net working capital and s&op

MALMÖ 10 NOVEMBER 2016

Net Working Capital and S&OP

A million is always a million

2

Content

Main speech 1: Net Working Capital – p. 3

Main speech 2: NWC and S&OP – p. 11

Café 1: Implement’s approach to Net Working Capital projects – p. 39

Café 2: Visual Management – Implement’s tool Stock Monitor – p. 46

Café 3: Reference case – Stock Killer project & One Arla project – p. 49

Café 4: Inventory – the definition & calculations behind Stock Monitor – p. 56

Café 6: How to involve Sales in project & Efficient scenario planning – p. 72

Café 5: Implement’s view on Simple forecasting – p. 63

Title slide 2

Use two colours in

the title: White text

+ Brown, Accent 4

Main Speech 1: Net Working Capital

By Jan Lythcke-Jørgensen

4

How many of you know Donald Duck?

Scrooge McDuck has a radical view on capital …

5

Net working capital (NWC) is a measure of a company’s financial strength

Net working capital = value of assets – liabilities

Why focus on net working capital and why optimise it?

Watch video

What is net working capital?

Freed up capital can be used to develop the business or reduce debt!!!

Why optimise net working capital?

Invest in

operations

Improve

service

Reduce

debt

Fund other

investments

https://www.youtube.com/watch?v=w-yFtdJ47Mk

https://www.youtube.com/watch?v=w-yFtdJ47Mk

6

Focus areas when optimising net working capital (DSO, DPO and DIO)

Usually, a significant amount of capital is tied up in running the business, which is something that the

CFO already know … Improving the DSO (-), DPO (+) and DIO (-) will free up $, i.e. change the capital balance, and secondly and even

more importantly, the focus will be on operating the business efficiently. i.e. the cash conversion cycle!

Days Inventory

Outstanding (DIO)

Days Sales

Outstanding (DSO)

Days Payable

Outstanding (DPO)

Focus area How to improve

» Negotiate credit terms (reduce credit period) with customers

» Make sure the customers obey payment terms, pay on time

» Negotiate better payment terms (longer payment period) with suppliers

» Always use the full payment period, do not pay early

» Reduce raw material inventory

» Reduce WIP

» Reduce FG inventory

Greatest

potential

7

Common pitfalls that typically drive up working due to other drivers of

business improvements

1. More convenient to purchase in large batches

2. Strong focus on unit cost/volume discount

3. No differentiated planning and low differentiated inventory

management

4. High unmonitored stock service level towards customers

5. Consider capital reduction projects to be CFO projects

6. Not a lean production set-up/long throughput times

7. Operating model does not differentiate between products

with different characteristics (one-model-fits-all)

Working capital pitfalls

8

Working capital is tied up in numerous places in the value chain

Minimise lead time and

minimise production batch sizes

Sales

Finished goods

inventory

Assembly,

finished

goods

Pre-assembly,

sub-parts

Manufacturing

processes

Raw material inventory

Purchasing Material planning

Reduce inventory

Negotiate longer credit period

towards suppliers

• Faster billing process • Negotiate shorter credit period

• Make customers pay on time

Move order production point upstream in the value chain

Value chain – production company (main control areas, i.e. BOM)

Working capital improvement areas can be identified by analysing the value chain of a company.

Free up capital by reducing stock/remove unnecessary inventory:

» Reducing throughput lead time and improve agility (JIT)

» Understanding variance in demand, supply and production better

» Pushing order production point upstream (make-to-order vs make-to-stock)

Differentiate products as late as possible in the production

9

When improving the DIO and reducing stock, we see a number of

solution hypotheses that involve all aspects of the supply chain

Flow and

production control principles

Supply chain

integration

Improve sales

forecasting and planning

Product pruning

and SKU reductions

» Improving sales forecasting

» Improving S&OP system and governance

» Improving planning and inventory control

» Clear definition of push/pull, segmented flow in

production and differentiated planning principles

» Balance through produce-to-stock on low-risk items

» Reducing the number of SKUs and/or raw materials

» Introducing standards or configurable items, allowing increased postponement (i.e. short lead times)

» Supplier integration to reduce lead times and

variance and increase operating flexibility

» Optimising the size of consignment stock (i.e. zero

lead times)

KPIs and visual

management focus

» Increased mutual management focus on the topics

» Effective KPIs with meaningful targets driving the desired behaviour balancing “trade-offs”

Reduce

inventory

10

Improving the NWC is only achieved through full engagement from the entire

business. The results can be tremendous and enable future growth

As-Is To-Be

» 100% P&L focus, cash consumption needed

to optimise EBIT

» Uncoordinated pricing strategy towards customer/product segments

» An extremely high service level always trumps inventory optimisation

» Strong focus on low unit cost instead of “one-piece flow” requirements

» Responsibility and management of NWC are

anchored to the CFO

» Inventory cost and write-downs are not

crucial as we will use these parts at some point later on

» Working capital as a strategic target to

change “the way of working”

