GB513 Unit 5: Time Series Forecasting - Discussion

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timer Asked: Mar 31st, 2017

Question Description

Unit 5 Discussion

This Discussion is based on the article, “Manager’s Guide to Forecasting” by David Georgoff and Robert Murdick (Attached), make sure to review this article before beginning this Discussion.

In this course, you will touch on a few forecasting methods, although there are many more approaches available to those managers who wish to do more. This week’s article provides an overview of many forecasting methodologies and provides a framework through which you can explore their differences.

Your objective in this Discussion is to learn to analyze a specific forecasting situation and identify the best suited methodology. You will complete three steps.

Step 1: Describe a specific forecasting need in your organization.

Step 2: Use the provided table below to analyze the requirements of the forecasting problem.

Step 3: Identify the best matching forecasting methodology to your situation and describe how it would be executed.

In Step 2, the analysis will be based on the table shown on pages 4 and 5 of the article. This table lists several questions about the nature of the forecasting situation, such as the urgency, detail required, and costs factors, and provides an overview of how well various forecasting methodologies will fit those requirements. For example, some forecasting methods cost more than others and depending on your financial resources, some of them may not be suitable. The same holds true with the math skills available or the need for high accuracy. So understand what each category is referring to, fill in the information, and follow the table to see which methodology is recommended for your specific case. You are learning how to analyze your situation so as to pick the best approach. Please note that these 2 pages in the article (pages 4–5) go side by side. You may wish to print them out and place them next to each other to read across the rows comfortably. The table shown below is based on the table in the article.

Please use the template below in your answers, so everyone can easily follow your answers to all the questions (copy and paste to your post).

Use this format for your Unit 5 Discussion.

Forecast need

Describe what question this forecast aims to answer, and why it is important for your organization to have this information.

Forecast situation analysis

Identify a forecast method by filling in the table below. The full table is on pages 4–5 of the article. You should fill in the table by answering the questions in the “Questions” column. Your answers will lead you to the methods that are most suitable for your forecasting need.

CategoryDimensionQuestionsAnswer to the questions
TimeSpanIs the forecast period a present, short/medium, or long-term projection?
UrgencyIs the forecast needed immediately?
FrequencyAre frequent forecast updates needed?
ResourceMath skillsAre quantitative skills limited?
ComputerAre computer capabilities limited?
FinancialAre only limited financial resources available?
InputAntecedentAre only limited past data available?
VariabilityDoes the primary series fluctuate substantially?
Internal consistencyAre significant changes in management decisions expected?
External consistencyAre significant environmental changes expected?
External stabilityAre significant shifts expected among variable relationships?
OutputDetailAre component forecasts required?
AccuracyIs a high level of accuracy critical?
Capability for reflecting direction changesShould turning points be reflected properly?
Capability for detecting direction changesShould turning points be identified early?
FormIs an internal or probabilistic forecast critical?


Recommendations

Which forecasting methodology listed in the article is the best match to your situation?

Describe how this forecast will be executed: Who will do it, where will the data come from, how frequently will it be repeated, and how will the results be used?

