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. 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. 28, No. 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). Once the manager and the forecaster have formulated their problem, the forecaster will be in a position to choose a method. The second, on the other hand, focuses entirely on patterns and pattern changes, and thus relies entirely on historical data. 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. This knowledge is not absolutely “hard,” of course, and pipeline dynamics must be carefully tracked to determine if the various estimates and assumptions made were indeed correct. 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. 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. Long- and short-term production planning. Sometimes forecasting is merely a matter of calculating the company’s capacity—but not ordinarily. Forecasting and Time-Phasing Remaining Hours, Materials, Equipment, etc. 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. It should be able to fit a curve to the most recent data adequately and adapt to changes in trends and seasonals quickly. 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. For example, the type and length of moving average used is determined by the variability and other characteristics of the data at hand. Computations should take as little computer time as possible. In today’s project management world, forward-thinking managers and leaders don’t adhere to a single methodology—they become well-versed in … One that forecasts total bulb demand more accurately for three to thirteen periods into the future. Exhibit VII Data Plots of Factory Sales of Color TV Sets. Specifically, it is often useful to project the S-shaped growth curves for the levels of income of different geographical regions. management, time-phasing and detailed forecasting, to achieve a reliable result. 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. 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. Where qualitative information is used, it is only used in an external way and is not directly incorporated into the computational routine. When black-and-white TV was introduced as a new product in 1948–1951, the ratio of expenditures on radio and TV sets to total expenditures for consumer goods (see column 7) increased about 33% (from 1.23% to 1.63%), as against a modest increase of only 13% (from 1.63% to 1.88%) in the ratio for the next decade. In general, for example, the forecaster should choose a technique that makes the best use of available data. These differences imply (quite correctly) that the same type of forecasting technique is not appropriate to forecast sales, say, at all stages of the life cycle of a product—for example, a technique that relies on historical data would not be useful in forecasting the future of a totally new product that has no history. During the initiation and planning stages, project managers will often complete "Forecasting" exercises to determine the project's scope, possible constraints, and potential risks. For a consumer product like the cookware, the manufacturer’s control of the distribution pipeline extends at least through the distributor level. The technique selected by the forecaster for projecting sales therefore should permit incorporation of such “special information.” One may have to start with simple techniques and work up to more sophisticated ones that embrace such possibilities, but the final goal is there. Analyses like input-output, historical trend, and technological forecasting can be used to estimate this minimum. Adaptive forecasting also meets these criteria. The implications of these curves for facilities planning and allocation are obvious. Exhibit II Flow Chart of TV Distribution System. Once the manager and the forecaster have formulated their problem, the forecaster will be in a position to choose a method. 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. The next step was to look at the cumulative penetration curve for black-and-white TVs in U.S. households, shown in Exhibit V. 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). In the early stages of product development, the manager wants answers to questions such as these: Forecasts that help to answer these long-range questions must necessarily have long horizons themselves. During the rapid-growth state of color TV, we recognized that economic conditions would probably effect the sales rate significantly. Since projects are usually temporary rather than ongoing, with definitive start and end dates to construct a time frame during which project objectives are meant to be achieved, forecasting is an extremely important element of the initiation stages of project management. 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. These decisions generally involve the largest expenditures in the cycle (excepting major R&D decisions), and commensurate forecasting and tracking efforts are justified. Virtually all the statistical techniques described in our discussion of the steady-state phase except the X-11 should be categorized as special cases of the recently developed Box-Jenkins technique. We conducted frequent marketing studies as well. What are the alternative growth opportunities to pursuing product. Primarily, these are used when data are scarce—for example, when a product is first introduced into a market. The graph of change in growth thus provides an excellent visual base for forecasting and for identifying the turning point as well. The forecasts using the X-11 technique were based on statistical methods alone, and did not consider any special information. The forecaster thus is called on for two related contributions at this stage: The type of product under scrutiny is very important in selecting the techniques to be used. This clarifies the relationships of interacting variables. At these meetings, the decision to revise or update a model or forecast is weighed against various costs and the amount of forecasting error. Most of the facilities planning has been squared away, and trends and growth rates have become reasonably stable. There are several approaches to resource forecasting, such as workload analysis, trend analysis, management judgment, etc. 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. While there are a wide range of frequently used quantitative budget forecasting tools, in this article we focus on the top four methods: (1) straight-line, (2) moving average, (3) simple linear regression, and (4) multiple linear regression. We call this product differences measurement.2. TeamAmp – https://certus3.com/ai-assurance-suite/teamamp/. One that does a reasonably good job of forecasting demand for the next three to six periods for individual items. 