ls industry deep dive … · ls industry deep dive eyeon pid november 13th 2019 1. 2 agenda 16h00...

LS industry deep dive

EyeOn PID

November 13th 2019

1

2

Agenda

16h00 Kick off

16h15 No-touch S&OP – the human factor (Freek Aertsen)

17h00 Getting your inventory health under control (Maarten Van Liempd / Vasco Werners)

17h45 Integrate industry specific constraints & requirements into your planning (Bart Paridaen)

18h30 Dinner

20h00 Wrap-up & Drinks

3

Introducing your hosts for today

Vasco Werners Maarten Van Liempd Bart Paridaen

Loek Lemmens Freek AertsenLuc Van Wouwe

70 Specialists in realizing forecasting and planning improvements

EyeOn Company profile

• Focus on design and implementation

• Line management experience (f.e. Sales, SCM & Finance) and university educated

• Cross functional & hands-on mentality

• Proven track records in interim management

• Data science team with experts in planning and forecasting modelling

• System independent

• Industry focus; industrial companies, its suppliers and customers

• Network facilitator

• Deliver concrete projects with a short throughput time

(max. 100 days) and clear deliverables

4

5

EyeOn - Full service offering inforecasting and planning

Project Interim Academy

Execute & improve

planning processes with

temporary resources

Advance forecasting &

planning specialists in their

career

Forecast Services

Deliver best possible

forecast. Optimize

inventory parameters

Data Science & Solutions

Deliver technology and

models solving key

customer challenges in

turnkey solutions.

Develop & implement

tailored planning &

forecasting solutions

6

EyeOnKnowledge partner

Industry networks

Expert Sessions / Idea Labs

Benchmarks

Teaching: EyeOn Academy

Research & Publications

7

Agenda

16h00 Kick off




18h30 Dinner


No-touch S&OPThe human factor!

Freek Aertsen

November 2019

8

10

No-touch planning is feasible in my organization in....

0%

5%

10%

15%

20%

25%

30%

35%

2 years 5 years 10 years 20 years never Don't want it

85

Simplify the world

Let’s put some sense in it!

11

σ 𝑃 − 𝐴

σ𝑃∗ 100 = 𝑥%

σ 𝐴 − 𝐹

σ𝐴∗ 100 = 𝑥%

Forecast Accuracy Manufacturing Schedule Adherence

How to improve (1) Forecasts and plans and (2) actuals

Improving!

Better forecasts and plans

Forecasting – using external data and analytics

• Causal forecasting using macro economic indicators

• NPI forecasting

• Promotion forecasting

• Improved time series forecasting

Supply planning – up to date parameters

• Data engineering and IOT to have up to date

planning parameters

• MEIO inventory optimization

Better actuals - reduce data latency

Market – what is happing with our product?

• Consumer insights, reviews

• Sell-through

Supply – faster response

• IOT predicting and preventing machine breakdown

• Integrate S&OP and S&OE

• Compliance improvement

• QC release by priority

12

Man and machine

13

No-Touch planning requires different building blocks

EyeOn vision

Integral process for immediate and fact based decision making- Along value chain

- Different functional areas

- Linking strategy to execution

Demand

Making the change

Inventory

Supply

E2E, S&OP, IBP

15


EyeOn vision




Demand

Making the change

Inventory

E2E, S&OP, IBP

Supply

Excellent data- Data engineering

- Collection (internal –

external)

- Data storage

- ……

EyeOn Analytics in Demand Planning benchmark shows limited use of

additional data sources!

Data collection

0 1 2 3 4 5 6 7 8 9 10

Order data

Point of sales - sell out

Point of sales - stock in trade

Consumer review data from the web

Price development of our and competitor products over time

Promotional activities of customers

Weather forecasts

Historical promotion information

Facebook data

Google search behaviour data

Webscraping

Other

Macro economic indicators

Usage of data sources (1 - 10 scale, N= 25)

Excellent data - process mining to determine missing master data to enable higher

planning reliability in Pharma company

Data engineering

>7M records of stock movements

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EyeOn vision




Demand

Making the change

Inventory

E2E, S&OP, IBP

Supply



external)

- Data storage

- ……

Application enabled- Advanced Planning

Systems

- Cloud based tools

- Visualization

From machine to human centred systems

Systems evolve

HCI (Human Computer Interaction)

HMI (Human Machine Interaction)

Human Machine Engineering / Human Machine Design

From machine to …….

