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. There are three basic types—qualitative techniques, time series analysis and projection, and causal models. 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. They are educated guesses by forecasters or experts based on … 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. To estimate the date by which a product will enter the rapid-growth stage is another matter. 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. In particular, when recent data seem to reflect sharp growth or decline in sales or any other market anomaly, the forecaster should determine whether any special events occurred during the period under consideration—promotion, strikes, changes in the economy, and so on. We agree that uncertainty increases when a forecast is made for a period more than two years out. Successful forecasting begins with a collaboration between the manager and the forecaster, in which they work out answers to the following questions. Once the manager and the forecaster have formulated their problem, the forecaster will be in a position to choose a method. 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. How much manufacturing capacity will the early production stages require? This reinforces our belief that sales forecasts for a new product that will compete in an existing market are bound to be incomplete and uncertain unless one culls the best judgments of fully experienced personnel. 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. 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. Once the manager and the forecaster have formulated their problem, the forecaster will be in a position to choose a method. The causal model takes into account everything known of the dynamics of the flow system and utilizes predictions of related events such as competitive actions, strikes, and promotions. The first uses qualitative data (expert opinion, for example) and information about special events of the kind already mentioned, and may or may not take the past into consideration. 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. For the year 1947–1968, Exhibit IV shows total consumer expenditures, appliance expenditures, expenditures for radios and TVs, and relevant percentages. 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. Codifying the estimates into a means of measuring project performance for work as it is accomplished. 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. 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. Systematic market research is, of course, a mainstay in this area. 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. ), Part C shows the result of discounting the raw data curve by the seasonals of Part B; this is the so-called deseasonalized data curve. Computer applications will be mostly in established and stable product businesses. The reader may find frequent reference to this gate-fold helpful for the remainder of the article. In 1965, we disaggregated the market for color television by income levels and geographical regions and compared these submarkets with the historical pattern of black-and-white TV market growth. 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. 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. 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. This initial forecast is what goes into the project hours and budget estimation and ultimately the price that is offered to the customer. Others have discussed different ones.3. 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. At the present time, most short-term forecasting uses only statistical methods, with little qualitative information. The forecaster, in turn, must blend the techniques with the knowledge and experience of the managers. 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. During the rapid-growth state of color TV, we recognized that economic conditions would probably effect the sales rate significantly. Project Management Forecasting Dr. Yiannis E. Polychronakis The Critical Nature of Forecasting Where do we use In the steady-state phase, production and inventory control, group-item forecasts, and long-term demand estimates are particularly important. 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. While there can be no direct data about a product that is still a gleam in the eye, information about its likely performance can be gathered in a number of ways, provided the market in which it is to be sold is a known entity. 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. 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. Thus the manufacturer can effect or control consumer sales quite directly, as well as directly control some of the pipeline elements. Qualitative forecasting methods, often called judgmental methods, are methods in which the forecast is made subjectively by the forecaster. The implications of these curves for facilities planning and allocation are obvious. Two CGW products that have been handled quite differently are the major glass components for color TV tubes, of which Corning is a prime supplier, and Corning Ware cookware, a proprietary consumer product line. 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. It has therefore proved of value to study the changes in growth pattern as each new growth point is obtained. 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. Regression analysis and statistical forecasts are sometimes used in this way—that is, to estimate what will happen if no significant changes are made. 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. Forecasting methods may be classified in the following categories: Time series Method: This method uses historical data to estimate future outcomes. This is almost never true. A trend and a seasonal are obviously two quite different things, and they must be handled separately in forecasting. When the retail sales slowed from rapid to normal growth, however, there were no early indications from shipment data that this crucial turning point had been reached. This will free the forecaster to spend most of the time forecasting sales and profits of new products. In concluding an article on forecasting, it is appropriate that we make a prediction about the techniques that will be used in the short- and long-term future. (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.). These decisions generally involve the largest expenditures in the cycle (excepting major R&D decisions), and commensurate forecasting and tracking efforts are justified. However, the development of such a model, usually called an econometric model, requires sufficient data so that the correct relationships can be established. Exhibit II displays these elements for the system through which CGW’s major component for color TV sets—the bulb—flows to the consumer. We hope to give the executive insight into the potential of forecasting by showing how this problem is to be approached. The prices of black-and-white TV and other major household appliances in 1949, consumer disposable income in 1949, the prices of color TV and other appliances in 1965, and consumer disposable income for 1965 were all profitably considered in developing our long-range forecast for color-TV penetration on a national basis. Finally, most computerized forecasting will relate to the analytical techniques described in this article. 