Emerging issues in sales forecasting and decision support systems

Emerging Issues in Sales Forecasting and Decision Support Systems

Essam Mahmoud University of North Texas

Gilllan Rice Consultant

Naresh Malhotra Georgia Institute of Technology

There are two distinct groups of emerging issues in the area of sales forecasting and decision support systems: methodological issues and implementational issues. This paper discusses each of them. The discussion of implementational issues includes some results of a survey of forecasting practice. The paper serves as a preface to the special section on sales forecasting and decision support systems and introduces the papers included in the special section.

Consistent with the emphasis of the special section, first we elaborate upon the linkages between sales forecasting and decision support systems. Recent literature in sales forecasting is selectively reviewed. Two distinct groups of emerging issues in sales forecasting and decision support systems are identified. These are methodological issues and implementational issues and they are discussed in some detail. Finally, we present an overview of other papers on forecasting contained in this special section.

INTRODUCTION

In the decade of the eighties, significant progress has been made in the areas of sales forecasting and decision support systems, yet, developments in these areas have been disjointed and are largely independent of each other. Hence, this special section attempts to bridge the gap by focusing on special aspects of the interrelationships between sales forecasting and decision support systems (DSS). The purpose of this paper is to present an overview of some of the emerging issues in sales forecasting and DSS. It also presents a synthesis of the papers in forecasting contained in this issue. In this sense, it serves as an introduction to this special section.

�9 1988, Academy of Marketing Science Journal of the Academy of Marketing Science Fall, 1988, Vol. 16, blo. 3&4, 047-061 0092-0703&4188/1603-4-0047 $2.00

THE LINK BETWEEN SALES FORECASTING AND DECISION SUPPORT SYSTEMS

Sales forecasting is an integral part of the marketing decision support system (DSS). Figure 1 is a diagrammatic presentation of how sales forecasting can be linked to the DSS. The DSS contains tools to help the forecaster prepare better forecasts; these tools are data, records of previous forecasting, and techniques. The forecasts assist marketing managers to improve decision-making. In an organizational design context, forecasting should not be regarded as a self- contained activity but should be integrated with the planning context of which it is a part (Wright et al. 1986). Note the overlap between the methodological and implementational processes of forecasting. A key element in the overlap between the methodological and implementational aspects is the combined sales forecast.

The choice of forecasting method should depend on the decisions that are to be based upon it (Wright et al. 1986). Therefore, the marketing manager must communicate full details of the decision needs and the situational context to the forecasting manager who can then apply the most ap-

JAMS 47 FALL, 1988

EMEROING ISSUF..,q IN SALES FORECASTING AND DECISION SUPPORT SYSTEMS MAHMOUD, RICE & MALHO'IRA

propriate technique(s). Wright et al. (1986) concluded that a current forecasting process should also take into account the past record of forecasting with respect to decisions. They emphasize that, with hindsight, forecasting methods should be judged, not according to forecast accuracy, but according to whether they lead to the correct or most profit- able decisions in the past. Hughes (1987) also stresses that "the ultimate test of a sales forecast is whether it made the marketing strategy a better strategy." In Figure 1 this is shown by the inclusion of "record of decision outcomes associated with forecast" in the DSS. The forecaster uses all information available in the DSS to provide a statistical forecast. As one possible forecasting strategy (illustrated in Figure 1), the marketing manager, using the DSS, develops an independent judgmental forecast. The two forecasts can then be combined using some objective combining technique. Based upon the final forecast, the marketing manager makes a decision, takes action and observes the result. In Figure 1 notice the feedback mechanism to the DSS and also the interaction between the techniques and the database. The latter is emphasized because the techniques that can be applied depend to a certain extent on the data available or on the data that will be collected.