The planning systems evolution

HMI (Human Machine Interaction)

HCI (Human Computer Interaction)

Human Machine Engineering / Human Machine Design

?

From machine to…

Changing APS landscape

Strong focus on:• Platform – connecting appilcations• Open architecture – connecting external data • ML / AI capabilities• User interface• Scenario’s, simulations

• Recommendations instead of exceptions (this will change the role of the planner!)

22


EyeOn vision




Demand

Making the change

Inventory

E2E, S&OP, IBP

Supply



external)

- Data storage

- ……


Systems

- Cloud based tools

- Visualization

High quality analytics- Applying algorithms in

the supply chain.

- Machine learning - AI

- Role of data scientists

- ….

What is all the fuzz about?

AI Taxonomy

Is part of a broader family of machine learning methods based on artificial neural networks. It has been applied to fields including image and speech recognition, natural language processing, audio recognition, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs.

The scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence

Deep learningMachine learning (ML) Artificial Intelligence (AI)

The study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. Mimic human intelligence.

More and clever machine learning techniques and algorithms

New techniques become available

• Gradient boosting• Generalized linear models• Support vector machines• Nearest neighbors• Decision trees• Neural networks• And so on…

25


EyeOn vision




Demand

Making the change

Inventory

E2E, S&OP, IBP

Supply



external)

- Data storage

- ……


Systems

- Cloud based tools

- Visualization


the supply chain.



- ….

Organizational

readiness- How:

• Is analytics organized?

• Training / coaching

• Changing R&R

- …

What will remain?

Trainers • Advanced algorithms need to be trained how to perform• Train algorithms statistical forecast based on historical demand and other available data sources like

Facebook, twitter, google or Point-of-Sales information• Requires deep analytical knowledge• Often centrally organized (cross- functional, size matters)

Explainers • AI delivers conclusions (forecasts, plans) from a black-box• Requires human experts in the field (Demand Planners) to explain the outcome to non-experts

(Sales)• Often decentral, close to the ‘student’

Sustainer • Unconstrained data gathering can harm privacy and break laws • AI uses data several data sources and interacts with humans • Continuously work to ensure that systems work properly, safely, responsibly, within the existing

laws and ethically • Often organized centrally

31


EyeOn vision




Demand

Making the change

Inventory

E2E, S&OP, IBP

Supply



external)

- Data storage

- ……


Systems

- Cloud based tools

- Visualization


the supply chain.



- ….

Organizational

readiness- How:

• Is analytics organized?

• Training / coaching

• Changing R&R

- …

Decision focused

culture• Fact based

collaboration

• Effective meetings

• Reducing cognitive

bias

• ….

The EyeOn Analytics benchmark shows decision making uses human knowledge and simple data and facts, limited use of advanced data analysis

Data driven culture: rely on data!

0 1 2 3 4 5 6 7 8 9 10

Intuition

Personal Experience

Consultation with others

The Hippo

Simple data and facts

Advanced data analysis

Decision making (1 - 10 scale, N = 25)

Understand what people do with it!

Relying on data!

Individual human behavior - bias • Segmentation - differentiation• Close system for small changes (<20%)• Close system for non-value adding updates (FVA)• Use planners for the good (thumbs – up)• Recommendations instead of exceptions

Group decision making• Understand dynamics• Dashboards for instant resolving• Roles in meeting – devil’s advocate

34


Summary - EyeOn vision

• Although it is extremely hyped, analytics really changes planning

• It is often old technology getting accessible / applicable

• Algorithms are demanding and need to be fueled by (a lot of) high quality data

• Though it is coined ‘no-touch’ it makes sense to prepare your organization

• Many small steps make a big difference

Let’s build the future together!