» Clear governance and control set-up

» Adjust logistics footprint

» Enable lean set-up and flow (internally and externally to strategic partners)

» Less “standalone” focus on P&L and unit cost

» Reduced lead time to create flow and

responsiveness

» Joint NWC business effort and maybe

bonus (not a CFO project)

Title slide 2

Use two colours in


+ Brown, Accent 4

Main Speech 2: NWC and S&OP

By Thomas Holm

12

When improving DIO and reducing stock, we see a number of

solution hypotheses that involve all aspects of the supply chain

Flow and production

control principles

Supply chain

integration

Improve sales

forecasting and

planning

Product pruning and

SKU reductions

• Improve sales forecasting

• Improve S&OP

• Improve planning and inventory control

• Clear definition of push/pull, segmented flow in

production and differentiated planning principles

• Achieve workload balance through production to stock on low risk/value items

• Reduce number of SKUs and/or raw materials

• Introduce standards or configurable items allowing increased postponement

• Supplier integration to reduce lead times, variance and

increase flexibility

• Optimising size of consignment stocks

KPIs and Visual

management focus

• Increased management focus on the topic

• Effective KPIs with meaningful targets driving the desired behaviour

Reduce

inventory

13

Agenda

Sales forecast and NWC

The best way to sales forecast

Sales & Operations Planning and NWC

Scenario planning

Conclusion

14

Medium-term sales forecast bias drives higher inventories & costs

Over sales forecasting (2-24 months)

Excess capacity because the capacity decisions are based on the too high sales forecast

• Excess capacity & manning

• Excess sourcing of raw materials, components

consumption materials, etc.

Under sales forecasting (2-24 months)

Lack of capacity because the capacity decisions are based on the too low sales forecast

• Lack of capacity & manning

• Shortage of raw materials, components

consumption materials, etc.

Increased inventories &

obsolete stock due to over production and purchasing

Increased costs

due to idle capacity

Delivery performance

issues

Increased

inventories & costs due to firefighting & overreaction

15

Medium-term sales forecast has huge business impact B

us

ine

ss

Imp

ac

t

Be

ha

vio

ura

l

Imp

ac

t

Reduce under sales

forecasting

Reduce over sales

forecasting

Improve financial

predictability Reduce costs

Sales & Operations Planning decisions:

• Production capacity

• Inventory targets for finished goods, components & raw materials

• Sourcing of external capacity, components and raw materials

Reduce inventories &

obsolete stock

Increase sales

forecasting stability

Reduce variability

from statistical

forecasting

Reduce tampering Reduce human bias

Sales forecasting & statistical facts

17

Sales Forecasting & Statistical facts

Company

Category, sales org.

Product grp, sales org.

Material, sales org.

Material, customer type, sales org.

Material, location, customer, sales org.

1. Law of large numbers: The relative variability

is less much for aggregated sales history

than on the lower levels

2. Not possible to accurately forecast sales on

the lowest level due to high unpredictable

demand e.g. due to few random lines per

week or high order size variance

3. Tampering is waste of time i.e. try to adjust

forecast within the variability of the sales.

Two ways of Sales Forecasting:

• Advanced Statistical

• Manually

19

Advanced Statistical Sales Forecasting

1. Find the right level to statistically forecast

on with appropriate variability

2. Use the forecast model that has the best

forecast accuracy for the different

materials

3. Disaggregate sales forecast to lower

levels

4. Aggregated in other dimensions like

sales organisation or customer type to

support the data needed by key

stakeholders

1

3

4

1

2

3

Company






20

Manual Sales Forecasting

1. Find the appropriate level to enter the

manual forecast on

2. Disaggregate sales forecast to lower

levels

3. Aggregated in other dimensions like

sales organisation or customer type to

support the data needed by key

stakeholders

1

1

2

2

3

Company






Is there a better way to sales forecast?

Is there a better scientific way?

23

Very simple statistical sales forecasting is scientifically better

than both advanced statistical & manual sales forecasting

1. Advanced statistical forecasting methods gives

lower forecast accuracy than simple ones as

shown by Professor J. Scott Armstrong at

Wharton University: “If you nevertheless use

forecasts from complex methods to help you

make decisions, expect to be confused about

how the forecasts were made and an accuracy

penalty of more than one quarter 25%)”; see

www.simple-forecasting.com

2. Aggregating the very simple statistical

forecasting on the lowest level – gives the same

result as if we had forecasted in the same way

on aggregated level

3. Only manually sales forecast leads to forecast

bias and is very time consuming

4. Disaggregation gives bad results – if there is not

a very simple forecast on lowest level

Company






http://www.simple-forecasting.com/



24

Very simple statistical sales forecasting combined with focused

insights from sales & marketing is scientifically the best way

1. Aggregated seasonality index on e.g.

combination of product group and sales org.

2. Very simple statistical forecasting on the

lowest level that takes seasonality into

account. The forecast is never used on this

level!

