Concept of Oligopoly and Kinked Demand Curve Model

Price rigidity under oligopoly in terms of kinked demand curve

Price rigidity in the oligopoly market is best explained by the kinked demand curve. The oligopoly is a reduced form of monopolistic competition. The term oligopoly has a Greek base and means few sellers, oligopoly as such, refers to markets with small number of large firms, each selling either differentiated or homogeneous product.

A few sellers imply a number so small or a few market share of each firm in so large that it can influence the market price. It also implies that each seller commands a sizeable proportion of the total market supply. The products traded by the oligopolists may be differentiated or homogeneous. Accordingly, the oligopoly market may be a heterogeneous oligopoly or a homogeneous (or pure) oligopoly. It seems the following features:

• Sellers are few in number.
• Any of them is of such a size that can increase and decrease in his output will appreciably affect the market price. In fact, the size of each seller’s output in relation to the total supply is the test.
• Each seller knows his competitors individually in each market.

Each oligopolist realizes that any change in his price and advertising policy may lead rivals to change their policies. Hence, an individual firm must consider the possible reaction of the other firms to its own policies. The smaller the number of firms, the more interdependent are their policies. The reactions of rivals will generally be immediate and strong, and tendencies to close collaboration in price determination are appeared.

It is the fewness of sellers that introduces interactions into the price and output decision problem under oligopoly a special form of oligopoly in duopoly, under which only two firms produce a particular product.

Kinked Demand Curve Model

The kinked demand curve model developed by Paul M. Sweezy, has features common to most of oligopoly pricing models. The kinked demand curve analysis does not deal with price and output determination. It seeks to establish that once a price-quantity combination is determined, an oligopoly firm will not find it profitable to change its price in response to a moderate change in cost of production. An oligopoly form believes that if it reduces the price of its product, rival firms would follow and neutralize the expected gain from price reduction. But, if it raises its price, rival firms would either maintain their prices or may even cut their price down. In either case, the price rising firm stands to lose, at least a part of its share in the market. This behavioral assumption is made by all the firms in respect of others. The oligopoly firms would therefore, find it more desirable to maintain their price and output at the existing level.

There are three possible ways in which rival firms may react:

1. The rival firms follow the price changes, both cut and hike; 
2. The rival firms do not follow the price changes;
3. Rival firms do not react to price-hikes but they do follow the price-cuts.

Kinked-demand curve is a demand curve with two distinct segments with different elasticities that join to form a kink. The primary use of the kinked-demand curve is to explain price rigidity in oligopoly. The two segments are:

(i) a relatively more elastic segment for price increase and

(ii) a relatively less elastic segment for price decreases.

The relative elasticities of these two segments are directly based on the interdependent decision-making of oligopolistic firms. Interdependence is the guiding behavioral principle of oligopoly firms in which the decision by one firm is both affected by the decisions of other firms and in turn affects the decisions of other firms. Such interdependence is characteristic of oligopoly firms that practice competition among the few. Interdependence is indicated by the kinked-demand curve, game theory, collusion, and mergers. Merger is the consolidation of two separately-owned businesses under single ownership. This can be accomplished through a mutual, “friendly” agreement by both parties, or through a “Hostile takeover,” in which one business gets ownership without cooperation from the other. Mergers fall into one of three classes –

(i) horizontal – two competing firms in the same industry that sell the same products,

(ii) vertical – two firms in different stages of the production of one good, such that the output of one business is the input of the other, and

(iii) conglomerate – two firms that are in totally, completely separated industries.

According to the kinked demand curve model, firm determines the price and output by intersection of MC and MR. But intersecting point lies on the discontinuous segment of MR. In this model, the demand curve faced by oligopolists has kink at the prevailing price. It means, the upper section of the kinked demand curve has higher price elasticity than lower part. Because, each oligopolist believes that if he reduces his price below the prevailing level, his competitors will follow him, and will accordingly lower their prices. So that an oligopolist firm which lowers the price could not increase its share of the market. Whereas if he raises the price above the prevailing level, his competitors will not follow him and they do not increase their price. So, an oligopolist will lose a considerable part of his customers. Because of this, an oligopolist tends to keep prices constant even if the cost and demand conditions are changed. This model is illustrated in figure.

