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Relevant Training / Workshops
Relevant Further Reading
Why do you need to Forecast:
1. Product life cycle
2. The Business Cycle
3. Supply chain lead-times greater than
consumer required lead-time
4. To provide input into long term decisions
such as capital investment.
5. Peak demand and average capacity
6. Spares
7. Transport costs
8. Manufacturing costs
Why forecasts are wrong
Implications of Forecast Error
Relevant Training Course / In-house Workshop Highlights:
SSC03 Advanced
Forecasting & Inventory Modelling (Using Spreadsheets) (Discusses the maths)
SSC05
Producing Accurate Forecasts (Discusses the business processes required)
Relevant Further Reading:
The following further articles were mentioned in this paper:
a. Permanently Maintained Website Articles:
Managing Demand
Lean & Agile Supply Chains
Participative Sales and Operations Planning
b. Previously Featured Articles from our Archives
(Up to 2 per organisation available on request):
Previous Best Practices:
B014: Effective Bill of Material Design
B022: Change Control
Previous Techniques:
Previous Questions:
Previous Malpractices:
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Reducing forecast error, reducing the need to forecast and avoiding the problems caused by
inaccurate forecasts:
Part 1. Why forecast, why forecasts are wrong, and why does that matter?
This article is part 1 of two articles on forecasting. This
first article deals with the need to forecast, why forecasts are
often wrong (contain errors), and the implications of forecast
error. The second article (available on
request) deals with improving the quality of
forecasts, reducing the need to forecast, and reducing the impact
of forecast error. This service is
not available to consultants.
Links to
related training and further reading on left
Why do you need to forecast?
There are a number of reasons why you may need to forecast.
Below are listed the ones we have encountered, together with
their characteristics.
The following diagram (figure 1.) illustrates a product life
cycle. It shows a growth in volume up to a peak. This peak which
may be short lived is sometimes called "the novelty curve" then
declines to a plateau of relatively stable demand and then a
decline into obscurity. The scale of the curves and slope of the
lines varies from product to product but the general shape is
applicable to many if not most products.
Figure 1. Product life cycle
The volume and duration where the changes in slope are most significant are:
- The most important to forecast
- The most difficult to forecast
- The ones where people want to exert maximum influence
Together with economic prosperity and decline goes a cycle of
what has come to be known as "the business cycle" (a
period of slow or negative growth followed by the next period of
rapid growth). It is also referred to as "boom and
bust".
The impact of this on capacity and stock is shown in figure 2 below:
Figure 2. The business cycle
Again it is important to identify the next upturn or downturn
accurately, or there will be the sort of implications shown in the diagram.
Where there is uncertain demand and the supply chain lead-time
is greater than the customer required lead-time, safety stock
needs to be held in anticipation of an order. I.e. Items cannot
be made to order.
4. To provide input into long term decisions such as capital investment
Often major capital equipment or expansion plans have to be
viewed against a forecast of what the demand will be.
5. Peak demand and average capacity
This is the problem caused when customers want things at a
faster rate than your capacity allows. For a period of time the
mismatch ((anticipated peak rate of demand - peak rate of supply)
x lead-time) has to be made in advance to ensure that sales are
made when required. It is not readily realised that in order to
give consistently high service levels, peak demand has to be
matched by peak capacity. This gives accountants concerns since
the capacity is therefore under-utilised for the remaining time.
Most management accounting systems and budgetary control systems
are based on top down spreading of annual targets which has a
smoothing (averaging) effect, rather than bottom up costing of
timed causal relationships, for example, a sales campaign leading
to increased costs at a particular time. This would not be a
problem if these budgets were not then used indiscriminately to
control expenditure rather than intelligently to explain
necessary variances. This cost tension then starts to create the
problems shown in figure 2. Extreme examples of this problem are
in seasonal demand such as vehicle batteries, and garden
equipment and success stories where products are selling better
than expected but cost pressures prevent increasing capacity.
Much worse however is where products are going rapidly into steep
decline but backward looking management accounting systems do not
exert cost pressures until too late.
Spares present two major problems:
- In early life the supply chain has to be primed on
estimates of mean-time-between-failures based on small sample testing.
- In later life as the original equipment is taken out of
production it is often necessary to estimate all-time
spares requirements with little knowledge of the decline in usage of the product.
Mid life spares forecasting whilst still potentially
problematic for long life components, is less troublesome, since
after the novelty phase (see figure 1) demand is more stable.
Transport costs inhibit the supply-to-order situation, which
is the ideal lean supply chain (See Lean &
Agile Supply Chains), forcing suppliers to "batch
up" to reduce unit transport costs.
The old adage "cheaper by the dozen" is often used
to justify large batches (See
Participative Sales and Operations Planning), which then require a
forecast to justify the stock holding costs and reduce the risk
of obsolescence.
Which of the lines in figure 3 represents the best forecast
after plot 3? There is a good argument for each assumption but they
may all be wrong. How would you know?.
Figure 3: Best-Fit Trend
There are a number of reasons for forecast error. We discuss the
59 we have encountered so far, under the
following 10 general headings in our
SSC05
Producing Accurate Forecasts training course:
- What decisions need forecasting & which do not?
- What data do you need and how far ahead should you look?
- Data sources:
- Where to collect data from (& where not to)
- Data collection processes
- Induced changes
- Customer's inventory planners
- Computer models Interpretation and analysis
- Not recognising natural variation & "the black swan" (when they are
usually white)
- The Mathematics of modelling (overview of SSC03 Advanced
Forecasting & Inventory Modelling (Using Spreadsheets)
The obvious implication of forecast error is an over or under
reaction to the latest trend. This gives rise to the following risks:
Figure 4: The implications of Forecast Error
Shown in figure 4 is the fine balance between being right and very wrong!
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