3. Focus insights from sales & marketing

4. Aggregated in other dimensions like sales

organisation or customer type to support the

data needed by key stakeholders

1

2

Company






2

3

1

4

3

When is it ok to use advanced statistical forecasting?

26

WHAT is a good sales forecast to support medium-term business

processes – and HOW to obtain this

1. Unbiased

2. Stable

3. Transparent

4. Market & customer insights

5. Minimize work load for sales & marketing

A good medium-term sales forecast

The six HOWs: 1. How to structure a clear sales forecasting process.

2. How to build a stable and transparent statistical forecast – that is easy to understand.

3. How to incorporate insights from Sales and Marketing with minimum workload.

4. How to handle sales forecasts with high uncertainty and impact.

5. How to handle new product introductions with high impact.

6. How to continuously improve the sales forecast.

27

Sales & supply chain impact segmentation helps to focus sales

forecasting efforts where it creates the largest impact

High

Low

Sa

les

fo

rec

as

t Im

pa

ct

Demand variability / unpredictability

Low High

• Trends

• Significant

step changes

• Review total

sales forecast

• Trends

• Scenarios

• Review total

sales forecast

No sales forecast

focus

No sales forecast

focus

High

Low

Sales

forecast

impact on

sales

Low High

Sales forecast impact on supply chain

Product group

sales forecast has

high impact on

sales in country,

region

Product group

sales forecast has

high impact on

both sales &

supply chain

No sales forecast

focus

Product group

sales forecast has

high impact on

supply chain

decisions

Sales & Operations Planning

and NWC

29

The S&OP process is a cross organisational process that involves

various stakeholders on many levels in the organisation

Financial

forecast

Division Category

Sales

region Product

group

Product Customer

SKU

Account Manager

‒ Financial sales forecast per

customer to reallocate

promotions and sales

activities and resources

BU VP:

‒ Financial forecast to overall resource

allocation

Strategic purchasing

- External capacity, component &

raw material requirements to

renew & adjust sourcing

CxO’s:

- Financial predictability

Production/Supply Chain

- Capacity load on key resources to adjust

capacity

Category Manager:

‒ Category forecast to adjust

marketing activities & resources

Master scheduling & Purchasing

- Mix forecast to plan production and purchasing

Sales Director

‒ Financial sales forecast per category

to reallocate marketing and sales


30

Keys in achieving the best forecast possible are by tailoring

according to needs and involving stakeholders in the process

In tailoring the forecast setup for the need, planning horizons and granularity are of vital importance

• The medium term forecast is used when taking capacity decisions, negotiating supplier agreements and purchasing materials with very long lead times

• The short term, operational forecast is used for master planning, purchasing, setting inventory levels etc.

Value Drivers

Strategic planning

5 years

• Effective budgeting

• Ability to forecast expected top line and profit

• Better capacity investments in machinery and plants


2-18 months

• Timely adjustments of capacity incl. hiring and dismissal of

manpower

• Efficient strategic sourcing

• Optimize balance between capacity, inventories and service

Master Scheduling 1-3 months • Better prices from sourcing partners

• Lower inventory levels to handle uncertainty

Operational planning

0-6 weeks

• Limited amount of self-induced rush orders

• Less scrapping or lost sales and penalties from customers

Focus level of the forecast

must be aligned with the purpose of the planning activities taking place.

Long

Term

S

hort

Term

M

ediu

m

Term

31

PRODUCT REVIEW SUPPLY PLANNING

INVENTORY PLANNING

DEMAND PLANNING

BALANCING & DECISIONS

The S&OP process is a cross organisational process to take and

execute the decisions that are best for the company as a whole

Create (statistical)

forecast

Review unconstrained demand plan

Sales approval

Sales input

Interface to NPD process

Prepare decision proposal

Executive decision meeting

Update revenue & cost

estimate

Pre-meeting based on

reconciliation

Analyse scenarios, incl. financial impact

All Sales & Finance SCM & Finance

EXECUTION

“Handover” from decision

meetings

Communication & decision execution

Update of latest estimate (quarterly)

Week 1 Week 2 Week 3 Week 4

Identify demand gaps & supply

issues

Develop initial supply chain

plan

Review critical resources &

assess scalability

Finance

Review target stock

Review Service Level Agreements

Decisions Demand gaps

& supply issues

Unconstrained forecast

The S&OP process consists of 4 general steps: Demand planning, Supply planning, Balancing & Decision Making and Execution

Decisions are executed

32

Stock increase is fast but inventory decrease takes long time when

right sizing stocks

Right sized inventory

based on differentiated

target service levels

Increased

targets

Decrease

targets

Increased

inventories

Slowly

decrease

inventories

33

Fine-tuning the balance between service level and inventory is a

continuous process

Plan: SL

improvement

Do: Adjust

inventory

Check: Balance

between service

level & inventories

Act: If

necessary

Need to improve SL

Determine additional

adjustments with

differentiate service levels

Calculate necessary

inventory adjustments to

stepwise improve SL

Implement inventory

adjustments

Monitor whether service

level improvement is

sufficient

Scenario planning is a vital part of


35

The game board is a simple and powerful tool to support scenario

planning

The financial, NWC, sales,

operations consequence of each

combination of scenario & choice

Best case

Base line

Worst case

Sce

na

rio

s

No change choice 1 choice 2

Choices

Conclusion

Planning problems can be complicated!