www.eyeon.nl

Eindhoven

Amsterdam

Antwerp

Geneva

DublinFreek [email protected]

T +31 6 29 07 23 87

36

Agenda

16h00 Kick off




18h30 Dinner


Inventory Health

Life Science Pre Event

November 13th, 2019

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Content

• Building blocks inventory management• Stock decomposition• Lead time analysis• Lot size analysis• Inventory Projection• Dashboarding and insights (demo)

Building blocks for inventory management

39

• regular check of actualperformance vs data setting

• clear process to ‘engineer’required parameters

• stage gate process forproduct pipeline

MASTER DATA

40

Transparent process to set theparameters for (groups of) SKUbased on agreed characteristicsand threshold. Appointed processowner.

PARAMETER DECISION




MASTER DATA


41

A process with clear ownershipand accountability to review andset targets and identify andimplement optimizationopportunities.Clear who are the ‘suppliers’ and‘customers’ of the process.

INVENTORY MANAGEMENT


PARAMETER DECISION




MASTER DATA


42



• plan towards targets• proactive management of

projected deviations• clear ‘escalation’ in case of

unexpected events• fact based decisions to

deviate

INVENTORY CONTROL


PARAMETER DECISION




MASTER DATA


43



• plan towards targets• proactive management of

projected deviations• clear ‘escalation’ in case of

unexpected events• fact based decisions to

deviate

INVENTORY CONTROL


PARAMETER DECISION




MASTER DATA


44

Inventory control tools

• Stock decomposition• Lead time analysis• Lot size analysis• Inventory Projection• Dashboarding and insights

Stock Decomposition

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46

Inventory decomposition

Safety stocks

Cycle

Stock

Regulatory stock

Anticipation stock

Stock type Description Determinants

Cycle stock Stock produced in lots or batches

that fulfills demand over replenishment cycle (time between two production starts)

Lead time, batch size Calculated

Safety stock Required stock to buffer for

uncertainty in demand (order quantity) or supply (lead-time)

Demand variability (Q) and

supply variability (@lead time)

Calculated

Anticipation stock Required stock to anticipate on

future known events

Tenders, quotes, anticipated

factory shut downs for maintenance, etc

Agreed

Regulatory stock Stock required for contractual or

legal reasons

Regulatory (mandatory) and

contractually agreed quantitiesC, SS and A are part of R

Agreed

47

Inventory decomposition shows stock health

Time

Inve

nto

ry

Overstock

Understock

Healthy Stock

Safety stock

Cycle stock

48

Example deliverable: making your inventory transparent

Safety Stock

Cycle Stock

Blocked Stock

Overstock• Excess• Obsolete• Strategic• …

• Returns• Quality Inspection• …

Safety45%

Cycle13%

Blocked23%

Total overstock

19%

Total Stock

49

Drivers for overstock and understock

WIP

Stock in

transit

On Hand Stock

Strategic Stock

Blocked Stock

Active stock

Safety stock

Cycle stock

Excess stock

Active stock

Safety stock Cycle stockExcess stock

• Imbalance safety stock• Lead time uncertainty

• Duplication of safety stock

• Lot size effects• Actual and planned lead

time different• Incorrect ROP

EOLEarlyBias

Pre-buildEtc.

Lead time analysis

50

Introduction lead time

• Analysis of the actual replenishment behavior of both receiving and supplying plants

Relevance for inventory management

• Safety stock: lead-time is an input to the “APICS” formula; purpose to cover demand and

supply uncertainty during the replenishment lead time

• Cycle stock: structural arrival of replenishment orders too early raises the physical cycle

stock.

• Service: structural late arrival of replenishment orders causes stock out and hence service

loss.

51

Impact of differences between actual and master data lead time

Typically in industry is the focus on late deliveries and not on early deliveries.Late deliveries directly impact the service provided.Early arrival of replenishments guarantee availability and have an often neglected working capital impact.

