Demand forecasting 12

Demand
Demand – The demand for a commodity
refers to the commodity which an individual
consumer is willing to purchase per unit of time
at a particular price.

Demand – the amount of goods consumers
desire to purchase at various alternative prices.

Demand – reflects the degree of value
consumers place on items – price and
satisfaction gained from purchase (utility)

Demand Forecasting
 General considerations:
2. Factors involved in demand forecasting
3. Purposes of forecasting
4. Determinants of demand
5. Length of forecasts
6. Forecasting demand for new products
7. Criteria of a good forecasting method
8. Presentation of a forecast to the management
9. Role of macro-level forecasting in demand forecasts
10. Recent trends in demand forecasting
11. Control or management of demand
 Methods of demand forecasting
 Approach to forecasting

Demand Forecasting
Accurate demand forecasting is essential for a firm to
enable it to produce the required quantities at the right
time and arrange well in advance for the various factors
of production, viz., raw materials, equipment, machine
accessories, labour, buildings, etc.
In a developing economy like India, supple forecasting
seems more important. However, the situation is
changing rapidly.
The National Council of Applied Economic Research.

Factors involved in Demand Forecasting
1. How far ahead?
a. Long term – eg., petroleum, paper, shipping. Tactical
decisions. Within the limits of resources already available.
b. Short-term – eg., clothes. Strategic decisions. Extending or
reducing the limits of resources.

Factors involved in Demand Forecasting

2. Undertaken at three levels:
b. Macro-level
c. Industry level eg., trade associations
d. Firm level
3. Should the forecast be general or specific (product-wise)?
4. Problems or methods of forecasting for “new” vis-à-vis
“well established” products.
5. Classification of products – producer goods, consumer
durables, consumer goods, services.
6. Special factors peculiar to the product and the market –
risk and uncertainty. (eg., ladies’ dresses)

Purposes of forecasting
 Purposes of short-term forecasting
b. Appropriate production scheduling.
c. Reducing costs of purchasing raw materials.
d. Determining appropriate price policy
e. Setting sales targets and establishing controls and incentives.
f. Evolving a suitable advertising and promotional campaign.
g. Forecasting short term financial requirements.
 Purposes of long-term forecasting
i. Planning of a new unit or expansion of an existing unit.
j. Planning long term financial requirements.
k. Planning man-power requirements.

Length of forecasts
 Short-term forecasts – upto 12 months, eg., sales quotas,
inventory control, production schedules, planning cash flows,
budgeting.
 Medium-term – 1-2 years, eg., rate of maintenance, schedule of
operations, budgetary control over expenses.
 Long-term – 3-10 years, eg., capital expenditures, personnel
requirements, financial requirements, raw material
requirements.
(Most uncertain in nature)

Forecasting demand for new products – Joel Dean
7. Project the demand for a new product as an outgrowth of an
existing old product.
8. Analyse the new product as a substitute for some existing
product or service.
9. Estimate the rate of growth and the ultimate level of demand
for the new product on the basis of the pattern of growth of
established products.

Presentation of a forecast to the
Management
 In presenting a forecast to the management, a managerial
economist should:
2. Make the forecast as easy for the management to understand as
possible.
3. Avoid using vague generalities.
4. Always pin-point the major assumptions and sources.
5. Give the possible margin of error.
6. Avoid making undue qualifications.
7. Omit details about methodology and calculations.
8. Make use of charts and graphs as much as possible for easy
comprehension.

Recent trends in demand forecasting
1. More firms are giving importance to demand forecasting than a
decade ago.
2. Since forecasting requires close cooperation and consultation with
many specialists, a team spirit has developed.
3. Better kind of data and improved forecasting techniques have been
developed.
4. There is a greater emphasis on sophisticated techniques such as
using computers.
5. New products’ forecasting is still in infancy.
6. Forecasts are usually broken down in monthly forecasts.
7. In spite of the application of newer and modern techniques,
demand forecasts are still not too accurate.
8. The usefulness of personal feel or subjective touch has been
accepted.
9. Top-down approach is more popular then bottom-up approach.

