves for total rejected portions and the waiting

percentage is moving further to the right from Figure 16 to Figure 21. This means that the

bigger salmons are becoming more and more feasible for production as the portion size

increases. Weight category one was not simulated for this portion size as it was not feasible

for the 125g portion size and therefore not for the bigger portion sizes either.

11,1 10,7 10,4 10,0 9,7 9,4 9,1

124,8 122,9 120,8 118,4 115,9113,2 110,5

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

5,4 5,6 5,8 6,0 6,2 6,4 6,6

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



43


Weight category two

The results for this portion size starts with weight category two which fulfills the

requirements of the rejection rate and waiting percentage only when the mean of arrival is 2,6

and 2,8. Theoretically the system should be able to handle this arrival rate but it is not certain

that the filleting machines upstream from the PortionCutter can deliver at this rate. The results

can be seen in Table 11 and Figure 22. It is interesting to see that the curve for the waiting

percentage of the robot almost has a linear trend.


Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

2,6 23,2 123,5 1,3 1% 8%

2,8 21,5 120,4 0,6 1% 10%

3,0 20,0 117,2 0,4 0% 12%

3,2 18,8 114,2 0,2 0% 15%

3,4 17,7 110,7 0,1 0% 17%

3,6 16,7 106,7 0,1 0% 20%

3,8 15,8 102,2 0,0 0% 24%

14,3 14,3 14,3 14,3 14,3 14,3

64,9

93,0

120,0129,5 131,0 131,1

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

1 2 3 4 5 6

Po

rtio

ns

an

d f

ille

ts

Weight category


44

Figure 22: Results from different arrival rates for 150g portions with weight category two.


Category three is well fitted for producing 150g portions. The optimum performance is

when the mean of arrival is around 4,0 to 4,2 which is the same as the physical arrival rate.

The rejection rate is then between two to four percentage and the robot is only waiting for

seven to ten percent of the time. With this setup 15,0 to 14,3 fillets are going through the

system on average. The results can be seen in Figure 23 and Table 12.

Table 12: Results for different arrival rates for weight category three producing 150g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

3,4 17,7 130,1 17,0 13% 3%

3,6 16,7 128,8 12,3 10% 4%

3,8 15,8 126,8 8,0 6% 5%

4,0 15,0 123,8 4,7 4% 7%

4,2 14,3 120,0 2,4 2% 10%

4,4 13,6 115,8 1,1 1% 13%

4,6 13,1 111,4 0,5 0% 17%

23,2 21,5 20,0 18,8 17,7 16,7 15,8

123,5 120,4 117,2 114,2 110,7106,7 102,2

0

20

40

60

80

100

120

140

0%

5%

10%

15%

20%

25%

2,6 2,8 3,0 3,2 3,4 3,6 3,8

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



45

Figure 23: Results from different arrival rates for 150g portions with weight category three.


Weight category four is also well suited for producing 150g pieces, the optimum

performance is achieved when the arrival rate is 4,8 to 5,0. The system is averaging 12,5 to

12,0 fillets with setup and rejection rate is six to four percent while the robot is waiting seven

to nine percentage of the time. The results can be seen in Figure 24 and Table 13.

Table 13: Results for different arrival rates for weight category four producing 150g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

4,2 14,3 129,5 21,5 17% 3%

4,4 13,6 128,3 15,9 12% 4%

4,6 13,1 126,6 11,4 9% 5%

4,8 12,5 124,5 7,8 6% 7%

5,0 12,0 121,9 5,1 4% 9%

5,2 11,5 118,8 3,2 3% 11%

5,4 11,1 115,6 2,0 2% 14%

17,7 16,7 15,8 15,0 14,3 13,6 13,1

130,1 128,8 126,8 123,8 120,0115,8 111,4

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

3,4 3,6 3,8 4,0 4,2 4,4 4,6

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



46


Weight category five

After running category five it became evident that categories five and six do not run well

for production of this size of portions. The robot is waiting fifteen percentage of the time

when the desired rejection rate has been achieved which is when mean of arrival is 6,4. The

setup is close to being acceptable with mean of arrivals at 5,8 but there the rejection rate is

around eight percentage which is too high. The results are presented in Figure 25 and Table

14

Table 14: Results for different arrival rates for weight category five producing 150g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

5,4 11,1 124,7 15,9 13% 7%

5,6 10,7 122,8 12,8 10% 8%

5,8 10,4 120,6 10,2 8% 10%

6,0 10,0 118,3 8,2 7% 12%

6,2 9,7 115,7 6,7 6% 13%

6,4 9,4 113,0 5,6 5% 15%

6,6 9,1 110,3 4,8 4% 18%

14,3 13,6 13,1 12,5 12,0 11,5 11,1

129,5 128,3 126,6 124,5 121,9118,8 115,6

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

4,2 4,4 4,6 4,8 5,0 5,2 5,4

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



47

Figure 25: Results from different arrival rates for 150g portions with weight category five

5.1.4 175g portion When using the physical arrival rate, the dark blue curve in Figure 26 presenting the

waiting of robot does not go under twenty percent until after category two. So the smaller

fillets are not going to be optimal for producing this portion size. The curve for total rejected

portions also rises quickly so the rate of rejection does quickly pass the five percentage mark.

In category six the line for total rejected portions is well over half of the processed portions so

the rejection rate should be more than 50% according to this scenario. The results for 125g

and 150g portions show that weight categories one and two are two small for producing 175g

portions. The first weight category for this portion size is therefore weight category three.

11,1 10,7 10,4 10,0 9,7 9,4 9,1

124,7 122,8 120,6 118,3 115,7113,0 110,3

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

5,4 5,6 5,8 6,0 6,2 6,4 6,6

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



48



The results for this category are presented in Table 15 and Figure 27. They show that the

optimum mean of arrival lies between 3,0 to 3,4. In this case the production will be from

twenty fillets per minute to just less than eighteen fillets per minute. The robot is then waiting

five to nine percent of the time while the rejected portions are two to four percent of the

processed ones. Table 15: Results for different arrival rates for weight category three producing 175g portions.

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

2,6 23,2 130,6 10,5 8% 2%

2,8 21,5 128,9 7,5 6% 4%

3,0 20,0 127,0 5,2 4% 5%

3,2 18,8 124,8 3,5 3% 7%

3,4 17,7 121,9 2,1 2% 9%

3,6 16,7 118,2 1,1 1% 12%

3,8 15,8 113,7 0,5 0% 15%

14,3 14,3 14,3 14,3 14,3 14,3

53,7

79,5

103,8

123,4129,8 131,0

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

1 2 3 4 5 6

Po

rtio

ns

an

d f

ille

ts

Weight category


49



The results for this setup can be seen in Figure 28 and Table 16. The optimal mean of arrivals

is at 4,2 to 4,4. This is in fact one of the few scenarios where the physical mean of arrival

fulfills the assumption made for robot waiting percentage and rejection rate. The system is

averaging 13,6 to 14,3 fillets per minute and rejection is low or two to four percentage. The

robot is waiting for some time but it is still within the assumptions made for robot waiting

time.

Table 16: Results for different arrival rates for weight category four producing 175g portions. Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

3,6 16,7 129,7 17,6 14% 3%

3,8 15,8 128,4 12,6 10% 4%

4,0 15,0 126,3 8,1 6% 6%

4,2 14,3 123,4 4,8 4% 8%

4,4 13,6 119,8 2,6 2% 10%

4,6 13,1 115,9 1,3 1% 13%

4,8 12,5 111,7 0,6 1% 16%

23,221,5

20,018,8

17,7 16,7 15,8

130,6 128,9 127,0 124,8 121,9118,2 113,7

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

2,6 2,8 3,0 3,2 3,4 3,6 3,8

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



50



According to the results from the simulation category five can also be used for production

of this size. The results are presented in Table 17 and

Figure 29. They show that the mean of arrival should be increased to between 5,0 and 5,2 .

This puts the rejection rate down to three to five percentages and the robot will be waiting

eight to ten percentage of the time. The system is averaging 11,5 to 12,0 fillets per minute

with this setup.

Table 17: Results for different arrival rates for weight category five

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

4,2 14,3 129,8 23,5 18% 3%

4,4 13,6 128,6 17,8 14% 4%

4,6 13,1 127,1 13,0 10% 5%

4,8 12,5 125,2 9,1 7% 6%

5,0 12,0 122,8 6,2 5% 8%

5,2 11,5 119,9 4,0 3% 10%

5,4 11,1 116,9 2,6 2% 13%

16,715,8

15,014,3

13,6 13,1 12,5

129,7 128,4 126,3 123,4 119,8115,9 111,7

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

3,6 3,8 4,0 4,2 4,4 4,6 4,8

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



51


Weight category six

The last weight category was also tested for this portion size. The results for weight

category six are shown in Figure 30 and Table 18. There is no arrival rate that meets the

criteria for maximum robot waiting percentage and rejection rate. Arrival rate with mean of

5,8 comes close but the rejection rate is too high.

Table 18: Results for different arrival rates for weight category six producing 175g portions

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

5,2 11,5 126,3 18,7 15% 6%

5,4 11,1 124,6 15,0 12% 7%

5,6 10,7 122,7 11,9 10% 8%

5,8 10,4 120,5 9,4 8% 10%

6,0 10,0 118,1 7,5 6% 12%

6,2 9,7 115,6 6,0 5% 14%

6,4 9,4 112,9 4,9 4% 16%

14,313,6

13,112,5

12,0 11,5 11,1

129,8 128,6 127,1 125,2 122,8119,9 116,9

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

4,2 4,4 4,6 4,8 5,0 5,2 5,4

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



52

Figure 30: Results from different arrival rates for 175g portions with weight category six

5.1.5 200g portions As Figure 31 shows the production of 200g portions works best with the bigger fillets that is

fillets from the higher weight categories. The waiting percentage goes under twenty percent

with category four so there should be an optimum point for mean of arrival for category four

but fillets from category three are likely to generate too few portions to hold up an effective

production for this portions size.


11,511,1

10,710,4

10,0 9,7 9,4

126,3 124,6 122,7 120,5 118,1115,6 112,9

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

5,2 5,4 5,6 5,8 6,0 6,2 6,4

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



14,3 14,3 14,3 14,3 14,3 14,3

47,7

67,3

90,7

110,6

125,9130,1

0,0

20,0

40,0

60,0

80,0

100,0

120,0

140,0

0,0%

10,0%

20,0%

30,0%

40,0%

50,0%

60,0%

70,0%

1 2 3 4 5 6

Po

rtio

ns

an

d f

ille

ts

Weight category


53


When looking at the results for weight category three which are presented in Figure 32 and

Table 19, then it is clear that the production will be difficult. An arrival rate with mean 2,6 is

going to fulfill the requirements but it is not certain that the system upstream from the portion

cutter can deliver with this rate of arrival. The system would be averaging 23,2 fillets per

minute while rejecting only 1% of the portions processed and the robot waiting for only eight

percent of the time.

Table 19: Results for different arrival rates for weight category three producing 200g portions

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

2,6 23,2 122,7 0,7 1% 8%

2,8 21,5 118,9 0,3 0% 11%

3,0 20,0 115,4 0,1 0% 14%

3,2 18,8 111,9 0,1 0% 16%

3,4 17,7 108,2 0,0 0% 19%

3,6 16,7 104,2 0,0 0% 22%

3,8 15,8 99,7 0,0 0% 25%

Figure 32: Results from different arrival ra tes for 200g portions with weight category three

23,221,5

20,018,8

17,7 16,7 15,8

122,7 118,9 115,4 111,9 108,2104,2 99,7

0

20

40

60

80

100

120

140

0%

5%

10%

15%

20%

25%

30%

2,6 2,8 3,0 3,2 3,4 3,6 3,8

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



54


As expected, category four is much better suited for this production then category three. The

speed of the production would have to be increased a bit, taking the mean of arrival down to

between 3,4-3,8 from 4,2. It would results in 17,7 to 15,8 fillets being processed per minute

and the robot would be waiting for five to ten percent of the time while the rate of rejection

would go from two to five percent. The results are presented in Table 20 and Figure 33.

Table 20: Results for different arrival rates for weight category four producing 200g portions

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

3,0 20,0 130,1 13,1 10% 3%

3,2 18,8 128,7 9,5 7% 4%

3,4 17,7 126,7 6,4 5% 5%

3,6 16,7 123,9 3,8 3% 7%

3,8 15,8 120,1 1,9 2% 10%

4,0 15,0 115,4 0,8 1% 14%

4,2 14,3 110,6 0,3 0% 17%


20,018,8

17,716,7

15,8 15,0 14,3

130,1 128,7 126,7 123,9 120,1115,4 110,6

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

3,0 3,2 3,4 3,6 3,8 4,0 4,2

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



55


Category five also works for this portion size and it has an optimum production point with

mean of arrival at 4,4. The system would be producing from 13,6 fillets per minute and the

robot would be waiting for eight percent of the time while the rejection rate would be four

percent. The physical arrival rate of 4,2 is close to fulfilling the criteria of waiting percentage

and rejection rate. The results for category five are presented in Figure 34 and Table 21

Table 21: Results for different arrival rates for weight category five producing 200g portions

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

3,8 15,8 129,6 17,2 13% 3%

4,0 15,0 128,0 12,0 9% 4%

4,2 14,3 125,9 7,7 6% 6%

4,4 13,6 123,1 4,5 4% 8%

4,6 13,1 119,6 2,5 2% 11%

4,8 12,5 115,7 1,2 1% 13%

5,0 12,0 111,8 0,6 1% 16%


15,815,0

14,313,6

13,1 12,5 12,0

129,6 128,0 125,9 123,1 119,6115,7 111,8

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

3,8 4,0 4,2 4,4 4,6 4,8 5,0

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



56

Weight category six

The results for weight category six and portion size of 200g can be seen in Table 22 and

Figure 35. It would be wise to slow down the arrivals for this salmon size as the scenario only

fulfills the criteria of robot waiting percentage and rejection rate when the mean of arrivals is

moved up to 5,2. The system is only taking in 11,5 fillets per minute but rejection rate goes

down to four percent by doing this and the robot is waiting for nine percent of the time.