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. Exhibit IV Expenditures on Appliances Versus All Consumer Goods (In billions of dollars), Certain special fluctuations in these figures are of special significance here. As we have already said, it is not too difficult to forecast the immediate future, since long-term trends do not change overnight. These predictions have been well borne out. Still, sorting-out approaches have proved themselves in practice. 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. Some of the techniques listed are not in reality a single method or model, but a whole family. Granting the applicability of the techniques, we must go on to explain how the forecaster identifies precisely what is happening when sales fluctuate from one period to the next and how such fluctuations can be forecast. What is the purpose of the forecast—how is it to be used? 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. This might be called the unseasonalized sales rate. For example, Quantum-Science Corporation (MAPTEK) has developed techniques that make input-output analyses more directly useful to people in the electronics business today. 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 main advantage of considering growth change, in fact, is that it is frequently possible to predict earlier when a no-growth situation will occur. Third, one can compare a projected product with an “ancestor” that has similar characteristics. Computer software packages for the statistical techniques and some general models will also become available at a nominal cost. 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 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. To estimate the date by which a product will enter the rapid-growth stage is another matter. 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. The need today, we believe, is not for better forecasting methods, but for better application of the techniques at hand. As one can see from this curve, supplier sales may grow relatively sharply for several months and peak before retail sales have leveled off. Trend forecasting takes the current project spending and extrapolates that rate of spending until the end of the project. Forecasts are essential for trying to get a predictory big picture view of the project.’ This term is defined in the 3rd and the 4th edition of the PMBOK. The availability of data and the possibility of establishing relationships between the factors depend directly on the maturity of a product, and hence the life-cycle stage is a prime determinant of the forecasting method to be used. Significant changes in the system—new products, new competitive strategies, and so forth—diminish the similarity of past and future. 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. Qualitative forecasting methods Forecast is made subjectively by the forecaster. It is very comprehensive: at a cost of about $10, it provides detailed information on seasonals, trends, the accuracy of the seasonals and the trend cycle fit, and a number of other measures. Equally, during the rapid-growth stage, submodels of pipeline segments should be expanded to incorporate more detailed information as it is received. Finally, most computerized forecasting will relate to the analytical techniques described in this article. 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. 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. This humping provided additional profit for CGW in 1966 but had an adverse effect in 1967. Heuristic programming will provide a means of refining forecasting models. A trend and a seasonal are obviously two quite different things, and they must be handled separately in forecasting. 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. All of these factors require a project manager to be as accurate as possible when making his or her predictions about any aspect of a given project's life cycle. Forecasting can help them […]. This is actually being done now by some of the divisions, and their forecasting accuracy has improved in consequence. Exhibit I shows how cost and accuracy increase with sophistication and charts this against the corresponding cost of forecasting errors, given some general assumptions. Add this growth rate (whether positive or negative) to the present sales rate. 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. Any regularity or systematic variation in the series of data which is due to seasonality—the “seasonals.”. 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. 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. Again, see the gatefold for a rundown on the most common types of causal techniques. In an EVM analysis, quite a number of time and cost forecasting techniques are available, but it is however a cumbersome task to select the right technique for the project under study. Use. Before we begin, let us note how the situations differ for the two kinds of products: Many of the changes in shipment rates and in overall profitability are therefore due to actions taken by manufacturers themselves. 1. The date when a product will enter the rapid-growth stage is hard to predict three or four years in advance (the usual horizon). Causal/Econometric Methods: This method assumes that it is possible to identify the underlying factors that might influence what is being forecasted. 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. 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. ALL RIGHTS RESERVED. Market research studies can naturally be useful, as we have indicated. The models will predict the behavior of consumers and forecast their reactions to various marketing strategies such as pricing, promotions, new product introductions, and competitive actions. Ongoing control of the estimate reliability. 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. Setting standards to check the effectiveness of marketing strategies. See Harper Q. 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. The objective here is to bring together in a logical, unbiased, and systematic way all information and judgments which relate to the factors being estimated. If this approach is to be successful, it is essential that the (in-house) experts who provide the basic data come from different disciplines—marketing, R&D, manufacturing, legal, and so on—and that their opinions be unbiased. As necessary, however, we shall touch on other products and other forecasting methods. For this same reason, these techniques ordinarily cannot predict when the rate of growth in a trend will change significantly—for example, when a period of slow growth in sales will suddenly change to a period of rapid decay. 