METHODOLOGICAL ISSUES IN SALES FORECASTING AND DECISION SUPPORT SYSTEMS

With this framework in mind, selected recent studies dealing with methodological and implementational aspects of sales forecasting and decision support systems are sum- marized in Table 1. In discussing these studies we focus on the issues conceming methodology and implementation. The primary methodological issues in sales forecasting re- late to the development of more accurate and reliable forecasting methods. These issues are discussed by examining the relative superiority of forecasting methods and procedures, forecasting over the product life cycle, statistical versus judgmental methods, the combining of forecasting methods, and descriptive versus predictive validity in marketing models that might be used for sales forecasting.

Relative superiority of forecasting methods and procedures

An assessment of current knowledge about forecasting is provided by Makridakis (1987). He emphasizes that studies have had contradictory results and that no study has shown a clear superiority of one method over another. The empirical studies included in Table 1 also confirm that there is no unique model which can work best for all situations. In this context, research generally has taken two directions. One direction is a focus on the comparison of methods for different situations. For example, for short-run forecasting applications, Yokum and Wildt (1987) com- pared constant and stochastic coefficients sales response models and showed that a first-order autoregressive stochastic coefficient model provided improved accuracy.

The situation-specific nature of sales forecasting will be elaborated upon in the remainder of this paper. The second research direction is a focus on introducing new methods or modified approaches to using existing forecasting methods. For example, Enns et al. (1982) found a multiple exponential smoothing model to have a number of structural and performance advantages over simple exponential smoothing. Makridakis (1987) writes that no current forecasting method can deal with all three possibilities of change (random, systematic temporary, and systematic permanent). Hence, it is important to concentrate on the development of methods that can.

Forecasting over the product life cycle

In sales forecasting it is important to match the most appropriate technique to the specific decision-task. Each task is characterized by different amounts of data available, varying time horizons and differing degrees of accuracy required, for example. This is clearly illustrated in the task of forecasting over the product life cycle (Mullick et al. 1987). For example, at the preproduct development stage an analysis of technological trends may use the Delphi method, historical analogies and other long-range forecasting techniques. New product forecasting is carefully reviewed by Assmus (1984). He concludes that the thrust of new product development has shifted to the early phases of the development process because of increased uncertainty. Sociocultural and economic changes along with technological advancement mean that markets are no longer so clearly defined. Hence there is emphasis on market identi- fication. The applicability of classic forecasting techniques has been limited in the area of new product entry because of the lack of historical data (Larreche 1987). Four classes of models discussed by Larreche that are suitable for new entry forecasting are: stochastic models, process models, aggregate models and product positioning models. Sto- chastic models attempt to estimate the long-term equilib- rium market share of a product from the past purchasing behavior of consumers. Process models assume that after the introduction of a new product, consumers move through stages from total ignorance to repeated purchase and brand loyalty. Aggregate models represent a situation at a global level in a single equation or a limited number of equations. Product positioning models explicitly take into account the influence of product characteristics on consumer choice. Assmus (1984) notes the widespread acceptance of computer aided forecasting procedures. For example, Cattin and Wittink (1982) report over a thousand applications of conjoint analysis during the seventies, several for the purpose of forecasting.

During the period of rapid growth in the product life cycle, statistical methods for short-term forecasting are appropriate (Mullick et al. 1987). The market saturation stage demands the use of estimates of trends and seasonals (time-series and projection techniques) as well as the use of causal models which can illustrate which fact6rs have a "lead" relationship with sales. Dino (1987) used the relationship between the product life cycle and price evolution to forecast the prices of electronic products at the different

JAMS 48 FALL, 1988

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TABLE 1 Selected Recent Research Studies in Sales Forecasting and Decision Support Systems

TOPIC

A. Methodological Issues

1. Relative superiority of methods

Introduction of a class of multiple exponential smoothing models for automated or minimal intervention industrial forecasting systems. Comparison with other models using seasonally adjusted, monthly unit sales for three auto product lines over five year period.

Presentation of a framework for market analysis which specifically models primary demand, competitive reaction and feedback effects of the market variables. Uses principles of time series analysis with standard econometric model building. Advantage of the model is that the relationship is m athe- marital and hence subject to statistical error.