For the exclusive use of B. May, 2016. Manager’s Guide to Forecasting by David M. Georgoff and Robert G. Murdick Harvard Business Review Reprint 86104 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. HBR J A N U A RY– F E B R U A RY 1 9 8 6 Manager’s Guide to Forecasting David M. Georgoff and Robert G. Murdick E arly in 1984, the Houston-based COMPAQ Computer Corporation, manufacturer of IBMcompatible microcomputers, faced a decision that would profoundly affect its future. Recognizing that IBM would soon introduce its version of the portable computer and threaten COMPAQ’s dominance in this profitable market, the company had two options. It could elect to specialize in this product line and continue to market its highly regarded portables aggressively, or it could expand market offerings to include desktop microcomputers. The latter move would force the year-old company to confront IBM on its home ground. Moreover, COMPAQ would have to make a substantial investment in product development and working capital and expand its organization and manufacturing capacity. COMPAQ’s management faced several important unknowns, including the potential market’s size, structure, and competitive intensity. Management recognized that the company’s vitality might seriously erode if it did not expand its product line. If the expansion were successful, COMPAQ might enjoy economies of scale that could help ensure its survival in a dynamic and very competitive industry. If COMPAQ’s market assumptions were incorrect, however, its future might be bleak. Many of today’s managers face similar new market realities and uncertainties. Continually confronted with issues critical to their companies’ competitive future, they must deal with novel and rapidly changing environments. In short, they must judge a broad range of dissimilar influences. For more than a decade, new forecasting techniques have theoretically helped managers evaluate these varied factors. Much of the promise of these techniques has been unrealized, however, even as a quickening succession of related advances have been overwhelming decision makers with new alternatives. As the number of techniques proliferates, management also realizes that some of its crucial assumptions and projections about the economy have become quite tenuous. Equipped only with a little history, meager and questionable data, and frail and changing theoretical tools, the forecaster must nevertheless make critical decisions about altered futures. As an example, COMPAQ Computer’s quandary was further complicated because new technologies, competitors, and products were already transforming Mr. Georgoff is professor of marketing at Florida Atlantic University and chairman of the Department of Management, Marketing, and International Business. He has published articles and worked as a consultant to large corporations in the areas of new product marketing, marketing planning, market research, and forecasting. Mr. Murdick is professor of management at Florida Atlantic University. Previously he worked at the General Electric Company for 14 years. Well known in the field of management information systems, he is the author or coauthor of 18 books on management and marketing, the most recent of which is MIS: Concepts and Design (Prentice-Hall, second edition, 1986). Authors’ note: We thank Steven C. Wheelwright for his valuable assistance in the preparation of this article. Copyright © 1986 by the President and Fellows of Harvard College. All rights reserved. This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. Scenario methods: smoothly unfolding narratives that describe an assumed future expressed through a sequence of time frames or snapshots. Jury of executive opinion: the consensus of a group of “experts,” often from a variety of functional areas within a company. Sales-force composite: a compilation of estimates by salespeople (or dealers) of expected sales in their territories, adjusted for presumed biases and expected changes. Naive extrapolation: the application of a simple assumption about the economic outcome of the next time period, or a simple, if subjective, extension of the results of current events. Judgment methods Historical analogy: predictions based on elements of past events that are analogous to the present situation. Delphi technique: a successive series of estimates independently developed by a group of “experts” each member of which, at each step in the process, uses a summary of the group’s previous results to formulate new estimates. Brief descriptions of methods Manager’s Guide to Forecasting Industrial market survey: data similar to consumer surveys but fewer, more knowledgeable subjects sampled, resulting in more informed evaluations. Consumer market survey: attitudinal and purchase intentions data gathered from representative buyers. Moving averages: recent values of the forecast variables averaged to preduct future outcomes. Market testing: representative buyers’ responses to new offerings, tested and extrapolated to estimate the products’ future prospects. Time series extrapolation: a prediction of outcomes derived from the future extension of a least squares function fitted to a data series that uses time as an independent variable. Adaptive filtering: a derivation of a weighted combination of actual and estimated outcomes, systematically altered to reflect data pattern changes. Exponential smoothing: an estimate for the coming period based on a constantly weighted combination of the forecast estimate for the previous period and the most recent outcome. Time series methods Counting methods Box-Jenkins: a complex, computer-based iterative procedure that produces an autoregressive, integrated moving average model, adjusts for seasonal and trend factors, estimates appropriate weighting parameters, tests the model, and repeats the cycle as appropriate. Time series decomposition: a prediction of expected outcomes