By reducing your net working capital, you release funds for investments and new growth opportunities. Beyond these effects, by reducing net working capital you also improve logistics processes, profitability and increase your stakeholders’ enterprise value.

1. MALMÖ 10 NOVEMBER 2016
Net Working Capital and S&OP
A million is always a million

2. 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

3. Main Speech 1:
Net Working Capital
By Jan Lythcke-Jørgensen

4. 4
How many of you know Donald Duck?
Scrooge McDuck has a radical view on capital …

5. 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

6. 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 secondlyand even
more importantly, the focus will be on operating the business efficiently.i.e. the cash conversioncycle!
Days Inventory
Outstanding
(DIO)
Days Sales
Outstanding
(DSO)
Days Payable
Outstanding
(DPO)
Focus area How to improve
» Negotiate creditterms (reduce credit period)with customers
» Make sure the customers obeypayment terms, pay on time
» Negotiate betterpayment 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. 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. 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 improvementareas can be identified by analysing the value chain of a company.
Free up capitalby reducing stock/remove unnecessary inventory:
» Reducing throughput lead time and improve agility (JIT)
» Understanding variance in demand, supply and productionbetter
» Pushing order productionpoint upstream (make-to-ordervs make-to-stock)
Differentiate products as late as possible in the production

9. 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
productioncontrol
principles
Supply chain
integration
Improve sales
forecasting and
planning
Productpruning
and SKU
reductions
» Improving sales forecasting
» Improving S&OP system and governance
» Improving planning and inventory control
» Clear definitionof push/pull, segmented flowin
productionand differentiated planning principles
» Balance through produce-to-stockon 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)
» Supplierintegration to reduce lead times and
variance and increase operating flexibility
» Optimising the size of consignmentstock(i.e. zero
lead times)
KPIs and visual
management focus
» Increased mutual managementfocus on the topics
» Effective KPIs with meaningful targets driving the
desired behaviour balancing “trade-offs”
Reduce
inventory

10. 10
Improving the NWC is only achieved through full engagement from the entire
business. The resultscan be tremendous and enable future growth
As-Is To-Be
» 100% P&L focus,cash consumptionneeded
to optimise EBIT
» Uncoordinated pricing strategy towards
customer/productsegments
» An extremely high service level always
trumps inventory optimisation
» Strong focus on low unit costinstead of
“one-piece flow” requirements
» Responsibilityand management of NWC are
anchored to the CFO
» Inventory costand 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 onP&Land unit
cost
» Reduced lead time to create flow and
responsiveness
» Joint NWC business effortand maybe
bonus (not a CFO project)

11. Main Speech 2:
NWC and S&OP
By Thomas Holm

12. 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 definitionof push/pull, segmented flowin
productionand differentiated planning principles
• Achieve workload balance through productionto stock
on low risk/value items
• Reduce numberof SKUs and/or raw materials
• Introduce standards or configurable items allowing
increased postponement
• Supplierintegration to reduce lead times, variance and
increase flexibility
• Optimising size of consignmentstocks
KPIs and Visual
management focus
• Increased managementfocus on the topic
• Effective KPIs with meaningful targets driving the
desired behaviour
Reduce
inventory

13. 13
Agenda
Sales forecast and NWC
The best way to sales forecast
Sales & Operations Planning and NWC
Scenario planning
Conclusion

14. 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
consumptionmaterials, etc.
Under sales forecasting (2-24 months)
Lack of capacity because the capacity decisionsare
based on the too low sales forecast
• Lack of capacity & manning
• Shortage of raw materials, components
consumptionmaterials, etc.
Increased inventories&
obsoletestock due to over
productionand purchasing
Increased costs
due to idle capacity
Delivery performance
issues
Increased
inventories& costs
due to firefighting &
overreaction

15. 15
Medium-term sales forecast has huge business impact
Business
Impact
Behavioural
Impact
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 tamperingReduce human bias

16. Sales forecasting & statistical facts

17. 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.

18. Two ways of Sales Forecasting:
• Advanced Statistical
• Manually

19. 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
Category, sales org.
Product grp, sales org.
Material, sales org.
Material, customer type, sales org.
Material, location, customer, sales org.

20. 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
Category, sales org.
Product grp, sales org.
Material, sales org.
Material, customer type, sales org.
Material, location, customer, sales org.

