##### Economics

# 4 Alternative Methods of Demand Forecasting

There are some other forecasting methods used by various enterprises in different situations. Each method has its own advantages and disadvantages. Out of many different methods, three methods are discussed here:

Alternative Methods of Demand forecasting are :-

(i) market Research Method,

(ii) Exponential Smoothing Method,

(iii) Markovian Process Method.

(iv) Method of Simulation, (this method is discussed separately, here)

**1. Market Research Method :** This method is generally used by big business houses. A separate marketing research cell is established in the organization which collects the required information both from internal and external sources for sales forecasting. Field surveys are also conducted to get direct information from the potential consumers and the retailers. The collected information is processed and analyzed by the department of the enterprise using statistical techniques like time series analysis, correlation analysis etc.

The following are the advantages and disadvantages of this method :

### Advantages of **Market Research Method** of Demand Forecasting

(i) It helps in quick decision making by supplying regular and systematic information.

(ii) The accuracy of the results can be tested by statistical methods.

(iii) Detailed study of the problem is possible.

### Disadvantages of **Market Research Method** of Demand Forecasting

(i) Method is expensive and can be afforded by big organizations only.

(ii) The results should be regularly reviewed and adjusted to changing conditions.

**2. Exponential Smoothing Method :** This method is an improvement over moving average method of forecasting. It tries to overcome the limitations of moving averages and eliminates the necessity of keeping extensive records of past data. It also tries to screen out the irregularities in the demand pattern. The method allows for trend and takes into consideration the short term fluctuations in the determination of the forecast. The fundamental concept of exponential smoothing method is that :

New estimate = old estimate of latest actual demand + σ (Latest actual demand — old estimate of latest actual demand)

Ft=σ Dt+ (1 —σ) Ft-1

where Ft is the Forecast at time t

Dt is the actual demand at time t

Ft-1 is the forecast at time (t- 1) a is the smoothing coefficient.

The performance of this method depends cn the value of the smoothing coefficient σ. The size of σ will depend on the speed with which one wishes to respond to real changes in demand. This has to be weighted against the risks of making forecasts errors, The value of σ lies between 0 and 1. It is observed that larger σ’s do not necessarily contribute to better forecasts. A high value of σ gives more weight to current values than to past ones. Thus if cyclical fluctuations are predominant in forecast then one should take low value of σ and if long term fluctuations are more dominant than σ to be high. Two main factors should be considered in deciding the value of σ namely (i) distribution of random errors and (ii) costs associated with forecasting errors.

**3. Markovian Process Method:** Any stochastic process which develops with time according to some law of probability is exactly in one of its possible states. The possible sates of a Markovian process arc the list of all possible outcomes at some point of time.

A Markov process is characterized by its transitional matrix which is a set of initial and transitional probabilities. The transitional matrix gives the step by step transitional probabilities of a Markov process. The matrix have following properties : (i) each element of matrix should be some probability, (ii) the sum of the elements of each row in the matrix should be equal to one i.e. the process at some point of time should be in any one of the states.

Thus, the foregoing discussion shows that there are many forecasting methods each having its own advantages and disadvantages. The suitability of any method basically depends on the nature of the product and its potential consumer.