Demand Forecasting | Methods | Requirements | Limitations

Methods of Forecasting Demand for New Products

Forecasting the demand for a new product is entirely different from forecasting demand for an established product. In case of new products, no historical data are available and, therefore, the statistical methods cannot be applied. Only an intensive study of the economic and competitive characteristics of the product in question will provide some guidelines for demand projections in the case of new product.

Demand Forecasting

Demand Forecasting – Methods, Requirements, Limitations

The following are the some of the methods or approaches suggested for estimating the demand for a new product.

1. Evolutionary Approach

In this method, the new product is regarded as an outgrowth and evolution of an existing old product. For example, it may be assumed that colour television is the evolution of black and white television sets. In the past, Black and White television set was an established product and from its trend in sales, we could get an idea about the likely demand for colour television sets.

2. Substitution Approach

In this method, the new product is analyzed as a substitute for some existing products. For example, synthetic fabrics may be regarded as a substitute for cotton fabrics. In this case, controlled laboratory tests can be advertised to impress upon the customers the fact that the product is a close and may be even an improved version of the substitutes. The demand for the product can be looked on the basis of a market share, by estimating the total market for all substitutes.

3. Growth Curve Approach

Under this method, the rate of growth and the ultimate level of demand for the new product is estimated on the basis of the pattern of growth of an established product, e.g., by analyzing growth curves of all established household appliances, a growth curve can be developed for new appliances.

4. Opinion Poll Approach

In this method, demand for a new product is estimated by direct enquiry of the ultimate purchasers, either by the use of samples or a full scale.

5. Sales Experience Approach

In this method, a new product is offered for sale in a sample market, say, a supermarket in a large city. The estimate so obtained that can be blown up to arrive at the total demand for the product for all channels.

6. Vicarious Approach

In this method, consumer reactions to a new product is surveyed indirectly through the eyes of specialized dealers who are supposedly informed about consumers’ needs and alternative opportunities.

These methods are mutually exclusive. A combination of several of them is often desirable where they can supplement and check one another. The evolutionary approach is useful only when the new product is so close to being merely an improvement of an existing product and that its demand can be a projection of the potential development of the underlying product. The substitute approach is applicable when the new product is a substitute for the old product. Most of the new products are substitutes of the old products.

The growth curve approach has narrow applicability and is useful primarily at the later stages of demand projection. Opinion polling or survey of buyers’ intentions has been widely used to explore the demand for new products.

Sales experience with a new product on a sample basis, when the experiment is properly controlled, puts estimates of demand on a more solid foundation. Troubles arise, however, in determining the allowances to be made for the immaturity of the sample market and its peculiar characteristics. The vicarious approach is very easy and distressingly hard to quantify.

Costs and Accuracy of Forecasts

There is a cost-accuracy trade off while selecting a forecasting method. The more sophisticated methods involve high costs of implementation and maintenance, but they provide accurate forecasts which brings operational economy. Thus, it is expected to balance the cost and accuracy and try to optimize the costs. The cost/accuracy trade off is given below:

Cost Accuracy of Forecast

Cost / Accuracy of Forecast

Requirements of a Good Forecasting Method

1. Simplicity

The method should be easy to understand and should be simple to use. It should not be too mathematical or beyond the understanding of an organization or its managerial personnel.

2. Reliability

The method selected should give minimum forecast errors at the optimal cost.

3. Economical

The cost to make the forecast should be reasonable i.e., not very expensive. The information required should be easily and quickly available from reliable sources.

4. Accuracy

The method selected should be stable in the sense that the changes should be minimum. Whole production planning strategy of an organization mainly depends on demand forecasts made. In case of discrepancy or too much variation in forecast of demand, the organization is likely to incur loss. Thus, the method selected should be able to provide reasonably accurate estimates.

Limitations of Demand Forecasting

There are several limitations of sales forecasting which the production and sales managers should understand and realize. Such limitations are given below:

1. Changes in consumers’ needs, tastes, fashions, etc.

In case of a consumer product the change in the needs, tastes, fashion and style of the consumers will affect the sales of the organization. If the commodity is well received by consumers, it will become popular and its sales will go up. Otherwise, the company will fail to achieve its sales forecast targets. To avoid this difficulty, the management must revise its sales estimates from time to time, taking in view the customers’ needs and preferences.

2. Lack of past data

For few products, it is very difficult to estimate the correct production sales figures because there is no past sales history. In such cases, management has to rely on such guesswork only.

3. Anticipatory growth element

It is very difficult to maintain a steady rate of growth over an extended period of time. The probable rate of growth should be considered while preparing sales estimates.

4. Psychological factors

Forecasting the psychological factors of customers is difficult. They may change suddenly from one that of confidence to apprehensiveness about the future. For example, a rumour of an impending war, for instance, would create a great demand for consumable items.