Planning problems can be complicated

solutions cannot

Title slide 2

Use two colours in


+ Brown, Accent 4

Café 1: Implement’s approach to Net Working Capital projects

By Jonas Sjögren

40

The overall approach to an NWC project, is divided into three overall phases,

where we later will deep dive into Phase I

Phase I:

Individual solution catalogue and

action plan for the customer

Phase II:

Implementation and benefit

realisation

Phase III:

Sustain results

Design future state and solution elements

across processes, planning and steering

principles

Plan implementation

Purpose

6-12 weeks, depending on scope / size 2-12 months 12+ months Duration

Implementation of plan and realisation of

identified potential for the customer

Bringing the management team together in

this joint effort, to reduce the NWC

Sustain results by using the S&OP process

for focus

Continuous monitoring and follow up

through Stock Monitor tool

ICG role and

involvement

ICG to drive project, conduct analysis,

prepare workshops and document solution

catalogue

Team usually consists of 2-4 consultants in

6-12 weeks depending on scope / size

Depending on customer resources, it could

range from full scale implementation

support to occasional reviews

Participation on steering team meetings

New slide

added

Usually no planned ICG activities, but not

seldom customers come back on additional

areas to explore, for help on realising

certain activities or just for some stochastic

advisory

OVERALL APPROACH TO AN IMPLEMENT NWC PROJECT

4 overall phases, being INSIGHTS,

EVALUATION, SOLUTION and

MONITORING

Content Implementation of established plan and

realisation of identified potential in phase I

Impact tracked in Stock Monitor and focus is

secured through the S&OP process

Content is designed to fit the customer’s

situation and ability to maintain and further

develop the setup

Solution catalogue

Solution success criteria

High level road map and target setting

Deliverables All selected prioritized initiatives are

implemented

Stock Monitor fully functional and is used

Stock Monitor follows the development of

the stock situation, and both corrective

actions and newly identified actions are

realised

41

The solution catalogue is developed with a high degree for key stakeholder

involvement in order to secure impact

Solution catalogue

• Prioritized catalogue of strategic levers to reduce NWC

• High level description of focus areas, strategic levers and components of the solutions.

• Easy-to-communicate document with reasoning, solutions and impact

Solution success criteria

• Simple levers • Focus on the most effective measures to

reduce the NWC

Fact pack

• Stock analysis, accounts receivable, accounts payable, Customer lead time

requirements, supplier performance, mapping of planning logic etc.

B

1

High level road map and target

setting

• Indication targets and timeline for impact and NWC reductions of individual initiatives

2

INSIGHTS INTO WORKING CAPITAL EVALUATION SOLUTION CATALOGUE & PLAN

Solution hypothesis

development

• Hypotheses development based on the value stream, current state fact pack and the build

up of data in the Stock Monitor tool

C

Evaluation of

strategic levers

and prioritization

• Evaluation of impact on cost

and customer service.

• Prioritization based on impact and ease of implementation

D

MONITORING & MEASURING

Value stream

• Product and production task overview • Current flows and stocking points in the

network • Utilization of network • Performance metrics

A

Stock monitor

• Integrated solution securing stock transparency, allowing deep dive analysis, setting stock targets and following up

• Developed to fit, by Implement

E

42

Examples of deliverables; value stream map with flow and steering principles, initial

fact pack and solution hypothesis

Value stream maps Total set of identified solution

hypotheses

A B C

Stocking points overview

related to planning logic A Demand pattern B

Stock level overview

Focus areas, made easy C

43

Examples of deliverables; further analysis for hypotheses testing, evaluation, road

map and project charters

D D 1,2 Further analysis related to testing of

hypothesis. Evaluation of potential

Evaluation of strategic levers

and prioritization

Road map; overview of strategic

levers, plan, idea of initiative

D Potential evaluation of specific

initiative

D Map of solution element in impact

and ease of implementation

1,2 One pager of initiative, idea, solution

description, targets, milestones

44

The project delivers also a Stock monitor, both for the initial analysis but also for

continuous transparency, target setting and follow up

The Stock monitor is a software, easy to set. It features:

• Robust tool developed in MS Access with reports in

Excel for easy distribution and subsequent analysis

• Integrates easily to various ERP systems

• Monitors stock level aggregated by group, business

units, sites etc.

• Drill down through all level of details to individual

material number (pivot table, with total stock data

repository)

• Target setting by business unit and type

• Follow up on stock movements and comparison to

targets

• View dead stock and monitor progress on campaign

actions

Easy to add modules for

• Optimal calculation of “how much to stock?”, i.e.

inventory parameter calculation, safety stock and

economic order quantity, based on automatically

identified demand patterns (normal, lumpy), lead time

and target service level

• Global stocking policy for “what are where to stock?”.