In the figure, dED is the demand curve faced by an oligopolistic firm and has a kink at point E which represents the prevailing market price. Above this point, demand curve dE is more elastic and below this point, it is less elastic. dABMR is the marginal revenue curve of the firm. MR has two segments; the upper segment dA corresponds to the upper part of the demand curve dE. The lower segment BMR corresponds to lower part of kinked demand curve ED. The kink at point E on the demand curve results in discontinuity ‘AB’ in the MR curve. Oligopolist firm can reach equilibrium position and determine the selling price, and quantity and maximize the profit by equating MC with MR. In the given figure, SMC cuts the discontinued segment of MR at point ‘C’ and the firm determines price QE and selling quantity OQ. This QE level of price will not be changed by firm. If SMC curve rises to SMC1 because of increasing costs and SMC curve goes down to SMC2 because of decreasing cost, this will not affect the pricing decision of the oligopolist. These two curves SMC1 and SMC2 allow the firm to fix the price QE and quantity OQ.

We may conclude that an oligopolist faced with a kinked demand curve will be extremely unwilling to change his price. For a fall in his price will cause no large increase in his sales whereas a price increases will cause a substantial decline in his sales. Thus, neither a price increase nor a price reduction will be an attractive proposition for the oligopolist. During inflationary periods, however, oligopoly firms often follow one another’s price increase, to this extent, the kinked demand curve analysis can be said not to hold true.

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Demand Forecasting for the New Products

If a firm is planning to market a new product and does not have past data on which to rely to project sales, it will have to find out and use other means in an effort to predict probable sales.

The special problem of demand forecasting for new products is that since each new product is to varying degrees, different from existing products, there are no directly relevant data available from past sales on which a forecast may be based. The more the new product is likely to be, the greater the problem. In addition, since a considerable amount of money has to be invested in developing and marketing a new product, we have to consider its sales over an extended period to be more precise over the whole of its expected commercial lifetime. It means that we need to estimate:

(i) the number of years for which the product will be sold and

(ii) the level of sales in each of those years.

There are, of course, a number of techniques available for the purpose. However, the choice of a particular technique depends on the circumstances faced by the forecaster.

1. Market Research

The forecaster can carry out market research in various forms. The marketing people may be asked to make different inquiries from the prospective ultimate buyers of the product. On the basis of such inquiries, they may try to discover whether and in what quantities customers are likely to buy the product, using various assumptions as to how, it would be marketed and at what price it would be sold.

This approach is no doubt, direct and practical. It can be applied for forecasting the demand of industrial goods because the buyers’ criteria are more precisely formulated and more stable.

However, this method is highly impracticable in the case of new consumer goods. It is so because the consumer is, for various reasons, unlikely to be able to assess reliably his own buying behavior in the hypothetical situation presented to him.

2. Test Marketing or Sales Experience Approach

It takes the form of a trial run of the product in a part of the intended market in conditions as closely similar to those that are expected to prevail if and when the product is ultimately marketed as are possible.

Frequently, a new product such as soap, toothpaste, food-stuff, etc. is put through test marketing, i.e. tested in a sample market in a bid to determine the probable demand for the product. It may be tasted at a particular price or at several different prices by seeing one or more test markets. From the test market results, a projection can be made regarding regional or national sales. The sales experience usually will give green or a red signal for the product.

This approach has been used with success for a wide range of products. However, the success of this method depends on the availability of a representative product for test marketing. This method is not applicable to the earlier stages of product development.

3. Opinion Sampling Approach

This approach is to bring together in a systematic way the informal judgments of executives, sales people, retailers and perhaps friendly customers as to the product’s probable performance.

Through use of a mail questionnaire, or by making a door survey, one can get some indication of the acceptance of new product. In this case, sampling of the potential customers may be polled directly, or there may be a poll of sources that have a feel of the actual buyer, such as retailers, wholesalers, jobbers and manufacturer representatives. This pool is likely to give some idea of market acceptance and price range.

