This tutorial Introduction to Demand Forecasting consists of meaning, definition, characteristics, methods & techniques, importance and steps in demand forecasting.
Table of Contents
- 1 What is Demand Forecasting?
- 2 Demand Forecasting Definition
- 3 Steps in Demand Forecasting
- 4 Characteristics of a Demand Forecasting Method
- 5 Methods & Techniques of Demand Forecasting
- 6 Importance of Demand Forecasting
Forecasting is an attempt to predict the future by examining the past. Business firms can estimate and minimise future risk and uncertainty through forecasting and forward planning. Without forecasting, forward planning will be directionless and meaningless.
What is Demand Forecasting?
Demand forecasting is an attempt to estimate the future level of demand on the basis of past as well as present knowledge and experience, to avoid both under production and overproduction.
It may be based on estimates of demand potential of the entire industry. The demand forecasting serves as the reference point for all marketing control efforts. It is indispensable in modern business.
Demand Forecasting Definition
Demand forecasting is an estimate of sales during a specified future period based on proposed marketing plan and a set of particular uncontrollable and competitive forces.Cundiff and Still
Steps in Demand Forecasting
Demand forecasting is a scientific exercise. It has to go through a number of steps. At each step, critical considerations are required to be made. The following steps are necessary for demand forecasting. These steps present a systematic way of initiating, designing and implementing a forecasting system.
Identification of Objective
Economist first should be clear about the uses of forecast data and how it is related to forward planning by the firm. Depending upon the scenario, the economist has to choose the type of forecast: short-run, active or passive, conditional or non-conditional, etc.
Nature of Product and Market
Nature of the product or service is an important consideration for which we are attempting a demand forecast. Forecasting of demand must examine carefully whether the product is a consumer good or producer good, perishable or durable and also consider the stage at which the product is i.e. introduction, growth, maturity and saturation, or obsolescence and decline. Finally, the nature of competition in the market (perfect or imperfect) should not be overlooked.
Determinants of Demand
Different determinants will assume a different degree of importance in different demand functions, depending on the nature of product and nature of forecasts, In addition, it is important to consider socio-psychological determinants; especially demographic, sociological and psychological factors affecting the demand.
Analysis of Factors
In an analysis of statistical demand function, it is customary to classify the explanatory factors into (i) trend factors, (ii) cyclical factors, (iii) seasonal factors and (iv) random factors. An analysis of factors is especially important depending upon whether it is the aggregate demand in the economy or the industry’s demand or the company’s demand or the consumer’s demand which is being predicted.
Choice of Method
The economist has to choose a particular technique from among various techniques of demand forecasting, depending upon the nature of the product.
There are various methods for testing statistical accuracy in a given forecast. Some of them are simple and inexpensive; others are quite complex and difficult. This testing is needed to avoid/reduce the margin of forecasting error and thereby to improve the decision-making.
Characteristics of a Demand Forecasting Method
Eight major characteristics can be identified with forecasting methods (techniques) to identify key characteristics of a good demand forecasting method.
The length of time over which a decision is being made has a bearing on the appropriate technique to use. The probability of forecasting error generally decreases with an increase in the length of the time horizon.
Level of Detail
The level of detail needed should match the focus of the decision-making unit in the forecast. For example, production planning must make its decision at the individual product level, whereas the corporate planning department is likely to be happy with aggregate demand forecasts by product categories.
Forecasting in situations that are relatively stable over time requires less attention than those that are in constant flux. In stable situations, the existing pattern is assumed to continue in the future and past patterns can be easily extrapolated in future.
Pattern of Data
Data required to use the underlying-relationships should be available on a timely basis. Each forecasting method is based on an underlying assumption about the data. As different forecasting methods vary in their ability to identify different patterns, it is useful to make the pattern in the data fit with the method that suits it the most.
Type of Model
Other assumptions are also made in each forecasting technique that must fit the situation under consideration. The technique used should be easily comprehended by the management to give quick meaningful results.
