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. For a consumer product like the cookware, the manufacturer’s control of the distribution pipeline extends at least through the distributor level. 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. Forecasting can help them […]. 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 will free the forecaster to spend most of the time forecasting sales and profits of new products. 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. We might mention a common criticism at this point. 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. The time series type of forecasting methods, such as exponential smoothing, moving average and trend analysis, employ historical data to estimate future outcomes. Such techniques are frequently used in new-technology areas, where development of a product idea may require several “inventions,” so that R&D demands are difficult to estimate, and where market acceptance and penetration rates are highly uncertain. When you initiate a new project, you're authorizing people to work under your auspices. Causal/Econometric Methods: This method assumes that it is possible to identify the underlying factors that might influence what is being forecasted. Unfortunately, most forecasting methods project by a smoothing process analogous to that of the moving average technique, or like that of the hypothetical technique we described at the beginning of this section, and separating trends and seasonals more precisely will require extra effort and cost. The X-11 method has also been used to make sales projections for the immediate future to serve as a standard for evaluating various marketing strategies. A causal model is the most sophisticated kind of forecasting tool. Although the X-11 was not originally developed as a forecasting method, it does establish a base from which good forecasts can be made. TeamAmp – https://certus3.com/ai-assurance-suite/teamamp/. The selection of a method depends on many factors—the context of the forecast, the relevance and availability of historical data, the degree of accuracy desirable, the time period to be forecast, the cost/ benefit (or value) of the forecast to the company, and the time available for making the analysis. The reader will be curious to know how one breaks the seasonals out of raw sales data and exactly how one derives the change-in-growth curve from the trend line. Trend forecasting takes the current project spending and extrapolates that rate of spending until the end of the project. Once the analysis is complete, the work of projecting future sales (or whatever) can begin. Setting standards to check the effectiveness of marketing strategies. Marketing simulation models for new products will also be developed for the larger-volume products, with tracking systems for updating the models and their parameters. 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. 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. It should not require maintenance of large histories of each item in the data bank, if this can be avoided. By identifying critical areas of management and forecasting the requirement of different resources like money, men, material etc., managers can formulate better objectives and policies for the organisation. Until computational shortcuts can be developed, it will have limited use in the production and inventory control area. 7. How successful will different product concepts be? 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. 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. When that is the case, the project manager should rely on trend forecasting - which is sometimes called "straight-line" forecasting. We expect that computer timesharing companies will offer access, at nominal cost, to input-output data banks, broken down into more business segments than are available today. The second, on the other hand, focuses entirely on patterns and pattern changes, and thus relies entirely on historical data. The output includes plots of the trend cycle and the growth rate, which can concurrently be received on graphic displays on a time-shared terminal. Exhibit I Cost of Forecasting Versus Cost of Inaccuracy For a Medium-Range Forecast, Given Data Availability. We shall return to this point when we discuss time series analysis in the final stages of product maturity.). On the other hand, a component supplier may be able to forecast total sales with sufficient accuracy for broad-load production planning, but the pipeline environment may be so complex that the best recourse for short-term projections is to rely primarily on salespersons’ estimates. Hence, two types of forecasts are needed: For this reason, and because the low-cost forecasting techniques such as exponential smoothing and adaptive forecasting do not permit the incorporation of special information, it is advantageous to also use a more sophisticated technique such as the X-11 for groups of items. Using data extending through 1968, the model did reasonably well in predicting the downturn in the fourth quarter of 1969 and, when 1969 data were also incorporated into the model, accurately estimated the magnitude of the drop in the first two quarters of 1970. This fluidity can be bucketed under risk breakdown structure that is found as a part of the feasibility study or it can be a purely financial assessment that you consider as you study markets, inflation or a sudden influx of revenue. Forecasting Revenue for a Fixed Price Project. The forecasts using the X-11 technique were based on statistical methods alone, and did not consider any special information. The raw data must be massaged before they are usable, and this is frequently done by time series analysis. The reason the Box-Jenkins and the X-11 are more costly than other statistical techniques is that the user must select a particular version of the technique, or must estimate optimal values for the various parameters in the models, or must do both. This technique requires considerably more computer time for each item and, at the present time, human attention as well. When it is not possible to identify a similar product, as was the case with CGW’s self-cleaning oven and flat-top cooking range (Counterange), another approach must be used. Qualitative forecasting methods Forecast is made subjectively by the forecaster. Many new products have initially appeared successful because of purchases by innovators, only to fail later in the stretch. 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. Because economic forecasts are becoming more accurate and also because there are certain general “leading” economic forces that change before there are subsequent changes in specific industries, it is possible to improve the forecasts of businesses by including economic factors in the forecasting model. Generally, even when growth patterns can be associated with specific events, the X-11 technique and other statistical methods do not give good results when forecasting beyond six months, because of the uncertainty or unpredictable nature of the events. For example, Quantum-Science Corporation (MAPTEK) has developed techniques that make input-output analyses more directly useful to people in the electronics business today. 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. Doubtless, new analytical techniques will be developed for new-product forecasting, but there will be a continuing problem, for at least 10 to 20 years and probably much longer, in accurately forecasting various new-product factors, such as sales, profitability, and length of life cycle. Graph the rate at which the trend is changing. 