Based on decision tree logic taking transport cost,

stocking cost, risk cost, service requirements, criticality

into account

Stock development and age profile

Stock overview by Group, BU, etc. Easy user interface

Tool is simple and adjustable

Dead stock overview and development Easy to change import files from ERP

E

E

E

E

E

E

45

Implement normally foresees three major challenges in the project and has a clear

strategy for how to succeed in solving them

There are conflicting

incentives

It’s not a quick fix!

One project across

several business units

1 2 3

THE LARGEST PROJECT CHALLENGES

Run entire project with large

involvement from business unit

organizations and co-develop

hypothesis as well as solutions.

Solutions shall be owned by the

people who bring them to life

Seek solutions of structural nature

and set the NWC agenda on top

of mind.

The Stock Monitor should be a

integrated part of daily business.

Use S&OP process keep focus.

Management support is vital.

Run project with multiple work

streams identifying only business

unit relevant levers to NWC

reductions. Stock Monitor will

ensure a full group overview.

Title slide 2

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+ Brown, Accent 4

Café 2: Visual Management – Implement’s tool Stock Monitor

By Peter Bundgaard and Adam Lewestam

47

An operational inventory model in MS Excel to track development of

demand and inventory levels.

48

The inventory model is built on five different key areas and is

designed to get operational with from “Day 1”.

Demand Input Demand & Variability

Calculations

Segmentation

Matrix

Simulated

Inventory

Profile

Stock

Value and

Potentials

Calculation

Title slide 2

Use two colours in


+ Brown, Accent 4

Café 3: Reference case – Stock Killer project & One Arla project

By Henrik Hahn Sørensen from Arla

10 November

2016 50

12,600+ OWNERS

THE 5TH LARGEST DAIRY COMPANY

MILK INTAKE 14+ BILLION KILO

19,000+ COLLEAGUES

10+ BILLION EURO

REVENUE PRODUCTS SOLD IN

100+ COUNTRIES

Goodness comes from within

51

We will deliver our mission by following Good Growth 2020

EXCEL

in 8 categories & 3 global

brands

Our identity: Healthy, Natural, Responsible & Cooperative

FOCUS

on 6 regions WIN

as ONE Arla

Our vision: Create the future of dairy to bring health and inspiration to the world, naturally

Our mission: To secure the highest value for our farmers’ milk while creating opportunities for

their growth

More milk – more opportunities

52

A change of mindset among key stakeholders was needed to solve the inventory challenge…

53

• Batch sizes • Cost

optimization • Capacity

utilization

• Max EBIT • High delivery

service

• Trustworthy available volumes

• Balancing milk intake

• High delivery service

• New product mix

• Low DIO

The key challenge is to balance inventory

levels, delivery service level, unit cost and

production capability and capacity assets…

...in an environment with where “each

stakeholder seek to optimize their business

within their area of responsibilities”

Supply Chain

Planning

Trading Sales BG’s

Finance

• P&L

• Investments

• Market requirements

• Balance sheet

Production

Capacity

Unit cost FG

stock

levels

Service Level

100% milk

utilization

• High delivery service

• Stocks 2nd priority

Inv.

Optimisa

tion

The Stock Killer Journey

54

How do Arla perform in terms of working capital?

55

”Reducing Arla’s inventory

is very much like being on

an ascending escalator.

The natural motion is up,

but we are doing everything

we can to climb down. And

that is hard work…”

Title slide 2

Use two colours in


+ Brown, Accent 4

Café 4: Inventory – the definition & calculations behind Stock Monitor

By Elin Aalders Hemmingsen

57

Aligning the inventory level to the target stock manually often results

in compromising the service level

Inventory level

Target service level

Service level

Order size

Lead time

Variance

Target stock level

Inventory level = target level

Target service level NOT achieved

A manually maintained stock level often results

in a compromised service level

With an inventory model, there is a fixed link

between the target service level and the resulting inventory level and service

58

The inventory optimization model

Segmenta-tion

Stable/ Lumpy items

Inventory Calculation

Safety Stocks/ ROP

Impact and reporting

Potential

Inventory Segmentation Model

Tuning of parameters

Diff. scenarios

ICG Inventory tool System

Defining service level targets for different

groups of products

Possibility to simulate different scenarios e.g.

shorter lead time,

higher target or similar

Projected potential of the proposed

inventory parameters

59

The Implement Segmentation Matrix is an excellent tool for finding the

best model for computing a re order point

Low

High

Low High

Lines/lead time

CV

(d

em

an

d d

uri

ng

le

ad

tim

e)

Erratic

Intermittent Stable/ predictable

60%

8 lines/lead time

The cut-off values are based on empirical experience

The Implement Segmentation Matrix uses frequency

and variance to segment items into groups and find

the best possible model to compute the re-order point.

Stable/predictable – This category contains items

characterised as having low variance and frequent demand

incidences. Items in this category do not give rise to any

forecasting or inventory control issues. Use normal

distribution.

