advanced demand planning sunny skies or rainy days? how to...
TRANSCRIPT
Advanced Demand Planning
Sunny skies or rainy days? How to
increase forecast accuracy
4 March 2013
© 2010 Deloitte and Touche Advanced Demand Planning 2
Contents
1. Introduction .............................................................................................................................. 3
2. Why Strive for Better Forecast Accuracy? ................................................................................ 4
3. Measuring Accuracy Meaningfully ............................................................................................ 5
4. The Demand Planning Process and the Effects of Incorrect Forecast ...................................... 6
5. True Demand versus Sales History .......................................................................................... 8
6. Forecasting at Optimal Hierarchy Level .................................................................................... 9
7. The Composite Forecast ........................................................................................................ 11
8. Inclusion of Non-quantitative Events as Causal Factors ......................................................... 12
9. Pre-setup Analysis and Post-implementation Diagnostic ....................................................... 13
10. Demand Planning Best Business Practices ............................................................................ 15
11. Key Success Factors ............................................................................................................. 16
12. Contact Details for More Information ...................................................................................... 17
© 2010 Deloitte and Touche Advanced Demand Planning 3
1. Introduction
Deloitte Consulting has provided many demand planning solution designs, analysis and
implementations to our clients, assisting them to improve potential forecast accuracies in
designed and existing Demand Planning solutions.
Demand Planning (DP) is the process of creating a forecast of market demand for a company’s products or
services. This is crucial for most businesses as it provides visibility into the future and drives supply. Striving
for the best forecast accuracy is usually the main goal of Demand Planning. The less uncertainty there is, the
better the ability to make supply planning decisions. Moreover, a better forecast accuracy can be converted
to higher profits.
We’d like to demonstrate why companies should strive for better forecast accuracy, what the consequences
of incorrect forecast are, how to alleviate possible drawbacks and also how to improve overall forecast
accuracy. This look at Demand Planning underlines the importance of capturing true demand versus sales
history, discusses forecast hierarchy and the optimal forecast generation level.
The optimal forecast generation level is regarded as the cornerstone of good DP design. The “best” possible
forecast accuracy is required at the hierarchy level where planning (decisions) are made. It does not
necessary mean that the forecast has to be generated at that level. Aggregation, forecast generation and
disaggregation, together with the right forecasting methods, could provide for the best forecast accuracy at
the “decision” level.
A proper set-up of composite forecast, inclusion of non-quantitative causal factors, pre set-up analysis and
post-implementation diagnostics are important DP best business practices and key factors in any successful
design and the usage of Demand Planning.
Let’s examine the technical and process aspects of Demand Planning. It is vital that people with the right skill
sets, knowledge and experience are acknowledged as fundamental factors for the successful design,
implementation and usage of a Demand Planning solution.
© 2010 Deloitte and Touche Advanced Demand Planning 4
2. Why Strive for Better Forecast Accuracy?
Companies very seldom realise to what extent demand forecast accuracy improvements
contribute to the increase in Earnings Before Interest, Depreciation, Tax and Amortisation
(EBIDTA).
Using Deloitte Consulting’s simple EBIDTA gauge, you can estimate how a relatively small improvement in
forecast accuracy may translate to a substantial EBIDTA increase (see Figure 1).
The tool incorporates the dependence of EBIDTA on service levels, forecast accuracy, lead times and
replenishment periods. It conservatively assumes that changes in EBIDTA are only due to the reduction in
inventory holding costs, which are estimated for an average South African company to be 35%. The holding
costs include cost of money, warehousing, insurance, potential wastage and handling costs and are
calculated as a percentage of the inventory value if it is kept in a warehouse for a period of one year.
Figure 1: EBIDTA dependence on forecast accuracy improvements
Assuming a 95% service level, eight-week lead time and two-week replenishment cycle, and a 3% increase
in forecast accuracy from the base of 80%, causes an EBIDTA increase of almost 5%, as illustrated.
The gauge shows the direct EBIDTA improvements based on inventory savings only. The other indirect
advantages of forecast improvements address the following shortcomings of forecast error:
Incorrect Raw Materials (RM) inventory in wrong places.
Excessive warehouse costs.
Inaccurate production scheduling (leading to production yield loss or increased costs).
Incorrect Work in Progress (WIP) inventory.
Excessive warehouse costs.
Stock outs and late orders.
Customer switching or increased safety stock levels.
Inadequate (too small or too big) shipments and inter-depot shipments (excessive transport costs).
