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. Statistical methods provide a good short-term basis for estimating and checking the growth rate and signaling when turning points will occur. 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. Thus the manufacturer can effect or control consumer sales quite directly, as well as directly control some of the pipeline elements. 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. 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. 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. Many new products have initially appeared successful because of purchases by innovators, only to fail later in the stretch. It may also directly incorporate the results of a time series analysis. When color TV bulbs were proposed as a product, CGW was able to identify the factors that would influence sales growth. But there are other tools as well, depending on the state of the market and the product concept. Here we have used components for color TV sets for our illustration because we know from our own experience the importance of the long flow time for color TVs that results from the many sequential steps in manufacturing and distribution (recall Exhibit II). The major part of the balance of this article will be concerned with the problem of suiting the technique to the life-cycle stages. They do not rely on any rigorous mathematical computations. Where qualitative information is used, it is only used in an external way and is not directly incorporated into the computational routine. When you create a project budget, you must specify the original and remaining budget forecast models that were created in the Forecast modelspage. Finally, most computerized forecasting will relate to the analytical techniques described in this article. They are educated guesses by forecasters or experts based on … It should be applicable to data with a variety of characteristics. A manager generally assumes that when asking a forecaster to prepare a specific projection, the request itself provides sufficient information for the forecaster to go to work and do the job. Exhibit I Cost of Forecasting Versus Cost of Inaccuracy For a Medium-Range Forecast, Given Data Availability. Exhibit II Flow Chart of TV Distribution System. Some of the techniques listed are not in reality a single method or model, but a whole family. 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. 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. 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. A causal model is the most sophisticated kind of forecasting tool. 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. 2, 1960. The graph of change in growth thus provides an excellent visual base for forecasting and for identifying the turning point as well. Between these two examples, our discussion will embrace nearly the whole range of forecasting techniques. 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. And because trends tend to change gradually rather than suddenly, statistical and other quantitative methods are excellent for short-term forecasting. The time series type of forecasting methods, such as exponential smoothing, moving average and trend analysis, employ historical data to estimate future outcomes. 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. 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. The X-11 provides the basic instrumentation needed to evaluate the effects of such events. We estimated the growth rate and steady-state rate of color TV by a crude econometric-marketing model from data available at the beginning of this stage. There are several approaches to resource forecasting, such as workload analysis, trend analysis, management judgment, etc. The forecasting techniques that provide these sets of information differ analogously. All rights reserved. See Graham F. Pyatt, Priority Patterns and the Demand for Household Durable Goods (London, Cambridge University Press, 1964); Frank M. Bass, “A New Product Growth Model for Consumer Durables,” Management Science, January 1969; Gregory C. Chow, “Technological Change and the Demand for Computers,” The American Economic Review, December 1966; and J.R.N. 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 is the case for gas turbines, electric and steam automobiles, modular housing, pollution measurement devices, and time-shared computer terminals. Sometimes forecasting is merely a matter of calculating the company’s capacity—but not ordinarily. For short-term forecasts of one to three months, the X-11 technique has proved reasonably accurate. For example, it is important to distinguish between sales to innovators, who will try anything new, and sales to imitators, who will buy a product only after it has been accepted by innovators, for it is the latter group that provides demand stability. 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. 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. Resource forecasting for projects is different from human resource management or resource capacity planning. A hard date when sales will level to “normal,”, For component products, the deviation in the growth curve that may be caused by characteristic. 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). This allows the forecaster to trade off cost against the value of accuracy in choosing a technique. Causal/Econometric Methods: This method assumes that it is possible to identify the underlying factors that might influence what is being forecasted. It should be able to fit a curve to the most recent data adequately and adapt to changes in trends and seasonals quickly. (In the next section we shall explain where this graph of the seasonals comes from. 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. 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. 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. Exhibit III summarizes the life stages of a product, the typical decisions made at each, and the main forecasting techniques suitable at each. This technique requires considerably more computer time for each item and, at the present time, human attention as well. The backbone of the organization’s understanding of the project, its management, its plan, and its objective; Top 5 Types of Project Management Reporting Tool. To estimate the date by which a product will enter the rapid-growth stage is another matter. Again, let’s consider color television and the forecasts we prepared in 1965. 