Erratic – This category contains items characterised as

having high variance and frequent demand incidences. Use

normal distribution with spike order procedures and reduce

possible bias. In special cases, gamma distribution is used.

Intermittent – This category contains items characterised

as having low variance and low and infrequent volumes.

Use normal distribution.

Lumpy – This category contains items characterised as

having low and infrequent volumes and high variance when

demand occurs. Items that belong in this category are the

most difficult to manage. Use Compound Poisson

distribution with spike handling procedures.

For lumpy items, we can further segment the items into

subgroups with the same characteristics. This is done by

finding the ratio between the average and the median order

size for each item.

60

This leads to the following decision tree for segmenting items into the

groups of distribution

Product age* < 4 months

Lines in LT > 8

No

Erratic (Stable)

Yes

Lumpy

No

Stable

Yes

Product age < 6 months

New product

Yes

𝐶𝑉𝐿𝑇 <= 60%

No

Mean/ median

<1,1

Slightly

lumpy

Mean /median

<1,7

Mod.

lumpy

Mean /median

<3

Highly

lumpy

Mean /median

>=3

Manual

review

61

Understanding demand and supply variability is important to estimate

safety stock – and to reduce it …

Demand and supply variability:

1. Demand variability during lead time. The safety

stock must cover the demand variability during

lead time. A longer lead time leads to higher

safety stocks.

2. Lead time variability. The total replenishment

lead time for supply may vary due to delays in

production, transport, quality control etc. Higher

lead time variability leads to higher safety

stocks.

The lead time variability is measured for a

group of materials. A good way to understand

what drives lead time variability is to measure

plan adherence and register why changes

occur.

3. Supply variability (order size). The supply may

vary due to component stock-out, capacity

issues, production variability, quality issues etc.

A higher supply variability leads to higher safety

stocks if the supply order size is lower than

demand during lead time plus safety stock. LT

Time

Qu

an

tity

Safety

stock

ROP

2. Lead time variability

3. Supply variability (order size)

1. Demand variability

during lead time

62

For Lumpy materials the re-order point is found by simulating the

connection between re-order point and service level

Finding the re-order point:

For the lumpy materials the safety stock and re-order

point resulting in certain service level cannot be

found via well known formulas. In order to find the

link between the re-order point and the corresponding

service level we need simulations that link the re-

order point and the resulting service level

Simulation:

The service level is simulated for combinations of the

rate of usage and a number of potential re-order

points.

This simulation is done once in Anylogic and the

outcome is a fixed table that can reside in an Excel

document. The re-order points are found via lookups

in Excel. No further simulations are needed going

forward.

0

1

2

3

4

1 2 3 4 5 6 7

Poisson Distribution

# Orders per period

# P

eri

od

s

Usages

during

leadtime

Re-order

point

Target

service

level

0,5 6 99%

1,0 7 99%

2,0 9 99%

3,0 10 99%

4,0 12 99%

5,0 13 99%

6,0 15 99%

7,0 16 99%

8,0 18 99%

9,0 19 99%

10,0 20 99%

Usages

during

leadtime

Re-order

point

Target

service

level

0,5 6 99%

1,0 7 99%

2,0 9 99%

3,0 10 99%

4,0 12 99%

5,0 13 99%

6,0 15 99%

7,0 16 99%

8,0 18 99%

9,0 19 99%

10,0 20 99%

Title slide 2

Use two colours in


+ Brown, Accent 4

Café 5: Implement’s view on Simple forecasting

By Andreas Kloow

64



1. Unbiased

2. Stable

3. Transparent




The six HOWs:

1. How to structure a clear sales forecasting process.






65

Don’t overcomplicate things – these are the few elements we need in

order to control the statistical forecast. Not More, Not Less.

Constant Forecast

Group Seasonality

Step Changes

Baseline

Forecast

History

Cleaning

The biggest challenge with statistical forecasting is complexity, leading to lack of transparency and a lot of frustration

on fitting various parameters trying to understand the outcome. As scientific literature shows it doesn’t create any better result to use complex algorithms, so why bother? Based on our experience, we actually only need a few simple elements in order to get a solid baseline statistical

forecast as illustrated below.

Long Term Trend

66

“Relying only on statistical forecast is like driving only looking in the

rear-mirror”

5 issues with statistical forecasting:

1. Lumpy and sporadic demand can’t be statistical forecasted with a reasonable forecast error (<60%):

• This is typically due to few unpredictable sales orders per month and / or high variability of order sizes

2. Optimizing of statistical forecasting models & parameters (“best fit”) is based on wrong assumptions:

• Step change or trend in the past always gives step change in the future (this might have been the reality in the 1960’s when this method was invented, but not any more)

3. Statistical forecasting of seasonality and trend does not work when the demand variability is higher than 10%

4. Complex statistical forecasting methods give lower forecast accuracy than simple ones as shown by Professor J.

Scott Armstrong at Wharton University: “If you nevertheless use forecasts from complex methods to help you make decisions, expect to be confused about how the forecasts were made and an accuracy penalty of more than one quarter 25%)”; see www.simple-forecasting.com