from trend, seasonal, cyclical, and random components, which are isolated from a data series. Econometric models: outcomes forecast from an integrated system of simultaneous equations that represent relationships among elements of the national economy derived from combining history and economic theory. Leading indicators: forecasts generated from one or more preceding variable that is systematically related to the variable to be predicted. Regression models: estimates produced from a predictive equation derived by minimizing the residual variance of one or more predictor (independent) variable. Correlation methods: predictions of values based on historic patterns of covariation between variables. Input-output models: a matrix model that indicates how demand changes in one industry can directly and cumulatively affect other industries. Association or causal methods Indicates weakness Indicates strength Ex = Execution time *Dev = Development time For the exclusive use of B. May, 2016. This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. Dimensions Time Questions Judgment methods Sales-force composite Jury of executive opinion Qualitative Scenario methods Delphi technique Historical analogy Consumer market survey Industrial market survey Present need to Medium Short or Medium Short or Medium Medium or Long Medium or Long Medium or Long Medium Medium Medium or Long Urgency Is the forecast needed immediately? Rapid results are a strong advantage of this technique.* Forecast can be assembled, combined, and adjusted relatively quickly. In-house group forecasts are quicker than outside experts’. Urgency seriously compromises quality. Urgency seriously compromises quality. Substantial lag is involved. Dev Moderate to Long Ex Moderate Dev Moderate Ex Moderate to Long Forecast can be computed quickly if data are available; data gathering may cause delay. Method of gathering data may cause a substantial time lag. Dev Short Ex Moderate Dev Short Ex Short to Moderate Can easily accommodate frequent updates. Are quantitative skills limited? Minimal quantitative capabilities are required. Computer Are computer capabilities limited? Computer capabilities are not essential. Nominal processing does not require a computer. Financial Are only limited financial resources available? Very inexpensive to implement and maintain. Inexpensive to implement and maintain. Antecedent Are only limited past data available? Some past data are required, but extended history is not essential. Past data are helpful but not always essential. Variability Does the primary series fluctuate substantially? Has difficulty adequately handling wide fluctuations. Internal consistency Are significant changes in management decisions expected? Can reflect changes. External consistency Are significant environmental changes expected? Can reflect changes, but quality can also vary substantially. External stability Are significant shifts expected among variable relationships? Often insensitive to shifts. Detail Are component Focus can be forecasts required? readily restricted. Can often provide Can reflect useful component breakdowns. forecasts, but is generally concerned with aggregate forecasts. Generally confined to aggregate forecasts. Accuracy Is a high level of accuracy critical? Can be very accurate or subject to substantial bias. Not particularly accurate, but usually most accurate when horizons are extended and conditions are dynamic. Often provides a limited practical level of accuracy. Forecast can be Can accomplish quickly compiled, quickly. but data collection restricts rapidity. Apt to miss turning points. Form Provides point forecast with crude estimated range. Is an interval or probabilistic forecast critical? A Dev Moderate Ex Long to Extended Frequency need is moderate; updates are generally provided as need arises. Usually used for one-time forecasts, but they can be revised as new information becomes available. Extended, Depending on basically used for methodology, one-time forecasts. frequent updates are possible, but updates are generally provided at extended intervals. Sophistication Technical level is variable, competencies are but some generally needed. quantitative skill is desirable. A computer may be helpful. Financial requirements are nominal for executive groups; they may be higher for outside experts. Usually expensive Expense depends for thorough on makeup and efforts. affiliation of participants. A computer is generally needed for data analysis. If data are readily Generally very available, expensive. out-of-pocket costs are minimal. Extended history is essential. Generally expensive for good controls. Past data are useful but not essential. Moderately expensive, depending on controls. Past data very helpful but not essential. Does not handle fluctuations well but can accommodate them if the panel meets frequently. Technique’s extended view dampens impact of short-run influences and random variability. Substantial fluctuations limit the accuracy of projections. Handles fluctuations poorly, but tracking improves performance. Wide fluctuations are frequently a significant concern. Significant changes are frequently not transmitted and/or realistically reflected. If changes come from an internal corporate group, technique can readily reflect them. Can readily reflect Can Can crudely internal changes. accommodate reflect changes at changes, but ease best. of reflecting them depends on group’s background. Can reflect changes well if they are incorporated into original research design. Generally cannot validly reflect changes. If changes are recognized, adjustments can be made. Generally has difficulty realistically reflecting changes. Reflects changes Reflects changes well; technique well. combines a range of expertise. Can handle changes, but forecast quality can vary substantially. Seriously weak in Ease of handling handling changes. changes depends on consumers’ awareness