Table 22: Results for different arrival rates for weight category six producing 200g portions

Mean of

arrival

Fillets

Arriving

Portions

Processed

Portions

Rejected

Rejection

rate

Robot

Waiting

4,4 13,6 129,2 20,8 16% 3%

4,6 13,1 127,9 15,7 12% 4%

4,8 12,5 126,2 11,4 9% 6%

5,0 12,0 124,2 8,0 6% 7%

5,2 11,5 121,7 5,3 4% 9%

5,4 11,1 118,8 3,6 3% 11%

5,6 10,7 115,7 2,3 2% 13%

Figure 35: Results from different arrival rates for 200g portions with weight category six

13,613,1

12,512,0

11,5 11,1 10,7

129,2 127,9 126,2 124,2 121,7118,8 115,7

0

20

40

60

80

100

120

140

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

4,4 4,6 4,8 5,0 5,2 5,4 5,6

Po

rtio

ns

an

d f

illl

ets

Mean of arrivals



57

5.1.6 Summary The first part of the study showed that by modifying the arrival rate the performance of

the system could be optimized. It was also made evident that not all weight categories were

optimal for production of the different portion sizes. As was expected, the smaller salmons

were best suited for producing small portions, as the portions size grew the bigger salmons

got more feasible for production.

Since the flow upstream from the PortionCutter is not a part of the study then it is not

certain that the flow can be increased or decreased as much as was done in this study.

Therefore to minimize manual handling but still keep production and throughput as high as

possible throughout the process, it would be wise to look at what weight of salmon runs best

with the different portion sizes when the mean of arrival is set at 4,2. Table 23 shows exactly

this.

Table 23: The performance of the optimum weight categories for each portions size

Portion size 100g 125g 150g 175g 200g Optimum weight category 1 2 2 3 3 4 4 5 5 6 Fillets per minute 14,3 14,3 14,3 14,3 14,3 14,3 14,3 14,3 14,3 14,3 Portions processed per min. 103 128 114 129 120 130 123 130 126 130 Portions rejected per min. 0 15 1 18 2 21 5 24 8 27 Rejection rate 0% 12% 1% 14% 2% 17% 4% 18% 6% 21% Robot waiting percentage 23% 4% 15% 3% 10% 3% 8% 3% 6% 3%

Maybe the performance of the system would be better if the size categories where

changed. Instead of using one kilograms interval it would be better to use other intervals. For

the 100g portions for an example the system is running too fast for weight category two but

too slow for weight category one, combining them could change the performance of the

system. There is also the possibility of using 2,5-3,5kg instead of two to three kilogram

salmons. Changing the weight categories changes the whole setup of the model and there is

no way of telling how it affects the production without running the model for the different

scenarios. This could be a subject for another study.

58

5.2 Productivity As mentioned earlier it could be problematic to change the arrival rate of fillets. The flow

line is a part of bigger production and the inflow is therefore in large part dependent on

machines upstream from it. It is therefore interesting to see if the efficiency of the portioning

can be increased by cutting an extra portion of the fillets if the offcut exceeds a certain

weight. In the results the performance of the system is compared when an extra portion is

allowed and when an extra portion is not allowed. Each scenario was simulated first for the

case with an extra portion, with the physical arrival rate and the same random parameters

where used for the same scenarios without extra portion. Each portion size was simulated for

all of the six weight categories. The weight of extra portions was set at 100g, except for the

100g portions where the extra portion weighs 50g. There are five different items that

contribute to the total processed weight; the tail, portions processed, portions rejected, offcuts

and extra portion. The different items were introduced in chapter 4.3.2, where the method of

calculating their weight was also introduced. For the cases where an extra portion is allowed

then the offcut is divided into two parts, offcut one and offcut two. The average weight of

offcuts varies from one weight category to the other and also between portion sizes. To find

the average weight the historical data was studied and the average weight calculated from it.

The total weight processed is then the sum of the five items. These five items are then divided

into two categories, usable products and unusable products. The usable products are portions

processed, rejected portions and extra portions. Rejected portions are usable but they have to

be manually packed or handled. The tail and offcuts are then unusable items but they can still

be used for making less valuable products such as small bits which are bite size cuts of

salmon or animal food. The ratios of usable and unusable products from the total amount of

material are analyzed for each of the portion sizes. All the weights are presented in kilograms

and the numbers are for one hour of processing. More results from the simulation with 95%

confident intervals can be seen in appendix three.

5.2.1 100g portions As mentioned before this portion size was simulated with and without a 50g extra portion.

The average weight of the offcuts can be seen in Table 24. The average weight of offcut one

is quiet close to 25g which should be the average if the fillets followed a normal distribution.

Same goes for the average weight of offcut two and the offcut for when no extra portions is

taken weighs around 50g.

59

Table 24: The average weight of offcuts for 100g portions

Extra portion No extra portion

Offcut 1 Offcut 2 Offcut Weight

category Average

weight[kg] Average

weight[kg] Average

Weight[kg]

1 0,027 0,022 0,050 2 0,025 0,028 0,054 3 0,023 0,026 0,048 4 0,028 0,025 0,049 5 0,025 0,024 0,053 6 0,024 0,028 0,052

The amount of different items that get processed in one hour according to the simulation is

listed in Table 25. The numbers are the same for both scenarios only the amount of extra

portions is zero in the case when extra portion is not taken and the offcut is then the sum of

offcut one and two. More portions are processed as the salmon fillets get bigger since the

robot does not have to wait as much because each fillet generates more portions. The amount

of rejected portions rises quickly and by category 5 the line is rejecting more portions than it

is packing. The reason for the variations in offcut is because the different distribution of fillets

with in each weight category.

Table 25: The amount processed of different items for 100g portions

Items Weight category

[kg per hour] 1 2 3 4 5 6

Portions 6189 7690 7860 7865 7865 7865

Rejected 5 917 3246 5764 8588 11346

Offcut 1 408 376 446 446 470 402

Offcut 2 321 384 311 319 289 384

Tail 858 858 858 858 858 858

Extra portion 428 482 403 403 368 456

60

Table 26 shows the total weight of the different items. In the bigger weight categories the

rejected portions are a big part of the total amount. This is because the portions are small

which reflects in more portions being generated.

Table 26: The total weight of items processed per hour for 100g portions

Items Weight Category

[kg per hour] 1 2 3 4 5 6

With extra portion

Portions 618,9 769,0 786,0 786,5 786,5 786,5 Rejected 0,5 91,7 324,6 576,4 858,8 1134,6

Offcut 20,3 22,0 20,2 21,9 20,1 21,8 Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 21,4 24,0 20,3 20,0 24,5 22,6

Total 746,8 992,5 1236,9 1490,5 1775,7 2051,3

Without extra

portion

Portions 618,9 769,0 786,0 786,5 786,5 786,5 Rejected 0,5 91,7 324,6 576,4 858,8 1134,6

Offcut 41,6 45,9 40,4 41,7 44,4 44,2 Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 0,0 0,0 0,0 0,0 0,0 0,0

Total 746,8 992,4 1236,8 1490,4 1775,5 2051,1

In Table 27 the percentage of usable and not usable items are presented both for when an

extra portion is allowed and also when not. The last row is then the difference between usable

percentage when an extra portion is allowed and when it is not allowed. It is interesting to see

that allowing the extra portion seems to give the best results when producing with small fillets

and then the advantage seems to whether out as the fillets get bigger. This is partly because

the extra portion is becoming increasingly smaller part of the total weight of the fillet.

61

Table 27: The percentage of usable and not usable products from 1 hour of processing of 100g portions


With extra

portion

Usable [kg] 640,8 884,7 1130,9 1382,8 1669,7 1943,7

Percentage 85,80% 89,20% 91,40% 92,80% 94,00% 94,80%

Not usable[kg] 106,1 107,8 106,0 107,7 105,9 107,6

Percentage 14,20% 10,80% 8,60% 7,20% 6,00% 5,20%

Without extra

portion

Usable [kg] 619,4 860,7 1110,6 1362,9 1645,3 1921,1 Percentage 82,60% 86,40% 89,70% 91,30% 92,50% 93,40%

Not usable[kg] 127,4 131,7 126,2 127,5 130,2 130,0

Percentage 17,40% 13,60% 10,30% 8,70% 7,50% 6,60%

Difference in usable kg 21 24 20 20 24 23

The results from Table 27 are shown graphically in Figure 36. Even though the results

don’t look high when looking at the difference, allowing for an extra portion is giving over

20kg per hour on average of valuable products which would otherwise have much less value

to the processor. In the long run this is big amount which adds added value to the process.

Figure 36: Difference between allowing for an extra portion or not when producing 100g portions

21

24

20 20

24

23

0

5

10

15

20

25

30

500,0

750,0

1000,0

1250,0

1500,0

1750,0

2000,0

1 2 3 4 5 6

Dif

fere

nce

[kg

]

Usa

ble

pro

du

ctio

n[k

g]

Weight category

Difference

With extra

portion

With no extra

portion

62

5.2.2 125g portions This is the first portion size where the extra portion weighs 100g. Table 28 shows the

average weight of offcuts, when extra portion is allowed and when it is not allowed.




category Average

weight[kg] Average

weight[kg] Average

Weight[kg]

1 0,057 0,013 0,078 2 0,048 0,013 0,082 3 0,051 0,013 0,071 4 0,052 0,014 0,072 5 0,044 0,013 0,076 6 0,056 0,013 0,080

The data for the quantity of items produced in one hour of production is presented in

Table 29, the data is the same for both with and without extra portion except that extra portion

is zero for the case when no extra portion is allowed and the offcut is the sum of offcut one

and two. The quantity of extra portions is much lower in this scenario than in the 100g one.

That is because the weight of the extra portion is much higher percentage of the portion size

here than it was in the 100g scenario. This is also evident when looking at the average weight

of offcuts without extra portion. In the last scenario it was close to the same as the weight of

the extra portion while in this one it is considerably lower.


Items Category

[kg per hour] 1 2 3 4 5 6

Portions 4710 6820 7740 7859 7865 7865

Rejected 0 48 1108 3093 5241 7388

Offcut 1 643 716 645 722 622 666

Offcut 2 120 70 119 85 131 98

Tail 858 858 858 858 858 858

Extra portion 215 128 206 136 223 178

63

The data in Table 30 is from multiplying the data from Table 29 with the weight of

different items. The rejected part of the last three categories is still very high so this portions

size is better suited for production with the smaller half of the weight categories. This

information was then used to find total weight of usable and not usable products which can be

seen in Table 31.



[kg per hour] 1 2 3 4 5 6

With extra portion

Portions 588,7 852,5 967,5 982,4 983,1 983,1

Rejected 0,0 5,9 138,5 386,7 655,1 923,5 Offcut 38,6 35,5 35,2 39,0 30,1 39,7

Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 22,0 13,0 20,3 13,7 22,1 17,7

Total 735,1 992,7 1247,4 1507,6 1776,2 2049,8

Without extra

portion

Portions 588,7 852,5 967,5 982,4 983,1 983,1

Rejected 0,0 5,9 138,5 386,7 655,1 923,5 Offcut 60,6 48,4 55,4 52,7 52,1 57,3

Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 0,0 0,0 0,0 0,0 0,0 0,0

Total 735,0 992,7 1247,3 1507,5 1776,1 2049,7

Table 31: The percentage of usable and not usable products from one hour of processing of 125g portions


With extra

portion


Not usable[kg] 124,4 121,3 121,0 124,8 115,9 125,5

Percentage 14,20% 10,80% 8,60% 7,20% 6,00% 5,20%

Without extra portion

Usable [kg] 588,7 858,5 1106,1 1369,1 1638,2 1906,6

Percentage 82,60% 86,40% 89,70% 91,30% 92,50% 93,40%

Not usable[kg] 146,3 134,2 141,2 138,5 137,9 143,1

Percentage 17,40% 13,60% 10,30% 8,70% 7,50% 6,60%


As the weight categories get bigger the usable percentage becomes higher. This is as

before because each extra portion is becoming an increasingly smaller portion of the total

64

weight of each fillet. The difference does not seem to be following any trend but rather a

random walk as can be seen in Figure 37. The difference between usable products when

allowing for an extra portion and when it is not allowed seems to be less than 20kg on

average, a bit lower than in the case of 100g portions.