21. Is there a better way to sales forecast?

22. Is there a better scientific way?

23. 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
Category, sales org.
Product grp, sales org.
Material, sales org.
Material, customer type, sales org.
Material, location, customer, sales org.

24. 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
Category, sales org.
Product grp, sales org.
Material, sales org.
Material, customer type, sales org.
Material, location, customer, sales org.
2
3
1
4
3

25. When is it ok to use
advanced statistical forecasting?

26. 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 clearsalesforecastingprocess.
2. How to build a stable and transparentstatisticalforecast – that is easy to understand.
3. How to incorporateinsights from Sales and Marketing with minimum workload.
4. How to handlesales forecastswith high uncertaintyand impact.
5. How to handlenew productintroductionswith high impact.
6. How to continuously improvethe sales forecast.

27. 27
Sales & supply chain impact segmentation helps to focus sales
forecasting efforts where it creates the largest impact
High
Low
SalesforecastImpact
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

28. Sales & Operations Planning
and NWC

29. 29
The S&OP process is a cross organisational process that involves
various stakeholders on many levels in the organisation
Financial
forecast
DivisionCategory
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
SalesDirector
‒ Financial sales forecast per category
to reallocate marketing and sales
activities and resources

30. 30
Keys in achieving the best forecast possible are by tailoring
according to needs and involving stakeholders in the process
In tailoring the forecastsetup for the need,planninghorizonsand granularity are ofvital importance
• The medium term forecastis used when taking capacity decisions,negotiating supplier agreements and
purchasing materials with very long lead times
• The short term, operational forecastis 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
Sales & Operations Planning
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
ShortTerm
Medium
Term

31. 31
PRODUCT REVIEW SUPPLYPLANNING
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
AllSales & 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. 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. 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

34. Scenario planning is a vital part of
Sales & Operations Planning

35. 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
Scenarios
No change choice 1 choice 2
Choices

36. Conclusion

37. Planning problems can be
complicated!

38. Planning problems can be
complicated
solutions cannot

39. Café 1:
Implement’s approach to Net
Working Capital projects
By Jonas Sjögren

40. 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+ monthsDuration
 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
 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. 41
The solution catalogue is developed with a high degree for key stakeholder
involvement in order to secure impact
Solution catalogue
• Prioritized catalogue ofstrategic levers to
reduce NWC
• High level description offocus areas,
strategic levers and components ofthe
solutions.
• Easy-to-communicate documentwith
reasoning,solutions and impact
Solution success criteria
• Simple levers
• Focus on the mosteffective measures to
reduce the NWC
Fact pack
• Stock analysis,accounts receivable,
accounts payable,Customer lead time
requirements,supplier performance,
mapping ofplanning logic etc.
B
1
High level road map and target
setting
• Indication targets and timeline for impact
and NWC reductions ofindividual initiatives
2
INSIGHTS INTO WORKING CAPITAL EVALUATION SOLUTION CATALOGUE & PLAN
Solution hypothesis
development
• Hypotheses developmentbased on the value
stream,currentstate fact pack and the build
up of data in the Stock Monitor tool
C
Evaluation of
strategic levers
and prioritization
• Evaluation of impacton cost
and customer service.
• Prioritization based on impact
and ease of implementation
D
MONITORING & MEASURING
Value stream
• Product and production task overview
• Currentflows 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. 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 patternB
Stock level overview
Focus areas, made easyC

43. 43
Examples of deliverables; further analysis for hypotheses testing, evaluation, road
map and project charters
D D 1,2Further 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. 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 developmentEasy to change import files from ERP
E
E
E
E
E
E

45. 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.

46. Café 2:
Visual Management –
Implement’s tool Stock Monitor
By Peter Bundgaard and Adam Lewestam

47. 47
An operational inventory model in MS Excel to track development of
demand and inventory levels.