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. It should be able to fit a curve to the most recent data adequately and adapt to changes in trends and seasonals quickly. 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. 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. Add this growth rate (whether positive or negative) to the present sales rate. Forecasting Revenue for a Fixed Price Project. 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. 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 time series type of forecasting methods, such as exponential smoothing, moving average and trend analysis, employ historical data to estimate future outcomes. This is accomplished by recognizing the realities of estimating accuracy, given the information on which it is based, and adjusting estimates for changes in scope or in the conditions of performance. 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. 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. Our predictions of consumer acceptance of Corning Ware cookware, on the other hand, were derived primarily from one expert source, a manager who thoroughly understood consumer preferences and the housewares market. 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. Exhibit IV Expenditures on Appliances Versus All Consumer Goods (In billions of dollars), Certain special fluctuations in these figures are of special significance here. From a project perspective, initial resource forecasting for an engagement is usually done upfront by the Account Manager during the sales process. These factors must be weighed constantly, and on a variety of levels. For a consumer product like the cookware, the manufacturer’s control of the distribution pipeline extends at least through the distributor level. Forecasters commonly use this approach to get acceptable accuracy in situations where it is virtually impossible to obtain accurate forecasts for individual items. For example, the type and length of moving average used is determined by the variability and other characteristics of the data at hand. It also should be versatile enough so that when several hundred items or more are considered, it will do the best overall job, even though it may not do as good a job as other techniques for a particular item. Again, see the gatefold for a rundown on the most common types of causal techniques. 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. 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. Before going any further, it might be well to illustrate what such sorting-out looks like. They use human judgment and rating schemes to turn qualitative information into quantitative estimates. Once the manager has defined the purpose of the forecast, the forecaster can advise the manager on how often it could usefully be produced. Although we believe forecasting is still an art, we think that some of the principles which we have learned through experience may be helpful to others. In planning production and establishing marketing strategy for the short and medium term, the manager’s first considerations are usually an accurate estimate of the present sales level and an accurate estimate of the rate at which this level is changing. (A similar increase of 33% occurred in 1962–1966 as color TV made its major penetration.). 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. Qualitative forecasting methods Forecast is made subjectively by the forecaster. 2. Exhibit VI Patterns for Color-TV Distributor Sales, Distributor Inventories, and Component Sales. Exhibit VII Data Plots of Factory Sales of Color TV Sets. Although the X-11 was not originally developed as a forecasting method, it does establish a base from which good forecasts can be made. How should we allocate R&D efforts and funds? As we have already said, it is not too difficult to forecast the immediate future, since long-term trends do not change overnight. Web sites about project management also included regression analysis, expert opinion, and causal/econometric methods. An important fact for you about project management methodologies: according to the PMI’s Pulse of the Profession,. This kind of trade-off is relatively easy to make, but others, as we shall see, require considerably more thought. That is, simulation bypasses the need for analytical solution techniques and for mathematical duplication of a complex environment and allows experimentation. 1. 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. 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. 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. Although the forecasting techniques have thus far been used primarily for sales forecasting, they will be applied increasingly to forecasting margins, capital expenditures, and other important factors. Before a product can enter its (hopefully) rapid penetration stage, the market potential must be tested out and the product must be introduced—and then more market testing may be advisable. 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. Part A presents the raw data curve. To estimate total demand on CGW production, we used a retail demand model and a pipeline simulation. 2, 1960. For example, priority pattern analysis can describe consumers’ preferences and the likelihood they will buy a product, and thus is of great value in forecasting (and updating) penetration levels and rates. Market tests and initial customer reaction made it clear there would be a large market for Corning Ware cookware. Many new products have initially appeared successful because of purchases by innovators, only to fail later in the stretch. 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. 