BBA I Semester Managerial Economics Demand Forecasting Study Material Notes

BBA I Semester Managerial Economics Demand Forecasting Study Material Notes: Meaning and Definition Objectives of Demand Forecasting Relevance of Demand Forecasting for Products Methods of Demand Forecasting Trend Projection or Time Series Method Experts Opinion Method Consumers Survey or Buyers Intentions methods Barometric Method Regression Metod Smoothing Methods utility or Importance of demand Forecasting Limitations of demand Forecasting :

MCom I Semester Accounts Holding Companies Study Material Notes

DEMAND FORECASTING

MEANING AND DEFINITION

Demand forecasting means the estimation of expected future demand for a good or service. As demand is directly related to sales, it is also known as sales forecasting. According to Evan Douglas. “Demand estimation may be defined as the process of finding values for demand in future time periods.” In the words of Phillip Kotler. “The demand estimate is the expected level of company’s sales based on a chosen marketing plan and assumed marketing environment.” Thus demand forecasting is an estimate of the likely demand of a firm’s product or service in future periods.

Demand Forecasting Study Material

Objectives of Demand Forecasting

Demand forecasting serves the main purpose of maintaining equilibrium in the economy. Some of the objectives are as follows:

(1) To Help Production. Demand forecasts help the producer to take up production planning so that the gap between demand and supply of goods is eliminated.

(2) To Supply Goods. Demand forecasting induces the producers to maintain sufficient inventory of products so that when supply falls and demand increases, the product may be released to the market to fill the gap.

(3) To Formule Price Policy. Demand foecasting helps the management to formulate appropriate price policies, so that the prices do not fluctuate over a period of time.

(4) To Formulate Sales Policy. One of the objectives of demand forecasting is to formulate sales policy. Since demand forecasting is made regionwise, movement of products may be arranged suitably. It helps the management to determine sales targets accordingly.

(5) To Arrange for Finance. On the basis of demand forecasting, the management can prepare the budget for institutional finance to procure materials and labour.

(6) To Determine the Production Capacity. Demand forecasting enables the companies to decide about the production capacity. By studying the demand pattern for the products, the organisation can plan for suitable plant and desired output.

(7) To Plan for Labour Force. Production of product requires skilled and trained labour force. If the demand forecasting indicates favourable trend, then management can take up measures to train labour force. This can ensure labour supply and there will be no hindrance in production process. Relevance (or Purpose) of Demand Forecasting

The relevance of demand forecasting in management depends on the time span of forecasting: short-run and long-run forecasting.

Demand Forecasting Study Material

Relevance of Short-run Forecasting.

The relevance of short-run demand forecasting is as follows:

1 Suitable Production Policy. In the short run, a firm is often faced with overor under-production leading to excess or short supply.

Policy. In the short run, a firm is often faced with over-production non leading to excess or short supply. To avoid this, the firm has to forecast the demand for its product based on a suitable production policy.

2. Suitable Sales Policy. Demand forecasting requires a suitable sales promotion consisting of sales targets in different areas, incentives to salesmen and dealers and advertising and propaganda.

3. Suitable Price Policy. Keeping in view the expected demand for its product, a firm may raise the price of its product when the demand is high and reduce it when the demand is low.

4. Financial Needs. Short-run demand forecasting requires forecasting the financial needs of a firm. The firm has to arrange for funds from different sources based on its production and sales levels on reasonal terms.

5. Suitable Inputs Policy. Besides finance, the firm has to evolve a suitable inputs policy relating to the demand for labour, raw materials and other inputs.

Relevance of Long-run forecasting. The relevance of long-run demand forecasting is as follows:

1 To Plan Changes in Production. Long-run demand forecasting requires a firm to change its production schedule. It has to assess the demand for its product in relation to its competitors. It there is need to change the quality of its product, it may start a new unit or change the existing unit. This also requires new machines and latest techniques.

2. To Plan for Financial Needs. In the long-run, a firm requires funds to meet changes in the demand for its product. Besides, it has to plan the repayment of the borrowed funds and the interest

3.To Plan for Manpower Requirements. On the basis of demand forecasting, a firm has to plan about its manpower requirements in the long run. For this, it has to recruit persons on permanent, temporary and contractual basis, keeping in view its future sales. A big firm also gives training to new recruits.