INPUTS
Service Level 95.00%
Present FCST Accuracy [%] 80.00%
FCST Accuracy Improvement [%] 2.00%
Product/Service Lead Time [weeks] 8.0
Production Cycle [weeks] 2.0
* Net Profit change % 3.19%
© 2010 Deloitte and Touche Advanced Demand Planning 5
3. Measuring Accuracy Meaningfully
When measuring forecast accuracy it is vital to make sure that it is representative
(appropriate) at all reporting levels.
Sometimes the market/product or combination of these means it makes sense to measure and report at a
slightly higher level. By not aggregating a forecast correctly or by reporting on a summated total, you can
easily be lulled into a false sense of security. Often forecast accuracy numbers on a very high level can
achieve in excess of 95%; but is this an honest number?
Forecast error is first measured at the lowest level in the hierarchy (often an item or SKU level). Let’s quickly
look at some forecast metrics:
Percentage Error (PE): this is shown as a % and has a range of 0 to infinity.
Mean Percentage Error (MPE): this is shown as a % between negative infinity and infinity.
Mean Absolute Percentage Error (MAPE): this is shown as a % between 0 and infinity.
Mean Absolute Percentage Error (MAPE*1): this is shown as a % between 0 and 100%.
When aggregating the forecast errors of multiple items (or products) it important to ensure that the total
number is representative of the true forecast error of the group. One may be tempted to aggregate the total
sales/usage for a range of items and compare this to the total forecast. Depending on the level this should
give errors between 0 and 10 % (accordingly the accuracy will be close to 100%). This may look very good
on a forecast report but may hide the reality of the performance of the forecast as a whole by cancelling out
the noise of over and under forecasted items. That is, if you were over by 100 items on one product and
under by 100 items on another product in total you were 100% accurate – this is not representative.
In order to aggregate the forecast error of Percentage Error, MPE and MAPE you must average the errors of
all the individual measures. This is a very dangerous option as high and low forecast items can cancel each
other out and skew the total forecast error. This number can also be very misleading in the cases where
there are forecasts errors greater than 100%, as they very quickly skew the data.
It is best to compare errors with a common range, i.e. a measure with a fixed minimum and maximum. The
MAPE* error calculation is one such measure with a range from 0 to 100%. Averaging this number will give
you a number that is always between 0 and 100% which gives a level of “badness” of the forecast.
To further calculate the forecast error accurately one should not simply average all the item level MAPE*’s
scores. This will result in an insignificant item (e.g. a washer) being compared with a critical item (e.g. car
chassis) with the same importance (or weighting). Ideally you would want to place a higher importance on
the critical items and a lesser importance on the non-critical items using a weighted average. The result is a
measure of “badness” of the items as a group at the level of aggregation.
In order to accurately report on the health of a forecast it is crucial that an honest metric is used to calculate
the error (and hence the accuracy) so that the forecast as a total is compared.
The next chapters focus on some aspects of the demand planning process aiming at increasing forecast
accuracy.
1 This is a normalised MAPE measure (i.e. range of values is from 0 to 1)
© 2010 Deloitte and Touche Advanced Demand Planning 6
4. The Demand Planning Process and the
Effects of Incorrect Forecast
Demand Planning is a process of creating a forecast of market demand for a company’s
products or services. It is a facility with an extensive ability to analyse data and predict
possible future trends and seasonality, add causal factors and mix them with a human
input.
The human element is required to inform the system about promotions, events, allocations, new product
launches, customer forecasts and many more. These are added to the baseline (system) forecast and
reviewed by the whole sales team in order to arrive at a single forecast (during a demand consensus
meeting) that the whole company operates to.
This stage of the process generates an unconstrained demand. What can the sales team sell? Only what the
factory can produce! The unconstrained demand will form one of the inputs to the Sales and Operational
Planning (S&OP) process where supply and demand is balanced. In the process, in the case of shortages,
conscious decisions are made:
Which customers do we disappoint?
Will we work overtime or outsource to not disappoint any customers?
By virtue the forecast is almost always incorrect. The external reason being demand instability (see Figure 2)
and the internal being sub-optimal demand planning design. The effect of incorrect forecast causes
disruptions in all supply chain areas in a number of ways.
© 2010 Deloitte and Touche Advanced Demand Planning 7
Figure 2: What contributes to demand instability?
Typically manufacturing facilities are equipped and optimised for long runs at high levels of efficiency.
Significant investments are required to ensure high flexibility in supply. Manufacturing operations have to
continually adapt to changes in the true demand versus what was forecasted. This approach typically results
in investments in:
Additional capacity.
Reduction in changeover times e.g. SMED methodology.
Additional shifts/overtime at short notice.
