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). The manager as well as the forecaster has a role to play in technique selection; and the better they understand the range of forecasting possibilities, the more likely it is that a company’s forecasting efforts will bear fruit. While the X-11 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. We have compared our X-11 forecasts with forecasts developed by each of several divisions, where the divisions have used a variety of methods, some of which take into account salespersons’ estimates and other special knowledge. Forecasting methods are one of the analytical techniques listed under tools and techniques for the PMI process to monitor and control project work.Some examples mentioned in the PMBOK (section 4.4.2.2 of the 5th edition) are time series, scenario building, and simulation. demand, this is the type of forecasting that is emphasized in our textbook and in this course.TYPES OF FORECASTING METHODS Qualitative methods: These types of forecasting methods are based on judgments, opinions, intuition, emotions, or personal experiences and are subjective in nature. The interested reader will find a discussion of these topics on the reverse of the gatefold. This humping provided additional profit for CGW in 1966 but had an adverse effect in 1967. Eventually we found it necessary to establish a better (more direct) field information system. Statistical methods and salespersons’ estimates cannot spot these turning points far enough in advance to assist decision making; for example, a production manager should have three to six months’ warning of such changes in order to maintain a stable work force. Projections designed to aid profit planning. ALL RIGHTS RESERVED. Market research studies can naturally be useful, as we have indicated. This suggested to us that a better job of forecasting could be done by combining special knowledge, the techniques of the division, and the X-11 method. We might mention a common criticism at this point. Any regularity or systematic variation in the series of data which is due to seasonality—the “seasonals.”. Heuristic programming will provide a means of refining forecasting models. We shall return to this point when we discuss time series analysis in the final stages of product maturity.). The graph of change in growth thus provides an excellent visual base for forecasting and for identifying the turning point as well. One should note, however, that there is some instability in the trend line for the most recent data points, since the X-11, like virtually all statistical techniques, uses some form of moving average. Significant changes in the system—new products, new competitive strategies, and so forth—diminish the similarity of past and future. 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. 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. The color TV set, for example, was introduced in 1954, but did not gain acceptance from the majority of consumers until late 1964. This allows the forecaster to trade off cost against the value of accuracy in choosing a technique. 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. 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. How should we allocate R&D efforts and funds? Over a long period of time, changes in general economic conditions will account for a significant part of the change in a product’s growth rate. The second, on the other hand, focuses entirely on patterns and pattern changes, and thus relies entirely on historical data. Again, let’s consider color television and the forecasts we prepared in 1965. 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. 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. The forecaster will use all of it, one way or another. Successful forecasting begins with a collaboration between the manager and the forecaster, in which they work out answers to the following questions. On the other hand, if management wants a forecast of the effect that a certain marketing strategy under debate will have on sales growth, then the technique must be sophisticated enough to take explicit account of the special actions and events the strategy entails. 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