5.2.3 150g portion Table 32: The average weight of offcuts for 150g portions



category Average

weight[kg] Average

weight[kg] Average

Weight[kg]

1 0,055 0,021 0,078 2 0,052 0,026 0,082 3 0,047 0,026 0,071 4 0,050 0,026 0,072 5 0,055 0,024 0,076 6 0,056 0,026 0,080

Table 32 shows the data for average weigh of offcuts both with and without an extra

portion. The average weight of offcut one for the simulation with an extra portion lies around

0,050 kg which looks right based in the distribution of the fillets. The other two offcuts lie

22

13

20

14

22

18

0

5

10

15

20

25

600,0

800,0

1000,0

1200,0

1400,0

1600,0

1800,0

1 2 3 4 5 6

Dif

fere

nce

[kg

]

Usa

ble

pro

du

ctio

n[k

g]

Weight category

Difference

With extra

portion

With no extra

portion

65

close to the middle of the range they span (0,050kg for offcut two and 0,149 for the offcut

without extra portion). The quantity of different items for each hour of production can be

found in Table 33. Rejected items are decreasing for the bigger weight categories as was

expected and the two smallest one are starting to underperform because inflow of fillets is not

high enough. This can be seen by looking at the rejections when they are zero then usually the

robot is waiting much of the time for new portions to pack.



[kg per hour] 1 2 3 4 5 6

Portions 3892 5581 7203 7772 7858 7865

Rejected 0 0 146 1288 2971 4784

Offcut 1 559 505 584 601 595 551

Offcut 2 213 285 201 196 201 230

Tail 858 858 858 858 858 858

Extra portion 299 342 256 256 256 290

The data from Table 33 and the information on weight of different items were used to find

the total weights for each hour of production. The information is presented in Table 34. The

total weight of extra portions is higher in this case than in the one for the 125g portions. This

is expected as the extra portion becomes increasingly smaller part of the portion size.

66



[kg per hour] 1 2 3 4 5 6

With extra portion

Portions 583,8 837,1 1080,4 1165,8 1178,7 1179,7

Rejected 0,0 0,0 21,9 193,1 445,6 717,5 Offcut 37,2 35,6 33,6 36,6 39,1 37,9

Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 29,7 34,4 25,8 25,3 25,9 29,2

Total 736,4 992,9 1247,5 1506,7 1775,1 2050,1

Without extra

portion

Portions 583,8 837,1 1080,4 1165,8 1178,7 1179,7

Rejected 0,0 0,0 21,9 193,1 445,6 717,5 Offcut 66,7 69,8 59,4 61,8 64,9 67,0

Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 0,0 0,0 0,0 0,0 0,0 0,0

Total 736,3 992,8 1247,4 1506,6 1775,0 2050,0



With extra

portion

Usable [kg] 613,5 871,5 1128,1 1384,3 1650,2 1926,4

Percentage 85,80% 89,20% 91,40% 92,80% 94,00% 94,80%

Not usable[kg] 123,0 121,3 119,4 122,4 124,9 123,7

Percentage 14,20% 10,80% 8,60% 7,20% 6,00% 5,20%

Without extra

portion


Not usable[kg] 152,5 155,6 145,1 147,6 150,7 152,7

Percentage 17,40% 13,60% 10,30% 8,70% 7,50% 6,60%


In Table 35 the percentages of usable and not usable items has been calculated. The usable

production and the difference between allowing an extra portion or not is then plotted on

Figure 38. The difference between the two cases is also in the same figure. As it was for the

other portion sizes the usable percentage increases as the fillets get bigger in this case. The

hourly average increase in usable products seems to be slowly rising as the portions grow

bigger. That is something that could be expected since heavier portions give heavier offcuts.

67


5.2.4 175g portion




category Average

weight[kg] Average

weight[kg] Average

Weight[kg]

1 0,058 0,040 0,102 2 0,053 0,032 0,086 3 0,049 0,039 0,083 4 0,048 0,043 0,089 5 0,047 0,036 0,094 6 0,058 0,037 0,096

As can be seen in Table 36 the average weight of offcuts is in increasing. This is because

the portions are bigger so the maximum weight of offcut is also getting higher. This does

though not apply for offcut one since it is always in the range of 0,001kg-0,099kg. The

quantity of different items produced each hour is also changing. The rejected part is getting

smaller and smaller as the portions get bigger. There is though still a considerable amount of

30

34

26 25 26

29

0

5

10

15

20

25

30

35

40

600,0

800,0

1000,0

1200,0

1400,0

1600,0

1800,0

1 2 3 4 5 6

Dif

fere

nce

[kg

]

Usa

ble

pro

du

ctio

n[k

g]

Weight category

Difference

With extra

portion

With no extra

portion

68

rejected portions in the top two weight categories. The information for processed items can be

found in Table 37.



[kg per hour] 1 2 3 4 5 6

Portions 3219 4768 6230 7404 7789 7858

Rejected 0 0 3 288 1412 2898

Offcut 1 393 484 534 485 403 437

Offcut 2 421 303 280 343 391 359

Tail 858 858 858 858 858 858

Extra portion 456 359 324 368 437 412

The data in Table 38 is gathered from multiplying the appropriate weight for each item in

Table 37. The weight of extra portions continuous to rise but the usable percentage is around

the same as in last scenario. The information on the percentage of usable and unusable parts

of the total production can be found in Table 39.



[kg per hour] 1 2 3 4 5 6

With extra portion

Portions 563,4 834,5 1090,3 1295,8 1363,1 1375,2

Rejected 0,0 0,0 0,5 50,5 247,1 507,1

Offcut 41,1 36,9 39,2 39,1 35,0 40,5 Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 45,6 36,1 32,3 37,1 44,1 40,9

Total 735,9 993,3 1248,1 1508,2 1775,2 2049,5

Without extra

portion

Portions 563,4 834,5 1090,3 1295,8 1363,1 1375,2

Rejected 0,0 0,0 0,5 50,5 247,1 507,1

Offcut 86,6 72,9 71,3 76,0 78,9 81,3 Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 0,0 0,0 0,0 0,0 0,0 0,0

Total 735,7 993,2 1248,0 1508,0 1775,0 2049,3

69



With extra

portion

Usable [kg] 609,0 870,6 1123,2 1383,3 1654,4 1923,2

Percentage 85,80% 89,20% 91,40% 92,80% 94,00% 94,80%

Not usable[kg] 126,9 122,7 124,9 124,9 120,8 126,3

Percentage 14,20% 10,80% 8,60% 7,20% 6,00% 5,20%

Without extra

portion


Not usable[kg] 172,3 158,7 157,1 161,8 164,7 167,0

Percentage 17,40% 13,60% 10,30% 8,70% 7,50% 6,60%


The difference in usable kg is slowly increasing as the portion sizes get bigger as can be seen

in Figure 39. This is due to the fact that the average weight of offcut is increasing which

means that more extra portions are being cut


46

36

32

37

44

41

0

5

10

15

20

25

30

35

40

45

50

600,0

800,0

1000,0

1200,0

1400,0

1600,0

1800,0

1 2 3 4 5 6

Dif

fere

nce

[kg

]

Usa

ble

pro

du

ctio

n[k

g]

Weight category

Difference

With extra

portion

With no extra

portion

70

5.2.5 200g portion Table 40 lists the values for average weights of offcuts. As before the average weight for

offcut, when there is no extra portion allowed, is increasing as the portions weight increases.


Extra portion No extra

portion


category Average

weight[kg] Average

weight[kg] Average

Weight[kg]

1 0,044 0,058 0,091 2 0,055 0,052 0,116 3 0,049 0,045 0,086 4 0,043 0,055 0,107 5 0,061 0,044 0,101 6 0,045 0,060 0,094

The amount of items processed in one hour of production can be seen in Table 41. There

are still many portions being rejected when producing with the bigger salmons, but in weight

category five the rate of rejection is getting close to being acceptable.



[Pieces per hour] 1 2 3 4 5 6

Portions 2865 4039 5443 6634 7553 7809

Rejected 0 0 0 19 460 1615

Offcut 1 492 307 523 355 437 485

Offcut 2 321 492 264 468 359 333

Tail 858 858 858 858 858 858

Extra portion 351 550 324 491 412 368

The values from Table 40 and Table 41 where used to get the total weight for different

items which can be found in Table 42

71



[kg per hour] 1 2 3 4 5 6

With extra portion

Portions 572,9 807,7 1088,5 1326,9 1510,6 1561,7

Rejected 0,0 0,0 0,0 3,9 92,0 323,1 Offcut 41,6 45,0 39,7 41,9 44,6 43,9

Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 35,4 54,7 33,0 48,9 41,3 36,6

Total 735,8 993,2 1247,1 1507,3 1774,3 2051,1

Without extra

portion

Portions 572,9 807,7 1088,5 1326,9 1510,6 1561,7

Rejected 0,0 0,0 0,0 3,9 92,0 323,1 Offcut 77,0 99,6 72,7 90,7 85,8 80,4

Tail 85,8 85,8 85,8 85,8 85,8 85,8

Extra portion 0,0 0,0 0,0 0,0 0,0 0,0

Total 735,7 993,1 1247,0 1507,2 1774,2 2050,9

Table 43 shows results from the calculations of percentage of usable and not usable products

from the total weight of items.



With extra

portion

Usable [kg] 608,4 862,4 1121,6 1379,7 1644,0 1921,4

Percentage 85,80% 89,20% 91,40% 92,80% 94,00% 94,80%

Not usable[kg] 127,4 130,8 125,5 127,7 130,3 129,7

Percentage 14,20% 10,80% 8,60% 7,20% 6,00% 5,20%

Without extra

portion


Not usable[kg] 162,8 185,4 158,4 176,4 171,5 166,1

Percentage 17,40% 13,60% 10,30% 8,70% 7,50% 6,60%


As before the average weight of offcut is increasing as the portions get bigger and the

difference in usable kg is therefore also getting bigger. This can be seen in Figure 40.

72


5.2.6 Summary All in all it does increase usable products and therefore yield to allow for an extra portion

to be collected when the offcut exceeds a certain weight. The increase is from being 13kg

each hour to around 55kg in a hour. Even though the increase is not always high, any increase

is likely to make it worth the while, especially when producing in this quantity of fillets.

Figure 41 shows the changes in production for the different weight categories and portion

sizes. In general, the yield is increasing as the portions get bigger though few cases look to be

lying outside of the norm. This is probably because of the distribution of fillets within each

weight category or because of the random features of the system.

35

55

33

49

41

37

0

10

20

30

40

50

60

600,0

800,0

1000,0

1200,0

1400,0

1600,0

1800,0

1 2 3 4 5 6

Dif

fere

nce

[kg

]

Usa

ble

pro

du

ctio

n[k

g]

Weight category

Difference

With extra

portion

With no extra

portion

73

Figure 41: The increase in production for all portion sizes when allowing an extra portion to be collected

0

10

20

30

40

50

60

1 2 3 4 5 6

We

igh

t [k

g]

Weight category

Difference in usable production

100g

125g

150g

175g

200g

74

6 Conclusion The first objective was to construct a simulation model that could efficiently predict the

performance of the system with respect to different scenarios based on different categories of

salmon sizes and portion size. The flow into the system was then optimized with the aim to

find the most suitable setup for each scenario and to find out if all salmon sizes work for

production of the different portion sizes.

A choice was made to look at six different size categories and five different portion sizes.

When looking just at these delimited scenarios, the results show that changing the inflow into

the system has big effect on the performance. The smaller weight categories work well with

the small portion size, but as the portion sizes grew the amount of portion generated from

each fillet decreased and the inflow had to increase to keep the production at an optimum

level for the small weight categories.

The bigger weight categories on the other hand generated too many of the small portions

and the inflow had to be decreased. The big salmon was better suited for the big portion sizes

but the smaller ones could not generate enough portions to keep up an efficient production.

None of the portion sizes worked with all of the weight categories.

It is not certain that the inflow can be altered as was assumed in the study because it relies

on other flow lines upstream from the portioning and packing line. But it is evident that the

production can be optimized by studying what salmon sizes are best suited for production

with the different portion sizes. By doing this the production companies can increase their

profits by minimizing manual handling but still maintaining high rate of production.

The second objective of the study was to find the efficiency of the current setup of the

system, that is how much of the total weight of raw material that goes into the system is

turned into value for the manufacturer and also to see if the efficiency changes if a smaller

extra portion is allowed to be cut off the rest of the fillet.

The results showed that the efficiency of the production did increase by allowing an extra

portion to be cut. The increase varies between scenarios but there is always some. The biggest

increase was for the smallest salmons since the extra portion is a bigger part of the total

weight of each fillet. In general the increase in production grew as the portion sizes got

bigger. The 100g portions are not compatible in comparison to the other portion sizes since it

had different size of extra portion. Even though the increase in percentage was not always

high it made a big difference when producing in such large quantities.

75

To figure out how much value the extra portion has to the total production, some kind of

value has to be assigned to each of the five different items that can be in each fillet. It was

assumed that the extra portion had the same value as the processed portions and the rejected

portions. In reality, the processed portions should have the highest value and the other items

should have lower value.

The problem seems to be bigger than was originally concluded. There are many different

parameters that affect the performance of the system so the options available for different

scenarios are much greater than was taken into account in the study. The portions can be

bigger or smaller, the salmon can be categorized differently and the size of the extra portion

can vary. One has to bear in mind that this is a simulation study and as mentioned in chapter

2.2, simulation studies are never 100% accurate but when carefully carried out they tend give

a good approach to the true performance of the system involved. The results could be more

accurate by running the actual data through the computer program used for portioning in the

portion cutter to get an exact performance of the system.

76

7 Future steps The study opens up many interesting options for further operational research studies

involving and around the system, both with simulation and other methods. The main objective

of production companies must be to increase profit of their production by minimizing cost and

maximizing income from selling products. The optimal performance can therefore vary from

one company to another. Companies with access to low cost labor can benefit from allowing

more manual handling while companies with high labor cost are likely to want the production

to have as little manual handling as possible. It would therefore be interesting to use the

results from the simulation and combine them with a linear optimization model. The objective

would be to maximize profit with the constraints of raw material available and the demand of

the market each time. A model like this could be used to determine how much manual

handling is optimal for different companies.