Several costs are associated with adopting a forecasting procedure. The variation in costs affects the selection of the forecasting method. There is a need for an economic consideration of balancing the benefits against the extra cost of providing the improved forecasting.
It is measured by the degree of deviations between past forecasts and current actual performance or present forecasts and future performance. If the likely state comes close to the actual state, it means that the forecast is dependable.
Ease of Application
Models must be chosen within the abilities of the users to understand them and within the time allowed for using them. This will enable management to properly interpret the results. The simplicity of handling the method matters in the selection of the method.
Methods & Techniques of Demand Forecasting
The choice is complicated because each situation might require a different method. Management should be aware of the factors favouring one method over another in a given demand-forecasting situation.
Under this approach, surveys are conducted about the intentions of consumers (individuals, firms or industries), opinion of experts or of markets. When taking sample surveys, a selected subset is surveyed and through study, inferences are drawn. These methods are usually suitable for short-term forecasts due to the nature of consumers’ intentions.
A few important survey methods:
Consumer Survey Method
A firm can ask consumers, what and how much they are planning to buy at various prices of the product for the forthcoming time period, usually a year.
Collective Opinion Method
Also called sales-force polling), salesmen or experts are required to estimate expected future demand of the product in their respective territories and sections.
It is also known as Reasoned Opinion. A variant of opinion poll and survey method is a Delphi method, developed by Rand Corporation of USA in the late 1940s for predicting technical changes.
Market Experiment Method
Under this method, the main determinants of the demand of a product like price, advertising, product design, packaging, quality, etc., are identified. These factors are then varied separately over different markets or over different time periods, holding other factors constant. The effect of the experiment on consumer behaviour is studied under actual or controlled market conditions, which is used for overall forecasting purpose.
These methods make use of historical data (time series or cross section) as a basis for extrapolating quantitative relationships to arrive at the future demand patterns and trends. The data may also be analysed through econometric models.
These are useful for long-term forecasting, for old products and for larger levels of aggregation. They are based on scientific ways of estimation, which are logical, unbiased and proved to be useful. However, the biggest disadvantage is that it is difficult to apply these methods.
Time Series Analysis
It is an arrangement of statistical data in chronological order, i.e., in accordance with its time of occurrence. It reflects the dynamic pace of steady movements of a phenomenon, over a period of time.
Regression analysis is perhaps the most popular method of forecasting among economists. It is a mathematical analysis of the average relation between two or more variables, in terms of the original units of the data.
Importance of Demand Forecasting
- Distribution of resources: We know that inputs are processed to result into output. These inputs include resources like materials, machinery and of course human resources. The business firm also has to make decisions regarding capital arrangement, manpower planning and so on. In short, the estimation of demand enables the firm to undertake critical business decisions.
- Helps in avoiding wastages of resources: Demand forecasting is not an option but compulsion in today’s competitive environment. In order to avoid wastages, it is always beneficial to have a sense of future demand for products and services.
- Serves as a direction to production: If there is a proper prediction of the demand, then it serves as a handy tool for the businesses to undertake future production activities. According to the demand in the market, the company can control their production.
- Pricing: The decision regarding the pricing of goods and services is perhaps one of the most critical business decisions. If there are sincere predictions about the future sales of the firm’s product then it could serve as a good aid to devise pricing strategies.
- Sales policy: Production is followed by sales. The business firms can plan their sales policy effectively on the backdrop of demand forecasting. This also implies that the distribution of goods and services can be done appropriately depending upon the predictions of the demand for the product.
- Decrease of business risk: Where there is a business there is a risk. Demand forecasting though does not completely remove the business uncertainties, helps in reducing the risks and uncertainties to a certain extent.
- Inventory management: Inventories is one of those aspects which is closely associated with demand. This is because inventories are kept by the producers to meet the demand in the coming times. Demand forecasting helps in devising appropriate inventory management policies