Why is forecasting important

What is the importance of demand forecast? What are the purposes of planning forecasting and decision making in management? What is the purpose of forecasting methods and how does it affect the organization in the future? How can Forecasting improve accuracy? What is the best measure of forecast accuracy? How do you calculate the accuracy?

What is the industry standard for forecast accuracy? Why forecasting is not always accurate? Why are capacity forecasts generally wrong? Why are forecasts generally wrong quizlet? What are the causes of bad forecasting? What can be done to reduce forecasting errors? How do you reduce MAPE? Previous Article What is quantitative method of forecasting? Next Article What are the types of forecasting? Back To Top.

This allows the forecaster to trade off cost against the value of accuracy in choosing a technique. For example, in production and inventory control, increased accuracy is likely to lead to lower safety stocks. Here the manager and forecaster must weigh the cost of a more sophisticated and more expensive technique against potential savings in inventory costs. Exhibit I shows how cost and accuracy increase with sophistication and charts this against the corresponding cost of forecasting errors, given some general assumptions.

The most sophisticated technique that can be economically justified is one that falls in the region where the sum of the two costs is minimal. Once the manager has defined the purpose of the forecast, the forecaster can advise the manager on how often it could usefully be produced.

From a strategic point of view, they should discuss whether the decision to be made on the basis of the forecast can be changed later, if they find the forecast was inaccurate. If it can be changed, they should then discuss the usefulness of installing a system to track the accuracy of the forecast and the kind of tracking system that is appropriate. What are the dynamics and components of the system for which the forecast will be made?

This clarifies the relationships of interacting variables. Generally, the manager and the forecaster must review a flow chart that shows the relative positions of the different elements of the distribution system, sales system, production system, or whatever is being studied.

Note the points where inventories are required or maintained in this manufacturing and distribution system—these are the pipeline elements, which exert important effects throughout the flow system and hence are of critical interest to the forecaster. Where data are unavailable or costly to obtain, the range of forecasting choices is limited.

The flow chart should also show which parts of the system are under the control of the company doing the forecasting. In Exhibit II, this is merely the volume of glass panels and funnels supplied by Corning to the tube manufacturers.

In the part of the system where the company has total control, management tends to be tuned in to the various cause-and-effect relationships, and hence can frequently use forecasting techniques that take causal factors explicitly into account.

The flow chart has special value for the forecaster where causal prediction methods are called for because it enables him or her to conjecture about the possible variations in sales levels caused by inventories and the like, and to determine which factors must be considered by the technique to provide the executive with a forecast of acceptable accuracy. Once these factors and their relationships have been clarified, the forecaster can build a causal model of the system which captures both the facts and the logic of the situation—which is, after all, the basis of sophisticated forecasting.

How important is the past in estimating the future? Significant changes in the system—new products, new competitive strategies, and so forth—diminish the similarity of past and future. Over the short term, recent changes are unlikely to cause overall patterns to alter, but over the long term their effects are likely to increase.

The executive and the forecaster must discuss these fully. Once the manager and the forecaster have formulated their problem, the forecaster will be in a position to choose a method. There are three basic types— qualitative techniques, time series analysis and projection, and causal models. The first uses qualitative data expert opinion, for example and information about special events of the kind already mentioned, and may or may not take the past into consideration.

The second, on the other hand, focuses entirely on patterns and pattern changes, and thus relies entirely on historical data. The third uses highly refined and specific information about relationships between system elements, and is powerful enough to take special events formally into account. As with time series analysis and projection techniques, the past is important to causal models. These differences imply quite correctly that the same type of forecasting technique is not appropriate to forecast sales, say, at all stages of the life cycle of a product—for example, a technique that relies on historical data would not be useful in forecasting the future of a totally new product that has no history.

The major part of the balance of this article will be concerned with the problem of suiting the technique to the life-cycle stages. We hope to give the executive insight into the potential of forecasting by showing how this problem is to be approached. But before we discuss the life cycle, we need to sketch the general functions of the three basic types of techniques in a bit more detail.

Primarily, these are used when data are scarce—for example, when a product is first introduced into a market. They use human judgment and rating schemes to turn qualitative information into quantitative estimates.

The objective here is to bring together in a logical, unbiased, and systematic way all information and judgments which relate to the factors being estimated. Some of the techniques listed are not in reality a single method or model, but a whole family. Thus our statements may not accurately describe all the variations of a technique and should rather be interpreted as descriptive of the basic concept of each.

A disclaimer about estimates in the chart is also in order. Estimates of costs are approximate, as are computation times, accuracy ratings, and ratings for turning-point identification. The costs of some procedures depend on whether they are being used routinely or are set up for a single forecast; also, if weightings or seasonals have to be determined anew each time a forecast is made, costs increase significantly. Still, the figures we present may serve as general guidelines.

The reader may find frequent reference to this gate-fold helpful for the remainder of the article. Once they are known, various mathematical techniques can develop projections from them. The matter is not so simple as it sounds, however. It is usually difficult to make projections from raw data since the rates and trends are not immediately obvious; they are mixed up with seasonal variations, for example, and perhaps distorted by such factors as the effects of a large sales promotion campaign.