1. When you use the Cost control page to view the current status of project costs, use the forecast models that were selected for the original and remaining budget. 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. Primarily, these are used when data are scarce—for example, when a product is first introduced into a market. 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. Heuristic programming will provide a means of refining forecasting models. However, the Box-Jenkins has one very important feature not existing in the other statistical techniques: the ability to incorporate special information (for example, price changes and economic data) into the forecast. 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. As necessary, however, we shall touch on other products and other forecasting methods. Stone and R.A. Rowe, “The Durability of Consumers’ Durable Goods,” Econometrica, Vol. The machine learning technology inside the tool analyzes how people are performing together as a team and optimizes the best route for them, counting the probability of project success in. It is possible that swings in demand and profit will occur because of changing economic conditions, new and competitive products, pipeline dynamics, and so on, and the manager will have to maintain the tracking activities and even introduce new ones. North and Donald L. Pyke, “‘Probes’ of the Technological Future,” HBR May–June 1969, p. 68. Here the manager and forecaster must weigh the cost of a more sophisticated and more expensive technique against potential savings in inventory costs. The forecasts using the X-11 technique were based on statistical methods alone, and did not consider any special information. A project that is subject to budget control uses two types of budgets: original and remaining. Exhibit III summarizes the life stages of a product, the typical decisions made at each, and the main forecasting techniques suitable at each. 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. A company’s only recourse is to use statistical tracking methods to check on how successfully the product is being introduced, along with routine market studies to determine when there has been a significant increase in the sales rate. That is, they do not separate trends from cycles. Note: Scales are different for component sales, distributor inventories, and distributor sales, with the patterns put on the same graph for illustrative purposes. 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. Market research studies can naturally be useful, as we have indicated. It should not require maintenance of large histories of each item in the data bank, if this can be avoided. 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). We are now in the process of incorporating special information—marketing strategies, economic forecasts, and so on—directly into the shipment forecasts. This determines the accuracy and power required of the techniques, and hence governs selection. See Harper Q. We now monitor field information regularly to identify significant changes, and adjust our shipment forecasts accordingly. You need to consider things at a more granular level. The X-11 provides the basic instrumentation needed to evaluate the effects of such events. Some examples of the type of information that must be weighed when making a complete forecast are examples such as the estimate at completion, or in other words, the estimate to complete. 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. Computations should take as little computer time as possible. What are the alternative growth opportunities to pursuing product. There are three basic types—qualitative techniques, time series analysis and projection, and causal models. One main activity during the rapid-growth stage, then, is to check earlier estimates and, if they appear incorrect, to compute as accurately as possible the error in the forecast and obtain a revised estimate. 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. 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. The model incorporated penetration rates, mortality curves, and the like. Therefore, we conducted market surveys to determine set use more precisely. Equally, during the rapid-growth stage, submodels of pipeline segments should be expanded to incorporate more detailed information as it is received. Eventually we found it necessary to establish a better (more direct) field information system. However, regardless of what approach you use, there are a … But, more commonly, the forecaster tries to identify a similar, older product whose penetration pattern should be similar to that of the new product, since overall markets can and do exhibit consistent patterns. It is occasionally true, of course, that one can be certain a new product will be enthusiastically accepted. Tactical decisions on promotions, specials, and pricing are usually at their discretion as well. Where data are unavailable or costly to obtain, the range of forecasting choices is limited. Exhibit I shows how cost and accuracy increase with sophistication and charts this against the corresponding cost of forecasting errors, given some general assumptions. What shall our marketing plan be—which markets should we enter and with what production quantities? 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. When color TV bulbs were proposed as a product, CGW was able to identify the factors that would influence sales growth. 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). Forecasting and Time-Phasing Remaining Hours, Materials, Equipment, etc. There are several approaches to resource forecasting, such as workload analysis, trend analysis, management judgment, etc. Forecasting can help them […]. For example, in production and inventory control, increased accuracy is likely to lead to lower safety stocks. Also, it is sometimes possible to accurately forecast long-term demands, even though the short-term swings may be so chaotic that they cannot be accurately forecasted. If the forecaster can readily apply one technique of acceptable accuracy, he or she should not try to “gold plate” by using a more advanced technique that offers potentially greater accuracy but that requires nonexistent information or information that is costly to obtain. 4castplus provides specialized tools to help project managers forecast remaining resource amounts to complete an activity. 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. 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. Further out, consumer simulation models will become commonplace. 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