4. Evolutionary Approach

If the product is supposed to be an improvement or has evolved out of an existing product, it can be assumed that the new product may have the same type of experience as that of an existing product. Color television sets, evolved from black and white sets, the jet engine from the propeller engine in aircraft, are the examples. In this manner, one can easily imagine what the demand for work processors will be in the office equipment industry if they became a nearly completely replacement for the electric typewriter.

5. Substitute Approach

If a new product seems to be a close substitute for a well established product, one can estimate what share of the market the new product may get by replacing some of the existing products. A new textbook may be a substitute for one of the many existing textbooks being used. After knowing the total sales, the producer of the new book may be in a position to estimate.

6. Sales Growth Approach

In the absence of the information about a new product, one may reasonably assume that the sales of the new product will simply displace those of an existing product and continue along the growth of distinct established by it. This is likely to be so when the new product is distinct, improvement on the existing one but not so radically different that buyers have learn to accept it. In the case of industrial investment, we assume that now there need to make any substantial investment in hardware or restraining. Alternatively, for consumer products, no changes in domestic habits or social attitudes and values are required.

No doubt, demand forecasting represents one of the most challenging aspects of business analysis. Continuous research has been going on in this area. The techniques of forecasting are being retired and have been improved enormously in recent years by the advent of computers. However, the use of sophisticated techniques is not enough. It is essential to exercise judgment and experience while carrying out any forecasting exercise. Techniques can only complement judgment and experience.

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Merits and limitations of market studies and experimentation as a method of demand forecasting

An alternative technique for obtaining useful information about a product’s demand function involves market experiments. The firm locates one or more markets with specific characteristics, and then varies prices, packaging, advertising, and other controllable variables in the demand function, with the variations occurring either over time or between markets.

One market experiment technique entails examining consumer behavior is actual markets. The firm may also be able to sue census or survey data to determine how such demographic characteristics as income, family size, educational level and ethnic background affect demand.

Market experimentation procedure utilizes a controlled laboratory experiment where in consumers are given funds with which to shop in a simulated store. By varying prices, product packaging, displays, and other factors, the experimenters can often learn a great deal about consumer behavior. The laboratory experiment, while providing similar information as field experiments has advantages / merits because of lower cost and greater control of extraneous factors. Merits and limitations of market experiments can be shown as below:

Merits of Market Experiments

1. Market experiments are based on actual consumer behavior and not on merely their intentions to buy the commodity. 
2. They provide more accurate returns than those of consumer survey because consumers are asked to make actual decisions regarding their purchase.

Limitations of Market Experiments

1. Market experiments are costly and much time consuming. 
2. If the price rises, the consumers may switch over to the products of the rival firms. If the price reduced to the original level, it may be difficult to regain the lost customers.
3. It is also difficult to select an area, which accurately represents the potential market.
4. Firm cannot control all the factors (i.e. bad weather, economic conditions, occupation situations etc.) that influence demand for a product.
5. The changes in price or adverting to know consumer’s response may go unnoticed by them in such a short period. 
6. The selected consumers may not respond accurately when they know they are a part of an experiment being conducted and their behavior is being recorded.

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Expert’s opinion survey differ from survey of Sales forces

The different between expert’s opinion and survey of sales forces can be explained as follows:

- A variant of the opinion poll and survey method is said to be expert’s opinion survey. The participants are supplied the responses to previous questions from others in the group by a coordinator or leader of same sort. The leader provides each expert with the responses of the others including their reasons.

In survey of sales forces, information is collected from firm’s sales-representatives or salesmen about their estimates of sales of its product in future.

- Outside experts such as consultant firms, investment analysts, who are professionally trained for the purpose of the forecasting demand, may be asked to estimate demand.

In survey of sales forces, information regarding likely sales is obtained from those who are closest to the market and have an initiate insight to the market.

- Each expert is told about the prediction of other experts and asked in the light of the other’s views whether he/she should revise his prediction about future demand. The experts are again shown each other’s revised forecasts and asked to reconsider their forecasts future till a consensus is reached or until referring the opinion of others.

In survey of sales forces, the responses of the various salesmen or representatives are then aggregated to arrive at total demand forecast for the product.

- Predictions of the demand by experts are not always based on any hard data but they can provide useful information about demand for the product.

