How does forecasting work

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.

Our expectation in mid was that the introduction of color TV would induce a similar increase. Thus, although this product comparison did not provide us with an accurate or detailed forecast, it did place an upper bound on the future total sales we could expect. The next step was to look at the cumulative penetration curve for black-and-white TVs in U. We assumed color-TV penetration would have a similar S -curve, but that it would take longer for color sets to penetrate the whole market that is, reach steady-state sales.

Whereas it took black-and-white TV 10 years to reach steady state, qualitative expert-opinion studies indicated that it would take color twice that long—hence the more gradual slope of the color-TV curve. At the same time, studies conducted in and showed significantly different penetration sales for color TV in various income groups, rates that were helpful to us in projecting the color-TV curve and tracking the accuracy of our projection.

With these data and assumptions, we forecast retail sales for the remainder of through mid see the dotted section of the lower curve in Exhibit V. The forecasts were accurate through but too high in the following three years, primarily because of declining general economic conditions and changing pricing policies. We should note that when we developed these forecasts and techniques, we recognized that additional techniques would be necessary at later times to maintain the accuracy that would be needed in subsequent periods.

These forecasts provided acceptable accuracy for the time they were made, however, since the major goal then was only to estimate the penetration rate and the ultimate, steady-state level of sales. Making refined estimates of how the manufacturing-distribution pipelines will behave is an activity that properly belongs to the next life-cycle stage. For the purposes of initial introduction into the markets, it may only be necessary to determine the minimum sales rate required for a product venture to meet corporate objectives.

Analyses like input-output, historical trend, and technological forecasting can be used to estimate this minimum. To estimate the date by which a product will enter the rapid-growth stage is another matter. As we have seen, this date is a function of many factors: the existence of a distribution system, customer acceptance of or familiarity with the product concept, the need met by the product, significant events such as color network programming , and so on.

As well as by reviewing the behavior of similar products, the date may be estimated through Delphi exercises or through rating and ranking schemes, whereby the factors important to customer acceptance are estimated, each competitor product is rated on each factor, and an overall score is tallied for the competitor against a score for the new product. As we have said, it is usually difficult to forecast precisely when the turning point will occur; and, in our experience, the best accuracy that can be expected is within three months to two years of the actual time.

It is occasionally true, of course, that one can be certain a new product will be enthusiastically accepted. Market tests and initial customer reaction made it clear there would be a large market for Corning Ware cookware. Since the distribution system was already in existence, the time required for the line to reach rapid growth depended primarily on our ability to manufacture it.

When a product enters this stage, the most important decisions relate to facilities expansion. Medium- and long-range forecasting of the market growth rate and of the attainment of steady-state sales requires the same measures as does the product introduction stage—detailed marketing studies especially intention-to-buy surveys and product comparisons.

When a product has entered rapid growth, on the other hand, there are generally sufficient data available to construct statistical and possibly even causal growth models although the latter will necessarily contain assumptions that must be verified later. We estimated the growth rate and steady-state rate of color TV by a crude econometric-marketing model from data available at the beginning of this stage.

We conducted frequent marketing studies as well. The growth rate for Corning Ware Cookware, as we explained, was limited primarily by our production capabilities; and hence the basic information to be predicted in that case was the date of leveling growth. Because substantial inventories buffered information on consumer sales all along the line, good field data were lacking, which made this date difficult to estimate.

Eventually we found it necessary to establish a better more direct field information system. While the ware-in-process demand in the pipeline has an S -curve like that of retail sales, it may lag or lead sales by several months, distorting the shape of the demand on the component supplier. Exhibit VI shows the long-term trend of demand on a component supplier other than Corning as a function of distributor sales and distributor inventories.

As one can see from this curve, supplier sales may grow relatively sharply for several months and peak before retail sales have leveled off. The implications of these curves for facilities planning and allocation are obvious. Exhibit VI Patterns for Color-TV Distributor Sales, Distributor Inventories, and Component Sales Note: Scales are different for component sales, distributor inventories, and distributor sales, with the patterns put on the same graph for illustrative purposes.

Here we have used components for color TV sets for our illustration because we know from our own experience the importance of the long flow time for color TVs that results from the many sequential steps in manufacturing and distribution recall Exhibit II. There are more spectacular examples; for instance, it is not uncommon for the flow time from component supplier to consumer to stretch out to two years in the case of truck engines.