and interpretation. Reflects changes indirectly; it is frequently very sensitive to them. Usually aware of shifts and can reflect them in the forecast. Can accommodate shifts crudely. Seriously weak in accommodating shifts. If carefully controlled, can handle shifts well. May be most accurate under dynamic conditions. Capability for Should turning Can be very reflecting points be reflected responsive to direction changes promptly? shifts. Capability for Should turning detecting points be direction changes identified early? Dev Moderate Ex Long to Extended Dev Moderate Ex Moderate Are frequent forecast updates needed? Resource Mathematical require- sophistication ments 4 Market survey Is the forecast period a: Present need, or Short-, Medium-, or Long-term projection? Frequency Output Market testing Span Dev Short Ex Short Input Counting methods Naive extrapolation Adapts well to shifts. Handles detail but scope can be limited. Inferential relationships are often tenuous; predictions are suspect. Can readily adjust if recognized, but long time horizon often precludes the need. Early turning point identification can be a strength under dynamic conditions. Can only provide crude, subjectively determined probabilistic forecast. B Can detect cyclical turning points early under dynamic conditions, but long time horizon often precludes the need. Only subjectively determined approximate range or frequency distribution is possible. C Technique is subjective, but distributions are an inherent part of technique. D Seldom reflects significant shifts. E Provides highest accuracy in new product and limited data conditions. Has limited Can be most predictability with accurate approach durables, in special cases. somewhat better with nondurables. Responsive, but this is not one of its purposes. Often highly responsive to demand shifts. Can only predict Early turning point noncyclical points identification is very crudely. not a purpose or capability of this technique. Can be responsive to turning points but usually cannot anticipate them. Can provide In limited situations, only an interval estimates. approximate range can be furnished. With probability sampling, accommodates any desired form. F G HARVARD BUSINESS REVIEW H Can be very sensitive to turning points. I January–February 1986 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. Time series methods Moving averages Association or Causal methods Exponential smoothing Short, Medium, or Present need to Long Short or Medium Adaptive filtering Time series extrapolation Time series decomposition Short or Medium Short, Medium, or Short or Medium Long Box-Jenkins Correlation methods Regression models Leading indicators Econometric models Input-output models Short, Medium, or Short, Medium, or Short, Medium, or Short, Medium, or Short, Medium, or Medium or Long Long Long Long Long Long 1 Rapid results are a strong advantage of this technique. Dev Short Ex Short Forecast can be produced quickly once programmed and past data are available. Computation is quick if data are available; data gathering can cause delays. Dev Moderate Ex Short Dev Short to Moderate Ex Short Program setup and data gathering may cause delays, but once programmed, computation is quick. Operationalizing program can take time, but forecast can be produced quickly. Dev Long Ex Moderate Dev Moderate Ex Short Data evaluation may cause delays, but forecast computation is quick. Dev Moderate Ex Short to Moderate Model formulation takes time, but forecast computation is quick. Dev Moderate to Long Ex Short to Moderate Data evaluation may cause delays, but forecast computation is quick. Dev Moderate Ex Short to Moderate Forecast can be systematically updated easily. A fundamental competency level is required. A computer is helpful for repetitive updating. A computer is essential. A computer is helpful for repetitive updating. If data are readily available, out-of-pocket costs are minimal. Forecast is moderately expensive to develop. If data are readily available, out-of-pocket costs are minimal. Can accommodate fluctuations with appropriate averaging period. Only recent forecasts and current data are required once alpha is determined. Past history is essential although detail and extent vary. Can accommodate fluctuations with suitable alpha. Absorbs random fluctuations and adjusts to systematic shifts. Wide fluctuations result in decreased confidence in projected outcomes. A high level of understanding is required. A fundamental competency level is required. A computer is essential. A computer is desirable. Moderately expensive to acquire, develop, and modify. Acquisition and If data are on modification costs hand, are expensive. development costs are moderate. Past history is essential with some detail required. Past history is essential with detail required. Cannot validly reflect shifts. Can only moderately reflect changes with prior trend. Cannot validly reflect changes. Can only moderately reflect shifts with prior trend. Cannot validly reflect shifts. Variable lags always exist. Confidence limits can be easily derived based on variability of data series. J Extended detailed history is required. 7 Insensitive to changes. Can only moderately reflect changes with prior trend. Insensitive unless they are related to predictor variables. Handles changes well if they are appropriately reflected in predictor variables. Sensitive to changes if they are reflected in appropriate indicators. Can only moderately reflect shifts with prior trend. Predictive accuracy is weakened if shifts occur. Effectively isolates Frequently the identifiable most accurate for components. short-to-mediumrange forecasts. When points are identified, adjusts quickly. Predictive accuracy can vary widely. Can adapt quickly to turning points. A weak predictive Can predict ability is possible. turning points only if a lagged relationship exists. Probability range is easily constructed. HARVARD BUSINESS REVIEW 6 Insensitive to changes unless they are reflected in the indicators. Generally cannot predict turning points unless series lags. M Development costs are substantial; operating costs are moderate. Insensitive to changes, but they can be reflected among predictor variables. Deals very well Very unresponsive. Generally with systematic responds slowly. shifts in variables. L 5 Insensitive to significant changes unless they are correlated with predictor variables. Depending on alpha value, can be very responsive. K A computer is essential for all cases. Time lag further reduces accuracy. Normally accurate for trends and stationary series. Generally only provides point forecast. 