The raw data must be massaged before they are usable, and this is frequently done by time series analysis. Time series analysis helps to identify and explain:. Unfortunately, most existing methods identify only the seasonals, the combined effect of trends and cycles, and the irregular, or chance, component.

That is, they do not separate trends from cycles. We shall return to this point when we discuss time series analysis in the final stages of product maturity. We should note that while we have separated analysis from projection here for purposes of explanation, most statistical forecasting techniques actually combine both functions in a single operation.

It is obvious from this description that all statistical techniques are based on the assumption that existing patterns will continue into the future. This assumption is more likely to be correct over the short term than it is over the long term, and for this reason these techniques provide us with reasonably accurate forecasts for the immediate future but do quite poorly further into the future unless the data patterns are extraordinarily stable.

For this same reason, these techniques ordinarily cannot predict when the rate of growth in a trend will change significantly—for example, when a period of slow growth in sales will suddenly change to a period of rapid decay. Such points are called turning points. They are naturally of the greatest consequence to the manager, and, as we shall see, the forecaster must use different tools from pure statistical techniques to predict when they will occur.

When historical data are available and enough analysis has been performed to spell out explicitly the relationships between the factor to be forecast and other factors such as related businesses, economic forces, and socioeconomic factors , the forecaster often constructs a causal model. A causal model is the most sophisticated kind of forecasting tool. It expresses mathematically the relevant causal relationships, and may include pipeline considerations i.

It may also directly incorporate the results of a time series analysis. The causal model takes into account everything known of the dynamics of the flow system and utilizes predictions of related events such as competitive actions, strikes, and promotions.

If the data are available, the model generally includes factors for each location in the flow chart as illustrated in Exhibit II and connects these by equations to describe overall product flow. If certain kinds of data are lacking, initially it may be necessary to make assumptions about some of the relationships and then track what is happening to determine if the assumptions are true. Typically, a causal model is continually revised as more knowledge about the system becomes available.

Again, see the gatefold for a rundown on the most common types of causal techniques. As the chart shows, causal models are by far the best for predicting turning points and preparing long-range forecasts. At each stage of the life of a product, from conception to steady-state sales, the decisions that management must make are characteristically quite different, and they require different kinds of information as a base.

The forecasting techniques that provide these sets of information differ analogously. Exhibit III summarizes the life stages of a product, the typical decisions made at each, and the main forecasting techniques suitable at each. Equally, different products may require different kinds of forecasting. Two CGW products that have been handled quite differently are the major glass components for color TV tubes, of which Corning is a prime supplier, and Corning Ware cookware, a proprietary consumer product line.

We shall trace the forecasting methods used at each of the four different stages of maturity of these products to give some firsthand insight into the choice and application of some of the major techniques available today.

Many of the changes in shipment rates and in overall profitability are therefore due to actions taken by manufacturers themselves. Tactical decisions on promotions, specials, and pricing are usually at their discretion as well.

Between these two examples, our discussion will embrace nearly the whole range of forecasting techniques. As necessary, however, we shall touch on other products and other forecasting methods.

In the early stages of product development, the manager wants answers to questions such as these:. Forecasts that help to answer these long-range questions must necessarily have long horizons themselves. A common objection to much long-range forecasting is that it is virtually impossible to predict with accuracy what will happen several years into the future.

We agree that uncertainty increases when a forecast is made for a period more than two years out. However, at the very least, the forecast and a measure of its accuracy enable the manager to know the risks in pursuing a selected strategy and in this knowledge to choose an appropriate strategy from those available. Systematic market research is, of course, a mainstay in this area.

But there are other tools as well, depending on the state of the market and the product concept. While there can be no direct data about a product that is still a gleam in the eye, information about its likely performance can be gathered in a number of ways, provided the market in which it is to be sold is a known entity.

We call this product differences measurement. Second, and more formalistically, one can construct disaggregate market models by separating off different segments of a complex market for individual study and consideration. Specifically, it is often useful to project the S -shaped growth curves for the levels of income of different geographical regions.

When color TV bulbs were proposed as a product, CGW was able to identify the factors that would influence sales growth. Then, by disaggregating consumer demand and making certain assumptions about these factors, it was possible to develop an S -curve for rate of penetration of the household market that proved most useful to us. In , we disaggregated the market for color television by income levels and geographical regions and compared these submarkets with the historical pattern of black-and-white TV market growth.

We justified this procedure by arguing that color TV represented an advance over black-and-white analogous to although less intense than the advance that black-and-white TV represented over radio. The analyses of black-and-white TV market growth also enabled us to estimate the variability to be expected—that is, the degree to which our projections would differ from actual as the result of economic and other factors. The prices of black-and-white TV and other major household appliances in , consumer disposable income in , the prices of color TV and other appliances in , and consumer disposable income for were all profitably considered in developing our long-range forecast for color-TV penetration on a national basis.

The success patterns of black-and-white TV, then, provided insight into the likelihood of success and sales potential of color TV. Our predictions of consumer acceptance of Corning Ware cookware, on the other hand, were derived primarily from one expert source, a manager who thoroughly understood consumer preferences and the housewares market.