48. 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

49. Café 3:
Reference case – Stock Killer
project & One Arla project
By Henrik Hahn Sørensen from Arla

50. 10 November
2016
50
12,600+
OWNERS
THE 5TH
LARGEST
DAIRY COMPANY
MILK INTAKE
14+ BILLION KILO
19,000+
COLLEAGUES
10+ BILLION EU
REVENU
PRODUCTS SOLD IN
100+ COUNTRIES
Goodness comes from within

51. 51
We will deliver our mission by following Good Gro
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

52. More milk – more opportunities
52

53. 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

54. The Stock Killer Journey
54

55. 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…”

56. Café 4:
Inventory – the definition &
calculations behind Stock Monitor
By Elin Aalders Hemmingsen

57. 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 stocklevel
Inventory level = target level
Targetservicelevel NOT achieved
A manually maintainedstock leveloften results
in a compromisedservice level
With an inventory model,there is a fixed link
betweenthe targetservice level and the resulting
inventory leveland service

58. 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. 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(demandduringleadtime)
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. 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. 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
Quantity
Safety
stock
ROP
2. Lead time variability
3. Supply variability (order size)
1. Demand variability
during lead time

62. 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
#Periods
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%

63. Café 5:
Implement’s view on Simple
forecasting
By Andreas Kloow

64. 64
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.

65. 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 biggestchallenge 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’tcreate any betterresult to use complexalgorithms, so why bother?
Based on our experience,we actually only need a few simple elements in order to get a solid baseline statistical
forecastas illustrated below.
Long Term Trend

66. 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 forecastedwith a reasonable forecast error (<60%):
• This is typically due to few unpredictable sales orders per month and / or high variability of ordersizes
2. Optimizing of statistical forecasting models & parameters (“bestfit”) is based on wrong assumptions:
• Step change or trend in the past always gives step change in the future (this might have beenthe reality in the
1960’s whenthis 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. Complexstatistical forecasting methods give lower forecastaccuracy than simple ones as shown by Professor J.
ScottArmstrong at Wharton University: “If you neverthelessuse forecastsfrom complexmethods to help you make
decisions,expectto be confusedabouthow the forecasts were made and an accuracy penalty ofmore than one
quarter25%)”; see www.simple-forecasting.com
5. All statistical forecasting methodswill 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. 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 reliablestatisticalbaseline forecast,we need to ensure that the input - in
the form of historical sales - are cleansedfrom significantoutliersand 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. 68
Simple exponential smoothing (SES) is robust, simple and does the
job
The SES is an excellentforecastmodel because of its smoothing factor alpha parameter (α). It is basically a
weighted moving average weighing the mostreason observation more.This means that we get the stable level of
the forecast from the average, and the reactivenessfrom 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
0
100
200
300
400
500
600
700
800
900
1 000
1 100
Demand Forecast
Volume
Stable
Forecast
The SES has smoothing factors (α) and is an excellent forecast model

69. 69
Seasonality cannot be ignored
We cannotignore seasonality since it can have a high impact on the decisionand thus forecast - However, using
traditional methods forcontrolling seasonality might only lead more complexity,less trust and thereby not achieving
the intended forecastaccuracy
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. 70
We need to incorporate market & customer insights on significant
step changes
Significantstep changes of demands create huge challenges for the statistical forecast,and can be a source to a
lot of manual effortto 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 Forecastw/o step change Forecastw/ 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 Forecastw/ step change

71. 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 Forecastw/ 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.

72. Café 6:
How to involve Sales in project
& Efficient scenario planning
By Thomas Holm

73. 73
The forecast is used by various stakeholders for different purposes
on multiple aggregation levels
Financial
forecast
DivisionCategory
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
SalesDirector
‒ Financial sales forecast per category
to reallocate marketing and sales
activities and resources

74. 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 horizonsbasedon supply chain
scalability and flexibility
Decision horizonsfor 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
AggregatedDetailed
Plan as aggregated aspossible to
supportthe business decisions
Avoid the suicide quadrant
AggregatedDetailed

75. 75
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 clearsalesforecastingprocess.
2. How to build a stable and transparentstatisticalforecast – that is easy to understand.
3. How to incorporateinsights from Sales and Marketing with minimum workload.
4. How to handlesales forecastswith high uncertaintyand impact.
5. How to handlenew productintroductionswith high impact.
6. How to continuously improvethe sales forecast.

76. 76
Sales & supply chain impact segmentation helps to focus sales
forecasting efforts where it creates the largest impact
High
Low
SalesforecastImpact
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. 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. 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
Scenarios
No change choice 1 choice 2
Choices

79. 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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