Sales forecasts provided by sales representatives are biased either upward or downward. However, as a result of experience, some corrective factors are applied to sales estimates furnished by salesmen.

The merits and limitations of expert’s opinion and survey of sales forces can be explained as below:

Merits of Expert’s Opinion

1. It facilitates the maintenance of anonymity of the respondent’s identity throughout the course. This enables the respondent to be candid and forth right in his view. 
2. It renders if possible to pose the problem to the experts at one time and have their response.

Limitations of Expert’s Opinion

1. Predictions for demand by experts should always be based on some hard data, but they are not based on any hard data. 
2. It is very costly or otherwise not possible to conduct complete enumeration. Outside experts may charge huge fees for giving their opinion.
3. The experts who consider themselves experts may not like to be influenced by the predictions of others on a panel of experts. As a result, there may not be any revision in subsequent rounds of seeking their opinion about other’s forecasts.

Merits of Survey of Sales Forces

1. It is easy and cheap to do. 
2. It has further advantage of increasing the motivation of salesmen to achieve the self-selected target for which they had made a forecast.

Limitations of Survey of Sales Forces

1. Sales representatives may not provide correct forecast. Some sales representatives would like to make too optimistic sales forecast. 
2. Some salesmen would like to make too pessimistic sales forecast so that they get higher payments for exceeding the targets based on their sales predictions.
3. Sales forecasts provided by sales representatives are biased forecast either upward or downward.

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Non-statistical methods used to forecast the demand for satellite TV signal decoder

Demand forecasting means predicting demand for a product. The information regarding future demand is essential for planning and scheduling production, purchase of raw materials, acquisition of finance and advertising. In order of accuracy as well as good forecasting, we can use various methods such as survey and statistical methods.

Generally in short term forecasting, we can use survey method and statistical method can be used for long-term forecasting.

As mention above in title, satellite TV signal decoder is a short-term program. To know the peoples intention about the program, we can use survey method. Under survey method, there are various methods, such as:

1. Consumer Survey Method: Direct interview method, complete enumeration method, sample survey method.
2. Opinion Poll Method: Experts opinion method, Delphi method, Market Studies and experiments. In case of forecasting demand for satellite TV signal decoder, we can use sample survey method.

Under this method, only a few potential consumers and users are selected from the relevant market through a sampling method are surveyed. This method of survey may be direct interview or method questionnaire to these sample consumers. On the basis of the information obtained that probable demand may be estimated through the following formula,

DP = HR / HS (H. AD)

Where, DP = Probable demand forecast

H = Census number of households from the relevant market

HS = Number of households for the product

AD = Average expected consumption by the reporting households

This method is simpler, less costly and less time consuming than the comprehensive survey method. The households who plan their future purchases generally use this method to estimate short-term demand from business firms, government departments and agencies, and so on. Business firms, government departments, and such other organization budget their expenditure at least for one year in advance. It is therefore possible for them to supply a fairly reliable estimate of their future purchases. Even the households making annual or periodic budget of their expenditure can provide reliable information about their purchases.

Sample survey method is widely used to forecast demand. However, this method has some limitations similar to those of complete enumeration or exhaustive survey method. The forecast therefore should not attribute reliability to the forecast more than warranted. Besides, sample survey method can be used to verify the demand forecast made by using quantitative or statistical methods. Some authors suggest that this method for forecasting rather than to replace it, this method can be gainfully used where market area is localized.

Sample survey method can be of greater use in forecasting where quantification of variables (e.g. feelings, opinion, expectations etc.) is not possible and where consumer’s behavior is subject to frequent changes. Satellite TV signal decoder forecasting is also a short and non-durable program. In such a case, sample survey method is only the appropriate non-statistical method.

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Different Methods of Demand Forecasting | Survey of Buyer's Intentions | Delphi Method | Time Series Analysis and Trend Projection | Market Studies and Experimentation | Regression Analysis

Demand forecasting is not an easy task. Two dangers must be guarded against. First, too much emphasis should not be placed on mathematical or statistical techniques of forecasting. Though statistical techniques are essential in clarifying relationships and providing techniques of analysis, they are not substitutes for judgment. The danger is that we may go to the opposite extreme and regard forecasting as something to be left to the judgment of the so-called experts.