These predictions have been well borne out. This reinforces our belief that sales forecasts for a new product that will compete in an existing market are bound to be incomplete and uncertain unless one culls the best judgments of fully experienced personnel. Frequently, however, the market for a new product is weakly defined or few data are available, the product concept is still fluid, and history seems irrelevant. This is the case for gas turbines, electric and steam automobiles, modular housing, pollution measurement devices, and time-shared computer terminals.

At CGW, in several instances, we have used it to estimate demand for such new products, with success. Input-output analysis, combined with other techniques, can be extremely useful in projecting the future course of broad technologies and broad changes in the economy.

The basic tools here are the input-output tables of U. Since a business or product line may represent only a small sector of an industry, it may be difficult to use the tables directly. However, a number of companies are disaggregating industries to evaluate their sales potential and to forecast changes in product mixes—the phasing out of old lines and introduction of others. For example, Quantum-Science Corporation MAPTEK has developed techniques that make input-output analyses more directly useful to people in the electronics business today.

Other techniques, such as panel consensus and visionary forecasting, seem less effective to us, and we cannot evaluate them from our own experience. Before a product can enter its hopefully rapid penetration stage, the market potential must be tested out and the product must be introduced—and then more market testing may be advisable. At this stage, management needs answers to these questions:.

Significant profits depend on finding the right answers, and it is therefore economically feasible to expend relatively large amounts of effort and money on obtaining good forecasts, short-, medium-, and long-range.

A sales forecast at this stage should provide three points of information: the date when rapid sales will begin, the rate of market penetration during the rapid-sales stage, and the ultimate level of penetration, or sales rate, during the steady-state stage. The date when a product will enter the rapid-growth stage is hard to predict three or four years in advance the usual horizon. Furthermore, the greatest care should be taken in analyzing the early sales data that start to accumulate once the product has been introduced into the market.

For example, it is important to distinguish between sales to innovators, who will try anything new, and sales to imitators, who will buy a product only after it has been accepted by innovators, for it is the latter group that provides demand stability. Many new products have initially appeared successful because of purchases by innovators, only to fail later in the stretch. Tracking the two groups means market research, possibly via opinion panels.

A panel ought to contain both innovators and imitators, since innovators can teach one a lot about how to improve a product while imitators provide insight into the desires and expectations of the whole market.

The color TV set, for example, was introduced in , but did not gain acceptance from the majority of consumers until late To be sure, the color TV set could not leave the introduction stage and enter the rapid-growth stage until the networks had substantially increased their color programming.

Although statistical tracking is a useful tool during the early introduction stages, there are rarely sufficient data for statistical forecasting. Market research studies can naturally be useful, as we have indicated.

But, more commonly, the forecaster tries to identify a similar, older product whose penetration pattern should be similar to that of the new product, since overall markets can and do exhibit consistent patterns. For the year —, Exhibit IV shows total consumer expenditures, appliance expenditures, expenditures for radios and TVs, and relevant percentages. Column 4 shows that total expenditures for appliances are relatively stable over periods of several years; hence, new appliances must compete with existing ones, especially during recessions note the figures for —, —, —, and — Certain special fluctuations in these figures are of special significance here.

Probably the acceptance of black-and-white TV as a major appliance in caused the ratio of all major household appliances to total consumer goods see column 5 to rise to 4. At the base level, an accurate forecast keeps prices low by optimizing a business operation - cash flow, production, staff, and financial management. It helps reduce uncertainty and anticipate change in the market as well as improves internal communication, as well as communication between a business and their customers.

It also helps increase knowledge of the market for businesses. Moreover, a promising forecast is compelling to investors who might be interested in putting money into a business. Effective forecasting also has a positive impact on product success rates. Learn more about the ways in which volumetric forecasting can help to improve your chances at product success. For a business to operate efficiently, it needs some idea of what the future will look like.

A forecast provides this look as a foundation upon which to plan. Every functional group within a business benefits from a forecast. For sales people, forecast numbers influence how the sales function is managed.

Forecasts also help to understand customer engagement and therefore shape marketing efforts. Since forecasts estimate an expected sales volume over a specified period of time, salespeople can use them to set their activity goals, and subsequent adjustments can be made to reach sales goals.

Marketers can use forecasts to gauge the effectiveness of their campaigns, decide which markets to enter and exit, and determine the life cycle of their products. Senior managers and finance teams use forecasts to prepare and evaluate financial plans, capitalize on production, and assess needs and logistics. A forecast can help inform critical decisions on how to allocate resources and set overhead levels within a business: personnel, rent, utilities, and other overhead.

Since forecasts attempt to look into the future, certain assumptions need to be made that form the basis of the forecast. Businesses need to take the following into account:. From here, businesses need to decide on the segmentation for their forecast; i.

Businesses then need to determine which forecasting method is appropriate. There are three general methods:. Qualitative approaches are generally used when data is not readily available —in instances when a business, product or service is new. Typically this technique uses expert opinions and informed judgements that are logical, systematic, and unbiased in their estimations, which are then quantified. As the name implies, they are not as rigorous generally as quantitative methods.