To estimate total demand on CGW production, we used a retail demand model and a pipeline simulation. The model incorporated penetration rates, mortality curves, and the like.

We combined the data generated by the model with market-share data, data on glass losses, and other information to make up the corpus of inputs for the pipeline simulation. The simulation output allowed us to apply projected curves like the ones shown in Exhibit VI to our own component-manufacturing planning.

That is, simulation bypasses the need for analytical solution techniques and for mathematical duplication of a complex environment and allows experimentation. Simulation also informs us how the pipeline elements will behave and interact over time—knowledge that is very useful in forecasting, especially in constructing formal causal models at a later date.

Statistical methods provide a good short-term basis for estimating and checking the growth rate and signaling when turning points will occur. In late it appeared to us that the ware-in-process demand was increasing, since there was a consistent positive difference between actual TV bulb sales and forecasted bulb sales.

Conversations with product managers and other personnel indicated there might have been a significant change in pipeline activity; it appeared that rapid increases in retail demand were boosting glass requirements for ware-in-process, which could create a hump in the S -curve like the one illustrated in Exhibit VI. This humping provided additional profit for CGW in but had an adverse effect in We were able to predict this hump, but unfortunately we were unable to reduce or avoid it because the pipeline was not sufficiently under our control.

The inventories all along the pipeline also follow an S -curve as shown in Exhibit VI , a fact that creates and compounds two characteristic conditions in the pipeline as a whole: initial overfilling and subsequent shifts between too much and too little inventory at various points—a sequence of feast-and-famine conditions. For example, the simpler distribution system for Corning Ware had an S -curve like the ones we have examined. When the retail sales slowed from rapid to normal growth, however, there were no early indications from shipment data that this crucial turning point had been reached.

Data on distributor inventories gave us some warning that the pipeline was over filling, but the turning point at the retail level was still not identified quickly enough, as we have mentioned before, because of lack of good data at the level.

We now monitor field information regularly to identify significant changes, and adjust our shipment forecasts accordingly. One main activity during the rapid-growth stage, then, is to check earlier estimates and, if they appear incorrect, to compute as accurately as possible the error in the forecast and obtain a revised estimate.

For example, the color-TV forecasting model initially considered only total set penetrations at different income levels, without considering the way in which the sets were being used. Therefore, we conducted market surveys to determine set use more precisely. Equally, during the rapid-growth stage, submodels of pipeline segments should be expanded to incorporate more detailed information as it is received.

In the case of color TV, we found we were able to estimate the overall pipeline requirements for glass bulbs, the CGW market-share factors, and glass losses, and to postulate a probability distribution around the most likely estimates.

Over time, it was easy to check these forecasts against actual volume of sales, and hence to check on the procedures by which we were generating them. We also found we had to increase the number of factors in the simulation model—for instance, we had to expand the model to consider different sizes of bulbs—and this improved our overall accuracy and usefulness. The preceding is only one approach that can be used in forecasting sales of new products that are in a rapid growth.

Others have discussed different ones. The decisions the manager at this stage are quite different from those made earlier. Most of the facilities planning has been squared away, and trends and growth rates have become reasonably stable. It is possible that swings in demand and profit will occur because of changing economic conditions, new and competitive products, pipeline dynamics, and so on, and the manager will have to maintain the tracking activities and even introduce new ones.

However, by and large, the manager will concentrate forecasting attention on these areas:. The manager will also need a good tracking and warning system to identify significantly declining demand for the product but hopefully that is a long way off.

To be sure, the manager will want margin and profit projection and long-range forecasts to assist planning at the corporate level. However, short- and medium-term sales forecasts are basic to these more elaborate undertakings, and we shall concentrate on sales forecasts. For Corning Ware, where the levels of the distribution system are organized in a relatively straightforward way, we use statistical methods to forecast shipments and field information to forecast changes in shipment rates.

We are now in the process of incorporating special information—marketing strategies, economic forecasts, and so on—directly into the shipment forecasts. This is leading us in the direction of a causal forecasting model. We find this true, for example, in estimating the demand for TV glass by size and customer.

In general, however, at this point in the life cycle, sufficient time series data are available and enough causal relationships are known from direct experience and market studies so that the forecaster can indeed apply these two powerful sets of tools. Historical data for at least the last several years should be available. The forecaster will use all of it, one way or another. We might mention a common criticism at this point. People frequently object to using more than a few of the most recent data points such as sales figures in the immediate past for building projections, since, they say, the current situation is always so dynamic and conditions are changing so radically and quickly that historical data from further back in time have little or no value.