These changes are felt all the way through to the raw material suppliers, who also have to adapt to the ever-
changing demand. They too then have to become more flexible, driving flexibility into their manufacturing
and distribution processes, resulting in an increase in their costs and subsequently the raw material prices.
Regardless of their intentions, organisations never seem to have sufficient “regular” capacity to meet this
ever changing demand. This is most keenly felt at the finished goods level where manufacturing capacity
and distribution capacity have been locked into producing a product and shipping it to a destination that may
ultimately require something else. The organisation has now been bitten twice by the same inaccuracy:
Inventory was produced for a projected demand that never became a reality.
Working capital is now tied up unnecessarily, on an item that may have a shelf-life or become
obsolete.
0
200
400
600
800
1000
1200
1400
1600
1800
Sale
s
Time
Series 1
Increased orders due to unreliable
delivery
Decreased orders due to late orders
being delivered (too much stock)
Pre-price increase buying
Post-price increase, decrease
in demand
Decreased orders due to Competitor
price activity
© 2010 Deloitte and Touche Advanced Demand Planning 8
Manufacturing and distribution capacity could have been better spent on producing an item that was ordered.
Additional capacity (in the form of overtime and /or extra shipping) now has to be employed, in order to meet
the true demand. Since the booking of this additional capacity is at late notice, it usually costs more that it did
when the organisation was producing the “wrong” item. The cost is therefore typically MORE THAN twice as
much as it should have been to meet the true customer demand. It will also disrupt production for pre-
existing orders. The described vicious circle is also known as “bull-whip” effect.
Many organisations have opted out of this vicious cycle by choosing to buffer with additional finished goods
inventory. Whilst this buffers the operations and suppliers from demand variability, it drives up finished
goods inventory significantly. Higher levels of finished goods inventory exposes the organisation to increased
risk of:
High stock obsolescence.
Shrinkage.
Additional warehouse capacity.
With holding costs of approximately 35% of the value of inventory, it represents a significant supply chain
cost component (working capital), which can be addressed starting with forecast accuracy improvements
initiatives.
It is clear that there is no single comprehensive solution to all companies facing this challenge. The best
approach is to determine the trade off between the increased costs of manufacturing flexibility and the
increased costs associated with the holding of excess stock. The tipping point will most likely contain a
combination of the two approaches. Deloitte Consulting has found that accurate business hypothesis
modelling is instrumental in making this decision.
5. True Demand versus Sales History
Another technique used to improve forecast accuracy is to move towards forecasting based
on true demand (POS data and stock-out information) versus forecasting based on sales
history. This is particularly important in businesses that experience periods of peak
demand and instances of stock shortages.
The following factors are prevalent in these circumstances:
When multiple customers are calling for inventory that is out of stock; capturing of lost sales by the
organisations’ order fulfilment clerks is usually very poor.
Where alternative items are available, substitution capturing may lead to skewed demand data.
Where there is general knowledge in the customer base that there is a short supply situation,
customers tend not to call and place orders so the true demand is lost.
One method to ensure that the organisation has a better sense of true demand is by ensuring that the order-
takers (be they sales reps, telesales or order fulfilment centres) implement a process for capturing back
orders and lost sales orders, thereby realising the following benefits:
© 2010 Deloitte and Touche Advanced Demand Planning 9
Customers will continue to call during a low-stock situation as the order will be captured, thereby
enhancing the organisation’s understanding of lost sales versus delayed sales.
The organisation will get a better understanding of substitution sales versus what the customer
actually wanted, and will move much closer towards capturing true demand on which to base the
statistical forecast.
6. Forecasting at Optimal Hierarchy Level
Many organisations are obsessed with forecasting at the most detailed level possible,
claiming that only at this level are they truly able to try and predict true customer demand in
line with customer ordering patterns (aiming to minimise potential ‘out of stock’ situations).
These are planning or ‘decision’ hierarchy levels.
To achieve this, they set up their demand forecasting applications or models to produce a statistical forecast
at a SKU level right at the point of consumption. This granularity could be represented:
By brand.
By pack size.
By day.
At the end-user location.
At that granularity level the demand signal tends to be very ‘spiky’, fluctuating between periods of high
demand and periods of no, or little, demand. This typically results in a highly variable forecast with low
accuracy.
© 2010 Deloitte and Touche Advanced Demand Planning 10
The other complication is that stakeholders in various business functions (marketing, sales, distribution,
manufacturing, purchasing, administration, finance, etc.) plan at strategic, tactical and execution levels. They
usually require forecasts at different hierarchy levels.
