important factors in choosing a forecasting technique are:divinity 2 respec talents

This is the method: In special cases where there are no seasonals to be considered, of course, this process is much simplified, and fewer data and simpler techniques may be adequate. Successful forecasting begins with a collaboration between the manager and the forecaster, in which they work out answers to the following questions. (We might further note that the differences between this trend-cycle line and the deseasonalized data curve represent the irregular or nonsystematic component that the forecaster must always tolerate and attempt to explain by other methods.). We now monitor field information regularly to identify significant changes, and adjust our shipment forecasts accordingly. Furthermore, where a company wishes to forecast with reference to a particular product, it must consider the stage of the products life cycle for which it is making the forecast. D. historical and associative. In some instances, models developed earlier will include only macroterms; in such cases, market research can provide information needed to break these down into their components. There's no getting around that in a world of uncertainty. Graph the rate at which the trend is changing. (Other techniques, such as panel consensus and visionary forecasting, seem less effective to us, and we cannot evaluate them from our own experience.). In this data exploration phase, it is important to identify these variables, understand them and transform them to fit the model. To avoid precisely this sort of error, the moving average technique, which is similar to the hypothetical one just described, uses data points in such a way that the effects of seasonals (and irregularities) are eliminated. 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. Using data extending through 1968, the model did reasonably well in predicting the downturn in the fourth quarter of 1969 and, when 1969 data was also incorporated into the model, accurately estimated the magnitude of the drop in the first two quarters of 1970. It is about understanding the problem (whether it be for descriptive or anomaly detection or clustering or regression or predictive,) categorizing the inputs and outputs, and knowing your data and its limitations. 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. It may be impossible for the company to obtain good information about what is taking place at points further along the flow system (as in the upper segment of Exhibit II), and, in consequence, the forecaster will necessarily be using a different genre of forecasting from what is used for a consumer product. 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. This is leading us in the direction of a causal forecasting model. 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. It may also directly incorporate the results of a time series analysis. Before going any further, it might be well to illustrate what such sorting-out looks like. Before we begin, let us note how the situations differ for the two kinds of products: Between these two examples, our discussion will embrace nearly the whole range of forecasting techniques. 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. Until computational shortcuts can be developed, it will have limited use in the production and inventory control area. 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. The costs of using these techniques will be reduced significantly; this will enhance their implementation. We expect that better computer methods will be developed in the near future to significantly reduce these costs. C)cost and accuracy. Exhibit III summarizes the life stages of a product, the typical decisions made at each, and the main forecasting techniques suitable at each. Probabilistic models will be used frequently in the forecasting process. As the chart shows, causal models are by far the best for predicting turning points and preparing long-range forecasts. The forecaster will use all of it, one way or another. 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. 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 I shows how cost and accuracy increase with sophistication and charts this against the corresponding cost of forecasting errors, given some general assumptions. Such points are called turning points. (In the next section we shall explain where this graph of the seasonals comes from. Accuracy Oc. 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. A version of this article appeared in the. Consider what would happen, for example, if a forecaster were merely to take an average of the most recent data points along a curve, combine this with other, similar average points stretching backward into the immediate past, and use these as the basis for a projection. 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. It defines the probability of happening of future events. Add this growth rate (whether positive or negative) to the present sales rate. We might mention a common criticism at this point. To handle the increasing variety and complexity of managerial forecasting problems, many forecasting techniques have been developed in recent years. Associate forecast involve identifying explanatory variables. Once the manager has defined the purpose of the forecast, the forecaster can advise the manager on how often it could usefully be produced. The manager must fix the level of inaccuracy he or she can toleratein other words, decide how his or her decision will vary, depending on the range of accuracy of the forecast. This is almost never true. In 1969 Corning decided that a better method than the X-11 was definitely needed to predict turning points in retail sales for color TV six months to two years into the future. For example, we will study market dynamics and establish more complex relationships between the factor being forecast and those of the forecasting system. The success patterns of black-and-white TV, then, provided insight into the likelihood of success and sales potential of color TV. 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. Sometimes forecasting is merely a matter of calculating the companys capacitybut not ordinarily. -The forecast should be reliable. 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. This will free the forecaster to spend most of the time forecasting sales and profits of new products. How successful will different product concepts be? Length of forecast horizon. 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. 11. However, the development of such a model, usually called an econometric model, requires sufficient data so that the correct relationships can be established. But there are other tools as well, depending on the state of the market and the product concept. These factors must be weighed constantly, and on a variety of levels. The analyses of black-and-white TV market growth also enabled us to estimate the variability to be expectedthat is, the degree to which our projections would differ from actual as the result of economic and other factors. It is an essential and basic tool for managing an organization of any size whether it is small or large. The two most important factors in choosing a forecasting technique are: - C. cost and accuracy. 