What is Demand Forecasting? Definition, Objectives, Importances, Methods.

What is Demand Forecasting?

Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses and quantitative methods, such as the use of historical sales data or current data from test markets.

Definition

Demand forecasting is an estimate of sales during a specified future period based on a proposed marketing plan and a set of particular uncontrollable and competitive forces”. As such, demand forecasting is a projection of firms’ expected level on a chosen marketing plan and assumed marketing environment. Cardiff and Still

Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements or in making decisions on whether to enter a new market. Forecasting may be defined as a technique of translating experience into a prediction of things to come.

It tries to evaluate the magnitude and significance of forces that will affect future operating conditions in an enterprise. Thus, demand forecasting is an estimation of future demand.

Often forecasting demand is confused with forecasting sales. However, failing to forecast demand ignores two important phenomena. There is a lot of debate in demand-planning literature about how to measure and represent historical demand since the historical demand forms the basis of forecasting.

The main question is whether we should use the history of outbound shipments or customer orders or a combination of the two as proxy for the demand.

Demand Forecast and Sales Forecast

Due to the dynamic nature of marketing phenomenon, demand forecasting has become a continuous process. It requires regular monitoring of the situation. In management circles, demand forecasting and sales forecasting are used interchangeably. Sales forecasts are first approximations in production planning. These provide foundations upon which plans may rest and adjustment may be made.

According to American Marketing Association, Sales forecast is an estimate of sales in monetary or physical units for a specified future period under a proposed business plan or programmer or under an assumed set of economic and other environment forces, planning premises, outside business/ antiquate which the -forecast or-estimateis made.

Components of Demand Forecasting System

Market research operations to get the relevant and reliable information about the trends in market.
A data processing and analysing system to estimateand evaluate the sales performance in various markets
Proper co-ordination of steps (i) and (ii) and then to place the findings before the top management for making final decisions.

Objectives of Demand Forecasting

Short Term Objectives

(a) Formulation of Production Policy: Demand forecast helps in formulating suitable production policy so that there may not be any gap between demand and supply of a product. This can further ensure:

Regular Supply of Material: By the determination of desired volume of production based on demand forecasts, one can evaluate the neces.
sary raw material requirements in the future to ensure a regular and continuous supply of the materials as well as controlling the size of inventory at the economic level.
Maximum Utilisation of Machines: The operations can be so planned that the machines are utilized to their maximum capacity.
Regular Availability of Labour: Skilled and unskilled workers can be properly arranged to meet the production schedule equipment.

(b). Price Policy Formulation: Demand forecasts enable the management to formulate appropriate pricing mechanism, so that the level of price does not fluctuate too much in the periods of depression or inflation.

Proper Control of Sales: Demand forecasts are calculated region wise and then the sales targets for various territories are fixed accordingly. This later on becomes the basis to evaluate sales performance.
Arrangement of Finance: Based on demand forecast, one can determine the financial requirements of the enterprise for the production of desired output. This can minimise the cost of procuring finance.

2.Long Term Objectives: If the period of a demand forecast is more than a year then it is termed as long term forecast. The following are the main objectives of such forecasts:

A.To decide about the Production Capacity: The size of the plant should be such that output conforms to sales requirements. Too small or too large size of the plant may not being the economic interest of the enterprise. By studying the demand pattern for the product and the forecasts for future the enterprise can plan for a plant/output of desired capacity.

Labour Requirements: Expenditure on labour is one of the most important components in cost of production. Reliable and accurate demand forecasts can help the management to assess appropriate labour requirements. This can ensure best labour facility and no hindrances in the production process.
Production Planning: Long-term production planning can help the management to arrange for long term finances on reasonable terms and conditions.
The analysis of long-term sales is more significant than short-term sales. Longterm sales forecast helps the management to take some policy decisions of great significance and any error committed in this may be very different or expensive to be rectified. Thus, the overall success of an enterprise mainly depends on the quality and reliability of sales forecasting mechanism.

Importance of Demand Forecasting

Management Decisions: An efficient demand forecast helps the management to take suitable decisions regarding plant capacity, raw- material requirement, space and building needs and availability of labour and capital. Production schedules can be prepared in conformity with demand requirement minimising inventory, production and other related costs.