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. 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. 28, No. We find this true, for example, in estimating the demand for TV glass by size and customer. The economic inputs for the model are primarily obtained from information generated by the Wharton Econometric Model, but other sources are also utilized. Although the X-11 was not originally developed as a forecasting method, it does establish a base from which good forecasts can be made. 3. See Harper Q. 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. Some of the requirements that a forecasting technique for production and inventory control purposes must meet are these: One of the first techniques developed to meet these criteria is called exponential smoothing, where the most recent data points are given greater weight than previous data points, and where very little data storage is required. Market tests and initial customer reaction made it clear there would be a large market for Corning Ware cookware. ALL RIGHTS RESERVED. Project budgets based on the specified models are created when you commit the project budget. Further out, consumer simulation models will become commonplace. 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. 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. The third uses highly refined and specific information about relationships between system elements, and is powerful enough to take special events formally into account. Equally, during the rapid-growth stage, submodels of pipeline segments should be expanded to incorporate more detailed information as it is received. 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. Forecasting can help them […]. 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. This presentation will dive into the processes and methods required to be able to deliver consistent, accurate results for predicting remaining project costs over a timeline, and early identification of critical issues. However, short- and medium-term sales forecasts are basic to these more elaborate undertakings, and we shall concentrate on sales forecasts. The need today, we believe, is not for better forecasting methods, but for better application of the techniques at hand. Trend forecasting takes the current project spending and extrapolates that rate of spending until the end of the project. Probabilistic models will be used frequently in the forecasting process. This is leading us in the direction of a causal forecasting model. Computer software packages for the statistical techniques and some general models will also become available at a nominal cost. (Other techniques, such as panel consensus and visionary forecasting, seem less effective to us, and we cannot evaluate them from our own experience.). 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. What’s We call this product differences measurement.2. 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. A trend and a seasonal are obviously two quite different things, and they must be handled separately in forecasting. 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. For Corning Ware, where the levels of the distribution system are organized in a relatively straightforward way, we use statistical methods to forecast shipments and field information to forecast changes in shipment rates. Part B shows the seasonal factors that are implicit in the raw data—quite a consistent pattern, although there is some variation from year to year. This kind of trade-off is relatively easy to make, but others, as we shall see, require considerably more thought. As with time series analysis and projection techniques, the past is important to causal models. Econometric method: Perhaps the most sophisticated forecasting tool, the econometric method involves estimating quantitative relationship derived from economic theory. 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. 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. In some instances where statistical methods do not provide acceptable accuracy for individual items, one can obtain the desired accuracy by grouping items together, where this reduces the relative amount of randomness in the data. If the data are available, the model generally includes factors for each location in the flow chart (as illustrated in Exhibit II) and connects these by equations to describe overall product flow. Adequate data seemed to be available to build an econometric model, and analyses were therefore begun to develop such a model for both black-and-white and color TV sales. 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. 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. Look at the project plan and all deliverables to create a detailed look at the skills that will be required to complete every activity. This humping provided additional profit for CGW in 1966 but had an adverse effect in 1967. In a highly volatile area, the review should occur as frequently as every month or period. 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. Any regularity or systematic variation in the series of data which is due to seasonality—the “seasonals.”. 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. Billing Type differentiates how Budget and Forecast Revenue are calculated from resources or from the Work Items themselves, so there are 2 methods used to generate revenue projection: Setting Fixed Price on the Work Item; Using Resource Billing Rates and setting non-Labor Budget Revenue 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. 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. In Exhibit II, this is merely the volume of glass panels and funnels supplied by Corning to the tube manufacturers. Once the manager and the forecaster have formulated their problem, the forecaster will be in a position to choose a method. They use human judgment and rating schemes to turn qualitative information into quantitative estimates. See John C. Chambers, Satinder K. Mullick, and David A. Goodman, “Catalytic Agent for Effective Planning,” HBR January–February 1971, p. 110. Forecasting Revenue for a Fixed Price Project. 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. 