4. To Plan for Machines and Materials. The firm has to plan for materials and machines needed on the basis of long-run demand forecasts for its products. It may be based on changes in the consumption patterns of the people.

Demand Forecasting Study Material

DEMAND FORECASTING FOR PRODUCTS

There are different forecasts for different types of products like (1) Forecasting demand for non-durable consumer goods, (2) Forecasting demand for durable consumer goods, (3) Forecasting demand for capital goods, and (4) Forecasting demand for new products. (1) Forecasting Demand for Non-Durable Consumer Goods

These are also known as ‘single-use consumer goods’ or perishable consumer goods. These vanish after a single act of consumption. These include goods like food, milk, medicine, fruits, etc. Demand for these goods depends upon household disposable income, price of the commodity and the related goods and population and characteristics. Symbolically.

A Text on-Managerial Economics

p=price of the commodity C

1. = price of its related goods

(i) Disposable income expressed as De=f() i.e. other things being equal, the demand for commodity c depends upon the disposable income of the household. Disposable income of the household is estimated after the deduction of personal taxes from the personal income. Disposable income gives an idea about the purchasing power of the household.

(ii) Price, expressed as De=f(p.p.) i.e. other things being equal, demand for commodity c depends upon its own price and the price of related goods. While the demand for a commodity is inversely related to its own price of its complements. It is positively related to its substitutes. Price elasticities and cross elasticities of non-durable consumer goods help in their demand forecasting.

(iii) Population, expressed as Dc = f(s) i.e. other things being equal, demand for commodity c depends upon the size of population and its composition. Besides, population can also be classified on the basis of sex, income, literacy and social status. Demand for nondurable consumer goods is influenced by all these factors. For the general demand forecasting population as a whole is considered, but for specific demand forecasting division of population according to different characteristics proves to be more useful.

The various steps involved in forecasting the demand for non-durable consumer goods are the following: (a) first identify the variables affecting the demand for the product and express them in appropriate forms, (b) gather relevant data or approximation to relevant data to represent the variables, and (c) use methods of statistical analysis to determine the most probable relationship between the dependent and independent variables.

(2) Forecasting Demand for Durable Consumer Goods

These goods can be consumed a number of times or repeatedly used without much loss to their utility. These include goods like car, T.V., air-conditioners, furniture etc. After their long use, consumers have a choice either these could be consumed in future or could be disposed of. The choice depends upon the following factors:

(i) Whether a consumer will go for the replacement of a durable good or keep on using it after necessary repairs, depends upon his social status, level of money income, taste and fashion, etc. Replacement demand tends to grow with increase in the stock of the commodity with the consumers. The firm can estimate the average replacement cost with the help of life expectancy table.

(ii) Most consumer durables are consumed in common by the members of a family. For instance, T.V., refrigerator, etc. are used in common by households. Demand forecasts for goods commonly used should take into account the number of households rather than the total size of population. While estimating the number of households, the income of the household, the number of children and sex-composition, etc. should be taken into account.

(iii) Demand for consumer durables depends upon the availability of allied facilities. For example, the use of T.V., refrigerator needs regular supply of power, the use of car needs availability of fuel, etc. While forecasting demand for consumer durables, the provision of allied services and their cost should also be taken into account.

(iv) Demand for consumer durables is very much influenced by their prices and their credit facilities. Consumer durables are very much sensitive to price changes. A small fall in their price may bring large increase in demand.

Demand Forecasting Study Material

Forecasting Demand for Capital Goods

Capital goods are used for further production. The demand for capital good is a derived one. It will depend upon the profitability of industries. The demand for capital goods is a case of derived demand. In the case of particular capital goods, demand will depend on the specific markets they serve and the end uses for which they are bought. The demand for textile machinery will, for instance, be determined by the expansion of textile industry in terms of new units and replacement of existing machinery. Estimation of new demand, as well as replacement demand, is thus necessary. Three types of data are required in estimating the demand for capital goods: (a) the growth prospects of the user industries must by known, (b) the norm of consumption of the capital goods per unit of each end-use product must be known, and (c) the velocity of their use.