We think this point of view had little validity. In practice, we find, overall patterns tend to continue for a minimum of one or two quarters into the future, even when special conditions cause sales to fluctuate for one or two monthly periods in the immediate future. For short-term forecasting for one to three months ahead, the effects of such factors as general economic conditions are minimal, and do not cause radical shifts in demand patterns.

And because trends tend to change gradually rather than suddenly, statistical and other quantitative methods are excellent for short-term forecasting. Using one or only a few of the most recent data points will result in giving insufficient consideration of the nature of trends, cycles, and seasonal fluctuations in sales.

Not directly related to product life-cycle forecasting, but still important to its success, are certain applications which we briefly mention here for those who are particularly interested.

While the X method and econometric or causal models are good for forecasting aggregated sales for a number of items, it is not economically feasible to use these techniques for controlling inventories of individual items. Some of the requirements that a forecasting technique for production and inventory control purposes must meet are these:.

One of the first techniques developed to meet these criteria is called exponential smoothing, where the most recent data points are given greater weight than previous data points, and where very little data storage is required. This technique is a considerable improvement over the moving average technique, which does not adapt quickly to changes in trends and which requires significantly more data storage.

Adaptive forecasting also meets these criteria. An extension of exponential smoothing, it computes seasonals and thereby provides a more accurate forecast than can be obtained by exponential smoothing if there is a significant seasonal.

There are a number of variations in the exponential smoothing and adaptive forecasting methods; however, all have the common characteristic at least in a descriptive sense that the new forecast equals the old forecast plus some fraction of the latest forecast error. Virtually all the statistical techniques described in our discussion of the steady-state phase except the X should be categorized as special cases of the recently developed Box-Jenkins technique.

If there is a trend that is predicted to take over the market, or data is showing changes in consumer behaviour it is important to readjust to the market overall and optimize resources to stand out from the competition.

Many common True CRM Solutions come with an integrated forecasting module, this can be used to create forecasted sales reports; enabling sales teams to fine-tune their selling strategy. Sales Representatives can gain visibility into items such as their quotas at any given moment, while Sales Managers can make more informed business decisions on how their team should manage its resources. Below is an example of a sales pipeline within Sage CRM.

This clickable diagram shows sales and management at a glance what opportunities are in the pipeline, addressing leads vs. Having this visual helps to ensure opportunities are not missed or forgotten about. Below is an example of a dashboard in Sage CRM, reporting on real-time data within the solution.

Similar to the pipeline shown above, in this dashboard the pipeline is represented as a funnel, along with weekly trends and actual sales. This provides businesses the ability to make informed business decisions, faster. These integrated solutions are able to provide you with historical, current, and future data.

BI tools use this data to create reports, summaries, dashboards, maps, graphs, and charts, providing detailed insights into business processes. Today, big data and artificial intelligence have transformed business forecasting methods and they are continually evolving according to business needs and technology.

As companies become more data-driven, efforts to share data and collaborate increases. A business intelligence system offers an effective way of acquiring the data that you need for better forecasting; with better forecasting comes a more efficient, productive, and cost-effective business.

Qualitative models are most successful with short-term projections. They are expert-driven, bringing up contrasting opinions and reliance on judgment over calculable data. Examples of qualitative models in business forecasting include:. Managers and forecasters must consider the stage of the product or business as this influences the availability of data and how you establish relationships between variables. A new startup with no previous revenue data would be unable to use quantitative methods in its forecast.

The more you understand the use, capabilities, and impact of different forecasting techniques, the more likely you will succeed in business forecasting. Any insight into the future puts your organization at an advantage. Forecasting helps you predict potential issues, make better decisions, and measure the impact of those decisions. By combining quantitative and qualitative techniques, statistical and econometric models , and objectivity, forecasting becomes a formidable tool for your company.

Business forecasting helps managers develop the best strategies for current and future trends and events. Today, artificial intelligence, forecasting software, and big data make business forecasting easier, more accurate, and personalized to each organization. Forecasting does not promise an accurate picture of the future or how your business will evolve, but it points in a direction informed by data, logic, and experiential reasoning.

While there are different forecasting techniques and methods, all forecasts follow the same process on a conceptual level. Standard elements of business forecasting include:. Successful business forecasting begins with a collaboration between the manager and forecaster. They work together to answer the following questions:.