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 inventoriesthat is, inventories that are adequate to customer demand but are not excessively costly. Therefore, the happening of future events can be precise only to a certain extent. We have found that an analysis of the patterns of change in the growth rate gives us more accuracy in predicting turning points (and therefore changes from positive to negative growth, and vice versa) than when we use only the trend cycle. 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 workforce. Econometric models will be utilized more extensively in the next five years, with most large companies developing and refining econometric models of their major businesses. Again, lets consider color television and the forecasts we prepared in 1965. In general, for example, the forecaster should choose a technique that makes the best use of available data. Because substantial inventories buffered information on consumer sales all along the line, good field data was lacking, which made this date difficult to estimate. 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. 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. Estimates of costs are approximate, as are computation times, accuracy ratings, and ratings for turning-point identification. Also, the feasibility of not entering the market at all, or of continuing R&D right up to the rapid-growth stage, can best be determined by sensitivity analysis. Pro forma statements are incredibly valuable when forecasting revenue, expenses, and sales. What every manager ought to know about the different kinds of forecasting and the times when they should be used. 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. List the elements of a good forecast. The continuing declining trend in computer cost per computation, along with computational simplifications, will make techniques such as the Box-Jenkins method economically feasible, even for some inventory-control applications. In the steady-state phase, production and inventory control, group-item forecasts, and long-term demand estimates are particularly important. Harvard Business Review also talks about the ability of a forecast to capture uncertainty as an extremely important factor to keep in mind while forecasting. The third uses highly refined and specific information about relationships between system elements, and is powerful enough to take special events formally into account. 3. Availability of forecasting software d. All of them Simple Linear Regression Assumptions are: a. That is, simulation bypasses the need for analytical solution techniques and for mathematical duplication of a complex environment and allows experimentation. Analyses like input-output, historical trend, and technological forecasting can be used to estimate this minimum. The two most important factors in choosing a forecasting technique are: cost and accuracy. One that does a reasonably good job of forecasting demand for the next three to six periods for individual items, One that forecasts total bulb demand more accurately for three to 13 periods into the future. True or false? Deciding whether to enter a business may. When color TV bulbs were proposed as a product, CGW was able to identify the factors that would influence sales growth. Once they are known, various mathematical techniques can develop projections from them. 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. What are the key factors and trade offs to consider when choosing a forecasting technique? 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. It is occasionally true, of course, that one can be certain a new product will be enthusiastically accepted. This determines the accuracy and power required of the techniques, and hence governs selection. 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. Eventually we found it necessary to establish a better (more direct) field information system. Statistical methods provide a good short-term basis for estimating and checking the growth rate and signaling when turning points will occur. In such cases, the best role for statistical methods is providing guides and checks for salespersons forecasts. Rule 1: Define a Cone of Uncertainty As a decision maker, you ultimately have to rely on your intuition and judgment. 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. 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. Human Resources (HR) urgently needed the patterns of HR management's key success factors towards the business departments and people. Because of lead-lag relationships and the ready availability of economic forecasts for the factors in the model, the effects of the economy on sales can be estimated for as far as two years into the future. The date when a product will enter the rapid-growth stage is hard to predict three or four years in advance (the usual horizon). This has been found to be especially effective for estimating the effects of price changes and promotions. 3. We shall illustrate the use of the various techniques from our experience with them at Corning, and then close with our own forecast for the future of forecasting. Choosing a model Further out, consumer simulation models will become commonplace. The model incorporated penetration rates, mortality curves, and the like. A graph of several years sales data, such as the one shown in Part A of Exhibit VII, gives an impression of a sales trend one could not possibly get if one were to look only at two or three of the latest data points. They use human judgment and rating schemes to turn qualitative information into quantitative estimates. Certain special fluctuations in these figures are of special significance here. Frequently one must develop a manual-override feature, which allows adjustments based on human judgment, in circumstances as fluid as these. 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 companys forecasting efforts will bear fruit. In this case, there is considerable difficulty in achieving desired profit levels if short-term scheduling does not take long-term objectives into consideration. This humping provided additional profit for CGW in 1966 but had an adverse effect in 1967. B. qualitative and quantitative. These are statistical techniques used when several years data for a product or product line are available and when relationships and trends are both clear and relatively stable. We have used it to provide sales estimates for each division for three periods into the future, as well as to determine changes in sales rates. At each stage of the life of a product, from conception to steady-state sales, the decisions that management must make are characteristically quite different, and they require different kinds of information as a base. Thus the manufacturer can effect or control consumer sales quite directly, as well as directly control some of the pipeline elements. Thus our statements may not accurately describe all the variations of a technique and should rather be interpreted as descriptive of the basic concept of each. Again, if the forecast is to set a standard against which to evaluate performance, the forecasting method should not take into account special actions, such as promotions and other marketing devices, since these are meant to change historical patterns and relationships and hence form part of the performance to be evaluated.

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