5. All statistical forecasting methods will create bias if:

• The sales has a trend

• The sales had a step change

“Relying only on statistical

forecast is like driving only

looking in the rear-mirror”




67

Proper data quality is a prerequisite for accurate forecasts

• We need to remove any outliers and event from the sales

history, such as promotion, stock-outs and exceptional sales

• We should not clean random noise or natural variance,

because these will be handled via a simple smoothing

forecast model. We should only clean for significant extremes

like promotions or outliers

• We need to establish an easy, simple and non-time

consuming approach for cleaning history, supported by alerts

and warnings

• Statistical models will not be able to forecast these events,

and thus promotions, tenders and other uplifts/drops needs to

be added on top of the baseline forecast based on input from

Sales and Marketing

If we are to achieve an accurate and reliable statistical baseline forecast, we need to ensure that the input - in

the form of historical sales - are cleansed from significant outliers and events, which otherwise will lead to a biased and inaccurate forecast

Time

Volume Uncleaned History

Time

Normal Sales

Volume Outliers / Events

The sales history used for the statistical forecast must consist of “normal sales”

68

Simple exponential smoothing (SES) is robust, simple and does the

job

The SES is an excellent forecast model because of its smoothing factor alpha parameter (α). It is basically a

weighted moving average weighing the most reason observation more. This means that we get the stable level of the forecast from the average, and the reactiveness from the weighted alpha factor.

*Makridakis’ forecasting competitions (M1, M2, M3)

• Creates a constant future forecast, with the alpha value

controlling the “reactiveness” of the model

• Simple Exponential Smoothing is better than moving average

because exponential smoothing reacts faster on trend and

step change than a moving average with same aging and it

has almost similar forecast variability;

a 12-month moving average has the same aging as

alpha = 2 / (12 + 1) = 0.15

• Often, optimisation of statistical models and parameters

makes the planning non-transparent and, worst of all, it

increases forecast variability. Forecast variability is noise and

is amplified in planning and gives more unstable plans

• Simple Exponential Smoothing has scientifically been proven

to perform at least as good as more complex models*

• We recommend having a low alpha value to create a stable

forecast, and handle changes in demand with the step change

functionality concept Time

Volatile

demand

0100200300400500600700800900

1 0001 100

Demand Forecast

Volume

Stable

Forecast

The SES has smoothing factors (α) and is an excellent forecast model

69

Seasonality cannot be ignored

We cannot ignore seasonality since it can have a high impact on the decision and thus forecast - However, using

traditional methods for controlling seasonality might only lead more complexity, less trust and thereby not achieving the intended forecast accuracy

Dealing with seasonality can be very tricky

• The “traditional” statistical models e.g. Holt Winters model,

can be complex and the output can be difficult to understand

due to its various input variables (Alpha, Beta, Gamma)

• In traditional models, products needs to have at least 1 year

of history in order to accept a seasonal forecast - This

challenges New Product Introductions (NPI)

• Seasonality pattern can be very difficult to spot on a

detailed SKU level, since noise and variance can obscure the

pattern

We recommend using group seasonality logics!

• Groups seasonality is simple and easy to understand since

it is basically just an index to add on top of the constant

baseline forecast

• We can use this for ALL products, even NPI with few or non

periods of sales

• We accumulate sales history across a range of products,

which, due to the law of large numbers, results in a much

more clear and smooth seasonal pattern with less noise and

variance.

Group Seasonality overcome these challenges

Calculate the seasonality indices based on groupings, and apply

it to the constants statistical forecast

0

0,2

0,4

0,6

0,8

1

1,2

%

0

0

0

1

1

1

1

Group seasonal index

Constant FC (SES) Baseline FC w/ season.

70

We need to incorporate market & customer insights on significant

step changes

Significant step changes of demands create huge challenges for the statistical forecast, and can be a source to a

lot of manual effort to manipulate history or adjusting future sales. We need to handle this in a simple manor.

Time

Volume

Reactive

Period

Step

Change

Today

Reactive step change – step changes in the past

Demand Forecast w/o step change Forecast w/ step change

• Since statistical forecast always is reactive, it can never

foresee step/level changes caused by e.g. new listings,

customers, etc. Sales needs to provide these information.

• Statistical forecast needs some periods of observations to

“catch up” – a reaction period. The step change logics resets

the forecast at the right level and thus achieve a better and

more accurate forecast.

Proactive step change – step changes in the future

Time

Volume

Step

Change

Today

• Expected step/level changes should be provide by Sales or

based on POS data, and added to the forecast.

• When the step change period is over in the past – then it

resets the forecast at the right level.

• This is added on the aggregation level which makes sense

e.g. customer, category or material, customer type.