Forecasting Demand for New Products

The methods of forecasting demand for new products are in many ways different from those for established products. Since the product is new to the consumers, an intensive study of the product and its likely impact upon other products of the same group provides a key to an intelligent projection of demand. Joel Dean has classified a number of possible approaches as follows:

(a) Evolutionary Approach. It consists of projecting the demand for a new product as an outgrowth and evolution of an existing old product.

(b) Substitute Approach. According to this approach the new product is treated as a substitute for the existing product or service.

(c) Growth Curve Approach. It estimates the rate of growth and potential demand for the new product as the basis of some growth pattern of an established product.

(d) Opinion-Poll Approach. Under this approach the demand is estimated by direct enquiries from the ultimate consumers.

(e) Sales Experience Approach. According to this method the demand for the new product is estimated by offering the new product for sale in a sample market.

(1) Vicarious Approach. By this method, the consumers’ reaction for a new product are found out indirectly through the specialised dealers who are able to judge the consumers’ needs, tastes and preferences.

Demand Forecasting Study Material

METHODS OF DEMAND FORECASTING

Demand forecasting is a difficult exercise. Making estimates for future under the changing conditions is a difficult task. There is no easy method or a simple formula which enables the manager to predict the future. Economists and statisticians have developed several methods of demnad forecasting. Each of these methods has its relative advantages and disadvantages. Selection of the right method is essential to make demand forecasting accurate. The more commonly used methods are discussed below.

There are two main methods of demand forecasting:

(1) Survey or opinion polling methods and (2) statistical methods.

1 Surveyor Opinion Polling Methods

In these methods, the opinions of the buyers, sales force and experts are gathered to determine the emerging trend in the market. The survey methods of demand forecasting are of three kinds:

(a) Consumers’ Survey or Buyers’ Intentions Methods

In this method, the consumers are directly approached to disclose their future purchase plans. This is done by interviewing all consumers or a selected group of consumers. Here demand is forecasted on the basis of information provided by the consumers. This method has the following merits and demerits:

Merits

(1) It is a very simple method.

(2) This method involves very little expenditure.

(3) It is useful in knowing the demand for new goods.

(4) It is useful for short-term forecasts.

Demerits

(1) This method is suitable only for short-run demand forecasting.

(2) This method is not much helpful in forecasting the demand for consumer goods as consumers’ tastes are subject to continuous change.

(3) It is costly and difficult method.

(4) Consumers are likely to give wrong and incomplete information. This method is of three types.

(i) Complete Enumeration Survey. Under the complete enumeration survey, the surveyers go for a door to door survey for the forecast period by interviewing all consumers in the area. This method has an advantage of unbiased information, yet it has its disadvantages also. (i) It requires lot of resources, manpower and time. (ii) Consumers may be reluctant to reveal their purchase plans due to personal privacy or commercial secrecy. (iii) At times the consumers may not express their opinion properly or may deliberately misguide the investigators.

(ii) Sample Survey Method. Under this method, some representative households are selected on random basis as samples and their opinion is taken as the generalised opinion. This method is based on the basic assumption that the sample truly represents the population. If the sample is the true representative, there is likely to be no significant difference in the results obtained by the survey. Apart from that, this method is less tedious and less costly.

(iii) End Use Method or Input-Output Method. This method is useful for industries which are mainly producers goods. In this method, the sale of the product is estimated on the basis of demand survey of the industries using this product as an intermediate product. The estimation of end use demand of an intermediate product involves many final good industries using this product at home and abroad. It helps us to understand inter-industry relations. In input-output accounting two matrices used are the transaction matrix and the input co-efficient matrix. The major efforts required by this type are not in its operation but in the collection and presentation of data.

(b) Collective or Sales Force Opinion Method. This is also known as collective opinion method. In this method, the opinion of the salesmen is sought. Each sales person in the company is required to make an individual forecast for his or her particular sales territory. These individual forecasts are discussed and agreed with the sales manager. All combined acte then constitute the sales forecast for the organisation. This method has the following

Merits:

(i) It is easy and cheap.

(ii) It does not involve any elaborate statistical treatment.

(iii) Its main merit lies in the collective wisdom of salesmen.

(iv) This method is more useful in forecasting sales of new products.

(v) In this method, forecast of demand is based on the opinions of such people who are directly linked with the market.