Methods of demand forecasting
Though statistical techniques are essential in clarifying relationships
and providing techniques of analysis, they are not substitutes for
judgement. What is needed is some common sense mean between
pure guessing and too much mathematics.

1. Survey of buyers’ intentions: also known as Opinion surveys.
Useful when customers are industrial producers. (However, a
number of biases may creep up). Not very useful for household
consumers.
Limitation: passive and “does not expose and measure the variables
under management’s control”

2. Delphi method: it consists of an effort to arrive at a consensus in an
uncertain area by questioning a group of experts repeatedly until
the results appear to converge along a single line of the issues
causing disagreement are clearly defined.
Developed by Rand Corporation of the U.S.A in 1940s by Olaf
Helmer, Dalkey and Gordon. Useful in technological forecasting
(non-economic variables).

Delphi method
Advantages
2. Facilitates the maintenance of anonymity of the respondent’s
identity throughout the course.
3. Saves time and other resources in approaching a large number
of experts for their views.
Limitations/presumptions:
5. Panelists must be rich in their expertise, possess wide
knowledge and experience of the subject and have an aptitude
and earnest disposition towards the participants.
6. Presupposes that its conductors are objective in their job,
possess ample abilities to conceptualize the problems for
discussion, generate considerable thinking, stimulate dialogue
among panelists and make inferential analysis of the
multitudinal views of the participants.

3. Expert opinion / “hunch” method
To ask “experts in the field” to provide estimates, eg., dealers,
distributers ,suppliers industry analysts, specialist marketing
consultants, etc.
Advantages:
3. Very simple and quick method.
4. No danger of a “group-think” mentality.

4. Collective opinion method
Also called “sales force polling”, salesmen are required to estimate
expected sales in their respective territories and sections.
Advantages:
9. Simple – no statistical techniques.
10. Based on first hand knowledge.
11. Quite useful in forecasting sales of new products.
Disadvantages:
13. Almost completely subjective.
14. Usefulness restricted to short-term forecasting.
15. Salesmen may be unaware of broader economic changes.

5. Naïve models
Naïve forecasting models are based exclusively on historical
observation of sales (or other variables such as earnings, cash
flows, etc). They do not explain the underlying casual
relationships which produces the variable being forecast.
Advantage: Inexpensive to develop, store data and operate.
Disadvantage: does not consider any possible causal relationships
that underlie the forecasted variable.

3-naïve models
1. To use actual sales of the current period as the forecast for the next
period; then, Yt+1 = Yt
2. If we consider trends, then, Yt+1 = Yt + (Yt – Yt-1)
3. If we want to incorporate the rate of change, rather than the
absolute amount; then,
Yt+1 = Yt (Yt / Yt-1)

6. Smoothing techniques
Higher form of naïve models:
A. Moving average: are averages that are updated as new
information is received. With the moving average a manager
simply employs, the most recent observations, drops the oldest
observation, in the earlier calculation and calculates an average
which is used as the forecast for the next period.
Limitations:
 One has to retain a great deal of data.
 All data in the sample are weighed equally.
B. Exponential smoothing: uses weighted average of past data as the
basis for a forecast.
Yt+1 = aYt + (1-a) Yt or Y new = a Y old + (1-a) Y’ old, where,
Y new = exponentially smoothed average to be used as the forecast
Y old = most recent actual data
Y’old = most recent smoothed forecast
a = smoothing constant
Smoothing constant (or weight) has a value between 0 and 1 inclusive.

Exponential smoothing
 .

Advantages:
Exponential smoothing is a forecasting method easy to use and
efficiently handled by computers. Although a type of moving
average technique, it requires very little record keeping of past
data. This method has been successfully applied by banks,
manufacturing companies, wholesalers and other organizations.