Another thing could be to combine product price with the results for the increased

productivity. The companies are getting different values for the production of different

products they get from the production. The extra portion is not as valuable as the main

portions and the tail and offcuts have some value as well.

Regarding the performance of the setup as it is, it could be interesting to see when the

robot stops being the bottleneck in the system and how the bottleneck change with different

setups, for example to see how the performance changes if another robot is added to the line.

In fact it could be interesting idea for Marel to construct a computer program where the setup

of each of their machines are setup and could easily be integrated to a simulation model based

on the desired setup at each time.

77

8 Bibliography [1] FAO Fisheries and Aquaculture Dept, The state of world fisheries and aquaculture

2012. London: Food and Agriculture Organization of the United Nations ; Eurospan,

2012.

[2] S. Arason, E. I. Ásgeirsson, B. Margeirsson, S. Margeirsson, P. Olsen, and H.

Stefánsson, “Decision Support Systems for the Food Industry,” in Handbook on

Decision Making, L. C. Jain and C. P. Lim, Eds. Springer Berlin Heidelberg, 2010,

pp. 295–315.

[3] P. Arbo and B. Hersoug, “The globalization of the fishing industry and the case of

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[5] H. L. Haraldsson, “Sjávarútvegur og byggðaþróun á Íslandi,” Byggðarstofnun, Mar.

2001.

[6] E. K. Guðfinsson, A. Á. Baldursson, F. Friðriksson, A. Möller, G. Sigurðardóttir, Á.

H. Elíasson, D. Jóhannsdóttir, and H. Einarsson, “Skýrsla nefndar um

framtíðarmöguleika fiskvinnslunnar,” Icelandic ministry of Fisheries and

Agriculture, Sep. 2001.

[7] Marel, “Marel.com”, Available: marel.com. [Accessed: 04-Feb-2013].

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systems: a critical review,” Int. J. Comput. Integr. Manuf., vol. 7, no. 6, pp. 340–355,

1994.

[9] F. S. Hillier and G. J. Lieberman, Introduction to Operations Research 8TH

EDITION. McGraw-Hil Publishing Company,2005.

[10] A. M. Law, Simulation Modeling and Analysis Fourth Edition. McGraw-Hill, 2007.

[11] B. W. Hollocks, “Intelligence, innovation and integrity—KD Tocher and the dawn of

simulation,” J. Simul., vol. 2, no. 3, p. 128, 2008.

[12] R. E. Nance, “History of programming languages—II,” T. J. Bergin,Jr. and R. G.

Gibson,Jr., Eds. New York, NY, USA: ACM, 1996, pp. 369–427.

[13] D. Goldsman, R. E. Nance, and J. R. Wilson, “A brief history of simulation,” in

Simulation Conference (WSC), Proceedings of the 2009 Winter, 2009, pp. 310 –313.

[14] J. H. Blackstone, Ed., APICS Dictionary, 13Th ed. APICS The Association for

Operations Management, 2010.

78

[15] A. M. Law and M. G. McComas, “Simulation of manufacturing systems,” in

Simulation Conference Proceedings, 1999 Winter, 1999, vol. 1, pp. 56 –59 vol.1.

[16] W. J. Hopp and M. L. Spearman, Factory physics : foundations of manufacturing

management. Boston: Irwin McGraw-Hill, 2000.

[17] Paul J. Sánchez, “FUNDAMENTAL OF SIMULATION MODELING,” in

Proceedings of the 2007 Winter Simulation Conference, 2007, pp. 54–62.

[18] S. M. Ross, Introduction to probability models, 9th ed. Amsterdam ; Boston:

Academic Press, 2007.

[19] S. Ólafsson, “A review of OR practice in Iceland,” Eur. J. Oper. Res., vol. 87, no. 3,

pp. 456–460, Dec. 1995.

[20] P. Jensson, “A SIMULATION MODEL OF THE CAPELIN FISHING IN

ICELAND,” in Applied Operations Research in Fishing, Boston, MA: Springer US,

1981.

[21] E. Jonatansson and S. U. Randhawa, “A network simulation model of a fish

processing facility,” SIMULATION, vol. 47, no. 1, pp. 5–12, Jul. 1986.

[22] T. Helgason and S. Ólafsson, “An Icelandic fisheries model,” Eur. J. Oper. Res., vol.

33, no. 2, pp. 191–199, Jan. 1988.

[23] S. U. Randhawa and E. T. Bjarnason, “A decision aid for coordinating fishing and

fish processing,” Eur. J. Oper. Res., vol. 81, no. 1, pp. 62–75, Feb. 1995.

[24] I. Arnarson and P. Jensson, “Impact of the cost of the time resource on efficiency of

economic processes,” Eur. J. Oper. Res., vol. 172, no. 2, pp. 616–630, Jul. 2006.

[25] Marel, “Marel”. [Online.]. Available: http://www.marel.com/company/brands/marel/.

[Accessed: 11-Mar-2013].

[26] Marel, “Research and development at Marel”. [online.]. Available:

http://marel.com/company/Research-and-development/. Accessed: 11-Mar-2013.

[27] Marel, “The company”. [Online.]. Available:

http://www.marel.com/company/company/. [Accessed: 11-Mar-2013].

[28] Marel, “Marel in figures”. [Online]. Available: http://marel.com/annual-

reports/2012/MarelinFigures.html. Accessed: 04-Feb-2013.

[29] Nordlaks, “About Nordlaks”. [Online]. Available: http://eng.nordlaks.no/About-

Nordlaks. [Accessed: 04-Mar-2013].

[30] J. Shalliker and C. Ricketts, An Introduction to Simulation in the Manufacturing

Industry using SIMUL8 2009, vol. 16. SIMUL8 Corporation, 2009.

79

8.1 Verbal Reference [31] J. B. Gunnarsson, “Meeting with manager at Marel,”, 04-Dec-2013.

80

9 Appendix one

Number of arrival Seconds between arrivals Number of arrival Seconds between arrivals 1 2 77 3,2 2 2,2 78 3,2 3 2,3 79 3,2 4 2,3 80 3,2 5 2,3 81 3,2 6 2,4 82 3,2 7 2,4 83 3,3 8 2,5 84 3,3 9 2,5 85 3,3 10 2,6 86 3,3 11 2,6 87 3,3 12 2,6 88 3,3 13 2,6 89 3,3 14 2,7 90 3,3 15 2,7 91 3,3 16 2,7 92 3,3 17 2,8 93 3,3 18 2,8 94 3,3 19 2,8 95 3,3 20 2,9 96 3,3 21 2,9 97 3,3 22 2,9 98 3,3 23 2,9 99 3,3 24 2,9 100 3,3 25 2,9 101 3,3 26 2,9 102 3,3 27 2,9 103 3,3 28 2,9 104 3,3 29 3 105 3,3 30 3 106 3,3 31 3 107 3,3 32 3 108 3,3 33 3 109 3,3 34 3 110 3,3 35 3 111 3,3 36 3 112 3,3 37 3 113 3,3 38 3 114 3,3 39 3 115 3,3 40 3 116 3,4 41 3 117 3,4 42 3 118 3,4 43 3,1 119 3,4 44 3,1 120 3,4 45 3,1 121 3,4 46 3,1 122 3,4 47 3,1 123 3,4 48 3,1 124 3,4 49 3,1 125 3,4 50 3,1 126 3,4 51 3,1 127 3,4 52 3,1 128 3,4

81

53 3,1 129 3,4 54 3,1 130 3,4 55 3,1 131 3,4 56 3,1 132 3,4 57 3,2 133 3,4 58 3,2 134 3,4 59 3,2 135 3,4 60 3,2 136 3,4 61 3,2 137 3,4 62 3,2 138 3,4 63 3,2 139 3,4 64 3,2 140 3,4 65 3,2 141 3,4 66 3,2 142 3,4 67 3,2 143 3,4 68 3,2 144 3,4 69 3,2 145 3,4 70 3,2 146 3,4 71 3,2 147 3,4 72 3,2 148 3,4 73 3,2 149 3,4 74 3,2 150 3,4 75 3,2 151 3,4 76 3,2 152 3,4

153 3,4 229 3,6 154 3,4 230 3,6 155 3,4 231 3,6 156 3,4 232 3,6 157 3,5 233 3,7 158 3,5 234 3,7 159 3,5 235 3,7 160 3,5 236 3,7 161 3,5 237 3,7 162 3,5 238 3,7 163 3,5 239 3,7 164 3,5 240 3,7 165 3,5 241 3,7 166 3,5 242 3,7 167 3,5 243 3,7 168 3,5 244 3,7 169 3,5 245 3,7 170 3,5 246 3,7 171 3,5 247 3,7 172 3,5 248 3,7 173 3,5 249 3,7 174 3,5 250 3,7 175 3,5 251 3,7 176 3,5 252 3,7 177 3,5 253 3,7 178 3,5 254 3,7 179 3,5 255 3,7 180 3,5 256 3,7 181 3,5 257 3,7 182 3,5 258 3,7 183 3,5 259 3,7 184 3,5 260 3,7 185 3,5 261 3,7 186 3,5 262 3,7

82

187 3,5 263 3,7 188 3,5 264 3,7 189 3,5 265 3,7 190 3,6 266 3,7 191 3,6 267 3,7 192 3,6 268 3,7 193 3,6 269 3,7 194 3,6 270 3,7 195 3,6 271 3,7 196 3,6 272 3,7 197 3,6 273 3,7 198 3,6 274 3,7 199 3,6 275 3,7 200 3,6 276 3,7 201 3,6 277 3,7 202 3,6 278 3,7 203 3,6 279 3,7 204 3,6 280 3,7 205 3,6 281 3,7 206 3,6 282 3,7 207 3,6 283 3,7 208 3,6 284 3,7 209 3,6 285 3,7 210 3,6 286 3,7 211 3,6 287 3,7 212 3,6 288 3,8 213 3,6 289 3,8 214 3,6 290 3,8 215 3,6 291 3,8 216 3,6 292 3,8 217 3,6 293 3,8 218 3,6 294 3,8 219 3,6 295 3,8 220 3,6 296 3,8 221 3,6 297 3,8 222 3,6 298 3,8 223 3,6 299 3,8 224 3,6 300 3,8 225 3,6 301 3,8 226 3,6 302 3,8 227 3,6 303 3,8 228 3,6 304 3,8

305 3,8 381 3,9 306 3,8 382 3,9 307 3,8 383 3,9 308 3,8 384 3,9 309 3,8 385 3,9 310 3,8 386 3,9 311 3,8 387 3,9 312 3,8 388 3,9 313 3,8 389 3,9 314 3,8 390 3,9 315 3,8 391 3,9 316 3,8 392 3,9 317 3,8 393 3,9 318 3,8 394 3,9 319 3,8 395 3,9 320 3,8 396 3,9

83

321 3,8 397 3,9 322 3,8 398 3,9 323 3,8 399 3,9 324 3,8 400 3,9 325 3,8 401 3,9 326 3,8 402 3,9 327 3,8 403 3,9 328 3,8 404 3,9 329 3,8 405 3,9 330 3,8 406 4 331 3,8 407 4 332 3,8 408 4 333 3,8 409 4 334 3,8 410 4 335 3,8 411 4 336 3,8 412 4 337 3,8 413 4 338 3,8 414 4 339 3,8 415 4 340 3,8 416 4 341 3,9 417 4 342 3,9 418 4 343 3,9 419 4 344 3,9 420 4 345 3,9 421 4 346 3,9 422 4 347 3,9 423 4 348 3,9 424 4 349 3,9 425 4 350 3,9 426 4 351 3,9 427 4 352 3,9 428 4 353 3,9 429 4 354 3,9 430 4 355 3,9 431 4 356 3,9 432 4 357 3,9 433 4 358 3,9 434 4 359 3,9 435 4 360 3,9 436 4 361 3,9 437 4 362 3,9 438 4 363 3,9 439 4 364 3,9 440 4 365 3,9 441 4 366 3,9 442 4 367 3,9 443 4 368 3,9 444 4 369 3,9 445 4 370 3,9 446 4 371 3,9 447 4 372 3,9 448 4 373 3,9 449 4 374 3,9 450 4 375 3,9 451 4 376 3,9 452 4 377 3,9 453 4 378 3,9 454 4

84

379 3,9 455 4 380 3,9 456 4

457 4 533 4,3 458 4 534 4,3 459 4,1 535 4,3 460 4,1 536 4,3 461 4,1 537 4,3 462 4,1 538 4,3 463 4,1 539 4,3 464 4,1 540 4,3 465 4,1 541 4,3 466 4,1 542 4,3 467 4,1 543 4,3 468 4,1 544 4,3 469 4,1 545 4,3 470 4,1 546 4,4 471 4,1 547 4,4 472 4,1 548 4,4 473 4,1 549 4,4 474 4,1 550 4,4 475 4,1 551 4,4 476 4,1 552 4,4 477 4,1 553 4,4 478 4,1 554 4,4 479 4,1 555 4,4 480 4,1 556 4,4 481 4,1 557 4,5 482 4,1 558 4,5 483 4,1 559 4,5 484 4,1 560 4,5 485 4,1 561 4,5 486 4,1 562 4,5 487 4,1 563 4,5 488 4,1 564 4,5 489 4,1 565 4,5 490 4,1 566 4,5 491 4,1 567 4,5 492 4,2 568 4,5 493 4,2 569 4,6 494 4,2 570 4,6 495 4,2 571 4,6 496 4,2 572 4,6 497 4,2 573 4,6 498 4,2 574 4,6 499 4,2 575 4,6 500 4,2 576 4,6 501 4,2 577 4,6 502 4,2 578 4,6 503 4,2 579 4,6 504 4,2 580 4,7 505 4,2 581 4,7 506 4,2 582 4,7 507 4,2 583 4,7 508 4,2 584 4,7 509 4,2 585 4,7 510 4,2 586 4,7 511 4,2 587 4,7 512 4,2 588 4,7