Demerits:

(1) This method involves the element of subjectivity as it is based on personal opinion of the salesmen.

(ii) This method is unable to provide a forecast of demand in the long-run.

(c) Experts’ Opinion Method

This method is also known as “Delphi Method” of investigation. The Delphi method requires a panel of experts, who are interrogated through a sequence of questionnaires in which the responses to one questionnaire are used to produce the next questionnaire. Thus any information available to some experts and not to others is passed on, so that all the experts have access to all the information for forecasting. The method is used for long term forecasting to estimate potential sales for new products. This method presumes two conditions: Firstly, the panellists must be rich in their expertise, possess wide range of knowledge and experience. Secondly, its conductors are objective in their job. This method has the following merits and demerits.

Merits

(i) This method is helpful in quick forecasting of demand.

(ii) This method is even simpler than buyers’ survey method and collective opinion method.

Demerits

(i) The opinions of the experts may be biased.

(ii) In the rapidly changing market environment, the opinions based on past experiences may loose their relevance.

2.Statistical Methods

Statistical methods have proved to be immensely useful in demand forecasting. In order to maintain objectivity, that is, by consideration of all implications and viewing the problem from an external point of view, statistical methods are used. The important statistical methods are:

Demand Forecasting Study Material

(i) Trend Projection or Time Series Method

A firm existing for a long time will have its own data regarding sales for past years. Such data when arranged chronologically yield what is referred to as ‘time series’. Time series shows the past sales with effective demand for a particular product under normal conditions. Such data can be given in a tabular or graphic form for further analysis. This is the most popular method among business firms, partly because it is simple and inexpensive and partly because time series data often exhibit a persistent growth trend.

Time series has got four types of components namely, Secular Trend (T), Secular Variation (S), Cyclical Element(C), and an Irregular or Random Variation (I). These elements are expressed by the equation O = TSCI. Secular trend refers to the long run changes that occur as a result of general tendency. Seasonal variations refer to changes in the short run Weather pattern or social habits. Cyclical variations refer to the changes that occur in industry during depression and boom. Random variation refers to the factors which are generally able such as wars, strikes, flood, famine and so on.

When a forecast is made the seasonal, cyclical and random variations are removed from the observed data. Thus only the secular trend is left. This trend is then projected. Trend projection fits a trend line to a mathematical equation. The trend can be estimated by using any one of the following methods: (a) The Graphical Method, (6) The Least Square Method.

( a) Graphical Method. This is the most simple technique to determine the trend. All values of output or sale for different years are plotted on a graph and a smooth free hand curve is drawn passing through as many points as possible. The direction of this free hand curve-upward or downward shows the trend. A simple illustration of this method is given in Table 2.

Table 2 : Sales of Firm

Fig. 1 In Fig. 1, AB is the trend line which has been drawn as free hand curve passing through the various points representing actual sale values.

(b) Least Square Method. Under the least square method, a trend line can be fitted to ime series data with the help of statistical techniques such as least square regression.

trend in sales over time is given by straight line, the equation of this line is of the form: y = a + br. Where ‘a’ is the intercept and b’ shows the impact of the

1 We have two variables the independent variable r and the dependent variable y The line of best fit establishes a kind of mathematical relationship between the two variables x and y. This is expressed by the regression y onx.

In order to solve the equation y = a + bx, we have to make use of the following normal equations:

Demand Forecasting Study Material

 (ii) Barometric Method

A barometer is an instrument of measuring change. Barometric methods are based on the idea that certain events of the present can be used to predict the directions of change in the future. This is done by the use of economic indicators which serve as barometers of economic change. These are known as leading indicators which are economic series that go down or up before gross domestic product (GDP) changes.

Forecasters forecast a firm’s sales fluctuations with three series: Leading Series, Coincident or Concurrent Series and Lagging Series.

(a) The Leading Series. The leading series comprise those factors which move up or down before the recession or recovery starts. They tend to reflect future market changes. For example, baby powder sales can be forecasted by examining the birth rate pattern five years earlier, because there is a correlation between the baby powder sales and children of five years of age and since baby powder sales today are correlated with birth rate five years earlier, it is called lagged correlation. Thus we can say that births lead to baby soaps sales.