4 May handle large Can readily adjust to systematic and fluctuations well with appropriate random patterns. independent variables. Generally rates high in accuracy for short-term forecasts. Cannot anticipate turning points. A computer is essential for most cases. N January–February 1986 O P 8 9 Highly sensitive to Can be modified relevant changes. to reflect changes. 10 Cannot validly reflect shifts without updated coefficients. A restricted focus might substantially compromise technique’s productive accuracy. Focus can be readily restricted, depending on indicators used. Generally confined to aggregate forecasts. Effectively reflects demand by SIC groups. Can be accurate if variable relationships are stable and the proportion of explained variance is high. Only moderately accurate under most conditions. Give spotty performances in dynamic environments. With stable relationships, predictive accuracy can be very good. Sensitive to changes once they are identified. 11 12 13 14 If relationships are stable, can effectively predict turning points. Especially effective in forecasting cyclical changes. Confidence limits are provided. Probability range is easily constructed. Q 2 3 A high level of understanding is required. Extended history is helpful in initial development. Focus can be readily restricted. Accurate under stable conditions. Dev Extended Ex Short to Moderate Technique is good if covariation is high; otherwise it is poor. Can isolate and Handles determine the variability level of effectively. component effects. Cannot validly reflect changes. Cannot validly reflect changes. Dev Long to Extended Ex Short to Moderate Original model may require up to a year to develop. Forecast can be updated quickly if data are available. Minimal quantitative capabilities are required. Past history is essential. Model building is lengthy, but producing forecast is quick. R Cannot anticipate turning points but can effectively predict outcomes. Confidence limits are provided. S Confidence limits can be developed. 15 16 T 5 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. a market that had been only recently established. COMPAQ’s forecast of the size, direction, and price trends of the 1984 microcomputer market was confounded by uncertainties about the market’s response to several vital factors:. M The entry of IBM’s new portable computer. M IBM’s 23% price cut in June 1984 and its potential erosion of margins. M The entry of lap portables introduced by Hewlett-Packard and Data General. MThe launch of IBM’s new PC AT, complicated by unexpected delivery delays and compatibility problems. M The introduction of desktop computers by Sperry, NCR, ITT, and AT&T. Eventually, COMPAQ entered the desktop segment of the market, even though 1984 was unforgiving and rampageous. Several large competitors restricted their programs; many smaller companies went into—or to the edge of—receivership. Financially and competitively, COMPAQ succeeded. During 1984, sales rose from $111 million to $329 million and earnings increased from $4.7 million to $12.8 million. The market’s dynamics, however, make such results increasingly difficult to achieve; positive and negative events—both expected and unforeseen—have a decisive effect. Even when managers anticipate outcomes, grave uncertainties about timing, form, and impact persist. Despite the difficulty, the vice president of marketing and the CEO—the two executives most directly involved with the decision—demonstrated what can be done. They used an extended series of consumer and dealer surveys coupled with periodic evaluations of the technology to assess the future market and to guide the development of products and programs to accommodate the industry’s fluid and rapidly evolving needs. Managers can use forecasting techniques to help them reach important decisions. A large and fast-growing body of research deals with the development, refinement, and evaluation of forecast techniques. Managers also have greater access to both internal and external data and can benefit from a multitude of computer software programs on the market, as well as easier access to computer capabilities for analyzing these data. FORECASTER’S CHART While each technique has strengths and weaknesses, every forecasting situation is limited by constraints like time, funds, competencies, or data. Balancing the advantages and disadvantages of techniques with re6 gard to a situation’s limitations and requirements is a formidable but important management task. We have developed a chart to help executives decide which technique will be appropriate to a particular situation; the chart groups and profiles a diverse list of 20 common forecasting approaches and arrays them against 16 important evaluative dimensions. We list techniques in columns and dimensions of evaluation in rows. Individual row-column intersections (cells) reflect our view of a technique’s characteristics as they apply to each dimension. Brief descriptions of the forecasting methods are given on the chart. We have used different shades of gray to show which dimensions represent a strength for a particular technique and which represent its weaknesses. The strengths are highlighted in light gray; weaknesses are