85

513 4,2 589 4,7 514 4,2 590 4,7 515 4,2 591 4,7 516 4,2 592 4,7 517 4,2 593 4,8 518 4,2 594 4,8 519 4,2 595 4,8 520 4,2 596 4,8 521 4,2 597 4,8 522 4,2 598 4,8 523 4,2 599 4,8 524 4,2 600 4,8 525 4,3 601 4,9 526 4,3 602 4,9 527 4,3 603 4,9 528 4,3 604 4,9 529 4,3 605 4,9 530 4,3 606 4,9 531 4,3 607 4,9 532 4,3 608 4,9

609 4,9 685 6,9 610 5 686 7 611 5 687 7 612 5 688 7,1 613 5 689 7,2 614 5 690 7,2 615 5 691 7,3 616 5 692 7,3 617 5 693 7,3 618 5 694 7,4 619 5,1 695 7,5 620 5,1 696 7,6 621 5,1 697 7,6 622 5,1 698 7,7 623 5,1 699 7,8 624 5,2 700 7,8 625 5,2 701 7,9 626 5,2 702 1,2 627 5,2 703 1,3 628 5,3 704 1,4 629 5,3 705 1,5 630 5,3 706 1,7 631 5,3 707 1,8 632 5,3 708 8 633 5,4 709 8,1 634 5,4 710 8,3 635 5,4 711 8,6 636 5,4 712 8,7 637 5,4 713 8,7 638 5,5 714 8,8 639 5,5 715 8,9 640 5,5 716 9 641 5,5 717 9 642 5,5 718 9 643 5,6 719 9 644 5,6 720 9 645 5,6 721 9,1 646 5,7 722 9,3

86

647 5,7 723 9,4 648 5,8 724 9,4 649 5,8 725 9,7 650 5,8 726 10,2 651 5,8 727 13,2 652 5,8

653 5,9 654 5,9 655 5,9 656 5,9 657 5,9 658 6 659 6 660 6 661 6,1 662 6,1 663 6,1 664 6,2 665 6,3 666 6,4 667 6,4 668 6,4 669 6,4 670 6,5 671 6,5 672 6,5 673 6,5 674 6,5 675 6,5 676 6,5 677 6,6 678 6,7 679 6,7 680 6,7 681 6,8 682 6,8 683 6,8 684 6,9

87

10 Appendix two

-95% Average 95% -95% Average 95% -95% Average 95%1 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,42 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,43 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,44 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,45 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,46 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

-0,95 Average 0,95 -0,95 Average 0,951 22,4 22,8 23,2 76,8 77,2 77,62 3,7 4,1 4,5 95,5 95,9 96,33 1,8 2,0 2,2 97,8 98,0 98,24 1,7 1,9 2,1 97,9 98,1 98,35 1,7 1,9 2,1 97,9 98,1 98,36 1,7 1,9 2,1 97,9 98,1 98,3

-0,95 Average 0,95 -0,95 Average 0,951 16,5 16,5 16,6 31,5 33,0 34,52 26,7 26,9 27,1 50,1 51,0 51,93 36,6 36,9 37,1 65,1 66,0 66,94 46,1 46,4 46,7 79,3 80,4 81,55 56,6 57,0 57,3 98,7 102,6 106,56 66,9 67,3 67,7 114,5 120,4 126,3

-0,95 Average 0,95 -0,95 Average 0,95 -0,95 Average 0,951 49255,3 49513,6 49771,9 6156,9 6189,2 6221,5 102,6 103,2 103,72 61261,3 61522,2 61783,1 7657,7 7690,3 7722,9 127,6 128,2 128,73 62743,4 62879,8 63016,2 7842,9 7860,0 7877,0 130,7 131,0 131,34 62788,5 62919,0 63049,5 7848,6 7864,9 7881,2 130,8 131,1 131,45 62789,5 62919,6 63049,7 7848,7 7865,0 7881,2 130,8 131,1 131,46 62789,5 62919,6 63049,7 7848,7 7865,0 7881,2 130,8 131,1 131,4




100g portions-Mean of arrival 4,2

Categ.Per run Per hour Per min

Rejected 1 portions


Rejected 2 portions


Total Rejected portions

Waiting % Working %

Fillets arrived per run Fillets arrived per hour

Processed portions


Fillets arrived per minCateg.

Robot

Average Queue size Max Queue sizeCateg.

Categ.

88

-95% Average 95% -95% Average 95% -95% Average 95%

3,0 9571,8 9623,4 9675,0 1196,5 1202,9 1209,4 19,9 20,0 20,23,2 8979,9 9025,8 9071,7 1122,5 1128,2 1134,0 18,7 18,8 18,9

3,4 8449,2 8492,0 8534,8 1056,2 1061,5 1066,8 17,6 17,7 17,8

3,6 7980,5 8020,6 8060,7 997,6 1002,6 1007,6 16,6 16,7 16,8

3,8 7555,0 7590,4 7625,8 944,4 948,8 953,2 15,7 15,8 15,94,0 7175,9 7205,4 7234,9 897,0 900,7 904,4 14,9 15,0 15,1

4,2 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

-0,95 Average 0,95 -0,95 Average 0,95

3,0 3,1 3,7 4,3 95,7 96,3 96,9

3,2 5,2 5,7 6,2 93,8 94,3 94,8

3,4 7,8 8,3 8,7 91,3 91,7 92,2

3,6 10,9 11,5 12,1 87,9 88,5 89,1

3,8 14,8 15,2 15,7 84,3 84,8 85,2

4,0 18,7 19,1 19,6 80,4 80,9 81,3

4,2 22,4 22,8 23,2 76,8 77,2 77,6

-0,95 Average 0,95 -0,95 Average 0,953,0 24,7 25,2 25,7 38,6 39,2 39,8

3,2 23,2 23,5 23,9 38,1 39,0 39,9

3,4 21,6 21,8 21,9 37,7 38,4 39,1

3,6 20,0 20,2 20,4 37,1 38,0 38,9

3,8 18,7 18,8 18,9 35,7 36,4 37,1

4,0 17,5 17,6 17,7 33,8 35,2 36,6

4,2 16,5 16,5 16,6 31,5 33,0 34,5

-0,95 Average 0,95 -0,95 Average 0,95 -0,95 Average 0,95

3,0 61439,4 61788,6 62137,8 7679,9 7723,6 7767,2 128,0 128,7 129,53,2 60204,7 60518,4 60832,1 7525,6 7564,8 7604,0 125,4 126,1 126,7

3,4 58555,3 58848,6 59141,9 7319,4 7356,1 7392,7 122,0 122,6 123,2

3,6 56390,0 56777,2 57164,4 7048,7 7097,2 7145,6 117,5 118,3 119,1

3,8 54069,9 54378,6 54687,3 6758,7 6797,3 6835,9 112,6 113,3 113,94,0 51602,6 51883,8 52165,0 6450,3 6485,5 6520,6 107,5 108,1 108,7

4,2 49255,3 49513,6 49771,9 6156,9 6189,2 6221,5 102,6 103,2 103,7


3,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

3,2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,03,4 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

3,6 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

3,8 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

4,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,04,2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

-0,95 Average 0,95 -0,95 Average 0,95 -0,95 Average 0,953,0 3356,5 3748,4 4140,3 419,6 468,6 517,5 7,0 7,8 8,6

3,2 2094,4 2348,8 2603,2 261,8 293,6 325,4 4,4 4,9 5,4

3,4 1158,2 1210,6 1263,0 144,8 151,3 157,9 2,4 2,5 2,63,6 539,7 577,6 615,5 67,5 72,2 76,9 1,1 1,2 1,3

3,8 212,9 236,2 259,5 26,6 29,5 32,4 0,4 0,5 0,5

4,0 71,6 100,8 130,0 8,9 12,6 16,3 0,1 0,2 0,3

4,2 31,0 38,2 45,4 3,9 4,8 5,7 0,1 0,1 0,1


3,0 3356,5 3748,4 4140,3 419,6 468,6 517,5 7,0 7,8 8,63,2 2094,4 2348,8 2603,2 261,8 293,6 325,4 4,4 4,9 5,4

3,4 1158,2 1210,6 1263,0 144,8 151,3 157,9 2,4 2,5 2,6

3,6 539,7 577,6 615,5 67,5 72,2 76,9 1,1 1,2 1,3

3,8 212,9 236,2 259,5 26,6 29,5 32,4 0,4 0,5 0,54,0 71,6 100,8 130,0 8,9 12,6 16,3 0,1 0,2 0,3

4,2 31,0 38,2 45,4 3,9 4,8 5,7 0,1 0,1 0,1

Max Queue sizeMEAN


100g portions-Weight category one

Rejected 1 portions

MEANPer run Per hour Per min

Processed portions


Fillets arrived per minMEAN

Robot

Average Queue size

Rejected 2 portions


Waiting % Working %


MEAN


89

-95% Average 95% -95% Average 95% -95% Average 95%4,2 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,44,4 6527,5 6548,8 6570,1 815,9 818,6 821,3 13,6 13,6 13,74,6 6245,9 6266,4 6286,9 780,7 783,3 785,9 13,0 13,1 13,14,8 5985,1 6005,6 6026,1 748,1 750,7 753,3 12,5 12,5 12,65,0 5750,7 5768,8 5786,9 718,8 721,1 723,4 12,0 12,0 12,15,2 5522,0 5543,2 5564,4 690,2 692,9 695,6 11,5 11,5 11,65,4 5319,8 5339,8 5359,8 665,0 667,5 670,0 11,1 11,1 11,2

-0,95 Average 0,95 -0,95 Average 0,954,2 3,7 4,1 4,5 95,5 95,9 96,34,4 5,1 5,5 5,8 94,2 94,5 94,94,6 6,9 7,3 7,6 92,4 92,7 93,14,8 9,1 9,4 9,7 90,3 90,6 90,95,0 11,5 11,8 12,1 87,9 88,2 88,55,2 14,1 14,4 14,8 85,2 85,6 85,95,4 16,8 17,1 17,5 82,5 82,9 83,2

-0,95 Average 0,95 -0,95 Average 0,954,2 26,7 26,9 27,1 50,1 51,0 51,94,4 25,0 25,2 25,3 46,0 48,4 50,84,6 23,5 23,6 23,8 43,8 46,8 49,84,8 22,1 22,3 22,4 40,1 43,8 47,55,0 21,0 21,1 21,2 38,2 41,2 44,25,2 19,9 20,0 20,2 37,3 39,6 41,95,4 19,0 19,1 19,2 35,4 36,8 38,2

-0,95 Average 0,95 -0,95 Average 0,95 -0,95 Average 0,954,2 61261,3 61522,2 61783,1 7657,7 7690,3 7722,9 127,6 128,2 128,74,4 60428,4 60642,6 60856,8 7553,6 7580,3 7607,1 125,9 126,3 126,84,6 59260,0 59501,0 59742,0 7407,5 7437,6 7467,7 123,5 124,0 124,54,8 57902,2 58139,6 58377,0 7237,8 7267,5 7297,1 120,6 121,1 121,65,0 56366,6 56584,4 56802,2 7045,8 7073,1 7100,3 117,4 117,9 118,35,2 54663,3 54904,8 55146,3 6832,9 6863,1 6893,3 113,9 114,4 114,95,4 52943,9 53172,2 53400,5 6618,0 6646,5 6675,1 110,3 110,8 111,3




100g portions-Weight category twoFillets arrived per min

Processed portions

MEAN Per run Per hour Per min

MEAN

Robot

Average Queue size Max Queue sizeMEAN

MEANWaiting % Working %



Rejected 1 portions


Rejected 2 portions



90


-0,95 Average 0,95 -0,95 Average 0,955,6 7,6 7,9 8,2 91,8 92,1 92,45,8 9,2 9,5 9,7 90,3 90,5 90,86,0 11,0 11,2 11,5 88,5 88,8 89,06,2 12,8 13,1 13,4 86,6 86,9 87,26,4 14,9 15,1 15,3 84,7 84,9 85,16,6 16,9 17,1 17,3 82,7 82,9 83,16,8 19,0 19,2 19,4 80,6 80,8 81,0

-0,95 Average 0,95 -0,95 Average 0,955,6 25,6 25,7 25,9 47,2 48,6 50,05,8 24,6 24,7 24,8 44,8 47,2 49,66,0 23,5 23,6 23,7 42,5 45,2 47,96,2 22,6 22,7 22,8 40,7 43,0 45,36,4 21,8 21,9 22,0 39,0 41,2 43,46,6 21,1 21,2 21,2 37,6 39,2 40,86,8 20,4 20,5 20,5 36,8 38,0 39,2





Processed portions


100g portions-Weight category threeFillets arrived per min

MEAN

Robot





Rejected 1 portions


Rejected 2 portions



91


-0,95 Average 0,95 -0,95 Average 0,957,2 21,8 22,0 22,2 77,8 78,0 78,27,4 23,8 24,0 24,2 75,8 76,0 76,27,6 25,8 25,9 26,1 73,9 74,1 74,27,8 27,7 27,8 28,0 72,0 72,2 72,38,0 29,5 29,6 29,7 70,3 70,4 70,58,2 31,2 31,3 31,4 68,6 68,7 68,88,4 32,8 32,9 33,0 67,0 67,1 67,2

-0,95 Average 0,95 -0,95 Average 0,957,2 24,7 24,8 24,9 44,2 45,2 46,27,4 24,0 24,1 24,2 42,7 44,6 46,57,6 23,4 23,5 23,5 42,8 43,8 44,87,8 22,8 22,9 23,0 42,2 43,2 44,28,0 22,2 22,3 22,3 40,6 42,2 43,88,2 21,8 21,8 21,8 39,2 40,8 42,48,4 21,2 21,3 21,3 38,8 39,8 40,8





Processed portions


100g portions-Weight category fourFillets arrived per min

MEAN

Robot





Rejected 1 portions


Rejected 2 portions



92


-0,95 Average 0,95 -0,95 Average 0,951 40,9 41,2 41,6 58,4 58,8 59,12 14,6 15,0 15,3 84,7 85,0 85,43 3,1 3,5 3,8 96,2 96,5 96,94 1,8 2,0 2,2 97,8 98,0 98,25 1,7 1,9 2,1 97,9 98,1 98,36 1,7 1,9 2,1 97,9 98,1 98,3

-0,95 Average 0,95 -0,95 Average 0,951 11,7 11,8 11,9 23,1 24,0 24,92 19,0 19,2 19,3 36,7 39,4 42,13 27,7 27,9 28,1 51,2 53,6 56,04 36,0 36,3 36,5 63,6 65,8 68,05 44,1 44,4 44,7 78,2 80,2 82,26 52,1 52,4 52,8 90,1 93,8 97,5





125g portions-Mean of arrival 4,2Fillets arrived per min

Categ.