Evaluation: Demand forecasting also helps in evaluating the performance of sales department.
Quality and Quantity Controls: Demand forecasting is a necessary and effective tool in the hands of the management of an enterprise to have finished goods of right quality and quantity at right time with minimum cost.
Financial Estimates: Demand forecasting is also very useful for a firm in estimating its financial requirements depending on sales level and production operations. Moreover, it also requires some time to get funds on reasonable terms. Sales forecasts will enable arrangement of sufficient funds on reasonable terms well as in advance.
Under and Over Production Avoided: Demand forecasting is essential for the old firms and new firms. It is much more important when the firm is engaged in large-scale production and there is a long gestation period in the production process. In such circumstances, an idea about future demand is necessary to avoid under production and over production.
Guideline for Future: Demand forecast for a particular product also provides a guideline for demand forecast of related industries. For example, the demand forecast for the automobile also helps the tyre industry in estimating the demand for 2 wheelers, 3 wheelers and 4 wheelers.
Importance for the Government: At macro-level, demand forecasting is useful to the government also for determining the targets of imports and exports for different commodities and planning the international business.

Methods of Demand Forecasting

There is no easy method or simple formula, which enables an individual or a business to predict the future with certainty or to escape the hard process of thinking. Two dangers must be guarded against. (i) 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. (ii) We may go to the opposite extreme and regard forecasting as something to be left to the judgment of the so-called experts. Some commonsense between pure guessing and too much mathematics is needed.

Survey of Buyers‘ Intentions

It is the most direct method of estimating demand in the short run. The customers are asked what they are planning to buy for the forthcoming time- period usually a year. This opinion survey is most useful when bulk of the sales is made to industrial producers. The burden of forecasting is shifted to the customer.

The Economic Times very often publishes surveys of Private Sector Investment intentions‘. The Centre for Monitoring Indian Economy (CMIE) makes an annual survey of the Investment Intentions of the Industry‘.

For example, according to the CMIE, 2,600 projects costing Rs. 3, 93,000 crores were to be taken up in the Eighth Plan. The Reserve Bank of India also makes occasional studies of corporate expenditure. For example, in 1992-93, the corporate sector was likely to incur a total expenditure of Rs. 22,343 crores. Yet it would not be wise to depend wholly on the buyer‘s estimates.

They should be used cautiously in the light of the sellers‘ own judgments. A number of biases may creep into the surveys. If shortages are expected, customers may tend to exaggerate their requirements. They may know what their total requirements are but they may misjudge 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 due to 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 but only a dream. This method is passive and idoes not expose and measure the variables under management‘s controli.

Delphi Method

The Delphi method is a systematic, interactive forecasting method, which relies on a panel of experts. The experts answer questionnaires in two or more rounds. After each round, a facilitator provides an anonymous summary of the experts‘ forecasts from the previous round as well as the reasons they provided for their judgments.

Thus, experts are encouraged to revise their earlier answers in light of the replies of other members of their panel. It is believed that during this process, the range of the answers will decrease and the group will converge towards the ‘correct’ answer. Finally, the process is stopped after a pre-defined stop criterion (e.g. number of rounds, achievement of consensus and stability of results) and the mean or median scores of the final rounds determine the results.

Delphi is based on the principle that forecasts from a structured group of experts are moreaccurate than from unstructured groups or individuals. The technique can be adapted for usein face-to-face meetings and is then called mini- Delphi or Estimate-Talk-Estimate (ETE). Delphi has been widely used for business forecasting and has certain advantages over another structured forecasting approach, prediction markets.

Collective Opinion

It is also called sales-force polling. In it, the sales representatives are required to estimate expected sales in their respective territories and sections, because being closest to the customers, they are likely to have the most intimate feel of the market, i.e., customer reaction to the products of the firm and their sales trends. The estimates of individual sales representatives are consolidated to find out the total estimated sales.

These are then reviewed to eliminate the bias of optimism on the part of some sales representatives and pessimism on the part of others. These revised estimates are further examined in the light of factors like proposed changes in selling prices, product designs and advertisement programmes, expected changes in competition, changes in secular forces like purchasing power, income distribution, employment, population, etc.

The final sales forecast emerges after these factors have been taken into account. This collective opinion method‘ takes advantage of the collective wisdom of sales representatives, departmental heads like production manager, sales manager, marketing manager, managerial economist, etc. and the top executives.

Analysis of Time Series and Trend Projections

A firm which has been in existence for some time will have accumulated considerable data on sales pertaining to different time periods which, when arranged chronologically, yield time series‘. The time series relating to sales represents 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 the method of least squares. The analyst chooses a plausible algebraic relation (linear, quadratic, logarithmic, etc.) between sales and the independent variable, time.

The trend lineis then projected into the future by extrapolation. There are two assumptions underlying this approach:

(1) The analysis of movements would be in the order of trend, seasonal variations and cyclical changes and.

(2) The effects of each component are independent of each other. This method is simple and inexpensive. Time series data often exhibit a persistent growth trend. Its basic assumption is that the past rate of change of the variable under study will continuein the future. It yields acceptable results so long as the time series shows a persistent tendency to movein the same direction. However, the trend projection breaks down whenever a turning point occurs.

