3. TERMINOLOGY
• DEMAND The demand for anything at a
given price, is the amount of it which will be
bought per unit of a time at that price.
• FORECASTING Forecasting generally means
prediction or estimation or a guess.
5. SO, DEMAND FORECASTING??
• DEMAND FORECASTING = DEMAND +
FORECASTING.
• Estimating the demand(increase in need)
which could probably arise prior to the release
or introduction of a new product.
• Prediction of the result of introduction of a
new product into the market(to make the
product a success).
7. • The basic types are based on the duration of
time over which the demand is forecasted.
1. SHORT TERM FORECAST:
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Forecasting the demand for about a year and is
periodically reviewed quarterly or half yearly.
Used in marketing activities, which don’t require a
huge investment.
2. LONG TERM FORECAST:
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Forecast the demand for over a long period of time.
Basically useful in large industrial sectors during
investment on a new unit or expansion of current unit.
Margin of error is large due to the duration involved.
10. 1. USER EXPECTATION METHOD
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Depends upon the survey of customers intention.
Direct interaction with customers.
An easy and inexpensive way of knowing
customer requirements.
Conclusions can be done after knowing the
desires of consumers.
Requires huge manpower or technical knowledge.
Misjudgment among people about the product.
11. 2. COLLECTIVE OPINION METHOD
• Depends on the opinion from the salesmen about
the demand for a particular type of commodity in
a locality.
• The salesmen report to the firm about the
probability of success of the firm’s product and
their expectation of results.
• Known as the HUNCH method of forecasting.
HUNCH A feeling by intuition.
• Requires a comparatively lesser manpower.
• Commissions/periodic settlement to the salesmen.
12. 3. EXPERTS OPINION METHOD
• A group are experts in that particular field of the
product are consulted for their opinions.
• The opinions are then discussed by a panel to
arrive at a conclusion which is basically the
average of all the opinions.
• Experts are consulted, so there is no risk involved.
• Based on opinions from multiple people, so there
is one way or the other to succeed always.
14. 1. TREND PROJECTION METHOD
• Based on the past demand record of a
product.
• The demand for the product are arranged
w.r.to time and this is called as the “TIME
SERIES”.
• No knowledge of economics is required since
it is based on trend.
• Valuable tool for freshers in the market.
• Past can’t predict the future always.
• Suitable only for Long term forecast.
15. 2. GRAPHICAL METHOD
• Plotting the sales of a firm over a period of
years and the improvement in sales of the
type of commodity to be introduced.
16. SIGNIFICANCE OF FORECASTING
• It provides appropriate production scheduling
so as to avoid the problem of over-production
& problem of short supply.
• Helping the firms to reduce the cost for
purchasing raw material.
• Manufacturers prefer “Make to Stock” rather
than “Make to Order". Demand Forecasting
helps to plan ahead and provide the finished
goods to their customers as soon as possible.
18. ERRORS
• There are many technical, human, data
accuracy and prediction errors which may lead
to inaccuracy in demand forecasting.
• This diminishes the purpose of forecasting the
demand for a product.
19. COMMON ERRORS
• Probability of success is just the wish of the
producer and the benefit of doubt is favored
towards profit.
• Prediction is normally based on the past
history of the commodity demand. History
need not be the case at all times in future.
20. MEASUREMENT ERRORS
• Record of data need not be accurate
always(depends on the type of record).
• Rounding off concept often affects the
accuracy of data, which is used in forecasting.
• Changes that occur at the time of
measurement is not considered and is always
assumed that there is no change during
measurement.
• Common human data entry errors.
21. DEMAND VARIABILITY
• Expected demand may sometimes be different
from the actual demand.
• Demand is affected by various factors such as
Price, Season, Quality, Govt policies…,
22. SCENARIO
• Suppose a jewellery shop is to be opened
shortly. The demand forecast insists a certain
period for the opening of the shop based on
the calculated demand.
• BUT unexpectedly too many jewel shops open
at that time with attractive offers.
• Here, the DEMAND BECOMES LESSER THAN
THE EXPECTED DEMAND DUE TO
COMPETITION.
23. SUPPLY INCONSISTENCY
• Due to various factors, a firm may not be able
to meet with the demands believing the
demand forecast.
• At that time the actual demand might have
been very much larger or much smaller than
the expected demand.
24. SCENARIO
• Suppose a HELMET manufacturer has expected a
certain demand and has manufactured
accordingly.
• Suddenly the GOVERNMENT ISSUES AN ORDER
that it is mandatory that both the persons must
wear helmet.
• This increases the demand for the helmets but
the manufacturer was not prepared to increase
the firm’s production.
• Thus it leads to inability to supply accordingly due
to GOVT POLICY CHANGE.
27. MODEL MISMATCHES
• The products which we introduce might be
suitable only for a certain range of people.
• The range may refer to either the affordability
or the need for the product.
• Sometimes demand forecasting may
concentrate only on the people who need it
which results in the success of the product
only among such people. (Partial success only)
28. HOW TO OVERCOME ERRORS IN
DEMAND FORECASTING
• Two approaches can reduce the impact and
effects of the inaccuracy in demand
forecasting.
Collaboration between customers and
suppliers to improve efficiency of forecasting.
Quicker response to demand changes.
29. SUPPLY MANAGEMENT
• The supplier must be able to meet the
situations where the actual demand is higher
than the expected demand.
• To meet this, the supplier can account with
the concept of:
CYCLE STOCK: To meet with the forecasted
demand.
BUFFER STOCK: To manage higher demand
situations.
30. IMPROVING FORECAST ACCURACY
• This can be achieved by COLLABORATION.
• Multilevel forecasting is performed by multiple
teams and increase inputs for forecasting.
Forecast from salesmen.
Survey from Consumers.
Past demand history.
Current trend.
• Conclusion based on the above forecasts will be
more accurate than the single level forecast.
31. AVOIDING COMPROMISES
• Compromises at various stages of forecasting
must be avoided.
• Examples of compromises:
Forecast review quarterly instead of monthly.
Avoiding data records of minorities.
Reducing forecast to reduce cost.
32. MORE FORECAST IN LESSER TIME
• Longer the forecast period, greater the
deviation of quantity of demand estimated.
• Performing forecasts frequently and reacting
to it instantaneously is important.
Content
Daily
Weekly
Monthly
Days in the cycle
1
5
22
Buffer stock requirement in %
10
23
46
33. CHANGES TO BE DONE
• Larger focus on the source that varies the demand
variability the most.
• Parallel processing of various steps in forecast
enabled.
• Processes in forecast that does not add any value
must be avoided.
• The step which adds more value and that takes
more time must be crucially dealt with large time.
• Changes in technology that increases forecast
accuracy and reduces time is essential.