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. Once the analysis is complete, the work of projecting future sales (or whatever) can begin. 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. A hard date when sales will level to “normal,”, For component products, the deviation in the growth curve that may be caused by characteristic. Here the manager and forecaster must weigh the cost of a more sophisticated and more expensive technique against potential savings in inventory costs. How successful will different product concepts be? 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. However, at the very least, the forecast and a measure of its accuracy enable the manager to know the risks in pursuing a selected strategy and in this knowledge to choose an appropriate strategy from those available. 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. Codifying the estimates into a means of measuring project performance for work as it is accomplished. This kind of trade-off is relatively easy to make, but others, as we shall see, require considerably more thought. Systematic market research is, of course, a mainstay in this area. The simulation output allowed us to apply projected curves like the ones shown in Exhibit VI to our own component-manufacturing planning. Predicting the final project duration and/or cost of a project in progress, given the current project performance, is a crucial step during project control. That is, they do not separate trends from cycles. The time series type of forecasting methods, such as exponential smoothing, moving average and trend analysis, employ historical data to estimate future outcomes. To be sure, the manager will want margin and profit projection and long-range forecasts to assist planning at the corporate level. In late 1965 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. Project Management Forecasting Dr. Yiannis E. Polychronakis The Critical Nature of Forecasting Where do we use 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. 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. Where the manager’s company supplies a component to an OEM, as Corning does for tube manufacturers, the company does not have such direct influence or control over either the pipeline elements or final consumer sales. Since a business or product line may represent only a small sector of an industry, it may be difficult to use the tables directly. Thus the manufacturer can effect or control consumer sales quite directly, as well as directly control some of the pipeline elements. Simulation is an excellent tool for these circumstances because it is essentially simpler than the alternative—namely, building a more formal, more “mathematical” model. 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. We agree that uncertainty increases when a forecast is made for a period more than two years out. Qualitative forecasting methods, often called judgmental methods, are methods in which the forecast is made subjectively by the forecaster. 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. Before going any further, it might be well to illustrate what such sorting-out looks like. Although statistical tracking is a useful tool during the early introduction stages, there are rarely sufficient data for statistical forecasting. This information is then incorporated into the item forecasts, with adjustments to the smoothing mechanisms, seasonals, and the like as necessary. In sum, then, the objective of the forecasting technique used here is to do the best possible job of sorting out trends and seasonalities. One of the basic principles of statistical forecasting—indeed, of all forecasting when historical data are available—is that the forecaster should use the data on past performance to get a “speedometer reading” of the current rate (of sales, say) and of how fast this rate is increasing or decreasing. We are now in the process of incorporating special information—marketing strategies, economic forecasts, and so on—directly into the shipment forecasts. Harvard Business Publishing is an affiliate of Harvard Business School. The third uses highly refined and specific information about relationships between system elements, and is powerful enough to take special events formally into account. Again, let’s consider color television and the forecasts we prepared in 1965. 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. Exhibit VI Patterns for Color-TV Distributor Sales, Distributor Inventories, and Component Sales. Still, the figures we present may serve as general guidelines. The manager must fix the level of inaccuracy he or she can tolerate—in other words, decide how his or her decision will vary, depending on the range of accuracy of the forecast. We expect that better computer methods will be developed in the near future to significantly reduce these costs. 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. Therefore, we conducted market surveys to determine set use more precisely. What shall our marketing plan be—which markets should we enter and with what production quantities? And because trends tend to change gradually rather than suddenly, statistical and other quantitative methods are excellent for short-term forecasting. Second, and more formalistically, one can construct disaggregate market models by separating off different segments of a complex market for individual study and consideration. 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. 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. Once they are known, various mathematical techniques can develop projections from them. This is almost never true. View Day5, Forecasting from INTERNATIO MCI-M5-OPS at Kedge Business School. It is obvious from this description that all statistical techniques are based on the assumption that existing patterns will continue into the future. As we have indicated earlier, trend analysis is frequently used to project annual data for several years to determine what sales will be if the current trend continues. For short-term forecasts of one to three months, the X-11 technique has proved reasonably accurate. 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. Next, in Part D, we have drawn the smoothest or “best” curve possible through the deseasonalized curve, thereby obtaining the trend cycle. The decisions the manager at this stage are quite different from those made earlier. For the illustration given in Exhibit VII, this graph is shown in. As we gain confidence in such systems, so that there is less exception reporting, human intervention will decrease. 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