Because substantial inventories buffered information on consumer sales all along the line, good field data were lacking, which made this date difficult to estimate. The third uses highly refined and specific information about relationships between system elements, and is powerful enough to take special events formally into account. Long- and short-term production planning. The forecaster will use all of it, one way or another. 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. To estimate the date by which a product will enter the rapid-growth stage is another matter. 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. These factors must be weighed constantly, and on a variety of levels. 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. Exhibit VII Data Plots of Factory Sales of Color TV Sets. 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. 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. management, time-phasing and detailed forecasting, to achieve a reliable result. How much manufacturing capacity will the early production stages require? 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. Market research studies can naturally be useful, as we have indicated. Probabilistic models will be used frequently in the forecasting process. Many new products have initially appeared successful because of purchases by innovators, only to fail later in the stretch. At CGW, in several instances, we have used it to estimate demand for such new products, with success. The forecaster might easily overreact to random changes, mistaking them for evidence of a prevailing trend, mistake a change in the growth rate for a seasonal, and so on. 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. 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. 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. As well as merely buffering information, in the case of a component product, the pipeline exerts certain distorting effects on the manufacturer’s demand; these effects, although highly important, are often illogically neglected in production or capacity planning. 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. Exhibit V Long-term Household Penetration Curves for Color and Black-and-White TV. How important is the past in estimating the future? The flow chart should also show which parts of the system are under the control of the company doing the forecasting. 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. 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. Exhibit I Cost of Forecasting Versus Cost of Inaccuracy For a Medium-Range Forecast, Given Data Availability. How shall we allocate our R&D resources over time? As necessary, however, we shall touch on other products and other forecasting methods. (A similar increase of 33% occurred in 1962–1966 as color TV made its major penetration.). Forecasting provides the knowledge of planning premises within which the managers can analyse their strengths and weaknesses and can take appropriate actions in advance before actually they are put out of market. Probably the acceptance of black-and-white TV as a major appliance in 1950 caused the ratio of all major household appliances to total consumer goods (see column 5) to rise to 4.98%; in other words, the innovation of TV caused the consumer to start spending more money on major appliances around 1950. 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. Deciding whether to enter a business may require only a rather gross estimate of the size of the market, whereas a forecast made for budgeting purposes should be quite accurate. The costs of using these techniques will be reduced significantly; this will enhance their implementation. 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. Techniques vary in their costs, as well as in scope and accuracy. A disclaimer about estimates in the chart is also in order. 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. Furthermore, where a company wishes to forecast with reference to a particular product, it must consider the stage of the product’s life cycle for which it is making the forecast. The X-11 provides the basic instrumentation needed to evaluate the effects of such events. 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. Forecasting Revenue for a Fixed Price Project. 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. 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. That is, they do not separate trends from cycles. 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. The color TV set, for example, was introduced in 1954, but did not gain acceptance from the majority of consumers until late 1964. The division forecasts had slightly less error than those provided by the X-11 method; however, the division forecasts have been found to be slightly biased on the optimistic side, whereas those provided by the X-11 method are unbiased. 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 1948–1949, 1953–1954, 1957–1958, and 1960–1961). The system is brimming with intelligent logic to know what was budgeted and the amounts burned to date, broken down by resource type. It is obvious from this description that all statistical techniques are based on the assumption that existing patterns will continue into the future. 2. With these data and assumptions, we forecast retail sales for the remainder of 1965 through mid-1970 (see the dotted section of the lower curve in Exhibit V). Equally, during the rapid-growth stage, submodels of pipeline segments should be expanded to incorporate more detailed information as it is received. At the present time, most short-term forecasting uses only statistical methods, with little qualitative information. This will free the forecaster to spend most of the time forecasting sales and profits of new products. In sum, then, the objective of the forecasting technique used here is to do the best possible job of sorting out trends and seasonalities. 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. Part A presents the raw data curve. One of the best techniques we know for analyzing historical data in depth to determine seasonals, present sales rate, and growth is the X-11 Census Bureau Technique, which simultaneously removes seasonals from raw information and fits a trend-cycle line to the data. 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. We hope to give the executive insight into the potential of forecasting by showing how this problem is to be approached. All the elements in dark gray directly affect forecasting procedure to some extent, and the color key suggests the nature of CGW’s data at each point, again a prime determinant of technique selection since different techniques require different kinds of inputs. Primarily, these are used when data are scarce—for example, when a product is first introduced into a market. The forecasting techniques that provide these sets of information differ analogously. Math involved. 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. To be sure, the manager will want margin and profit projection and long-range forecasts to assist planning at the corporate level. 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. Until computational shortcuts can be developed, it will have limited use in the production and inventory control area. See John C. Chambers, Satinder K. Mullick, and David A. Goodman, “Catalytic Agent for Effective Planning,” HBR January–February 1971, p. 110. There are several approaches to resource forecasting, such as workload analysis, trend analysis, management judgment, etc. 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. What are the alternative growth opportunities to pursuing product. 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