Robot

Average Queue size Max Queue size

Processed portions

Categ. Per run Per hour Per min

Per min

Rejected 1 portions


Rejected 2 portions



Categ.

Categ. Waiting percentage Working %


Categ. Per run Per hour

93


-0,95 Average 0,95 -0,95 Average 0,952,6 20,2 20,7 21,3 78,7 79,3 79,82,8 22,5 23,2 23,9 76,1 76,8 77,53,0 24,9 25,5 26,1 73,9 74,5 75,13,2 27,1 27,6 28,0 72,0 72,4 72,93,4 29,5 29,9 30,4 69,6 70,1 70,53,6 32,0 32,6 33,1 66,9 67,4 68,03,8 35,0 35,5 35,9 64,1 64,5 65,0

-0,95 Average 0,95 -0,95 Average 0,952,6 16,2 16,3 16,4 26,2 27,8 29,42,8 15,6 15,8 15,9 26,6 27,2 27,83,0 15,1 15,2 15,3 26,1 27,0 27,93,2 14,7 14,8 14,9 26,6 27,2 27,83,4 14,1 14,2 14,3 26,5 27,6 28,73,6 13,6 13,7 13,8 25,0 26,8 28,63,8 12,9 13,1 13,2 23,9 24,6 25,3





MEAN

MEAN Waiting percentage Working %


125g portions-Weight category one

Rejected 1 portions


Processed portions


Fillets arrived per minMEAN

Robot

Average Queue size Max Queue size

Rejected 2 portions




94


-0,95 Average 0,95 -0,95 Average 0,953,2 3,0 3,3 3,6 96,4 96,7 97,03,4 4,0 4,4 4,8 95,2 95,6 96,03,6 5,7 6,1 6,5 93,5 93,9 94,33,8 8,0 8,5 8,9 91,1 91,5 92,04,0 11,1 11,5 12,0 88,0 88,5 88,94,2 14,6 15,0 15,3 84,7 85,0 85,44,4 18,2 18,5 18,8 81,2 81,5 81,8

-0,95 Average 0,95 -0,95 Average 0,953,2 26,5 26,7 26,8 46,5 48,0 49,53,4 25,0 25,2 25,4 44,7 47,0 49,33,6 23,5 23,7 23,9 45,5 47,0 48,53,8 21,9 22,1 22,2 41,3 43,4 45,54,0 20,4 20,5 20,6 37,1 41,6 46,14,2 19,0 19,2 19,3 36,7 39,4 42,14,4 17,9 18,0 18,1 34,4 37,2 40,0





Per min

MEAN Per run Per hour Per minTotal Rejected portions

Processed portions


Rejected 2 portions


Rejected 1 portions

MEAN Per run Per hour


MEAN

Robot


MEANWaiting percentage Working %


95


-0,95 Average 0,95 -0,95 Average 0,954,2 3,1 3,5 3,8 96,2 96,5 96,94,4 4,3 4,6 4,9 95,1 95,4 95,74,6 5,8 6,1 6,5 93,5 93,9 94,24,8 7,7 8,0 8,3 91,7 92,0 92,35,0 9,8 10,1 10,4 89,6 89,9 90,25,2 12,2 12,6 13,0 87,0 87,4 87,85,4 14,8 15,2 15,6 84,4 84,8 85,2

-0,95 Average 0,95 -0,95 Average 0,954,2 27,7 27,9 28,1 51,2 53,6 56,04,4 26,0 26,2 26,3 50,2 52,0 53,84,6 24,5 24,6 24,7 45,2 49,4 53,64,8 23,0 23,1 23,3 42,9 46,6 50,35,0 21,9 21,9 22,0 41,3 43,6 45,95,2 20,7 20,8 21,0 40,8 41,8 42,85,4 19,8 19,9 19,9 37,1 40,0 42,9






MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


96


-0,95 Average 0,95 -0,95 Average 0,955,4 6,3 6,6 6,9 93,1 93,4 93,75,6 7,7 8,1 8,4 91,6 91,9 92,35,8 9,4 9,7 9,9 90,1 90,3 90,66,0 11,2 11,4 11,7 88,3 88,6 88,86,2 13,0 13,3 13,6 86,4 86,7 87,06,4 15,1 15,3 15,5 84,5 84,7 84,96,6 17,1 17,3 17,6 82,4 82,7 82,9

-0,95 Average 0,95 -0,95 Average 0,955,4 26,4 26,5 26,6 47,2 52,8 58,45,6 25,1 25,3 25,5 46,5 49,6 52,75,8 24,1 24,2 24,3 45,7 48,0 50,36,0 23,1 23,2 23,3 44,0 46,0 48,06,2 22,2 22,3 22,5 41,4 44,0 46,66,4 21,4 21,5 21,6 39,2 42,4 45,66,6 20,7 20,8 20,8 36,4 39,4 42,4






MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


97


-0,95 Average 0,95 -0,95 Average 0,951 51,2 51,4 51,7 48,3 48,6 48,82 30,0 30,4 30,8 69,2 69,6 70,03 9,7 10,2 10,6 89,4 89,8 90,34 2,8 3,1 3,4 96,6 96,9 97,25 1,8 2,0 2,2 97,8 98,0 98,26 1,7 1,9 2,1 97,9 98,1 98,3

-0,95 Average 0,95 -0,95 Average 0,951 9,4 9,5 9,6 19,2 19,8 20,42 14,3 14,5 14,6 27,2 28,2 29,23 21,1 21,2 21,4 39,0 42,4 45,84 28,6 28,8 29,0 52,6 54,2 55,85 35,6 35,8 36,0 62,7 64,6 66,56 42,4 42,7 43,0 74,8 76,8 78,8





150g portions-Mean of arrival 4,2Fillets arrived per min

Categ.

Robot


Categ.Waiting % Working %




Processed portions


Rejected 2 portions


Rejected 1 portions


98


-0,95 Average 0,95 -0,95 Average 0,952,6 7,0 7,6 8,2 91,8 92,4 93,02,8 9,2 9,9 10,7 89,3 90,1 90,83,0 11,7 12,3 12,9 87,1 87,7 88,33,2 14,2 14,6 15,0 85,0 85,4 85,83,4 16,7 17,2 17,7 82,3 82,8 83,33,6 19,5 20,2 20,9 79,1 79,8 80,53,8 23,1 23,6 24,0 76,0 76,4 76,9

-0,95 Average 0,95 -0,95 Average 0,952,6 21,3 21,5 21,8 34,7 36,4 38,12,8 20,0 20,3 20,6 35,2 36,2 37,23,0 19,2 19,4 19,6 34,3 36,4 38,53,2 18,5 18,6 18,8 32,7 35,4 38,13,4 17,6 17,8 17,9 33,5 35,0 36,53,6 16,8 16,9 17,1 32,8 35,2 37,63,8 16,0 16,1 16,2 29,8 32,2 34,6






MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


99


-0,95 Average 0,95 -0,95 Average 0,953,4 2,3 2,7 3,0 97,0 97,3 97,73,6 3,3 3,7 4,1 95,9 96,3 96,73,8 4,7 5,2 5,6 94,4 94,8 95,34,0 7,0 7,4 7,8 92,2 92,6 93,04,2 9,7 10,2 10,6 89,4 89,8 90,34,4 13,0 13,4 13,7 86,3 86,6 87,04,6 16,4 16,7 17,0 83,0 83,3 83,6

-0,95 Average 0,95 -0,95 Average 0,953,4 27,9 28,1 28,3 49,5 50,6 51,73,6 26,1 26,4 26,6 48,2 49,6 51,03,8 24,4 24,6 24,8 44,8 47,2 49,64,0 22,7 22,8 23,0 41,0 45,2 49,44,2 21,1 21,2 21,4 39,0 42,4 45,84,4 19,7 19,8 20,0 38,0 41,2 44,44,6 18,6 18,7 18,8 34,4 37,8 41,2






MEAN

Robot


MEAN Waiting % Working %



Processed portions


Rejected 2 portions


Rejected 1 portions


100


-0,95 Average 0,95 -0,95 Average 0,954,2 2,8 3,1 3,4 96,6 96,9 97,24,4 3,7 4,0 4,3 95,7 96,0 96,34,6 5,0 5,3 5,6 94,4 94,7 95,04,8 6,6 6,9 7,2 92,8 93,1 93,45,0 8,5 8,8 9,1 90,9 91,2 91,55,2 10,8 11,1 11,4 88,6 88,9 89,25,4 13,2 13,5 13,9 86,1 86,5 86,8

-0,95 Average 0,95 -0,95 Average 0,954,2 28,6 28,8 29,0 52,6 54,2 55,84,4 26,9 27,0 27,2 50,3 52,4 54,54,6 25,3 25,4 25,6 47,8 50,8 53,84,8 23,8 23,9 24,1 44,3 48,2 52,15,0 22,6 22,7 22,7 42,4 45,2 48,05,2 21,4 21,5 21,6 41,2 42,8 44,45,4 20,4 20,5 20,6 37,9 40,6 43,3






MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


101


-0,95 Average 0,95 -0,95 Average 0,955,4 6,4 6,7 7,0 93,0 93,3 93,65,6 7,8 8,1 8,4 91,6 91,9 92,25,8 9,5 9,8 10,0 90,0 90,2 90,56,0 11,3 11,5 11,8 88,2 88,5 88,76,2 13,1 13,4 13,7 86,3 86,6 86,96,4 15,2 15,5 15,7 84,3 84,5 84,86,6 17,3 17,5 17,7 82,3 82,5 82,7

-0,95 Average 0,95 -0,95 Average 0,955,4 26,0 26,1 26,2 46,3 50,6 54,95,6 24,8 24,9 25,1 46,4 48,2 50,05,8 23,8 23,9 24,0 43,4 46,4 49,46,0 22,8 22,9 23,0 41,5 44,0 46,56,2 21,9 22,0 22,1 39,6 42,6 45,66,4 21,1 21,2 21,3 38,4 41,4 44,46,6 20,5 20,5 20,5 36,9 38,6 40,3





150g portions-Weight category fiveFillets arrived per min

MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


102


-0,95 Average 0,95 -0,95 Average 0,951 59,6 59,8 60,0 40,0 40,2 40,42 40,2 40,5 40,8 59,2 59,5 59,83 22,0 22,3 22,6 77,4 77,7 78,04 7,3 7,7 8,1 91,9 92,3 92,75 2,6 2,9 3,2 96,8 97,1 97,46 1,8 2,0 2,2 97,8 98,0 98,2

-0,95 Average 0,95 -0,95 Average 0,951 7,7 7,7 7,8 15,6 16,2 16,82 11,9 12,0 12,1 23,1 24,0 24,93 16,5 16,6 16,7 30,7 32,6 34,54 22,6 22,8 22,9 42,1 44,8 47,55 29,2 29,4 29,6 52,0 54,0 56,06 35,3 35,5 35,8 62,2 63,8 65,4






Processed portions



Rejected 1 portions


Rejected 2 portions




Robot


Categ.Waiting % Working %


103


-0,95 Average 0,95 -0,95 Average 0,952,6 1,9 2,3 2,7 97,3 97,7 98,12,8 3,1 3,6 4,0 96,0 96,4 96,93,0 4,6 5,0 5,4 94,6 95,0 95,43,2 6,3 6,7 7,1 92,9 93,3 93,73,4 8,4 8,8 9,1 90,9 91,2 91,63,6 11,0 11,5 12,1 87,9 88,5 89,03,8 14,5 14,9 15,4 84,6 85,1 85,5