(b) The Coincident or Concurrent Series. The coincident or concurrent series are those which move up or down simultaneously with the level of the economy. They are used in confirming or refuting the validity of the leading indicator used a few months afterwards. Common examples of coinciding indicators are G.N.Pitself, industrial production, trading and the retail sector.

(C) The Lagging Series. The lagging series are those which take place after some time lag with respect to the business cycle. Examples of lagging series are, labour cost per unit of the manufacturing output, loans outstanding, leading rate of short term loans, etc.

The leading and coincident series are explained in Fig. 2. If two series of data frequently increase or decrease at the same time, one series may be regarded as a coincident indicator of the other. For example, in Figure 2(a) series 1 is a coincident indicator of series 2 because the two series have their peaks and troughs in the same periods.

If changes in one series consistently occur prior to changes in another series, a leading indicator has been identified. In figure 2(b), series 1 can be considered a leading indicator of series 2 because the peaks and troughs of series 1 consistently occur before the corresponding peaks and troughs of series 2.

For purposes of forecasting, leading indicators are of primary interest. Much as a meteorologist uses changes in barometric pressure to predict the weather, leading indicators can be used to forecast changes in general economic conditions. Consequently, the use of such indicators is commonly referred to as barometric forecasting,

Demand Forecasting Study Material

The value of a leading indicator depends on several factors. First, the indicator must be accurate. That is, its fluctuations must correlate closely with fluctuations in the series that it is intended to predict. Second, the indicator must provide adequate lead time. Even if two series are highly correlated, an indicator will be of little use if the lead time is too short. Third, the lead time should be relatively constant.

The leading indicators method has the following merits and demerits:

Merits

(i) It is a simple method.

(ii) It overcomes the regression method’s problem of forecasting the values of the independent variables in the prediction period.

(iii) Since there is a lead relationship, the exact values of the independent variable for the lead period are known.

Demerits

(i) It is not always possible to find a leading indicator for the variable to be forecast.

(ii) The lead period may change over time. Therefore, this method is available for short-term forecasting only.

(iii) It lacks accuracy because changes in indicators do not correctly indicate changes in other economic variables.

(iv) This method requires a special knowledge to identify the appropriate indicator for every variable.

(iii) Regression Method independent and one dependent). variable from the specific value of the tempts to assess the relationship between at least two variables (one or more nt and one dependent), the purpose being to predict the value of the dependent the specific value of the independent variable. The basis of this prediction any is historical data. This method starts from the assumption that a basic relationship exists between two variables. An interactive statistical analysis computer package is used formulate the mathematical relationship which exists.

For example, one may build up the sales model as: Quantum of Sales = a. price + b. advertising + c. price of the rival

products + d. personal disposable income + u Where a, b, c, d are the constants which show the effect of corresponding variables as sales. The constant u represents the effect of all the variables which have been left out in the equation but having effect on sales. In the above equation, quantum of sales is the dependent variable and the variables on the right hand side of the equation are independent variables. If the expected values of the independent variables are substituted in the equation, the quantum of sales will then be forecasted.

The regression equation can also be written in a multiplicative form as given below:

Quantum of Sales =(Price) +(Advertising) + (Price of the rival products) + (Personal disposable income)”+u

In the above case, the exponent of each variable indicates the elasticities of the corresponding variable. Stating the independent variables in terms of notation, the equation form is

QS=po8. 40:42. R0.83. Y,06% 40 Then we can say that 1 per cent increase in price leads to 0.8 per cent change in quantum of sales and so on.

If we take logarithmic form of the multiple equation, we can write the equation in an additive form as follows:

log QS = a log P + blog A + c log R+ d log Y,+log u In the above equation, the coefficients a,b,c, and d represent the elasticities of variables P, A, R and Y respectively.

The co-efficient in the logarithmic regression equation are very useful in policy decision making by the management.

The various statistical methods used for demand forecasting have the following merits and demerits:

Merits

(1) As these methods are based on mathematics, they provide accurate information.

(2) These methods are less expensive because no field work is required.

(3) The reseults obtained through these methods are objective and free from personal bias.

Demerits

(1) Being mathematical, the calculations under these methods are complex.

(2) A specialised knowledge is required for making use of these methods.

(3) Some statistical methods like graphs, time series etc. present only the trends but do not measure these trends.