In all cases the planning and decisions are made based on forecasts at certain product/customer/geography/
time hierarchy levels. It is then crucial to obtain the best possible forecast accuracies at those hierarchy
levels. It does not necessary mean that the forecasts need to be generated at those levels. The forecast can
be generated at any level, as long as through the process of aggregation, forecast generation and dis-
aggregation, the best accuracies at the planning (decision) levels are obtained. The generated forecast is a
composite forecast (see Chapter 7) with judgemental inputs added at the adequate hierarchy levels (see
Figure 3).
The design of the hierarchy, aggregation of historical data, reconciliation of forecasts (Figures 3 and 4),
conversion between various units of measure and the identification of the optimal forecast level are vital
issues to achieve the best overall forecast accuracy. The reconciled forecasts render planning (decision-
making) of different stakeholders in various business functions in required timescales.
History Future
Sales
Materials
Judgment Input
Level
Decision Level
FCST Level
SKU Sales SKU
Weighted Composition
Aggregation, dis-aggregation
Figure 3: Input, forecast and decision hierarchy levels can be different
If any of these issues are neglected various forecasts in the company will not be accurate or compatible and
will be manifested in unnecessary inefficiencies, since the demand planning creates the base for further
decision making in the supply chain.
© 2010 Deloitte and Touche Advanced Demand Planning 11
History in months FCST in months
Forecast
Product/national/monthHistory
Product/national/month
History
Product/depot/monthResult
Product/depot/month
Aggregation, Dis-aggregation
Figure 4: Aggregation, dis-aggregation concepts
7. The Composite Forecast
The final (composite) forecast is usually a weighted combination of a univariate (time
series), causal analysis (usually handled using multiple linear regression - MLR) and
judgemental input.
The allocation of the weights of the components is performed based on the forecast accuracy of each
component in the previous “periods”: thus the higher the forecast accuracy the bigger the weight factor. In
this way the composite forecast “rewards” providers of the past top performing forecast accuracies, see
Figure 5.
If the weight factors are not calculated based on “past” accuracies, then the opportunity to obtain an
objective overall best forecast is limited.
© 2010 Deloitte and Touche Advanced Demand Planning 12
Figure 5: The composite forecast
8. Inclusion of Non-
quantitative Events as Causal Factors
If causal analysis is one of the components of the composite forecast it normally
incorporates only quantitative factors (those which can be expressed as a series of
numbers, for example temperature, discount promotions, wages, etc. see Figure 6).
The technique used is based on quantifying the deviation (calculating “deviation” coefficients) of the “sales”
in the past caused by a factor and assuming that the factor would have a similar impact in the future. In order
to estimate the forecast the calculated coefficients are used to extrapolate the impact of the factors.
These factors include, for example, temperature, income, promotional discounts, Easter, fishing quotas,
impacts of legislation and special announcements. It is often impossible to include qualitative factors, such
History Forecast
Time Series ( Univariate )
Causal (Regression) Judgment Univariate Composite
Causal (MLR) Calculate
Judgment
History Forecast
Sales
Temp - X 1 Promo – X 2
Promo Effect
Sales Fcst Temp Fcst
Future Promo NOW
Forecast
• Composite forecast – weighted average of univariate , causal and judgmental forecasts
• Weighs – based on historical forecast accuracy of each component
or not?
weights
© 2010 Deloitte and Touche Advanced Demand Planning 13
as holidays, sport events and non-value-adding promotions since it is challenging to find their numeric
representation. These factors can have a substantial influence on sales patterns and are usually planned in
advance or known (promotions, events, and holidays) and therefore it would be beneficial to include them in
the causal analysis.
Deloitte Consulting uses a propriety method of generating dummy variables out of these non-quantitative
factors, which proved to be very successful in causal analyses forecast accuracy improvements.
Figure 6: Temperature (quantitative) and promotion (qualitative) influence on sales
9. Pre-setup Analysis and
Post-implementation Diagnostic
In many businesses the crucial part of Demand Planning is a statistical forecast and its
accuracy, with judgemental input being on the other side of the spectrum. Generally the
forecast is based on historical sales data, which is usually captured and maintained in a
hierarchy structure pertaining to product; geography, key clients and time.
Therefore, the main principle of the pre-set-up analysis is to differentiate the forecast generation level and
sales (or usage) data capturing level. The main reason for this is that data at those levels are usually
sporadic, intermittent or exhibit “noise”. Forecasts generated at those levels do not necessarily provide the
best accuracy. For these reasons, generally it is not advisable to design a Demand Planning solution based
on generating forecasts at the “data capturing” levels (refer to Chapter 5).