indicated by a dark gray cell. Naive extrapolation, for example, is strong in internal consistency in that it easily reflects changes in management decisions. It is weak, however, in forecast form. It is important to keep these distinctions in mind when you are using the chart. The chart is useful in two ways. The first is in deciding which technique will suit your particular needs as a forecaster. The second is in deciding how to combine techniques to further improve the result. In this section, we discuss the simpler approach; we talk more about combining methods later. To use the chart, look at the 16 questions listed in the first column after the dimensions. They are the most common questions a manager will ask when deciding to use a certain forecast. The first question sets out the various time spans a forecast would have to cover. Everyone who uses the chart will have to answer question 1. But each of the following questions can be answered with a yes or no. If you answer no to a question, you don’t have to look across that row. In responding to question 1, make note of those techniques whose time span matches your needs. We have found it easiest for forecasters to write down the technique’s column letter. The row number of each dimension and the column letter of each technique are written along the horizontal and vertical axes. With regard to question 1, for example, if your forecast horizon is short-term, you can write down the cell letters for naive extrapolation (A), sales-force composite (B), jury of executive opinion (C), and so forth. But you would ignore the letters for scenario methods (D), Delphi technique (E), historical analogy (F), and so on. The columns you have now listed represent techniques that are qualified for further consideration. Next read down the column of each of these techniques and note any gray cells. If these gray cells are HARVARD BUSINESS REVIEW January–February 1986 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. associated with questions to which you have answered yes, then the dimension either precludes use of the technique or the technique can be used but it has difficulty accommodating that dimension. Such precautions will help you determine whether you must—or wish to—eliminate certain techniques from further consideration. An arrow in a cell indicates that its evaluation is the same as the cell to its left. After you have answered all the questions and have a list of surviving techniques, note the cells that are highlighted in light gray. Those cells represent specific strengths of a technique and can guide you in making a final selection. In the course of the exercise, you may have eliminated a technique that you like, have heard about, or routinely use. You can go back to that one and compare its strengths and weaknesses with those of the methods that the chart has indicated would be best for you. You can then decide whether you would rather proceed with the technique that the chart indicates corresponds most closely to your specific requirements or whether you can accommodate the eliminating factors in order to use the technique that you initially favored. Important Considerations When considering each question, you should remember some “tricks of the trade” concerning: Time horizon. Most managers will want the forecast results to extend as far into the future as possible. Too long a period, however, may make the technique selection process even more confusing because of the varying abilities of the techniques to accommodate different time spans. In choosing an extended time horizon, the forecaster increases the complexity, cost, and time required to develop the final product. You can break down the time needed to produce a forecast into development (Dev) and execution (Ex) time. Development time includes the gathering and entry of data, the modification of programs to the company’s specific requirements, and the start-up of the system. Execution time is the time it takes to produce a forecast with a particular technique. Initially, of course, development time is a significant concern for the forecaster; once the forecast technique is firmly established, however, execution time is a more appropriate concern. Technical sophistication. Experience shows that computer and mathematical sophistication is integral to many techniques. Although many executives have improved their skills in this area, not all have sharpened their quantitative skills enough to be comfortable with some of the forecast results a computer will spill out. Cost. The cost of any technique is generally more HARVARD BUSINESS REVIEW January–February 1986 important at the beginning when it is being developed and installed; after that, any technique’s potential value to a decision maker usually exceeds the expense of generating an updated forecast. Data availability. Before choosing a technique, the forecaster must consider the extensiveness, currency, accuracy, and representativeness of the available data. More data tend to improve accuracy, and detailed data are more valuable than those presented in the aggregate. Because a technique’s ability to handle fluctuations is important to a forecast’s success, the manager must match the sensitivity and stability of a technique to the random and systematic variability components of a data series. Variability and consistency of data. Beyond changes that might occur in the company’s structure or its environment, the manager must look at the kind of stable relationships assumed among a model’s independent variables (represented by the “external stability” dimension). For example, while most historically oriented quantitative forecasts might use expected levels of automobile production as a basis for determining demand for steel, the forecast model may not reflect changes over time in the average amount of steel used in automobiles. These relationships sometimes do change, but any variation is usually