Related Topic: Demand Forecasting

Commonly for pure guessing, we can use following methods:

1. Survey of Buyer’s Intentions

The most direct method of estimating demand in the short run is to ask customers what they are planning to buy for the forthcoming time period generally a year. This method is also known as public opinion surveys, the most useful when bulk of the sales is made to industrial producers. In this method, the burden of forecasting is shifted to the consumers. But it would not be wise to depend wholly on the buyers’ estimates and they should be used cautiously in the light of the sellers’ own judgment.

A number of biases may creep into the surveys. If shortages are expected, customers may tend to exaggerate their requirements. The customers may know what their requirements are but they may misuse or mislead or may be uncertain about the quantity they intend to purchase from a particular firm. This method is not very useful in the case of household customers for several reasons, viz. irregularity in customers’ buying intentions, their inability to foresee their choice when faced with multiple alternatives, and the possibility that the buyers’ plans may not be real only wishful thinking.

2. Delphi Method

A variant of the opinion poll and survey method is Delphi method. It consists of an attempt to arrive at a consensus in an uncertain area by questioning a group of experts repeatedly until the responses appear to coverage along a single line or the issues causing disagreements are clearly defined. The participants are supplied the responses to previous questions from others in the group by a coordinator or leader of same sort. The leader provides each expert with the responses of the others including their reason. Others given the opportunity to react to the information or considerations advance each expert but interchange is anonymous so as to avoid or reduce halo effect and ego involvement associated with publicly expressed opinions.

It has some exclusive advantages such as: (a) It facilitates the maintenance of anonymity of the respondent’s identity throughout the course. This enables the respondent to be candid and forth right in his view. (b) Delphi renders if possible to pose the problem to the experts at one time and have their response.

Though it posses wide knowledge and experience of the subject and have an aptitude and earnest disposition towards the participants.

3. Time Series Analysis and Trend Projection

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 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 such as method of least squares.

The analyst chooses a plausible algebraic relation between sales and the independent variable such as time. The trend line is then projected into the future by extrapolation. It is popular method because it is simple and inexpensive and partly because time series data often exhibit a persistent so long as the time shows a persistent tendency to move in the some direction.

4. Market Studies and Experimentation

An alternative technique for obtaining useful information about a product’s demand function involves market experiments. The firm locates one or more markets with specific characteristics, and then varies prices, packaging, advertising, and other controllable variables in the demand function with the variations occurring either over time or between markets. One market experiment technique entails examining consumer behavior is actual markets. The firm may also be able to use census or survey data to determine how such demographic characteristics as income, family size, educational level and ethnic background affect demand.

Market experiments have many serious shortcoming, they are expensive and are therefore usually undertaken on a scale too small to allow high levels of confidence in the results. Market experiments are seldom run for sufficiently long periods to indicate the long-run effects of various price, advertising or packaging strategies. The experimenter is thus forced to examine short run data and attempt to extend it to a longer period.

Various difficulties related with the uncontrolled parts of the market experiment also reduce its value as an estimating tool. A change in economic condition during the experiment is likely to invalidate the results, especially if the experiment includes the use of several separated markets, a local strike or layoffs by a major employer in one of the market areas. There is also the danger that customers lost during the experiment as a result of price manipulations cannot be regained when the experiment ends.

Market experimentation procedure utilizes a controlled laboratory experiment where in consumers are given funds with which to shop in a simulated store. By varying prices, product packaging, displays, and other factors, the experimenter can often learn a great deal about consumer behavior. The laboratory experiment, while providing similar information as field experiments, has an advantage because of lower cost and greater control of extraneous factors.

5. Regression Analysis

Regression analysis is to specify the variables that are expected to influence demand. Product demand, measured in physical units, is the dependent variable. The list of independent variables, or those which influence demand, always includes the price of the product and simply includes such factors as the prices of complementary and competitive products, advertising expenditures, consumer income and population of the consuming group. Demand function for expensive durable goods such as the houses and automobiles, include interest rates and other credit terms, those for beverages, or air conditioners include weather conditions. Demand determinants for capital goods, such as industrial machinery, include corporate profitability output to capacity ratios and wage rate trends.