>

52

Early deliveries Late deliveries

52

Inbound lead time

53

Production time Goods Receipt

Actual lead timeArrived

early

Total replenishment lead timeInbound view

• In case replenishment orders from different suppliers structurally arrive earlier than planned at the receiving plant,

there is a high probability that the Goods Receipt component is too high.

• Hypothesis: local inventory targets for receiving plants provide a pervert incentives to plan with longer lead times

and wait with Goods Receipt processing till needed.

Observed behavior inbound view

Expected behavior Unexpected behavior

Loc 1:

Loc 2:

Loc 3:

Loc 4:

OK Late → Early

OK Late → Early OK Late → Early

OK Late → Early

54

How to improve inventory health?

• Insights are key in understanding your inventory! Analyze and visualize your inventory periodically

• Focus is often on optimizing safety stock levels, while cycle stock also makes up big part of total inventory

• Are your master data lead times similar to the actual lead times?

• Are there any strange lot sizes, such as an MOQ of one year of demand

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Total Stock

56

Drivers for overstock and understock

WIP

Stock in

transit

On Hand Stock

Strategic Stock

Blocked Stock

Active stock

Safety stock

Cycle stock

Excess stock

Active stock

Safety stock Cycle stockExcess stock

• Imbalance safety stock• Lead time uncertainty not

included• Duplication of safety stock

• Lot size effects• Actual and planned lead

time different• Incorrect ROP

EOLEarlyBias

Pre-buildEtc.

Lot sizes

57

High MOQ Effect - stock height driven by MOQ value rather than demand

58

Inve

nto

ry

Replenishment lead-time

ROP

Safety stock

Equal to the demand over the replenishment lead-time

Inve

nto

ry

Replenishment lead-time

ROP

Safety stock

Equal to MOQ

Situation 2: MOQ >> Demand during LT

Situation 1: No MOQ

1. In situation 1, the replenishment quantity is equal to the demand over the lead-time. Assuming that orders are coming in as expected, the next replenishment will be planned after one cycle of replenishment lead-time

2. In situation 2, the replenishment quantity is driven by the MOQ. In case the MOQ is very high, it will take a long time before the inventory has moved to the minimum stock level.

>

In case of high MOQs the safety stock can be relatively low

59

Inve

nto

ry

Situation 2: High MOQ

1. Risk of a stock-out increases at the end of the replenishment cycle2. In case the MOQ is very high, the number of replenishment cycles is limited. Therefore the number of

occasions when the stock is developing towards the minimum stock level is also limited. This effect results in lower safety stock values.

>

Situation 1: No MOQ

Inventory policy for “rare demand of large orders”

60

Production

Product A

Replenishment LT: 2 weeksMOQ: 11,4 kgAvg. demand: 0,84 kg / weekStd. demand: 1,03 kg / weekPolicy: ROP (k = 2,05 → 98%)

Product B

Replenishment LT: 9 weeksMOQ: 75 kgAvg. demand: 0,84 kg / weekStd. demand: 1,03 kg / weekPolicy: non-stocking item

The current policy ignores the effect of the large demand orders. If an order occurs the available stock quantity is not enough and replenishment takes two weeks. Demand is rare, only for 2 out of the 89 weeks there will be shortage →97,7 % service.

Demand for B is a rare event; happens approximately once per 89 weeks (= 75 / 0,84)

If there is demand of B it triggers a demand of 75 kg of A.

Starting point

Implication

• Recommendation: Keep no stock of A (97,7% is also realized with ROP = 0). The current ROP is useless.• Alternative: set the reorder point equal to the demand order quantity (service will be 100%)

Recommendation

Inventory Projection

62

Latest inventory snapshot

Supply Plans

Net Requirement Plans

63

Inventory projection

Root cause analysis on stock development

using inventory projection

• Root cause analysis helps in

understanding main drivers of stock

increase

• Example:

- Detect mismatch between Supply

and Net Requirements per month

or per location to prevent

obsolete stock

64

Stock coverage projection to detect

excess and obsolete cases

• Based on latest Net Requirement

Plans projected inventory can be

categorized in stock:

