Which profitability models are generally used for forecasting

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.

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.

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Financials We can calculate interest charges or increase material costs and add them to expenditures while projecting increasing revenue due to higher sales. Trends and Analytical model. Products By Bayt. Use Our Mobile App. Get Fresh Updates On your job applications, and stay connected. Download Now. Quantitative financial forecasting takes a straightforward approach to generating forecasts based on hard data. Typically, quantitative forecasting is more effective when dealing with data points such as future sales growth and tax topics, rather than subject matter that has less concrete data to guide these forecasts.

Although quantitative forecasting takes the guesswork out of the process, it can also be limited by a lack of human expertise, resulting in a deficit of important context that could alter forecasts significantly. A straight-line forecasting method is one of the easiest to implement, requiring only basic math and providing reasonable estimates for what businesses can anticipate in future financial scenarios. Straight-line forecasting is commonly used when a business is assuming revenue growth in the future.

Your business may use its past revenue growth rate as a standard for growth in the future. If revenues have grown by an average of 7 percent over the past three years, for example, you could assume a similar growth rate for the next years with the straight-line method.

Of course, many variables will affect not only your revenue growth, but also your net profits over that period of time. A moving average is the average performance of a specific metric over a specific period of time. Typically, a moving average is used to evaluate on monthly time frames, rather than yearly time frames.