Regression analysis is to obtain accurate estimates of the variables, measures of price, credit terms, output, capacity ratios, advertising expenditures, incomes and so on. Obtaining estimates of these variables is not always easy especially if the study involves data for past years. Some key variables, such as consumer attitudes toward quality and their expectations about future business conditions – which are very important in demand functions for many consumer goods, may have to be obtained by survey techniques, which introduces on element of subjectivity into the data or by market or laboratory experiments, which may produce biased data.

6. Barometric Method

A barometric or indicator, forecasting is based on the observation that there are lagged relationships among many economic time series. Changes in some series appear consistently to follow changes in one or more other series. The theoretical basis for some of these lags is obvious. For example, building permits issued precede housing starts and orders for plant and equipment lead production in durable goods industries. The reason is that each of these indicators refers to plans or commitment for the activity that follows. Other barometers are not also directly related to the economic variables they forecast. An index of common stock prices, for example, is a good leading indicator of general business activity. Although the causal relationship here is not readily apparent, stock prices reflect an aggregation of profit expectation by business managers and others and hence composite expectation of the level of business activity.

Theoretically, barometric forecasting requires the isolation of an economic time series that consistently leads the series being forecast. This relationship established; forecasting directional changes in the lagged series is simply a matter of keeping track of movement in the leading indicator. Several problems prevent such as easy solution to the forecasting problem.

• Few series always correctly indicate changes in another economic variable. Even the best leading indicators of general business conditions forecast with only to go present accuracy.
• Second, even the indicators that have good records of forecasting directional changes generally fail to lead by a consistent period. If a series is to be an adequate barometer, it not only must indicate directional changes but also, additionally, must provide a constant lead-time. Few series meet the test of lead-time consistency.
• Finally, barometric forecasting refers that even when leading indicators proved to be satisfactory from the stand point of consistently indicating directional change with a stable lead time, they provide very little information about the magnitude of change in the forecast variable.

Mainly two techniques that have been used with some success to overcome at least partially the difficulties in barometric forecasting are composite indexes and diffusion indexes. Composites indexes are weighted averages of several leading indicators. The combining of individual series into a composite index results in a series with less random fluctuation or noise. The smoother composite series has a lower tendency to produce false signals of change in the predicted variable.

Diffusion indexes are similar to composite indexes. Instead of combining a number of leading indicators into a single standardized index, the methodology consists of noting the percentage of the total number of leading indicators that are rising at given point in time.

Even with the use of composite and diffusion indexes, the barometric forecasting technique is a relatively poor tool for estimating the magnitude of change in an economic variable. Thus, although it represents a significant improvement over simple extrapolation techniques for short term forecasting.

7. Input-output Analysis

A forecasting method known as input-output analysis provides the most complete examination of all the complex interrelationships within an economic system. It shows how an increase or decrease in the demand for one’s industry output will affect other industries. An increase in the demand for trucks will lead to increased production of plastic, steel, tires, glass and other materials. The increase in the demand for these materials will have secondary effects. The increase in the demand for glass will lead to a further increase in the demand for steel, as well as for trucks used in the manufacture of glass, steel and so on. Input-output analysis traces through all these inter-industry relationships to provide information about the total input on all industries of the original increase in the demand for trucks.

It is based on set of tables that describe the interrelationships among all the component and parts of the economy. Input output analysis has a variety of uses ranging from forecasting the sales of an individual firm to probing the implications of national economic programs and policies. The major contribution of input-output analysis it that it facilitates measurement of the effects on all industrial sectors that changes in activity in any one sector.

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Process of demand forecasting by the time series analysis

A firm, which has been in existence for some time will have accumulated/ collected considerable data on sales pertaining to different time periods. Such data when arranged chronologically yield ‘time series’. 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 well known method of analysis of time series is to project the trend of the time series. The trend line can be fitted through a series either visually or by means of statistical techniques such as least square method. The analyst chooses a plausible algebraic relation between sales and the independent variable, time.

The trend line is then projected into the future by extrapolation. The basic assumption of the trend method is that the past rate of change of the variable under study will continue in the future. 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, the trend projection breaks down. Nevertheless, a forecaster could normally expect to be right in most forecasts especially if the turning points are few and spaced at long intervals from each other.