Nevertheless, a forecaster could normally expect to be right in most forecasts particularly if the turning points are few and spaced at long intervals from each other. Thus forecasting must predict turning points rather than trends. On turning points, the management will have to alter and revise its sales and production strategies drastically.

Four sets of factors are responsible for the characterisation of time series by fluctuations and turning points in a time series: trend, seasonal variations, cyclical fluctuations, and irregular or random forces. The problem is to separate and measure each of these four factors. The basic 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 an irregular movement (I).

It is generally assumed that these elements are bound together in a multiplicative relationship expressed by the equation O = TSCI. The usual practice is to compute the trend from the original data first. 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 decomposition of time series data is a useful analytical device for understanding the nature of business fluctuations. However, in actual business forecasting, it is of limited value.

The trend and the seasonal factor can be forecast, but the prediction of cycles is hazardous because there is no regularity in the cyclical behavior.

Use of Economic Indicators

This approach bases demand forecasting on following economic indicators:

Construction contracts sanctioned for the demand of building materials, say, cement Personal income for the demand of consumer goods.
Agricultural income for the demand of agricultural inputs, implements, fertilizers and so on.
Automobile registration for the demand of car accessories, petrol and so on.

These economic indicators are published by specialised organisations like the C.S.O., which publishes national income estimates. Steps in the Use of Economic Indicators.

See whether 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 curvilinear relationships as well.
Once regression equation is derived, the value of Y i.e. ‘demand’ can be estimated for any given value of x.
Past relationships may not recur. Hence, there is need for value judgement as well. New factors may also have to be taken into consideration.

Controlled Experiments

Controlled experiments have sufficient potential to become a major method for business research and analysis in future. In this method, an effort is made to separately vary certain determinants of demand, which can be manipulated e.g. price, advertising etc., and conduct the experiments assuming that the other factors remain constant.

The effect of demand determinants like price, advertisement, packaging, etc., on sales can be assessed by either varying them over different markets or by varying them over different periods in the same markets. For example, different prices would be associated with different sales. On that basis, the price-quantity relationship is estimated in the form of regression equation and used for forecasting purposes. The market divisions here must be homogeneous with regard to income, tastes, etc.

Controlled experiments have often been conducted in the U.S.A. to gauge the effect of a change in some demand determinants like price, advertising, product design, etc. For example, the Parker Pen Co. used this method to find out the effect of a price rise on the demand for Quink ink.

Judgmental Approach

In this method, the management may have to use its own judgment, when:

Analysis of time series and trend projections is not feasible because of wide fluctuations in sales or because of anticipated changes in trends.
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. If statistical methods areused, it might be desirable to supplement them by use of judgement for the following reasons:
Even the most sophisticated statistical methods cannot incorporate all the potential factors affecting demand as, for example, a major technological breakthrough in product or process design.
For industrial products, demand may be concentrated in a small number of buyers. If the management anticipates loss or addition of a few such large buyers, it could be taken into account only through the judgemental approach.
Statistical forecasts are more reliable for larger levels of aggregations. Thus whileit may be possible to forecast the total national demand more or less accurately, it may be more difficult to accurately forecast demand by sales territory, sizes and models. In such cases, one has to depend on judgement for developing forecasts that are more detailed.

Steps in Forecasting

Identify and clearly state the objectives of forecasting — short-term or long- term, market share or industry as a whole.
Select appropriate method of forecasting.

Identify the variables affecting the demand for the product and express them in appropriate forms.
Gather relevant data or approximations to relevant data to represent the variables.
Determine the most probable relationship between the dependent and the independent variables using statistical techniques.
Prepare the forecast and interpret the results. Interpretation is more mportant to the management.
Following two different assumptions may be made for forecasting the company‘s share in the demand.
The ratio of the company sales to the total industry sales will continueas in the past.
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.
However, it would be useful to prepare alternative forecasts which are more meaningful than a single forecast. As forecasts are based on certain assumptions, these must be revised when improved information is available. In long-term forecasts, the projections may be revised every year, sometimes known as rolling forecasts.
Forecast may be made either in terms of physical units or in terms of rupees of sales volume. The latter may be converted into physical units by dividing it by the expected selling price
Forecasts may be made in terms of product groups and then broken for individual products based on past percentages. Product group may be divided into individual products in terms of sizes, brands, labels, colours, etc.
Forecasts may be made on annual basis and then divided monthly or weekly based on past records.
For determining the month-wise break-up of the forecast sales of a new product, either: (i) use may be made of other firms‘ data, if available or (ii) some survey may be necessary. The situation will be similar whenthe forecast sales of a product-line have to be divided productwise.
Sales may change over time by a constant proportion rather than by a constant absolute amount. For example, if a firm is projecting its sales for five years into the future and if it has determined that sales are increasing at an annual rate of 10 per cent, the projection would simply involve multiplying the 10 per cent growth factor for 5 years times present sales.