Sales
Temperature
Promotion
Promotion
Effect
Sales
Forecast
Temperature
Forecast
Future
Promotion
NOW
© 2010 Deloitte and Touche Advanced Demand Planning 14
The main principle of the sound design of a forecasting solution is based on forecast generation at the
appropriate levels. Using adequate methods which, after disaggregation, provide the best accuracy of
information at the hierarchy level where planning is performed (the decision level).
The proper “DP design” is crucial to attain the best accuracies. The principles of the “good” design can also
be used as benchmarks for post-implementation forecast diagnostics.
Figure 7: Back testing (ex-post forecast) concept
The pivotal aspect of Deloitte Consulting’s forecast diagnostic approach is the data analysis required in order
to determine:
Optimal forecasting hierarchy input (judgemental) and generation level.
Best forecasting methods – with reference to the three possible input areas: univariate, causal and
judgemental input.
Significant causal factors and their projections into the future.
Best methods of disaggregation, e.g. based on proportional factors, forecasts generated at lower
hierarchy levels or other factors.
Whenever possible, the main criterion for selecting the best method/technique is based on back testing (ex-
post forecast), Figure 7, rather than fitting the curve to historical sales (best-fit or interpolation). In many
cases using the best fit provides for dismal forecast accuracy because of exponential effect of most recent
history.
Many businesses believe that it should be possible to significantly improve forecast accuracy but find it very
difficult to do it in practise. The Deloitte Consulting team has designed many Demand Planning solutions.
0
50000
100000
150000
200000
250000
300000
350000
1 2 3 4 5 6 7 8 9 10 11 Time
Demand Back Tested FCST Best fit
Now
A B Test bench period
© 2010 Deloitte and Touche Advanced Demand Planning 15
The team has also assisted clients who have existing Demand Planning systems to significantly improve
their forecast accuracy. The areas of improvement include processes, new approaches, fine-tuning methods
and enabling advanced functionality such as causal analysis. The fine-tuning of Demand Planning is the key
to realising several related benefits, such as: a well-managed supply chain, reliable client service and
significant cost reduction.
10. Demand Planning Best
Business Practices
In our experience, companies that perform Demand Planning efficiently and effectively, and
therefore prosper, have many attributes in common.
These companies make use of a robust forecasting and planning system which enable their Demand
Planning processes. These planning solutions are customised to their specific needs; unlike an ERP system
– one size does not fit all. Demand Planning usually is a part of Integrated Business Planning.
They adhere to clearly defined processes which enhance their DP capabilities. They have a clearly defined
Sales and Operations Planning process where a demand consensus is reached and fed back into the DP
system where stock policy is calculated.
© 2010 Deloitte and Touche Advanced Demand Planning 16
There is a broad view of the supply chain, not only within the organisation but between customers and
suppliers too. This so called CPFR (Collaborative Planning Forecasting and Replenishment) focus allows
better planning by adding input from client’s forecasting system into the DP system and feeding more
information to suppliers.
These companies also manage their demand by an ABC or Pareto classification. By doing this not all
forecasted items need to reviewed, only the items that will significantly influence the business. During the
S&OP process only the ‘star’ items (and new items) are considered and collaborated on.
Lastly companies that successfully make use of their DP system understand that no company is too complex
or too simple to forecast. Understanding the demand is a vital part of managing a supply chain.
11. Key Success
Factors
Based on the experience Deloitte Consulting has in the supply chain industry, the key success factors for
effective and efficient Demand Planning design, analysis and implementation projects have been identified:
The active involvement of the executive sponsor will drive the DP solution from the top; this ensures
buy-in from the stakeholders within the organisation.
The understanding of the impact that forecast accuracy has on the business and knowing which
measures to monitor (e.g. EBITDA).
The quality and availability of demand and other data that influence the forecast (causal factors etc.)
from a well maintained source (e.g. ERP).
© 2010 Deloitte and Touche Advanced Demand Planning 17
The alignment of the planning department with other departments in the business who contribute to
the S&OP process (sales, marketing, manufacturing etc.).
The skill of the personnel involved in planning and the transfer of knowledge within the business.
The amount of continuous Demand Planning training that is provided.
12. Contact Details for More Information
Dr Tomek Jekot
Deloitte Associate
Tel: +27 83 4411 626
Email: [email protected]
© 2010 Deloitte and Touche Advanced Demand Planning 18
Stephen Povey
AngloGold Ashanti
Tel: +27 83 449 4332
Email: [email protected]
Clinton Houston
Supply Chain Strategy
Deloitte Consulting ZA
Tel: +27 82 419 0913
Email: [email protected]
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