The real challenge of forecasting is in the prediction of turning points rather than in the projection of trends. It is when turning points occur that management will have to alter and revise its sales and production strategies most drastically.

There are primarily four sets of factors, which are responsible for the characterization of time series by fluctuations and turning points in a time series, trend, seasonal variations, cyclical fluctuations, and irregular or random forces. The problem in forecasting is separate and measures each of these four factors.

The fundamental approach is to treat the original time series data (O or observed data) as composed of four parts: a secular trend (T), a seasonal factor (S), a cyclical element (C) and on irregular movement (I). It is generally assumed that these elements are bound together in a multiplicative relationship presented by the equation O = TSCI.

The usual practice is to first compute the trend from the original data. The trend values are then eliminated from observed data (TSCI/T). The next step is to calculate the seasonal index, which is used to remove the seasonal effect (SCI/S). A cycle is then fitted to the remainder, which also contains the irregular effect.

The foregoing approach of decomposition of time series data is a useful analytical device for understanding the nature of business fluctuations. The trend and seasonal factors can be forecast, but the prediction of cycle is hazardous for the simple reason that there is no regularity in the cyclical behavior.

Though, there are two assumptions underlying this approach:

• The analysis of movements would be in the order of trend, seasonal variation and cyclical charges, and
• The effects of each component are independent of each other.

For the use of economic indicators, the following steps have to be taken:

• See if a relationship exists between the demand for a product and certain economic indicators.
• Establish the relationship through the method of least squares and derive the regression equation. Assuming the relationship to be linear, the equation will be of the form y = a + bx. There can be curve-linear relationships as well.
• Once regression equation is derived, the value of Y i.e. demands, can be estimated for any given value of X.
• Past relationships may not recur, hence the need for value judgment as well. New factors may also have to be taken into consideration.

Merits and Limitations of Time Series Analysis

Merits

1. The trend method is based on least square principle of demand forecasting which is quite popular due to simplicity.
2. It provides good result, which is particularly suitable for long run.
3. It is very much simple in the sense that it doesn’t require the knowledge of economic theory and market structure.

Limitations:

1. This method is based on the assumption that future events will follow the same path, which may not be true for every time.
2. It is not suitable for short-term demand forecasting. This method cannot usually explain the turning points of the business cycle.

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• Demand Forecasting

• Steps of Demand Forecasting

• Different Methods of Demand Forecasting | Survey of Buyer's Intentions | Delphi Method | Time Series Analysis and Trend Projection | Market Studies and Experimentation | Regression Analysis

• Demand Forecasting for the New Products

• Purpose of Forecasting Demand in a Business Firm

• Merits and limitations of market studies and experimentation as a method of demand forecasting

Steps of Demand Forecasting

The objective of demand forecasting is achieved only when forecast is made systematically and scientifically and when it is fairly reliable. The following steps are generally taken to make systematic demand forecasting:

Related Topic: Demand Forecasting

1. Specifying the objective

The objective or the purpose of demand forecasting must be clearly specified. The objective may be specified in terms of;

(a) short-term or long-term demand,

(b) the overall demand for a product or for a firm’s own product,

(c) the whole or only a segment of the market for its product,

(d) firm’s market share.

The objective of demand forecasting must be determined before the process of forecast is started. This has to be the first step.

2. Determining the time perspective

Depending on the firm’s objective, demand may be forecast for a short period, that is, for the next 2 to 3 years, or for a long period. In demand forecasting for a short period, 2 to 3 years, many of the demand determinants can be taken to remain constant or not to change significantly. In the long-run, however, demand determinants may change significantly. Therefore, the time perspective of demand forecasting must be specified.

3. Making choice of method for demand forecasting

There are a number of methods available for demand forecasting which we shall introduce in another section of the analysis. However, all methods are not suitable for all kinds of demand forecasting because the purpose of forecasting, data requirement and availability of data for the use of a method, and time frame of forecasting differ from method to method. Therefore, the demand forecaster has to choose a fitting method keeping in view the purpose and requirements. The choice of a forecasting method is generally based on the purpose, experience and skill/ knowledge of the forecaster. It depends also to a great extent on the availability of required data. The choice of a suitable method saves not only time and cost but also ensures the reliability of forecast to a great extent.