(iv) Smoothing Methods

There are two types of smoothing methods : simple smoothing or moving average and weighted or exponential smoothing.

Moving Average. In moving average, a simple average of the specific number of observations is taken. They are updated as new information is received, A manager uses the most recent observations and drops the oldest observation in the earlier calculation. He then calculates an average which he uses as the forecast for the next period.

The moving average method is simple and easy to use and understand. But it has two limitations:

(a) A lot of data are required and used from one forecast period to other forecast periods.

(b) All data are weighted equally. This gives wrong forecasts because past data cannot be used for the forecasting of demand in future.

Exponential Smoothing. Exponential smoothing is a method for short-run forecasting which uses a weighted average of past data as the basis for demand forecasting. Since the recent observations are more relevant than the older observations, more weights are given to them and less weights to older observations. The weights are given in a descending order as one moves from the current observations to the past observations. As economic environment changes very fast, the past data cannot be used for forecasting future demand. The exponential smoothing method tries to remove this defect by forecasting demand on the basis of shortrun fluctuations of demand in the past. Exponential smoothing is a time series method of forecasting that gives more weight to current observations. The formula for exponential smoothing is

Demand Forecasting Study Material

UTILITY OR IMPORTANCE OF DEMAND FORECASTING

Demand Forecasting has the following utility:

(1) Useful in Planning. In modern times, most of firms prepare their business plans on the basis of forecasts of demand for their products. In this way, it proves to be very helpful in decision-making and formulation of policies.

(2) Mass Production. Demand forecasting provides a prior idea about the fact that which goods are going to be in much demand in future. It leads to mass production of such goods. Mass production is useful for both the producers and the consumers. On the one hand, producers enjoy economies of large scale production and on the other, consumers get goods at reasonable prices.

(3) Research and Development. Demand forecasting makes the business firms to undertake research and development activities. Such activities are undertaken on the basis of predictions of demand in future. As a result of research and development new products are made available in the market.

(4) Optimal Utilisation of Resources. If production is done in a haphazard way and environment of uncertainty, then a number of resources are wasted. But this type of wastage is minimised due to demand forecasting because there production of such goods which are demanded in casting because the resources are employed in the helpful in optimal utilisation of resources. which are demanded in the market. Thus, demand forecasting is

(5) Development of Economy. Demand forecasting of Economy. Demand forecasting is useful in the development of economy. In fact, demand forecasting creates an environment of certa world which attracts more investments. As a result creates an environment of certainty in the business acts more investments. As a result of increase in investment, the economy develops at a faster rate.

(6) Price Stability. The major cause of fluctuations in Dility. The major cause of fluctuations in prices is under or over supply of goods. This situation takes place due to imperfect knowledge o on takes place due to imperfect knowledge of demand conditions. But as a result of demand forecasting, demand for various goods can be estimated more accuracy ply can be made accordingly. In this way, stability in the price level can be maintained.

(7) Increase in Knowledge. Demand forecasting is also helpful in increasing the knowledge of the marketing manager. With the help of demand forecasting, he can know about the various determinants of market demand. On their basis, he can influence the market demand.

(8) Helpful to Government. Demand forecasting helps the government in a number of ways. Most of the government’s revenue and expenditure policies are formulated on the basis of demand forecasting. For example, if the government comes to know that a particular product is going to be in much demand, then it may earn more revenue by imposing tax on its production and sale.

Demand Forecasting Study Material

Limitations of Demand forecasting

Although demand forecasting is very useful for business management yet it has a number of limitations.

(1) Need of Past Data. Most of the methods adopted for the forecasting of demand rely upon the availability of past data regarding sales. These data are necessary for determining the past trends. But in most cases, these data are not available and even if available, they are not reliable.

(2) Uncertainty of Future. The modern business world is to a number of changes. As a result, the future has become highly uncertain. So the forecasting made on the basis of past and present trends may prove to be wrong in future. Thus, demand forecasting loses its importance due to the uncertainty of future.

(3) Expensive. Forecasting of demand requires a lot of expenses to be made. As a result, demand forecasting proves to be expensive in terms of both time and money. As small are generally not able to incur these expenses, they are deprived of the benefits of demand forecasting.