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. 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. (Other techniques, such as panel consensus and visionary forecasting, seem less effective to us, and we cannot evaluate them from our own experience.). How much manufacturing capacity will the early production stages require? This determines the accuracy and power required of the techniques, and hence governs selection. How should we allocate R&D efforts and funds? This is actually being done now by some of the divisions, and their forecasting accuracy has improved in consequence. The simulation output allowed us to apply projected curves like the ones shown in Exhibit VI to our own component-manufacturing planning. 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. We find this true, for example, in estimating the demand for TV glass by size and customer. Forecasters commonly use this approach to get acceptable accuracy in situations where it is virtually impossible to obtain accurate forecasts for individual items. While some companies have already developed their own input-output models in tandem with the government input-output data and statistical projections, it will be another five to ten years before input-output models are effectively used by most major corporations. Our purpose here is to present an overview of this field by discussing the way a company ought to approach a forecasting problem, describing the methods available, and explaining how to match method to problem. Exhibit V Long-term Household Penetration Curves for Color and Black-and-White TV. Basically, computerized models will do the sophisticated computations, and people will serve more as generators of ideas and developers of systems. Sometimes forecasting is merely a matter of calculating the company’s capacity—but not ordinarily. Exhibit II Flow Chart of TV Distribution System. 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. It has therefore proved of value to study the changes in growth pattern as each new growth point is obtained. A time series is a group of data that’s recorded over a specified period, such as a company’s sales by quarter since the year 2000 or the annual production of Coca Cola since 1975. Estimates of costs are approximate, as are computation times, accuracy ratings, and ratings for turning-point identification. 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. Techniques vary in their costs, as well as in scope and accuracy. They are educated guesses by forecasters or experts based on … How shall we allocate our R&D resources over time? However, the macroanalyses of black-and-white TV data we made in 1965 for the recessions in the late 1940s and early 1950s did not show any substantial economic effects at all; hence we did not have sufficient data to establish good econometric relationships for a color TV model. 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. Within five years, however, we shall see extensive use of person-machine systems, where statistical, causal, and econometric models are programmed on computers, and people interacting frequently. Finally, through the steady-state phase, it is useful to set up quarterly reviews where statistical tracking and warning charts and new information are brought forward. Equally, during the rapid-growth stage, submodels of pipeline segments should be expanded to incorporate more detailed information as it is received. In a highly volatile area, the review should occur as frequently as every month or period. In sum, then, the objective of the forecasting technique used here is to do the best possible job of sorting out trends and seasonalities. (In the next section we shall explain where this graph of the seasonals comes from. At CGW, in several instances, we have used it to estimate demand for such new products, with success. Others have discussed different ones.3. These decisions generally involve the largest expenditures in the cycle (excepting major R&D decisions), and commensurate forecasting and tracking efforts are justified. 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. Equally, different products may require different kinds of forecasting. 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. 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. We found this to be the case in forecasting individual items in the line of color TV bulbs, where demands on CGW fluctuate widely with customer schedules. Primarily, these are used when data are scarce—for example, when a product is first introduced into a market. Therefore, we conducted market surveys to determine set use more precisely. 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. 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. We are now in the process of incorporating special information—marketing strategies, economic forecasts, and so on—directly into the shipment forecasts. Furthermore, the executive needs accurate estimates of trends and accurate estimates of seasonality to plan broad-load production, to determine marketing efforts and allocations, and to maintain proper inventories—that is, inventories that are adequate to customer demand but are not excessively costly. The first three core concepts for forecasting can be summarized by the use of three related project systems: 1. Some of the techniques listed are not in reality a single method or model, but a whole family. Copyright © 2020 Harvard Business School Publishing. The matter is not so simple as it sounds, however. In general, for example, the forecaster should choose a technique that makes the best use of available data. For example, the simpler distribution system for Corning Ware had an S-curve like the ones we have examined. 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. In this method of forecasting, the management may bring together top executives of different functional areas of the enterprise such as production, finance, sales, purchasing, personnel, etc., supplies them with the necessary information relating to the product for which the forecast has to be made, gets their views and on this basis arrives at a figure. Many of the techniques described are only in the early stages of application, but still we expect most of the techniques that will be used in the next five years to be the ones discussed here, perhaps in extended form. North and Donald L. Pyke, “‘Probes’ of the Technological Future,” HBR May–June 1969, p. 68. To do this the forecaster needs to build. What are the dynamics and components of the system for which the forecast will be made? Computer applications will be mostly in established and stable product businesses. Project management is a process that involves several component aspects such as initiation, planning, executing, controlling, and closing. Finally, most computerized forecasting will relate to the analytical techniques described in this article. During the initiation and planning stages, project managers will often complete "Forecasting" exercises to determine the project's scope, possible constraints, and potential risks. To be sure, the manager will want margin and profit projection and long-range forecasts to assist planning at the corporate level. 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. 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