4. Collection of data and data adjustment

Once method of demand forecasting is decided on, the next step is to collect the required data, primary or secondary or both. The required data is often not available in the required type/ form. In that case, data needs to be adjusted – even messaged, if necessary – with the purpose of building data series consistent with data requirement. Sometimes the required data has to be generated from the secondary sources.

5. Estimation and interpretation of results

As mentioned earlier, the availability of data often determines the method, and also the potential/feasible equation to be used for demand forecasting. Once required data is collected and forecasting method is finalized, the final step in demand forecasting is to make the estimate of demand for the predetermined years or the period. Where estimates appear in the form of an equation, the result must be interpreted and presented in a usable form.

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• Demand Forecasting

• Non-statistical methods used to forecast the demand for satellite TV signal decoder

• Different Methods of Demand Forecasting | Survey of Buyer's Intentions | Delphi Method | Time Series Analysis and Trend Projection | Market Studies and Experimentation | Regression Analysis

• Process of demand forecasting by the time series analysis

• Demand Forecasting for the New Products

• Purpose of Forecasting Demand in a Business Firm

• Uses of Price Elasticity of Demand in Managerial Decision-making

Purpose of Demand Forecasting in a Business Firm

An accurate demand forecasting is essential for a firm to enable it to product the required quantities at the right time and arrange well in advance for the various factors of production (raw materials, equipment, machine accessories, labor, building, etc.). It helps a firm to assess the probable demand for its products and plan its production accordingly.

In fact, forecasting is an important aid in effective and efficient planning. It is also helpful in better planning and allocation of national resources.

The purpose of demand forecasting refers the estimation of volume of production; price of the commodity either short-run as well as long term forecasting.

Under the short-run, purposes of forecasting includes following headings:

 

1. Purpose of Short-Term Forecasting

• Appropriate production scheduling so as to avoid the problem of over-production and the problem of short supply. For this purpose, production schedules have to be geared expected sales.
• It helps the firm in reducing price policy so as to avoid an increase when the market conditions are expected to be weak and a reduction when the market is going to be strong.
• Determining appropriate price policy so as to avoid an increase when the market conditions are expected to be weak and a reduction when the market is going to be strong.
• Setting sales targets and establishing controls and incentives. If targets are set too high, they will be discouraging sales-man who fails to achieve them, if set too low, the targets will be achieved easily and hence incentives will prove meaningless.
• Evolving a suitable advertising and program.
• Forecasting short-term financial requirements. Cash requirements depend on sales level and production operations. It takes time to arrange for funds on reasonable terms. Sales forecasts will, therefore, enable arrangement of sufficient funds on reasonable terms well in advance.

2. Purposes of Long-Term Forecasting

• Planning of a new unit or expansion of an existing unit, it requires an analysis of the long-term demand potential of the products in question. A multi-product firm must ascertain not only the total demand situation, but also the demand for different items separately. If the company has better knowledge that its rivals of the growth trends of the aggregate demand and of the distribution of the demand over various products, its competitive position would be much better. 
• Planning long-term financial requirements. As planning for raising funds requires considerable advance notice, long-term sales, forecasts are quite essential to assess long-term financial requirements.
• Planning manpower requirements. Training and personnel development are long-term propositions, taking considerable time to complete. They can be started well in advance only on the basis of estimates of manpower requirements assessed according to long-term sales forecasts.

Most of modern firms forecast their demand on the basis of past demand, present condition of demand. Business/ Demand forecasting is the prediction of future situation for a firm’s product in the market.

   SOME RELATED LINKS:   

• Demand Forecasting

• Steps of Demand Forecasting

• Different Methods of Demand Forecasting | Survey of Buyer's Intentions | Delphi Method | Time Series Analysis and Trend Projection | Market Studies and Experimentation | Regression Analysis

• Process of demand forecasting by the time series analysis

• Demand Forecasting for the New Products

• Merits and limitations of market studies and experimentation as a method of demand forecasting

• Factors Determining Elasticity of Demand