Levels of Forecasting in Agri-economics - Agribusiness Education and Research International

Levels of Forecasting

Levels of Forecasting in Agri-economics has a lot of Importance. Forecasting demand can be carried out at various levels,

i.e., i) Macro-economic level,

(ii) Industry level,

(iii) Company level, and

(iv) Product line level.

Macro-economic level:

The whole economy is covered by macro-economic forecasts. Economic measures such as national income, gross investment, manufacturing performance, wholesale index, etc. assist in assessing the level of operation of the organization. Company businesses have to focus their predictions on ‘simple macro-economic parameters.’

Industry level:

Companies operating in the industry have to forecast demand at the industry level. The trade association provides its members with data relating to trends in a particular industry. A business making electronic components, for example, may be interested in understanding how the electronic industry will act in the future. The company can formulate its plan on the basis of available data with regard to production, sales, capacity expansion, etc.

Firm-level:

A firm can independently undertake demand forecasting of its own products. For example, Tata Oil Mills Ltd. could, in order to assess its position vis-a-vis its competitors in the market, make a forecast of its own goods.

Product line level:

A company engaged in the manufacture of diversified goods may make estimates at the product line level in order to determine which product or goods should be allocated more funds to maximize the overall profits of the company.

Forecasting purpose:

The scope of the forecast is also defined by its aims, that is to say, whether general or particular. For a firm, it is simpler to go for general forecasting. However, if the general forecast is broken into particular forecasts, e.g. domestic and foreign forecasts, etc., a very useful function is served.

Nature of the product:

The extent of the forecast varies according to the product’s design. For the ‘established’ products and the ‘new’ products, the methods and problems of demand forecasting differ. Sales trends are available for well-established products such as watches, TVs, refrigerators, etc., but the company will have to conduct independent surveys for demand forecasting for new products such as hi-tech music systems, microprocessors, etc. Products are also categorized into non-durable ‘consumer goods’ and ‘consumer durables’ and ‘capital goods’ according to their end-use. The demand pattern may be different for each category of these goods, and therefore separate forecasting is required to determine their market demand.

Diverse Factors:

In addition, there may be many other variables that may affect the spectrum of demand forecasting. If the company considers that the impact of these variables is very important, it cannot disregard them when forecasting demand. If their influence on demand patterns is insignificant, it is better for a company to ignore them. These variables include socio-psychological factors, degree of competition, risk and uncertainty impact, degree of forecast accuracy, change in population composition, distribution of income from money, etc.

FORECASTING POSITION AND PRIORITIES OF DEMAND

In this dynamic world, where changes are taking place quite rapidly and where future events are very unpredictable and risky, demand forecasting becomes highly indispensable and relevant in the sense of business policy decisions.
The forecast of demand for a specific product is useful not only for the business manufacturing that product, but also for other related industries as well. For example, the demand forecast for cotton textiles may also provide an idea of the likely sales for the ready-mades garment industry in the textile machinery industry.
Demand forecasting is also an important guide for the government as the government can determine if imports are required to meet the domestic supply deficit or not similarly export promotion policies are also built on the basis of demand forecasting The need for forecasting differs according to the time span of forecast that we can discuss below the significance of demand forecast.

Importance of forecasting short-period demand

Depending on the nature of the market, short-period demand forecasting can span a period of three months to one year. Generally speaking, it concerns forecasting products. Short-period forecasting is useful in the following ways:

Basis of Effective Production Scheduling: By forecasting seasonal variation in demand, the organization can avoid the problem of overproduction and the problem of short supply. Production schedules have to be oriented towards anticipated demand for this reason.
Appropriate Price Policy Basis: Appropriate pricing policy can be determined on the basis of anticipation of the conditions of market demand. It may also prevent a price rise if the market conditions are projected to be poor and a decline is projected. Price, when there is going to be a strong demand.
Appropriate Purchase Policy Basis: Demand forecasting helps the company to reduce the cost of purchasing raw materials and control inventory by determining its future requirement for resources.
Setting Reasonable Sales Goals for Salesmen: If goals are set too high, they will deter salesmen, Who fad to reach them. If objectives are set at an unrealistically low level, the target will be set. It will be easily achieved and incentives provided by management will therefore prove to be meaningless.
Financial Requirements for Forecasting: Cash requirements depend on demand and 144 operations for production. In addition, arranging for funds on fair terms requires time. Therefore, demand forecasts will allow sufficient funds to be arranged on reasonable terms well in advance for production, advertising, etc.

Importance of predicting long-period demand

Forecasting long-term demand covers a period of five to twenty years. It provides information for major strategic decisions. Invariably, the introduction of new products involves a long-term forecast. In this scenario, researchers need to take into account long-term demographic shifts, consumers’ tastes, and expectations, technology, product life cycle, etc. The significant applications of long-term demand forecasting are as follows:

Business Planning Basis:

The establishment of a new unit or expansion of an existing production unit requires an analysis of the long-term demand potential of the products concerned. Not only the overall sales situation but also the demand and its distribution over multiple products must be determined by a multi-product firm. If a company had better knowledge of the growth trends of aggregate demand and its distribution across different products than its competitors, its competitive position would be much better.

Planning Man-Power Requirements:

Long-term plans are training and staff development, requiring some time to complete. Only on the basis of projections of manpower needs calculated according to long-term demand forecasts can they be implemented well in advance.

Financial planning:

Substantial advance notice is needed for planning to raise funds. In order to evaluate long-term financial requirements for purchasing machines, raw materials, research and development programs, etc., long-term demand is essential.

CRITERIA OF A DECENT FORECASTING OF DEMAND

The dynamics for demand change very rapidly with the advent of new technologies and highly developed means of transport and communication. With regular shifts in demand, it is becoming increasingly difficult for businesses to keep pace. Employing vast production personnel makes it all the more necessary for businesses to accurately perform demand forecasting. For demand forecasting, many methods are available, and it is a difficult choice for the firm to choose the most suitable forecasting tool. For choosing the best approach for forecasting, the following parameters can be used:

Accuracy:

Historical data provides the basis for present success and can be predicted on the basis of the accuracy of current results. According to the objectives of the forecast, the degree of accuracy can vary. The accuracy of the forecast can be calculated by I the degree of difference between forecasts and actual changes, and (ii) the degree of performance in forecasting directional changes.

Economy:

This organization is definitely interested in reliable predictions, but the outcomes that can be obtained by a forecasting system must be measured against the expense of the system at the same time. An organization must strike a balance between the advantages of enhanced precision and the added expense of delivering enhanced forecasting.

Availability:

The effects of a prediction should be conveniently accessible and well-understood. The processes, assumptions, weaknesses and probabilities must be thoroughly understood in these forecasts. It is not as significant as what can be done by a system of forecasting, but it has actually been accomplished.

Timeliness:

Between the occurrence of an event and its prediction, there is often a time difference. It is regarded as the time of the ‘lead.’ The longer the prediction leads prior to the case, the greater its utility would be. Often, instead of sacrificing ‘lead’ for accuracy, a company can sacrifice some precision to obtain a ‘lead’. Adjustments in forecasting should also be possible in line with the forthcoming market patterns.

Simplicity:

Mathematical demand forecasting techniques may be precise, but it is not possible for intricate techniques to be used by all producers. A much less precise, straightforward forecasting technique should be preferred to a cumbersome and complex technique.

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