Demand Forecast w/ step change

71

Long-term trend must be handled by a combination of statistical forecasting

and input from Sales and Marketing

Small monthly trends in the market – growth or decline – have a significant impact on our planning, and thus we need to included these

expectations to our forecast. We cannot rely on past statistical trends, because then we’re already too late. We need support from

Category and Marketing.

Include long term trends on aggregate level

Time

Volume

Trend

Expectations

Today

Demand Forecast w/ trend

• Add long term trends on a high aggregation level – it is

impossible to catch the trends in the details

• Short term market changes should be managed by event

planning, uplifts and adjustments – not market trends

• Include Marketing in the trends discussions, they are the ones

with the long term expectations

• Use historical trend patterns as input to the discussion with

Marketing, don’t rely on them solely

Future trends will rarely mirror past trends

• Due to different stages in the Product Life Cycle (PLC) we

cannot expect past trends to mirror to the future

• Trend has a great impact on the long-term forecast, but not so

much on the short-term forecast

• Trying to catch short term trends is time-consuming and

difficult du to the random variation in demand.

• Optimizing trend parameter algorithms via the traditional 𝛽

value (Beta), is another element to increasing complexity and

instability.

Title slide 2

Use two colours in


+ Brown, Accent 4

Café 6: How to involve Sales in project & Efficient scenario planning

By Thomas Holm

73

The forecast is used by various stakeholders for different purposes

on multiple aggregation levels

Financial

forecast

Division Category

Sales

region Product

group

Product Customer

SKU

Account Manager

‒ Financial sales forecast per

customer to reallocate

promotions and sales


BU VP:

‒ Financial forecast to overall resource

allocation

Strategic purchasing

- External capacity, component &

raw material requirements to

renew & adjust sourcing

CxO’s:

- Financial predictability

Production/Supply Chain

- Capacity load on key resources to adjust

capacity

Category Manager:

‒ Category forecast to adjust

marketing activities & resources

Master scheduling & Purchasing

- Mix forecast to plan production and purchasing

Sales Director

‒ Financial sales forecast per category

to reallocate marketing and sales


74

How to define on which level in the product hierarchy & time horizon

for sales & marketing to focus on to minimize their work load

Business area

Category

Product line

Product Group

Material

Decision horizons based on supply chain

scalability and flexibility

Decision horizons for sales & marketing

activities

Promotion x x

Marketing campaign x

Sales meetings x

Change product grp focus x

Time

Plan the Volume

Manage

the Mix

Suicide

Quadrant

Time

Ag

gre

ga

ted

D

eta

ile

d

Plan as aggregated as possible to

support the business decisions

Avoid the suicide quadrant

Ag

gre

ga

ted

D

eta

ile

d

75



1. Unbiased

2. Stable

3. Transparent




The six HOWs: 1. How to structure a clear sales forecasting process.






76

Sales & supply chain impact segmentation helps to focus sales

forecasting efforts where it creates the largest impact

High

Low

Sa

les

fo

rec

as

t Im

pa

ct

Demand variability / unpredictability

Low High

• Trends

• Significant

step changes

• Review total

sales forecast

• Trends

• Scenarios

• Review total

sales forecast

No sales forecast

focus

No sales forecast

focus

High

Low

Sales

forecast

impact on

sales

Low High

Sales forecast impact on supply chain

Product group

sales forecast has

high impact on

sales in country,

region

Product group

sales forecast has

high impact on

both sales &

supply chain

No sales forecast

focus

Product group

sales forecast has

high impact on

supply chain

decisions

77

The process for scenario planning consists of 5 steps

• Identify critical uncertainties that drive change/assumptions

• Monitor and examine the current environment to determine which are the most important factors that will decide the

nature of the future environment within which the organisation operates

• Link these drivers together to provide a meaningful framework usually with 5-10 logical groupings of drivers

1. Identify

drivers for

scenarios

2. Produce

scenarios

3. Describe

possible

choices

4. Assess risk

and impact

5. Create

decision

proposal

• Identify and describe scenarios based on different assumptions, and understand if they are interlinked. What does

each assumption represent?

• Reduce 2-3 realistic core scenarios and define probability for each

• Identify and describe the choices for 2-3 core scenarios

• Design the “game board” with combinations of scenarios and choices

• If scenarios are not interlinked, a game board for each group of scenarios can be designed

• List and assess risks (probability X consequence) for each combination of scenario and choice

• Analyse impact on key metrics such as profit, cost, networking capital, utilisation, lost sales, gained

sales/opportunities, lead times, service levels etc.

• Create a one-pager with supporting appendices

• Include recommendations, plan for monitoring and risk mitigation proposal

78

The game board is a simple and powerful tool to support scenario

planning

The financial, sales , operations,

supply chain consequence of each

combination of scenario & choice

Best case

Base line

Worst case

Sce

na

rio

s

No change choice 1 choice 2

Choices

Implementconsultinggroup.com

Implement Consulting Group Implement Consulting Group is a leading Scandinavia based management consultancy, specialised in driving strategic transformations with a strong differentiator on “making change happen” – delivering documented Change with Impact.

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