- Sold within 1 year

- Sold between 1 and 2 years

- Sold after 2 years

- No requirements at all

• Early detection can reduce the future

excess and/or obsolete stock

65

Scenario Analysis

• By changing the original supply or net

requirement plan, the inventory

projection is updated

• Benefits:

- Quantitative arguments in

discussions

- Which adjustment to make to close

the budget gap

66

Inventory Control and

visualization

67

Online demo

68

Go to our website:

https://www.eyeon.nl/insights/powerbi/

for an online demo

68

https://www.eyeon.nl/insights/powerbi/


www.eyeon.nl

Eindhoven

Amsterdam

Antwerp

Geneva

DublinMaarten van LiempdT: +31 (0)88 883 00 25

[email protected]

replenishment lead time

requested customer lead time

Replenishment lead-time = required time to have new stock available again

Make-to-Order: We have sufficient time available to produce the material and deliver the customer within the requested lead-time

replenishment time

requested customer lead time

Make-to-Stock/Forecast: We don’t have sufficient time available to produce the material and deliver the customer within the requested lead-time. And as a result stock is required to meet the requested lead-time

Lead time vs. stock

72

73

Agenda

16h00 Kick off




18h30 Dinner


74

Integrate industry specific constraints & requirements into your planning

Key elements for planning:• Master data strategy• Planning signal• Planning specifics

Focus Topic 2: Flavour management

Focus Topic 1: New product introductions

75

Focus Topic 1: New product introductionsIntroduction

• Disparate systems and data sources

• NPI requires a cross functional approach – involvement of planning

• Forecast consensus – 1 aligned input for planning

• Planning signal visibility – ability to propagate your planning signal upstream

• Assessing the impact of NPIs on existing portfolio (phase in-phase out, cannibalization)

• Ability to simulate E2E impact of NPI scenarios

• Conversion between value and volume

76

Focus Topic 1: New product introductionsMaster data strategy: NPI codes & “early” MD

• Setup of “early” master data to capture forecast signal and propagate upstream in the supply chain• Trade off between effort and speed• Enable local simulation modelling in your planning system

A customer example

78

Focus Topic 1: New product introductionsPlanning signal: consolidate different demand inputs

• Consolidation of different demand inputs (e.g. LRFP, commercial forecast, clinical forecast)

• Agree on demand signal to be used for operational supply planning

• Anticipate upside and downside scenarios to assess RCCP impact

• Estimating and systemizing brand cannibalization impact (predecessor – succesor relations)

79

Focus Topic 1: New product introductionsA customer example – Forecast Integration Session

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81


82


83


84


85

Focus Topic 1: New product introductionsPlanning specifics – E2E visibility

• Create E2E plan visibility

• Embed regulatory dependencies (approval timings)

• Clinical vs commercial supply

• Mid term RCCP vs operational planning (launch plans)

• Formalize risk based decisions in S&OP

86

Focus Topic 2: Flavour managementIntroduction

87

Focus Topic 2: Flavour managementMaster data strategy: SKUs vs recipes

Key considerations

• Stability of the process

• Stability of the plan

• Reporting at aggregated level

• Data maintenance effort and cost

88

Focus Topic 2: Flavour managementPlanning signal: upstream flavour propagation

• Balance between regulatory constraints and planning decisions

• Capture flavour preferences as part of the demand signal

• Upstream flavour propagation

89

Focus Topic 2: Flavour managementPlanning specifics: balancing flavour demand and supply

Key considerations

• At what level do youtake your decisions?

• A dynamic planning challenge

• Flavour maintenance

• Assess bridging impact of regulatory changes


www.eyeon.nl

Eindhoven

Amsterdam

Antwerp

Geneva

Dublin

Bart ParidaenT: +31 (0)88 883 00 12

[email protected]

Many different nationalities, one team!

EyeOn as a partner

91

500+Years of combined experience

12Nationalities Consultants with different backgrounds: SCM, finance, sales & marketing, data science

89Companies where projects were delivered in 2018

10Countries where we operated in 2018

ls industry deep dive … · ls industry deep dive eyeon pid november 13th 2019 1. 2 agenda 16h00...

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