A new perspective devoted to forecasting: demand planning is a very challenging job, that is why multinationals manage forecasting poorly. How can they improve it?

1. perspective JUL 2013
DEMYSTIFYING
FORECAST
ACCURACY
ACHIEVING HIGH
SALES FORECAST
ACCURACY IS
A MATTER
OF DISCIPLINE
Alberto Calvo, Alessandro Barmettler, Alberto Oteri

2. Demystifying forecast accuracy:
achieving high sales forecast
accuracy is a matter of discipline
Published by
Value Partners Management Consulting
via Vespri Siciliani 9
20146 Milan, Italy
July 2013
Written and edited by:
Alberto Calvo, Alessandro Barmettler,
Alberto Oteri
If you would like an electronic copy
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For more information on the issues
raised in the report please contact:
alberto.calvo@valuepartners.com
alessandro.barmettler@ valuepartners.com
alberto.oteri@valuepartners.com
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3. 3
executive summary
My wife and I walked into a Milan patisserie to buy
a small cake. We walked out with a tray full
of mouthwatering cannoli, in a quantity suitable
for a small army. For free.
How did that happen?
Something with the shop’s forecasting had gone
terribly wrong that day. Whilst thanking the chef
for this unexpected tray-full of bliss we found out
that more or less each evening he has to somehow
‘dispose’ of unsold cakes, either supplying them
to soup kitchens or handing them out for free to
unsuspecting clients.
If an apparently simple operation such as a
patisserie – employing 10 skilled workers who have
crafted the same products for decades – isn’t able
to plan its production accurately, then how can
a multinational company successfully manage its
own forecasting?
Like our patisserie, multinationals struggle
with forecasting though this is not altogether
surprising – demand planning is one the most
challenging jobs around.
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4. 4
Annex 1
Europe, Quarterly variation of Sales, Percentage, 1Q 2000 – 1Q 2013
2
1,5
1
31%
0,5
14%
57%
0
31%
-0,5
-1
-1,5
-2
2000
2001
2002
2003
2004
2005
2006
2007
Standard deviation
Source: Eurostat.
perspective DEMYSTIFYING FORECAST ACCURACY
2008
2009
Automotive
2010
2011
2012
2013
Food, beverages, tobacco

5. 5
have you to cope
with increasing
volatility ?
Over the last few years a number of
firms have suffered the effects of
increased market demand volatility,
especially those with a varied product
offering and a high number of SKUs.
(see annex 1)
As the opening example showed, the
process of demand forecasting is not
only crucial to a company’s success, but
is also inherently challenging. Within the
context of larger firms, this process may
become further complicated through
the interplay of many factors such as
investment decisions, supply chain management and strategic planning.
From our experiences, we believe that
the process of company forecasting
seeks to address four key objectives:
1.
To guide and support commercial
planning – helping to align objectives regarding volume, channels,
client, price and strategy
2. To calibrate the supply chain and
operations – enabling an increase in
service levels through the optimisation of logistics and manufacturing
planning in the short term
3. To decide which products to manufacture in which plant – optimising
the investment decisions in the
medium-long term
4. To quantify the expected economic
and financial results – helping anticipate and forecast future financial incomes, maintain control over
strategic directives, manage cash
flows, etc.
However when addressing the
issue, many firms mistakenly try
to find solutions through the use However, despite the clarity of these
objectives, the process of accurate
forecasting is becoming increasingly
of IT, investing a considerable
complicated and having correspondingamount of resources in software- ly detrimental effects on the accuracy
of forecasting models (see annex 2).
based programs such as data
Typically, the complexity of a forecastanalysis and statistical software, ing model for an individual product
(which may include multiple SKUs)
will be determined by four key facwithout developing a more
tors: the time-frame of the model, the
geographical coverage, the product’s
comprehensive and wider view channel presence and the total number
of clients served.
of the issue.
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6. 6
Annex 2
MAPE %, 2000-2012
120%
100%
80%
60%
40%
20%
0%
2000
2001
2002
2003
2004
2005
2006
Automotive company
Source: Value Partners.
perspective DEMYSTIFYING FORECAST ACCURACY
2007
2008
2009
Consumer goods (food)
company
2010
2011
2012
Consumer goods (white)
company