so gradual that it will not affect a short-term forecast. When the forecasts are long-term, however, or when the company expects a substantial change in a vital relationship, the forecaster should either apply judgment in a quantitative technique or use a qualitative method. Amount of detail necessary. While aggregate forecasts are easy to prepare, the manager will need specific information (including individual product classes, time periods, geographic areas, or productmarket groupings, for example) to determine quotas or allocate resources. Since forecasts vary widely in their ability to handle such detail, the manager may want a technique that can accurately predict individual components and then combine the results into an overall picture. Otherwise, the forecaster can use one technique to provide an overall picture and then use past patterns or market factors to determine the component forecasts.1 Accuracy. While accuracy is a forecaster’s holy grail, the maximum accuracy one can expect from a technique must fall within a range bounded by the average percentage error of the random component of a data series. Also, because of self-defeating and selffulfilling prophecies, accuracy must be judged in light of the control the company has over the predicted outcome and within the time and resource constraints imposed on the forecaster. Remember also that accuracy alone is not the most important criterion. The forecaster may wish to forgo 7 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. Forecasting Strategies [There are] three basic strategies of forecasting. . . . The deterministic strategy assumes that the present has a close causal relation to the future. This is the strategy that would be used by a cardsharp, who had stacked the deck of cards, to predict the deal. In economic forecasting, the strategy would be used to predict construction expenditures by a knowledge of construction contract awards already made. The symptomatic strategy assumes that present signs show how the future is developing; such signs do not “determine” the future but reveal the process of change that is already taking place. Thus, a falling barometer may reveal a coming storm, or a rising body thermometer an incipient illness. In economic forecasting, this strategy calls for the spotting of “leading indicators”—time series whose movements foreshadow rises or declines in general business activity. The systematic strategy assumes that, though changes in the real world may seem accidental or chaotic, careful analysis can reveal certain underlying regularities (sometimes called principles, theories, or laws). The way to find these regularities is to black out much of reality and hold only to the abstractions that make up a system, such as a solar system, or a nuclear system, or an economic system. Though the theories that result from this process of abstraction are “unreal,” they may nevertheless possess the power to affect the real world—provided, of course, that the theories are sound. The test of the soundness of a theory is how it measures up when applied to reality: An atomic explosion confirms Einstein’s E = mc2. Similarly, a price cut that leds to increased sales confirms the hypothetical demand curve that no man has ever seen outside an economics textbook. To be sure, economic “laws” do not have the consistency of those in the physical sciences. Nevertheless, economic relations or theories, derived from a study of the past, may be useful tools for prediction, within some acceptable range of probable error. some accuracy in favor of, for example, a technique that signals turning points or provides good supplemental information. Turning points. Because turning points represent periods of exceptional opportunity or caution, the manager will want to analyze whether a technique anticipates fundamental shifts. Some techniques give false turn signals, so the forecaster must keep in mind not only a technique’s ability to anticipate changes but also its propensity to give erroneous information. Form. Final form varies greatly; it is always advisable to use a technique that provides some kind of mean or central value and a range of possible outcomes. If even remotely accurate, such information helps the manager determine more explicitly risk exposure, expected outcomes, and likelihood distributions. searching for a better approach to technique selection. In part, these attempts have explored the strengths and performance characteristics of various techniques.2 Our chart extends this approach by helping the forecaster match different techniques’ strengths and characteristics to the needs and constraints of the required forecast. Managers can improve their projection in the following ways: IMPROVING THE FORECAST Because no dramatic breakthroughs in technique development have occurred during the past several years, efforts to improve forecasts have shifted to 8 From Business Forecasting: With a Guide to Sources of Business Data by Leonard S. Silk and M. Louise Curley (New York: Random House, 1970), p. 3. Copyright © 1970 by Random House, Inc. Reprinted with the permission of the publisher. M Combining forecasts. M Simulating a range of input assumptions. M Selectively applying judgment. Combining forecasts The research on combining forecasts to achieve improvements (particularly in accuracy) is extensive, persuasive, and consistent.3 The results of combined forecasts greatly surpass most individual projections, techniques, and analyses by experts. Because toprated experts and the most popular techniques cannot consistently outperform an approach that combines results, and because the manager cannot predetermine which experts or techniques will be superior in HARVARD BUSINESS REVIEW January–February 1986 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. any situation, combining forecasts—particularly with techniques that are dissimilar—offers the manager an assured way of