Forecasting Demand for New Products

Joel Dean has suggested following possible approaches to the problem of forecasting demand for new products:
Project the demand for the new product as an outgrowth of an existing old product.
Analyse the new product as a substitute for some existing product or service.
Estimate the rate of growth and the ultimate level of demand for the new product based on the pattern of growth of established products.
· Estimate the demand by making direct enquiries from the ultimate purchasers, either by the use of samples or on a full scale.

Offer the new product for salein a sample market e.g. by direct mail or through one multiple shop organisation.
Survey the reaction of the consumers to a new product indirectly through specialised dealers. These dealers are supposed to have knowledge about consumers‘ need and alternative opportunities.

These methods are not mutually exclusive and it would be desirable to try to combine several of them so that crosschecking is possible. To some extent, the methods of forecasting demand for an established product may also be applied or adapted for new products.

Criteria of a Good Forecasting Method

Accuracy: It is necessary to check the accuracy of past forecasts against present performance and of present forecasts against future performance. The accuracy of the forecast is measured by: (a) the degree of deviations between forecasts and actual and (b) the extent of success in forecasting directional changes.
Simplicity and Ease of Comprehension: For proper interpretation of the results, management must beable to understand. They should have confidence in the techniques used. If management does not really understand the procedure or what the forecaster is doing, elaborate mathematical and econometric procedures may be judged less desirable.
Economy: Costs must be weighed against the importance of the forecast to the operations of the business. The criterion here is the economic consideration of balancing the benefits from increased accuracy against the extra cost of providing the improved forecasting.Economy: Costs must be weighed against the importance of the forecast to the operations of the business. The criterion here is the economic consideration of balancing the benefits from increased accuracy against the extra cost of providing the improved forecasting.
Availability: The techniques employed should be able to produce meaningful results quickly. Techniques, which take a long time to work out may produce useful information too late for effective management decisions.
Maintenance of Timeliness: The forecast should be capable of being maintained on an up-to-date basis. According to Norman N. Barish this has three aspects.
The relationships underlying the procedure should be stable so that they will carry into the future for a significant amount of time.
Current data required to use these underlying relationships should be available on timely basis.
The forecasting procedure should permit changes to be made in the relationships as they occur.

Role of Macro-Level Forecasting in Demand Forecasts

Macro-level forecasting precedes micro-level demand forecasting. The macroparameters such as Gross National Product (GNP), population growth, per capita income, aggregate savings, level of investment etc., provide boundaries within which projections of demand for an industry, a firm or a product fit in.

For example, if the level of national savings is projected to rise fast, the disposable consumer expenditure on products will decline. Thus, savings parameter has a bearing on future demand for consumer goods, especially durable consumer goods. Likewise, rising population indicates that the market for various commodities is in general expanding.

Macro-parameters Useful for Demand Forecasting

National income and per capita income: Increase in these parameters indicates rising market potential for consumer goods.
Savings: If the level of savings is high, this would dampen consumer goods demand.
Investment: An increase in investment would raise demand for intermediate goods or vice versa.
Population Growth: The future demand for all types of goods would rise with population growth.
Government Expenditure: High level of public expenditure would stimulate investment in the private sector. In the context of Indian economy, the increase in public expenditure has a decisive role in stimulating private investment, aggregate demand and the level of spending in general.
Taxation: Taxation can also influence demand pattern. Certain taxes would depress the demand of commodities taxed. For example, high level of excise duties on semi-luxury and luxury goods such as electrical appliances, refrigerators, air-conditioners etc. would depress the demand for these goods. Further, this in turn would depress investment in these industries and as such demand for capital goods employed in these industries.
Credit Policy: Such policies influence cost of credit, credit availability and company finance. The time pattern of investment is largely affected by credit policies. Again, inventories are largely affected by credit policies through their effects on carrying costs of inventories. Credit policies affect holding capacities of all business sections — producers, dealers and retailers.

In India, information and data about macro parameters are mostly available in various publications of Government organisations, National Council of Applied Economic Research and Central Statistical Organisation. Forecasts regarding national parameters would influence and determine firm‘s demand projections.

A good crop forecast and higher rural incomes would lower cost of materials and boost demand for various products. The data pertaining to national income, per capita income, production, prices, taxes, etc., present a reasonable basis for good forecasts.