advanced demand planning sunny skies or rainy days? how to...

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


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.


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%


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)


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.


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


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:


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.


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.


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.


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


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


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

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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


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.


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).


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]


Stephen Povey

AngloGold Ashanti

Tel: +27 83 449 4332


Clinton Houston

Supply Chain Strategy

Deloitte Consulting ZA

Tel: +27 82 419 0913


Deloitte refers to one or more of Deloitte Touche Tohmatsu, a Swiss Verein, and its network of member firms, each of which is a legally separate and independent entity. Please see www.deloitte.com/za/aboutus for a detailed description of the legal structure of Deloitte Touche Tohmatsu and its member firms. Deloitte provides audit, tax, consulting, and financial advisory services to public and private clients spanning multiple industries. With a globally connected network of member firms in more than 140 countries, Deloitte brings world-class capabilities and deep local expertise to help clients succeed wherever they operate. Deloitte’s more than 168,000 professionals are committed to becoming the standard of excellence. Deloitte’s professionals are unified by a collaborative culture that fosters integrity, outstanding value to markets and clients, commitment to each other, and strength from cultural diversity. They enjoy an environment of continuous learning, challenging experiences, and enriching career opportunities. Deloitte’s professionals are dedicated to strengthening corporate responsibility, building public trust, and making a positive impact in their communities. © 2010 Deloitte & Touche. All rights reserved. Member of Deloitte Touche Tohmatsu

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