improving quality. The forecasting chart can help the manager select the best combination of techniques. As the chart shows, each method has strengths and weaknesses. By carefully matching two or more complementary techniques, the forecaster can offset any technique’s limitations with the advantages of another, all the while retaining the strengths of the first. Simply compare an approach’s highlighted cells against those of other qualified methods. Various techniques incorporate very different underlying notions. Not knowing which of these will ultimately prove to be most accurate in a particular economic environment, forecasters can add to their awareness of possible outcomes by evaluating the range and the distribution of the projections produced by the various methods.4 Simulating various outcomes The manager can also establish a range of probable outcomes by varying the combination and the levels of inputs of a particular technique. Such sensitivity analysis can underscore the most critical variables, the range and distribution of expected outcomes, and the probable outcomes from different assumptions. Using judgment While many quantitative forecasts incorporate some subjectivity, forecasters should rely more heavily on the output of a quantitative forecast than on their own judgment. Forecasting research has concluded that even simple quantitative techniques outperform the unstructured intuitive assessments of experts and that using judgment to adjust the values of a quantitatively derived forecast will reduce its accuracy.5 This is so because intuitive predictions are susceptible to bias and managers are limited in their ability to process information and maintain consistent relationships among variables.6 The forecaster should incorporate subjective judg- HARVARD BUSINESS REVIEW January–February 1986 ments in dynamic situations when the quantitative models do not reflect significant internal and external changes. Even in these cases, the forecaster should incorporate the subjective adjustments as inputs in the model rather than adjusting the model’s final outcome. When confronted with extended horizons or with novel situations that have limited data and no historical precedent, judgment or counting methods should be used. Applying judgment in such situations, however, should be done on a structured basis. The forecaster should also employ judgment to stimulate thought and explore new relationships but, where possible, quantitative techniques should be incorporated to test and support assumptions. The two-part article on scenario forecasts by Pierre Wack in the September-October 1985 and November-December 1985 issues of HBR provides a good example of this. 1. For additional discussion, see G. David Hughes, “Sales Forecasting Requirements,” in The Handbook of Forecasting: A Manager’s Guide, ed. Spyros Makridakis and Steven C. Wheelwright (New York: John Wiley & Sons, 1982), p. 13. 2. For a discussion of examples, see Spyros Makridakis et al., “The Accuracy of Extrapolation (Time Series) Methods,” Journal of Forecasting, April–June 1982, p. 111; and Steven P. Schnaars, “Situational Factors Affecting Forecast Accuracy,” Journal of Marketing Research, August 1984, p. 290. 3. See Essam Mahmoud, “Accuracy in Forecasting: A Survey,” Journal of Forecasting, April–June 1984, p. 139; Spyros Makridakis and Robert L. Winkler, “Averages of Forecasts: Some Empirical Results,” Management Science, September 1983, p. 987; and Victor Zarnowitz, “The Accuracy of Individual and Group Forecasts from Business Outlook Surveys,” Journal of Forecasting, January–March 1984, p. 10. 4. See Hillel J. Einhorn and Robin M. Hogarth, “Prediction, Diagnosis, and Causal Thinking,” Journal of Forecasting, January–March 1982, p. 23. 5. For survey articles that address this issue, see Mahmoud, p. 139; and Robin M. Hogarth and Spyros Makridakis, “Forecasting and Planning: An Evaluation,” Management Science, February 1981, p. 115. 6. Lennart Sjoberg, “Aided and Unaided Decision Making: Improved Intuitive Judgment,” Journal of Forecasting, October–December 1982, p. 349. 9 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017. For the exclusive use of B. May, 2016. Harvard Business Review HBR Subscriptions Harvard Business Review U.S. and Canada Subscription Service P.O. Box 52623 Boulder, CO 80322-2623 Outside U.S. and Canada Tower House Sovereign Park Lathkill Street Market Harborough Leicestershire LE16 9EF Telephone: (800) 274-3214 Telephone: 44-85-846-8888 Fax: (617) 496-8145 Fax: 44-85-843-4958 American Express, MasterCard, Visa accepted. Billing available. HBR Article Reprints HBR Index and Other Catalogs HBS Cases HBS Press Books Harvard Business School Publishing Customer Service - Box 230-5 60 Harvard Way Boston, MA 02163 Telephone: U.S. and Canada (800) 545-7685 Outside U.S. and Canada: (617) 495-6117 or 495-6192 Fax: (617) 495-6985 Internet address: custserv@cchbspub.harvard.edu HBS Management Productions Videos Harvard Business School Management Productions videos are produced by award-winning documentary filmmakers. You’ll find them lively, engaging, and informative. HBR Custom Reprints Please inquire about HBR’s custom service for quantity discounts. We will print your company’s logo on the cover of reprints, collections, or books in black and white, two color, or four color. The process is easy, cost effective, and quick. Telephone: (617) 495-6198 or Fax: (617) 496-8866 Permissions For permission to quote or reprint on a one-time basis: Telephone: (800) 545-7685 or Fax: (617) 495-6985 For permission to re-publish please write or call: Permissions Editor Harvard Business School Publishing Box 230-5 60 Harvard Way Boston, MA 02163 Telephone: (617) 495-6849 This document is authorized for use only by Bryan May in GB513 Business Analytics 5_2_2017 taught by Chris Osadczuk, Kaplan University from November 2016 to May 2017.

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