(4) Need of Forecasting Experts. As demand forecasting is a crucial task. so it reanimac a special knowledge but generally, the experts having this knowledge are not avai result, it becomes difficult to have an accurate forecasting of demand. (5) Psychological factors. The demand for any product is also affected by a numbe

tors These factors do not remain the same in every situation. The demand psychological factors. These factors do not remain the

noes if any change occurs in these factors. So, it is not possible to tell for a product also changes if any change occurs in these factors. Sa certainly what will be the future trend in demand.

Criteria of a Good Forecasting Method

There are thus, a good many ways to make a guess about future sales. They show contrast in cost, flexibility and adequate skills and sophistication. Therefore, there is a problem of choosing the best method for a particular demand situation. There are certain economic criteria of broader applicability. They are: (1) Accuracy, (ii) Plausibility, (iii) Durability, (iv) Flexibility, (v) Availability, (vi) Economy. (vil) Simplicity and (viii) Consistency.

(1) Accuracy. The forecast obtained must be accurate. How is an accurate forecast possible? To obtain an accurate forecast, it is essential to check the accuracy of past Torecasts against present performance and of persent forecasts against future performance. Accuracy cannot be tested by precise measurement but buy judgement.

Demand Forecasting Study Material

(ii) Plausibility. The executive should have good understanding of the technique chosen and they should have confidence in the techniques used. Understanding is also needed for a proper interpretation of results. Plausibility requirements can often improve the accuracy of results.

(iii) Durability. Unfortuately, a demand function fitted to past experience may back cost very greatly and still fall apart in a short time as a forecaster. The durability of the forecasting power of a demand function depends partly on the reasonableness and simplicity of functions fitted, but primarily on the stability of the understanding relationships measured in the past. Of course, the importance of durability determines the allowable cost of the forecast.

(iv) Flexibility. Flexibility can be viewed as an alternative to generality. A long lasting function could be set up in terms of basic natural forces and human motives. Even though fundamental, it would nevertheless be hard to measure and thus not very useful. A set of variables whose co-efficient could be adjusted from time to time to meet changing conditions in more practical way to maintain intact the routine procedure of forecasting.

(v) Availability. Immediate availability of data is a vital requirement and the search for reasonable approximations to relevance in late data is a constant strain on the forecasters patience. The techniques employed should be able to produce meaningful results quickly. Delay in result will adversely affect the managerial decisions.

(vi) Economy. Cost is a primary consideration which should be weighted against the importance of the forecasts to the business operations. A question may arise: How much money and managerial effort should be allocated to obtain a high level of forecasting accuracy ? The criterion here is the economic consideration.

(vii) Simplicity. Statistical and econometric models are certainly useful but they are intolerably complex. To those executives who have a fear of mathematics, these methods would appear to be Latin or Greek. The procedure should, therefore, be simple and easy so that the management may appreciate and understand why it has been adopted by the forecaster.

(viii) Consistency. The forecaster has to deal with various components which are independent. If he does not make an adjustment in one component to bring it in line with forecast of another, he would achieve a whole that would appear consistent.

Conclusion

In fine, the ideal forecasting method is one that yields returns over cost with accuracy, Reasonable, can be formalised for reasonably long periods, can meet new circumstances seems reasonable, can be can give up-to-date results. The method of forecasting is not the same for all adeptly and can give up-to-date result products.

There is no unique method for forecasting the sale of any commodity. The forecaster may try one or the other method depending upon his objective, data availability, the urgency with which forecasts are needed, resources he intends to devote to this work and the type of commodity whose demand he wants to forecast.

Demand Forecasting Study Material

EXERCISES

1 Define demand forecasting.

2. Explain the objectives of demand forecasting.

3. Discuss the relevance of demand forecasting.

4. Explain the different criteria of a good forecasting method.

5. Classify and explain the concept of demand forecasting for different types of products.

6. Explain Joel Dean’s approaches to forecasting demand for new products.

7. Discuss in detail the various time-series demand forecasting techniques.

8. Explain the “Barometric method of demand forecasting”.

9. Explain the expert opinion method of demand forecasting.

10. Explain the consumer survey method of demand forecasting.

11. Explain the leading indicator method of demand forecasting.

12. Explain the exponential methods of demand forecasting.

Demand Forecasting Study Material