7. 7
By considering each of these four factors in turn, one can deduce the origins
of increasing forecasting complexity.
For instance, with respect to the
geographic coverage, the internationalisation of many firms means that they
are now operating in multiple localities
with a corresponding increase in the
number of inputs to be considered in
the forecasting process. Additionally,
with respect to channel presence, the
emerging trend of ‘omni-channel’ behaviour by businesses where companies
are selling products online, in-stores
and over-the-phone further complicates
their forecasting processes.
These complications increase significantly for firms with a wide-ranging
product portfolio as each individual
product is likely to have a unique set of
drivers and delivery channels and the
sales of one product may have a direct
effect upon the sales of another.
Indeed, these ‘internal’ complications
are mirrored by additional complications
emerging from ‘external’ factors. With
wide-ranging supply chains and target
markets, many companies are becoming
increasingly exposed to a wide range
of market volatilities. Furthermore, it is
important to consider the general health
and direction of the economy overall.
For instance, in a fast and booming
economy, forecasts are likely to have to
take account of growing demand levels
and hence supply volumes will necessarily need to increase as well.
perspective DEMYSTIFYING FORECAST ACCURACY
In a recessive economy, those firms
which ignore the general economic
trends may see a corresponding overestimation of overall volumes.
Despite the complexity of forecasting
increasing, the costs of low levels of
accuracy have remained as high as ever.
Fundamentally, if the aforementioned
complexities are not correctly managed, the consequentially low forecasting accuracy will generate inefficiencies
for the firm, negatively impacting upon
the company’s cash-flow and P&L. More
specifically, these inaccuracies can lead
to inefficiencies in a range of different
ways such as mis-matched levels of
stock, poor levels of service and lower
levels of production flexibility through
poor system planning.
However, when addressing the issue,
many firms mistakenly try to find solutions through the use of IT, investing a
considerable amount of resources in
software-based programs such as data
analytics and statistical software, without developing a more comprehensive
and wider view of the issue.

8. 8
Annex 3
• High level KPls
• ABC error
• Operative KPls
• ...
• ...
1
7
Define key
metrics
• IT systems evolution
Analyze error
determinants
2
• Tools simplicity
• ...
Optimize
tools
FCST
6
Design
the new
process
• Roles
Qualify
the FCST
model
Identify
information
ownership
5
• Activities work flows
• Responsibility
• Info work flows
• ...
Source: Value Partners.
perspective DEMYSTIFYING FORECAST ACCURACY
• Rules & definitions
• ...
3
• Market estimates
• Demand segmentation
4
• ...
• Objectives
Restate
the “basics”
• ...

9. 9
coming to grips with
forecast accuracy:
the power of discipline
is always underrated
It is within this context, and based upon
our extensive experience, that Value
Partners would like to present a range
of ways in which we feel that companies
are able to refine and improve their
forecasting abilities. (see annex 3)
1. Understanding the major causes
of forecasting errors. It is essential to
analyse the deviation between forecast
predictions and recorded data with
respect to two main variables timeframe
and granularity. Firstly, it is necessary
to assess the ability of a firm to develop
estimates in both the short and mediumlong term and to establish whether any
differences emerge between the two.
Annex 4
MAPE %
Forecast Timeframe
+79%
201%
164%
+38%
112%
+45%
81%
56%
1m
68%
2m
3m
Production frozen 60-80%
6m
12m
CAPEX allocation
Source: Value Partners.
perspective DEMYSTIFYING FORECAST ACCURACY
24m
Secondly, it is important to ensure that
the correct level of detail is captured
such that changing trends in total
volume and SKU mix are correctly built
in to the forecast.
Through our previous work, Value
Partners have noticed that the ability
for firms to forecast over different
time periods may vary. For instance,
when analysing different forecast
timeframes during an advisory project
for a major global automotive player,
it emerged that the firm was entirely
focussed on the short term (i.e. 1-2
months), resulting in a considerable
elevation of MAPE (Mean Average
Percentage Error; one of the main
KPIs adopted by enterprises to assess
forecast accuracy) for longer term
forecasts (i.e. over 3 months).
(see annex 4)
Another example can be found in the
case of an international company operating in the white goods space that was
experiencing a limited ability to forecast
their sales mix. This was due to a low
accuracy in estimating total volumes as
a result of a budget that was misaligned
from wider market trends.
Causes for high deviation are numerous, usually hard to identify and driven
by erroneous activities that have often
become common practice over time.
For these reasons it is necessary to
analyse every process and sub-process,
the consistency of the timeframe,
information quality and responsibility
allocation, both at central and local
levels. (see annex 5)

10. 10
Annex 5
Unpredictable demand
Orders to be received
difficult to foreseen
Volumes
+
Almost certain demand
Part of demand to be
segmented with specific
rules
+
Certain demand
Contracted orders
to be delivered
short-term
medium-term
FCST timeframe
Source: Value Partners.
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long-term