-0,95 Average 0,95 -0,95 Average 0,952,6 26,4 26,7 26,9 41,2 42,8 44,42,8 24,9 25,2 25,5 41,4 42,8 44,23,0 23,7 23,9 24,1 41,0 42,4 43,83,2 22,5 22,7 22,8 41,1 42,0 42,93,4 21,2 21,4 21,5 39,8 40,8 41,83,6 19,9 20,1 20,3 40,2 40,8 41,43,8 18,7 18,9 19,0 36,8 37,8 38,8






MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


104


-0,95 Average 0,95 -0,95 Average 0,953,6 2,5 2,9 3,3 96,7 97,1 97,53,8 3,5 3,9 4,4 95,6 96,1 96,54,0 5,1 5,5 5,9 94,1 94,5 94,94,2 7,3 7,7 8,1 91,9 92,3 92,74,4 10,0 10,4 10,7 89,3 89,6 90,04,6 12,9 13,3 13,7 86,3 86,7 87,14,8 16,1 16,5 16,8 83,2 83,5 83,9

-0,95 Average 0,95 -0,95 Average 0,953,6 27,8 28,0 28,3 51,1 52,0 52,93,8 26,1 26,3 26,5 48,6 50,2 51,84,0 24,3 24,5 24,6 43,2 47,6 52,04,2 22,6 22,8 22,9 42,1 44,8 47,54,4 21,1 21,3 21,4 41,2 42,8 44,44,6 19,9 20,0 20,1 39,0 41,2 43,44,8 18,8 18,9 18,9 36,0 38,8 41,6






MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


105


-0,95 Average 0,95 -0,95 Average 0,954,2 2,6 2,9 3,2 96,8 97,1 97,44,4 3,5 3,7 4,0 96,0 96,3 96,54,6 4,5 4,9 5,2 94,8 95,1 95,54,8 6,0 6,3 6,6 93,4 93,7 94,05,0 7,9 8,1 8,4 91,6 91,9 92,15,2 9,9 10,3 10,7 89,3 89,7 90,15,4 12,2 12,6 12,9 87,1 87,4 87,8

-0,95 Average 0,95 -0,95 Average 0,954,2 29,2 29,4 29,6 52,0 54,0 56,04,4 27,4 27,6 27,8 52,0 53,4 54,84,6 25,9 26,0 26,1 48,8 51,8 54,84,8 24,3 24,5 24,6 45,4 48,6 51,85,0 23,1 23,2 23,3 42,8 45,4 48,05,2 21,8 22,0 22,1 40,1 43,6 47,15,4 20,9 21,0 21,0 39,3 41,4 43,5






MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


106


-0,95 Average 0,95 -0,95 Average 0,955,2 5,3 5,5 5,8 94,2 94,5 94,75,4 6,5 6,8 7,0 93,0 93,2 93,55,6 7,9 8,2 8,5 91,5 91,8 92,15,8 9,5 9,8 10,1 89,9 90,2 90,56,0 11,3 11,6 11,9 88,1 88,4 88,76,2 13,2 13,5 13,9 86,1 86,5 86,86,4 15,3 15,6 15,9 84,1 84,4 84,7

-0,95 Average 0,95 -0,95 Average 0,955,2 26,9 27,1 27,3 47,9 51,6 55,35,4 25,8 25,9 26,0 45,4 50,0 54,65,6 24,6 24,7 24,9 44,7 47,4 50,15,8 23,6 23,7 23,8 43,2 45,6 48,06,0 22,6 22,7 22,8 41,4 44,0 46,66,2 21,7 21,8 21,9 39,1 42,0 44,96,4 20,9 21,0 21,1 38,0 40,6 43,2





175g portions-Weight category sixFillets arrived per min

MEAN

Robot





Processed portions


Rejected 2 portions


Rejected 1 portions


107


-0,95 Average 0,95 -0,95 Average 0,951 64,1 64,2 64,4 35,6 35,8 35,92 49,4 49,6 49,8 50,2 50,4 50,63 31,9 32,1 32,4 67,6 67,9 68,14 16,9 17,3 17,7 82,3 82,7 83,15 5,5 5,8 6,1 93,9 94,2 94,56 2,3 2,6 2,9 97,1 97,4 97,7

-0,95 Average 0,95 -0,95 Average 0,951 6,7 6,8 6,9 15,0 15,0 15,02 9,9 9,9 10,0 20,0 20,0 20,03 13,9 14,0 14,1 27,2 27,8 28,44 18,1 18,2 18,4 35,7 37,6 39,55 24,1 24,3 24,4 44,0 46,2 48,46 30,1 30,3 30,5 54,7 56,6 58,5






Processed portions


Rejected 1 portions



Rejected 2 portions




Robot


Categ. Waiting % Working %


108


-0,95 Average 0,95 -0,95 Average 0,952,6 7,6 8,2 8,7 91,3 91,8 92,42,8 10,4 11,0 11,7 88,3 89,0 89,63,0 13,2 13,7 14,1 85,9 86,3 86,83,2 15,9 16,3 16,6 83,4 83,7 84,13,4 18,7 19,0 19,4 80,6 81,0 81,33,6 21,5 22,1 22,6 77,4 77,9 78,53,8 25,0 25,4 25,7 74,3 74,6 75,0

-0,95 Average 0,95 -0,95 Average 0,952,6 20,7 20,9 21,1 33,3 35,6 37,92,8 19,5 19,7 19,9 32,8 34,8 36,83,0 18,6 18,7 18,8 33,2 34,6 36,03,2 17,9 18,0 18,0 32,5 33,6 34,73,4 17,1 17,2 17,3 31,2 32,6 34,03,6 16,2 16,4 16,5 30,4 31,8 33,23,8 15,5 15,6 15,7 29,0 30,4 31,8





Processed portions



MEAN

Robot





Rejected 1 portions


Rejected 2 portions



109


-0,95 Average 0,95 -0,95 Average 0,953,0 2,3 2,7 3,1 96,9 97,3 97,73,2 3,4 3,7 4,1 95,9 96,3 96,63,4 4,8 5,2 5,6 94,4 94,8 95,23,6 6,8 7,3 7,8 92,2 92,7 93,23,8 9,7 10,2 10,6 89,4 89,8 90,34,0 13,2 13,6 14,1 85,9 86,4 86,84,2 16,9 17,3 17,7 82,3 82,7 83,1

-0,95 Average 0,95 -0,95 Average 0,953,0 27,0 27,2 27,4 45,3 46,4 47,53,2 25,6 25,7 25,9 44,9 45,6 46,33,4 23,9 24,1 24,3 43,2 44,8 46,43,6 22,3 22,5 22,8 43,2 44,6 46,03,8 20,8 21,0 21,1 40,0 42,0 44,04,0 19,4 19,5 19,6 36,4 40,8 45,24,2 18,1 18,2 18,4 35,7 37,6 39,5





Processed portions



MEAN

Robot





Rejected 1 portions


Rejected 2 portions



110


-0,95 Average 0,95 -0,95 Average 0,953,8 2,7 3,1 3,4 96,6 96,9 97,34,0 3,9 4,2 4,6 95,4 95,8 96,14,2 5,5 5,8 6,1 93,9 94,2 94,54,4 7,6 7,9 8,3 91,7 92,1 92,44,6 10,2 10,5 10,8 89,2 89,5 89,84,8 13,1 13,4 13,7 86,3 86,6 86,95,0 16,1 16,4 16,6 83,4 83,6 83,9

-0,95 Average 0,95 -0,95 Average 0,953,8 27,7 27,9 28,1 51,0 52,4 53,84,0 25,9 26,0 26,1 47,0 50,0 53,04,2 24,1 24,3 24,4 44,0 46,2 48,44,4 22,5 22,6 22,8 42,8 44,8 46,84,6 21,2 21,2 21,3 40,7 43,6 46,54,8 19,9 20,0 20,0 37,1 40,2 43,35,0 19,0 19,0 19,0 35,3 38,4 41,5





Processed portions



MEAN

Robot





Rejected 1 portions


Rejected 2 portions



111


-0,95 Average 0,95 -0,95 Average 0,954,4 3,1 3,3 3,6 96,4 96,7 96,94,6 4,0 4,3 4,6 95,4 95,7 96,04,8 5,3 5,6 5,9 94,1 94,4 94,75,0 6,8 7,1 7,4 92,6 92,9 93,25,2 8,6 9,0 9,3 90,7 91,0 91,45,4 10,8 11,2 11,5 88,5 88,8 89,25,6 13,1 13,4 13,8 86,2 86,6 86,9

-0,95 Average 0,95 -0,95 Average 0,954,4 28,3 28,5 28,7 52,5 54,0 55,54,6 26,7 26,9 27,0 49,5 52,6 55,74,8 25,2 25,3 25,4 45,7 49,8 53,95,0 23,9 24,0 24,1 43,3 47,0 50,75,2 22,6 22,7 22,9 41,7 44,6 47,55,4 21,6 21,7 21,8 39,4 43,8 48,25,6 20,6 20,7 20,8 38,0 40,6 43,2





Processed portions


200g portions-Weight category sixFillets arrived per min

MEAN

Robot





Rejected 1 portions


Rejected 2 portions



112

11 Appendix three

100g portions

Processed portions



1 49255,3 49513,6 49771,9 6156,9 6189,2 6221,5 102,6 103,2 103,7 2 61261,3 61522,2 61783,1 7657,7 7690,3 7722,9 127,6 128,2 128,7 3 62743,4 62879,8 63016,2 7842,9 7860,0 7877,0 130,7 131,0 131,3 4 62788,5 62919,0 63049,5 7848,6 7864,9 7881,2 130,8 131,1 131,4 5 62789,5 62919,6 63049,7 7848,7 7865,0 7881,2 130,8 131,1 131,4 6 62789,5 62919,6 63049,7 7848,7 7865,0 7881,2 130,8 131,1 131,4

Rejected 1 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 4 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 5 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 6 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

Rejected 2 portions



1 31,0 38,2 45,4 3,9 4,8 5,7 0,1 0,1 0,1 2 7235,9 7334,4 7432,9 904,5 916,8 929,1 15,1 15,3 15,5 3 25675,1 25970,2 26265,3 3209,4 3246,3 3283,2 53,5 54,1 54,7 4 45745,0 46111,0 46477,0 5718,1 5763,9 5809,6 95,3 96,1 96,8 5 68303,4 68703,6 69103,8 8537,9 8588,0 8638,0 142,3 143,1 144,0

6 90261,1 90767,0 91272,9 11282,6 11345,9 11409,1 188,0 189,1 190,2

113




1 31,0 38,2 45,4 3,9 4,8 5,7 0,1 0,1 0,1 2 7235,9 7334,4 7432,9 904,5 916,8 929,1 15,1 15,3 15,5 3 25675,1 25970,2 26265,3 3209,4 3246,3 3283,2 53,5 54,1 54,7 4 45745,0 46111,0 46477,0 5718,1 5763,9 5809,6 95,3 96,1 96,8 5 68303,4 68703,6 69103,8 8537,9 8588,0 8638,0 142,3 143,1 144,0 6 90261,1 90767,0 91272,9 11282,6 11345,9 11409,1 188,0 189,1 190,2

Tail



1 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 2 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 3 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 4 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 5 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 6 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

Offcut 1



1 3225,2 3264,4 3303,6 403,2 408,1 412,9 6,7 6,8 6,9 2 2968,2 3005,2 3042,2 371,0 375,7 380,3 6,2 6,3 6,3 3 3537,2 3564,0 3590,8 442,2 445,5 448,8 7,4 7,4 7,5 4 3537,2 3564,0 3590,8 442,2 445,5 448,8 7,4 7,4 7,5 5 3707,6 3759,2 3810,8 463,5 469,9 476,3 7,7 7,8 7,9 6 3176,5 3213,2 3249,9 397,1 401,7 406,2 6,6 6,7 6,8

Offcut 2



1 2551,1 2571,8 2592,5 318,9 321,5 324,1 5,3 5,4 5,4 2 3041,7 3068,8 3095,9 380,2 383,6 387,0 6,3 6,4 6,4 3 2471,3 2487,8 2504,3 308,9 311,0 313,0 5,1 5,2 5,2 4 2533,3 2551,0 2568,7 316,7 318,9 321,1 5,3 5,3 5,4 5 2300,7 2311,0 2321,3 287,6 288,9 290,2 4,8 4,8 4,8 6 3045,5 3075,4 3105,3 380,7 384,4 388,2 6,3 6,4 6,5

114

Extra



1 3401,1 3421,4 3441,7 425,1 427,7 430,2 7,1 7,1 7,2 2 3814,0 3857,0 3900,0 476,7 482,1 487,5 7,9 8,0 8,1 3 3206,0 3221,8 3237,6 400,7 402,7 404,7 6,7 6,7 6,7 4 3206,0 3221,8 3237,6 400,7 402,7 404,7 6,7 6,7 6,7 5 2919,5 2943,8 2968,1 364,9 368,0 371,0 6,1 6,1 6,2 6 3613,2 3649,0 3684,8 451,7 456,1 460,6 7,5 7,6 7,7

125g portions

Processed portions



1 37451,4 37677,0 37902,6 4681,4 4709,6 4737,8 78,0 78,5 79,0 2 54315,0 54562,6 54810,2 6789,4 6820,3 6851,3 113,2 113,7 114,2 3 61690,7 61922,8 62154,9 7711,3 7740,4 7769,4 128,5 129,0 129,5 4 62733,3 62873,6 63013,9 7841,7 7859,2 7876,7 130,7 131,0 131,3 5 62789,5 62919,6 63049,7 7848,7 7865,0 7881,2 130,8 131,1 131,4 6 62787,3 62918,0 63048,7 7848,4 7864,8 7881,1 130,8 131,1 131,4