 The following rules of thumb may be given :
2. When the magnitude of the random variations is
large, give a lower value to “a” so as to average out
the effects of the random variation quickly.
3. When the magnitude of the random variation is
moderate, a large value can be assigned to the
smoothing constant “a”.
4. It has been found appropriate to have “a” between
0.1 and 0.2 in many systems

7. Analysis of time series and trend
projections
 The time series relating to sales represent the past pattern of
effective demand for a particular product. Such data can be
presented either in a tabular form or graphically for further
analysis. The most popular method of analysis of the time series is
to project the trend of the time series.a trend line can be fitted
through a series either visually or by means of statistical
techniques. The analyst chooses a plausible algebraic relation
(linear, quadratic, logarithmic, etc.) between sales and the
independent variable, time. The trend line is then projected into
the future by extrapolation.
 Popular because: simple, inexpensive, time series data often
exhibit a persistent growth trend.
 Disadvantage: this technique yields acceptable results so long as
the time series shows a persistent tendency to move in the same
direction. Whenever a turning point occurs, however, the trend
projection breaks down.
The real challenge of forecasting is in the prediction of turning points
rather than in the projection of trends.

Analysis of time series and trend
projections
 Four sets of factors: secular trend (T), seasonal variation (S),
cyclical fluctuations (C ), irregular or random forces (I).
O (observations) = TSCI
Assumptions:
4. The analysis of movements would be in the order of trend,
seasonal variations and cyclical changes.
5. Effects of each component are independent of each other.

8. Use of economic indicators
The use of this approach bases demand forecasting on certain
economic indicators, eg.,
2. Construction contracts sanctioned for the demand of building
materials, say, cement;
3. Personal income for the demand of consumer goods;
4. Agricultural income for the demand of agricultural inputs,
implements, fertilizers, etc,; and
5. Automobile registration for the demand of car accessories,
petrol, etc.
Steps for economic indicators:
7. See whether a relationship exists between the demand for the
product and certain economic indicators.
8. Establish the relationship through the method of least squares
and derive the regression equation. (Y= a + bx)
9. Once regression equation is derived, the value of Y (demand)
can be estimated for any given value of x.
10. Past relationships may not recur. Hence, need for value
judgement.

Use of economic indicators
 Limitations:
2. Finding an appropriate economic indicator may be difficult.
3. For new products – no past data exists.
4. Works best when the relationship of demand with a particular
indicator is characterized by a time lag. Eg., construction
contracts will result in a demand for building materials but with
a certain amount of time lag.

9. Controlled experiments
 Under this method, an effort is made to vary separately certain
determinants of demand which can be manipulated, e.g., price,
advertising, etc., and conduct the experiments assuming that the
other factors remain constant.
 Example – Parker Pen Co.
 Still relatively new and untried:
4. Experiments are expensive as well as time consuming.
5. Risky – may lead to unfavourable reaction on dealers,
consumers, competitors, etc.
6. Great difficulty in planning the study.difficult to satisfy the
condition of homogeneity of markets.

10. Judgemental approach
 Required when:
2. Analysis of time series and trend projections is not feasible
because of wide fluctuations in sales or because of anticipated
changes in trends; and
3. Use of regression method is not possible because of lack of
historical data or because of management’s inability to predict
or even identify causal factors.
Even statistical methods require supplementation of judgement:
5. Even the most sophisticated statistical methods cannot
incorporate all the potential factors, e.g., a major technological
breakthrough in product or process design.
6. For industrial products – if the management anticipates loss or
addition of few large buyers, it could be taken into account only
through judgement approach.
7. Statistical forecasts are more reliable for larger levels of
aggregations.

Approach to forecasting
1. Identify and clearly state the objectives of forecasting.
2. Select appropriate method of forecasting.
3. Identify the variables.
4. Gather relevant data.
5. Determine the most probable relationship.
6. For forecasting the company’s share in the demand, two different
assumptions may be made:
(g) Ratio of company sales to the total industry sales will continue
as in the past.
(h) On the basis of an analysis of likely competition and industry
trends, the company may assume a market share different from
that of the past. (alternative / rolling forecasts)
7. Forecasts may be made either in terms of units or sales in rupees.
8. May be made in terms of product groups and then broken for
individual products.
9. May be made on annual basis and then divided month-wise, etc.

Demand forecasting 12

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