11. 11
An in-depth evaluation of forecasting
would enable the tracing of each component of the errors that lead to a high
deviation in forecast accuracy. The overview and identification of every cause
of error is the first step towards the
development of a new forecast model,
which must include every strategic and
operational aspect of the firm.
2. Clarification of the basic rules and
meaning of forecasting. The meaning of
the term ‘forecast’ is vague and could
therefore be interpreted differently by
different firms. In addition, the dimensions upon which forecasts are built are
numerous (orders, shipments, turnover,
etc.) and may have correspondingly
different implications for the forecasting
process. The understanding and interpretation of this term should therefore
be clarified for all involved.
Fundamentally, forecasting accomplishes three main tasks and becomes the input of three different business functions:
Sales (commercial planning), Operations
& Supply-Chain (logistics optimisation
and investment allocation) Corporate
Finance and Control (impact appraisal
and economic/financial results).
An understanding of the desired outputs from a forecasting model should
also be understood in order to ensure
accurate outputs and avoid subsequent
complications.
perspective DEMYSTIFYING FORECAST ACCURACY
For instance, many firms use order
forecasting as a proxy for estimating
turnover, however turnover forecasts
can result in a number of paybacks
(e.g. more coherent financial projections with the firm’s real potential) especially when sales forecasts are based
on complex operating models.
3. Definition of the firm’s specific
forecast model. First and foremost
sales estimates should be based upon
realistic market projections.
The developments of recent years have
proven that the market and the general
economic context can rapidly alter.
For this reason, forecasts (e.g. budget,
strategic plan, etc.) with 6-12 month
timeframes can swiftly prove to be mistaken in their structure and content.
In many cases, it is more appropriate
to define a composite model to estimate the total volumes for each market
and each strategic segment before
improving the capability of forecasting
which is a combination of these separate elements. Such a model should
necessarily include competitive intelligence, which incorporates the actions
and strategies of a company’s main
competitors.
Secondly, clients should be constantly
monitored in order to positively refine
the accuracy of forecasts. Depending
on the business of the firm, segmenting the customer base can facilitate the
assessment of the market at the same
time as helping to capture all of the
relevant phenomena.

12. 12
Annex 6
Commercial planning
(by client)
Service level
(e.g. less Lost
Sales)
2
3
MIX
detail
by sku
Optimization
of product
allocations
and investments
(e.g. moulds)
dimensions
Stock level
rationalization
1
Commercial planning
(by client)
Volumes
by macrO
segment
Factory planning
and investments
short-term
medium-term
FCST timeframe
Source: Value Partners.
perspective DEMYSTIFYING FORECAST ACCURACY
long-term

13. 13
For example, some firms have been
implementing joint-forecast models together with their clients, even integrating their forecast and order management systems to gain a real-time view
over clients’ orders.
4. Identification of information owners.
It is important to assign or identify a
point of contact within a firm with responsibility for data gathering, and for
this individual or group to be appraised
on a performance basis.
Lastly, to guarantee accuracy throughout the processes of market and client
base assessment it is necessary to rigorously define the criteria and practices
that underpin effective demand segmentation.
In addition to a point of contact within a
firm, it is equally important throughout
the forecast process to review the exact
timing of sharing of information on different external/ internal processes.
The forecast should be built upon two
types of demand (with minor differences depending on the business): ‘certain’
demand, relating to received orders;
and ‘open’ demand, referring to potential upcoming orders. (see annex 6)
Of the two, the second demand type is
the most challenging to assess. In order
to gain full understanding it is therefore
crucial to define and employ appropriate methodologies (e.g. the use of
statistics).
The overview and identification
of all the causes is the first
step towards the development
of a new forecast model,
which will have to include
every strategic and operational
aspect of the firm.
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Furthermore, if the quality of the overall
process is contingent on the demand
Planning function, it is also necessary to
develop forecasting in conjunction with
further business functions and integrate
the different information streams to create a holistic picture. Demand Planning,
Sales, Logistics and Pricing should work
together to ensure that all the relevant
data is captured, shared and included in
the forecast.