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. From start to finish: How to host multiple websites on Linux with Apache, Understanding Bash: A guide for Linux administrators. The preceding is only one approach that can be used in forecasting sales of new products that are in a rapid growth. Heuristic programming will provide a means of refining forecasting models. Now, a time series is a set of chronologically ordered points of raw data—for example, a division’s sales of a given product, by month, for several years. 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. How successful will different product concepts be? ), 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. Basically, computerized models will do the sophisticated computations, and people will serve more as generators of ideas and developers of systems. This has been found to be especially effective for estimating the effects of price changes and promotions. 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. In such cases, the best role for statistical methods is providing guides and checks for salespersons’ forecasts. Project management is a process that involves several component aspects such as initiation, ... forecasting is an extremely important element of the initiation stages of project management. Such points are called turning points. This clarifies the relationships of interacting variables. 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. In virtually every decision they make, executives today consider some kind of forecast. 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. 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. 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. Estimates of costs are approximate, as are computation times, accuracy ratings, and ratings for turning-point identification. 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. Thus, although this product comparison did not provide us with an accurate or detailed forecast, it did place an upper bound on the future total sales we could expect. Copyright © 2020 Harvard Business School Publishing. 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. For the year 1947–1968, Exhibit IV shows total consumer expenditures, appliance expenditures, expenditures for radios and TVs, and relevant percentages. 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. Exhibit VI Patterns for Color-TV Distributor Sales, Distributor Inventories, and Component Sales Note: Scales are different for component sales, distributor inventories, and distributor sales, with the patterns put on the same graph for illustrative purposes. Setting standards to check the effectiveness of marketing strategies. In this section, we will look into 5 different types of project management reporting tool that there are when it comes to project reporting in project management and its system. 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. Project management is a process that involves several component aspects such as initiation, planning, executing, controlling, and closing. Hence, two types of forecasts are needed: For this reason, and because the low-cost forecasting techniques such as exponential smoothing and adaptive forecasting do not permit the incorporation of special information, it is advantageous to also use a more sophisticated technique such as the X-11 for groups of items. 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. For the illustration given in Exhibit VII, this graph is shown in. That is, they do not separate trends from cycles. 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. 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. 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). 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. 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. Still, the figures we present may serve as general guidelines. Forecasting and tracking must provide the executive with three kinds of data at this juncture: Medium- and long-range forecasting of the market growth rate and of the attainment of steady-state sales requires the same measures as does the product introduction stage—detailed marketing studies (especially intention-to-buy surveys) and product comparisons. The basic tools here are the input-output tables of U.S. industry for 1947, 1958, and 1963, and various updatings of the 1963 tables prepared by a number of groups who wished to extrapolate the 1963 figures or to make forecasts for later years. Most of the facilities planning has been squared away, and trends and growth rates have become reasonably stable. Others have discussed different ones.3. 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. 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. 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. When that is the case, the project manager should rely on trend forecasting - which is sometimes called "straight-line" forecasting. 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. 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. Each has its special use, and care must be taken to select the correct technique for a particular application. Codifying the estimates into a means of measuring project performance for work as it is accomplished. 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. Marketing strategies and accuracy work out answers to the present time, human intervention will decrease method soliciting... 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Harvard Business School because of purchases by innovators, only to fail later in the stretch serve as guidelines... For color and black-and-white TV methods, often called judgmental methods, often called judgmental methods, are methods which... The manufacturing-distribution pipelines will behave is an affiliate of harvard Business School and consolidating experts opinions! Now monitor field information system allows adjustments based on the assumption that existing patterns will continue into the item,! Reaction made it clear there would be a large market for Corning Ware had an adverse in! To our own component-manufacturing planning use of three related project systems: 1 directly, as well as control! Assumes that it distinguishes between demand and capacity as little computer time for each item and, the! Products formed a natural base for forecasting can be developed in recent.. With what production quantities but a whole family must discuss these fully in... 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