115

Rejected 1 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 4 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 5 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 6 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

Rejected 2 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 331,7 380,4 429,1 41,5 47,6 53,6 0,7 0,8 0,9 3 8772,7 8865,6 8958,5 1096,6 1108,2 1119,8 18,3 18,5 18,7 4 24455,5 24746,0 25036,5 3056,9 3093,3 3129,6 50,9 51,6 52,2 5 41619,3 41925,4 42231,5 5202,4 5240,7 5278,9 86,7 87,3 88,0 6 58686,9 59103,4 59519,9 7335,9 7387,9 7440,0 122,3 123,1 124,0




1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 331,7 380,4 429,1 41,5 47,6 53,6 0,7 0,8 0,9 3 8772,7 8865,6 8958,5 1096,6 1108,2 1119,8 18,3 18,5 18,7 4 24455,5 24746,0 25036,5 3056,9 3093,3 3129,6 50,9 51,6 52,2 5 41619,3 41925,4 42231,5 5202,4 5240,7 5278,9 86,7 87,3 88,0 6 58686,9 59103,4 59519,9 7335,9 7387,9 7440,0 122,3 123,1 124,0

Tail



1 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 2 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 3 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 4 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 5 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 6 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

116

Offcut 1



1 5096,8 5142,8 5188,8 637,1 642,9 648,6 10,6 10,7 10,8 2 5679,7 5725,0 5770,3 710,0 715,6 721,3 11,8 11,9 12,0 3 5109,2 5161,4 5213,6 638,6 645,2 651,7 10,6 10,8 10,9 4 5737,8 5775,6 5813,4 717,2 722,0 726,7 12,0 12,0 12,1 5 4916,9 4974,2 5031,5 614,6 621,8 628,9 10,2 10,4 10,5 6 5278,7 5327,2 5375,7 659,8 665,9 672,0 11,0 11,1 11,2

Offcut 2



1 921,7 957,8 993,9 115,2 119,7 124,2 1,9 2,0 2,1 2 530,0 556,4 582,8 66,2 69,6 72,9 1,1 1,2 1,2 3 915,9 948,4 980,9 114,5 118,6 122,6 1,9 2,0 2,0 4 653,7 679,8 705,9 81,7 85,0 88,2 1,4 1,4 1,5 5 1015,6 1051,6 1087,6 126,9 131,5 136,0 2,1 2,2 2,3 6 753,9 780,2 806,5 94,2 97,5 100,8 1,6 1,6 1,7

Extra



1 1696,4 1719,4 1742,4 212,0 214,9 217,8 3,5 3,6 3,6 2 992,2 1020,0 1047,8 124,0 127,5 131,0 2,1 2,1 2,2 3 1617,2 1648,0 1678,8 202,2 206,0 209,8 3,4 3,4 3,5 4 1063,0 1086,6 1110,2 132,9 135,8 138,8 2,2 2,3 2,3 5 1751,3 1785,8 1820,3 218,9 223,2 227,5 3,6 3,7 3,8 6 1400,6 1427,0 1453,4 175,1 178,4 181,7 2,9 3,0 3,0

117

150g portions

Processed portions



1 30934,6 31136,6 31338,6 3866,8 3892,1 3917,3 64,4 64,9 65,3 2 44402,2 44647,0 44891,8 5550,3 5580,9 5611,5 92,5 93,0 93,5 3 57330,3 57621,6 57912,9 7166,3 7202,7 7239,1 119,4 120,0 120,7 4 61987,4 62178,0 62368,6 7748,4 7772,3 7796,1 129,1 129,5 129,9 5 62722,8 62865,0 63007,2 7840,3 7858,1 7875,9 130,7 131,0 131,3 6 62787,3 62918,0 63048,7 7848,4 7864,8 7881,1 130,8 131,1 131,4

Rejected 1 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 4 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 5 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 6 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

Rejected 2 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,8 2,2 0,0 0,1 0,3 0,0 0,0 0,0 3 1098,0 1165,6 1233,2 137,2 145,7 154,2 2,3 2,4 2,6 4 10151,0 10301,0 10451,0 1268,9 1287,6 1306,4 21,1 21,5 21,8 5 23510,6 23767,0 24023,4 2938,8 2970,9 3002,9 49,0 49,5 50,0 6 37940,0 38268,2 38596,4 4742,5 4783,5 4824,5 79,0 79,7 80,4

118




1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,8 2,2 0,0 0,1 0,3 0,0 0,0 0,0 3 1098,0 1165,6 1233,2 137,2 145,7 154,2 2,3 2,4 2,6 4 10151,0 10301,0 10451,0 1268,9 1287,6 1306,4 21,1 21,5 21,8 5 23510,6 23767,0 24023,4 2938,8 2970,9 3002,9 49,0 49,5 50,0 6 37940,0 38268,2 38596,4 4742,5 4783,5 4824,5 79,0 79,7 80,4

Tail



1 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 2 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 3 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 4 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 5 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 6 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

Offcut 1



1 4412,6 4474,0 4535,4 551,6 559,3 566,9 9,2 9,3 9,4 2 3987,6 4041,0 4094,4 498,4 505,1 511,8 8,3 8,4 8,5 3 4624,1 4669,2 4714,3 578,0 583,7 589,3 9,6 9,7 9,8 4 4768,3 4810,8 4853,3 596,0 601,4 606,7 9,9 10,0 10,1 5 4722,3 4761,2 4800,1 590,3 595,2 600,0 9,8 9,9 10,0 6 4353,1 4405,0 4456,9 544,1 550,6 557,1 9,1 9,2 9,3

Offcut 2



1 1667,3 1700,6 1733,9 208,4 212,6 216,7 3,5 3,5 3,6 2 2254,9 2278,8 2302,7 281,9 284,9 287,8 4,7 4,7 4,8 3 1587,6 1608,4 1629,2 198,4 201,1 203,7 3,3 3,4 3,4 4 1541,3 1565,4 1589,5 192,7 195,7 198,7 3,2 3,3 3,3 5 1587,6 1608,4 1629,2 198,4 201,1 203,7 3,3 3,4 3,4 6 1813,6 1841,2 1868,8 226,7 230,2 233,6 3,8 3,8 3,9

119

Extra



1 2350,7 2388,2 2425,7 293,8 298,5 303,2 4,9 5,0 5,1 2 2705,5 2736,4 2767,3 338,2 342,1 345,9 5,6 5,7 5,8 3 2034,6 2051,4 2068,2 254,3 256,4 258,5 4,2 4,3 4,3 4 2034,6 2051,4 2068,2 254,3 256,4 258,5 4,2 4,3 4,3 5 2034,6 2051,4 2068,2 254,3 256,4 258,5 4,2 4,3 4,3 6 2294,5 2321,0 2347,5 286,8 290,1 293,4 4,8 4,8 4,9

175g portions

Processed portions



1 25622,1 25753,4 25884,7 3202,8 3219,2 3235,6 53,4 53,7 53,9 2 37932,7 38147,2 38361,7 4741,6 4768,4 4795,2 79,0 79,5 79,9 3 49635,4 49843,2 50051,0 6204,4 6230,4 6256,4 103,4 103,8 104,3 4 58954,6 59235,0 59515,4 7369,3 7404,4 7439,4 122,8 123,4 124,0 5 62131,2 62314,8 62498,4 7766,4 7789,4 7812,3 129,4 129,8 130,2 6 62719,9 62864,2 63008,5 7840,0 7858,0 7876,1 130,7 131,0 131,3

120

Rejected 1 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 4 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 5 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 6 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

Rejected 2 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 17,2 24,4 31,6 2,1 3,1 4,0 0,0 0,1 0,1 4 2252,3 2306,4 2360,5 281,5 288,3 295,1 4,7 4,8 4,9 5 11138,8 11296,8 11454,8 1392,4 1412,1 1431,8 23,2 23,5 23,9 6 22936,1 23182,0 23427,9 2867,0 2897,8 2928,5 47,8 48,3 48,8




1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 17,2 24,4 31,6 2,1 3,1 4,0 0,0 0,1 0,1 4 2252,3 2306,4 2360,5 281,5 288,3 295,1 4,7 4,8 4,9 5 11138,8 11296,8 11454,8 1392,4 1412,1 1431,8 23,2 23,5 23,9 6 22936,1 23182,0 23427,9 2867,0 2897,8 2928,5 47,8 48,3 48,8

Tail



1 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 2 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 3 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 4 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 5 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

6 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

121

Offcut 1



1 3108,1 4474,0 4535,4 551,6 559,3 566,9 9,2 9,3 9,4 2 3987,6 4041,0 4094,4 498,4 505,1 511,8 8,3 8,4 8,5 3 4624,1 4669,2 4714,3 578,0 583,7 589,3 9,6 9,7 9,8 4 4768,3 4810,8 4853,3 596,0 601,4 606,7 9,9 10,0 10,1 5 4722,3 4761,2 4800,1 590,3 595,2 600,0 9,8 9,9 10,0 6 4353,1 4405,0 4456,9 544,1 550,6 557,1 9,1 9,2 9,3

Offcut 2



1 3342,2 3367,4 3392,6 417,8 420,9 424,1 7,0 7,0 7,1 2 2400,9 2425,6 2450,3 300,1 303,2 306,3 5,0 5,1 5,1 3 2228,4 2239,0 2249,6 278,5 279,9 281,2 4,6 4,7 4,7 4 2712,8 2747,6 2782,4 339,1 343,5 347,8 5,7 5,7 5,8 5 3100,2 3127,0 3153,8 387,5 390,9 394,2 6,5 6,5 6,6 6 2841,1 2875,4 2909,7 355,1 359,4 363,7 5,9 6,0 6,1

Extra



1 3613,2 3649,0 3684,8 451,7 456,1 460,6 7,5 7,6 7,7 2 2847,2 2874,0 2900,8 355,9 359,3 362,6 5,9 6,0 6,0 3 2573,6 2592,8 2612,0 321,7 324,1 326,5 5,4 5,4 5,4 4 2919,5 2943,8 2968,1 364,9 368,0 371,0 6,1 6,1 6,2 5 3475,0 3499,8 3524,6 434,4 437,5 440,6 7,2 7,3 7,3 6 3273,8 3294,8 3315,8 409,2 411,9 414,5 6,8 6,9 6,9

122

200g portions

Processed portions



1 22790,2 22917,4 23044,6 2848,8 2864,7 2880,6 47,5 47,7 48,0 2 32155,7 32308,4 32461,1 4019,5 4038,6 4057,6 67,0 67,3 67,6 3 43374,4 43541,0 43707,6 5421,8 5442,6 5463,4 90,4 90,7 91,1 4 52819,6 53075,8 53332,0 6602,5 6634,5 6666,5 110,0 110,6 111,1 5 60222,5 60425,4 60628,3 7527,8 7553,2 7578,5 125,5 125,9 126,3 6 62271,6 62469,4 62667,2 7783,9 7808,7 7833,4 129,7 130,1 130,6

Rejected 1 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 4 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 5 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 6 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0

Rejected 2 portions



1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 0,0 0,4 1,5 0,0 0,1 0,2 0,0 0,0 0,0 4 133,5 154,8 176,1 16,7 19,4 22,0 0,3 0,3 0,4 5 3634,2 3680,2 3726,2 454,3 460,0 465,8 7,6 7,7 7,8 6 12782,9 12922,6 13062,3 1597,9 1615,3 1632,8 26,6 26,9 27,2

123




1 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 2 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 3 0,0 0,4 1,5 0,0 0,1 0,2 0,0 0,0 0,0 4 133,5 154,8 176,1 16,7 19,4 22,0 0,3 0,3 0,4 5 3634,2 3680,2 3726,2 454,3 460,0 465,8 7,6 7,7 7,8 6 12782,9 12922,6 13062,3 1597,9 1615,3 1632,8 26,6 26,9 27,2

Tail



1 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 2 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 3 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 4 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 5 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4 6 6834,9 6862,2 6889,5 854,4 857,8 861,2 14,2 14,3 14,4

Offcut 1



1 3881,4 3935,8 3990,2 485,2 492,0 498,8 8,1 8,2 8,3 2 2409,0 2459,6 2510,2 301,1 307,5 313,8 5,0 5,1 5,2 3 4136,6 4181,0 4225,4 517,1 522,6 528,2 8,6 8,7 8,8 4 2808,3 2842,4 2876,5 351,0 355,3 359,6 5,9 5,9 6,0 5 3473,0 3492,6 3512,2 434,1 436,6 439,0 7,2 7,3 7,3 6 3837,8 3877,2 3916,6 479,7 484,7 489,6 8,0 8,1 8,2

Offcut 2



1 2533,4 2564,4 2595,4 316,7 320,6 324,4 5,3 5,3 5,4 2 3884,3 3936,4 3988,5 485,5 492,1 498,6 8,1 8,2 8,3 3 2099,0 2111,8 2124,6 262,4 264,0 265,6 4,4 4,4 4,4 4 3701,9 3740,0 3778,1 462,7 467,5 472,3 7,7 7,8 7,9 5 2841,1 2875,4 2909,7 355,1 359,4 363,7 5,9 6,0 6,1 6 2635,4 2662,8 2690,2 329,4 332,9 336,3 5,5 5,5 5,6

124

Extra



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using simulation to predict performance of salmon portioning …€¦ · · 2017-05-15predict...

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