14. 14
Annex 7
+
<50%
<20%
<30%
fragmentation
(# clients / channels)
<40%
–
–
+
Granularity
(# sku)
Source: Value Partners.
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15. 15
5. Design of the new forecasting process. Once the objectives and nature of
the forecast under development are
established and all required information (including the identification of an
individual responsible for information
gathering), it is then possible to design
a forecasting process that is capable of
improving overall efficiency.
The design of the forecasting process should start from the definition of
strategic objectives, identifying the right
balance of main operational constraints;
a delayed forecast with fewer operational constraints would likely be more
accurate, however the level of service
and the ability of the supply chain to
react promptly would be impaired.
(see annex 7)
Firms with a substantial geographical
heterogeneity should also include the
sharing of information from central and
local sources in the process, as information could be stored at a local level (e.g.
local regulations, client data, local commercial strategy, etc.) as well as at central level (e.g. long-term trends, regional
level trends, product strategy, etc.).
Like all business processes it is always
necessary to ensure substantive commitment from all the parties involved.
Coherently empowering all of the actors
involved in the process and identifying
specific KPIs to appraise every actor’s
contribution to the overall quality of the
forecast facilitates a more productive
and coordinated focus aligned with the
importance of the process.
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Based on our experience we would
argue that many firms suffer low commitment to sales forecasting due to a
deficiency in the clarity of objectives,
absence of rules and a lack of consensus on the value of projects.
6. Support from IT systems to activities. As previously mentioned, the
employment of IT resources does not
directly augment forecasting accuracy,
and may even be an obstacle to the
correct integration of processes and
information in some instances. However,
it remains of primary importance within
the overall process.
Statistical tools must be considered
differently. Several firms have achieved
positive results thanks to the employment of statistical forecasting; many
of these firms are in the spare parts sector which is predominantly influenced
by historical sales rather than market
trends.
When employing statistical tools it becomes critical to ensure that algorithms
are not exclusively based upon historical data though take into consideration
future trends, market trends, macroeconomic metrics (GDP, consumer
index, etc.), market characteristics and
exogenous factors (e.g. new regulations, climate changes, supply chain
stock, etc.).

16. 16
7. Identification of KPIs to monitor.
In order for an organisation’s forecasting performance to be evaluated
successfully it is necessary to identify a
simple and exhaustive set of KPIs along
two main dimensions:
• Forecast timeframe – both in the
short and medium-long term
However the MAPE of a single month is
not sufficient to ensure understanding
of the nature of main forecast errors; to
guarantee comprehensive understanding it must be integrated with other
metrics measuring the main forecast
dimensions (e.g. granularity, timeframe,
etc.).
• Forecast granularity – measuring the
forecast accuracy on macro figures,
product mix and SKU
Amongst the metrics identified and analysed, MAPE (Mean Average Percentage
Error) is the most valid and accurate
when measuring the difference between
actual and forecast data, as it does not
have the problem concerning averaging positive and negative errors which
afflicts other metrics.
Indeed the forecast accomplishes
three main tasks and becomes the
input of three different business
functions: Sales (commercial
planning), Operations & Supply-Chain
(logistics optimisation and investment
allocation) Corporate Finance and
Control (impact appraisal and
economic/financial results).
perspective DEMYSTIFYING FORECAST ACCURACY

17. 17
Conclusions
In summary, the adoption of an easy
and efficient forecast model based
on sales predictions derived from an
analysis of market trends and the firm’s
commercial plans produces more accurate and realistic forecasts.
The ability to assess the market, understand the main trends and drivers and
react responsively to a market slowdown as well as expansion is not just a
matter of being internally efficient but
also having a competitive advantage
that is hard to replicate.
Based on our extensive experience in
demand forecasting for large industrial
players, we have been able to record
and benchmark errors to develop a
framework for acceptable forecast
errors which should be used by all
organisations.
Such values (which vary depending on
the business) are based upon two main
directives: number of SKUs and market fragmentation, depending on the
number of channels and clients served.
(see annex 8)
Annex 8
Q&A
• Demand Planning is one of the roles that people try always
to avoid, I wonder why.
• It seems impossible that to have a decent service level,
you must set so high stock targets levels.
• the Demand Planner is often alone, neither Logistics nor Sales
are supporting him. Why this lack of inter-functionality?
• When Forecast error is high: Demand Planning blames
Logistics and Logistics blame Sales, and so on. So who should
be responsible for MAPE?
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18. 18
AUTHORS
Alberto Calvo
Partner, Milan Office
alberto.calvo@valuepartners.com
alessandro barmettler
Senior Engagement Manager, Milan Office
alessandro.barmettler@valuepartners.com
alberto oteri
Associate, Milan Office
alberto.oteri@valuepartners.com
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19. 19
About
Value Partners
Value Partners is a global management consulting firm that works
with multinational corporations
and high-potential entrepreneurial
businesses to identify and pursue
value enhancement initiatives across
innovation, international expansion,
and operational effectiveness.
In 2007 Value Partners acquired
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leading UK company specialized in
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strengthening its international presence. Today Value Partners is a leading advisor in the telecom, media
and technology sectors worldwide.
Founded in Milan in 1993, Value Partners’ rapid growth testifies to the
value it has created for clients over
time. Today it draws on 25 partners
and 280 professionals from 23 nations, working out of offices in Milan,
London, Istanbul, São Paulo, Buenos
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and Singapore.
For more information on the issues
raised in this note please contact
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