Use of a time series modeling technique to model a series of sales data in which seasonality causes distinct spike peaks. Model consists of a scalar time series decomposed linearly into twoparts, one deterministic and the other stochastic. The application uses two sets of data for one market: total market demand for all products of a firm and its competitors, and market demand of the single firm's products (71 4-week periods).

Use of intervention analysis, an extension of Box-Jenkins univariate time series model, to measure the effect on the market of a change in the environment. Empirical analysis using 2 data sets: investigation of effect of American Dental Association's endorsement of Crest dental cream on market share; investigation of the impact of price deals on sales of wet cat food.

Comparison of sales forecasts using constant and stochastic coefficients sales-response models. Selected models are applied to 6 sets of bimonthly data and one set of annual advertising and sales data to assess forcasting accuracy for time horizons of various lengths.

Comparison of simple versus complex extrapolation models using 44 time series of monthly shipping data for consumer products of three food processing companies.

Discussion of thematic content analysis as a new forecasting methodology to examine structural change. Application to the foreseeable future in leisure lifestyles with attempt to identify trends.

Discussion of a special case of multiple time series models in which the auto-regression and moving average parameter matrices are diagonal. The models are appropriate if the random shocks which drive the series are only contempora- neously correlated. Flexibility and usefulness of the ap-

MAJOR FINDING(S)

Multiple exponential smoothing model tested has a number of structural advantages and performed as well as or better than simple exponential smoothing and the Trigg andLeach version of adaptive exponential smoothing.

An illustration using a model of competition for a city pair in the U.S. domestic air travel market reveals that flight scheduling has a market expansive or a competitive effect, depending on the competitor, and that advertising does not have a significant impact on performance.

The forecasted sales for both times series fell within the range of -21% to +2% with the actual sales falling well within the 95% confidence intervals for the forecasted values. In a comparison with forecasts obtained using Box-Jenkins and generalized least squares methods, the proposed method provided more accurate forecasts than the other two methods.

Both of the analyses provided models that were infor- mative and parsimonious in variables and which provided accurate descriptions of the impact of the intervention that occurred. Also, both situations indi- cated that not only accurate descriptive information was provided, but also that forecasts of the short-ran and long-run impact of such a change could be made available to management who could use it in developing future marketing strategies.

The results show improved accuracy for a lst-order autoregressive stochastic coefficient model, particularly in short-run forecasting applications.

Results of the Box-Jenkins methodology support the application of simple models to forecast at the stock- keeping unit level. Results also support the hypothesis that improving the model fit does not necessarily improve the forecasts.

Results show the existence of a global lifestyle for people under 21; pro-technology, conservative, em- ployment-oriented. This will result in cultural disso- nance and then a new emphasis on ethnicity and nation- alism.

Forecasts from the approach compare favorably with those from the univariate models.

SOURCE

Enns, Ma- chak, and Spivey (1982)

Hanssens (1980)

Kapoor, Madhok, and Wu (1981)

Leone (1987)

Yokum and Wildt (1987)

Koehler (1985)

Restall (1987)

Umashankar and LedoRer (1983)

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TABLE 1 (continued) Selected Recent Research Studies In Sales Forecasting and Decision Support Systems

TOPIC

p~ach illustrated in the context of a competitive market situation with five sales series where individual components follow different univariate time series models.

Consideration of what methods used by manufacturers to forecast sales can be applied to forecast accurately retail sales, or whether new models should be developed. To test the accuracy of the models, monthly sales data over a 13-year period were collected and analyzed for Phoenix retail depart- ment stores. Study is an in-depth case study of a forecasting situation.

2. Forecasting over the product life cycle

Forecasting long-run demand for new products using example of residential solar heating in Canada. Purpose is to find out if the forecasting ability of the regression and multinomial logit models is inherently dependent upon the level of aggregation of the data.

3. Statistical versus judgmental forecasts

Investigates whether multiple scenarios make unexpected forecasts less surprising, whether scenarios make users less confident of their predictions, and whether scenarios improve judgmental forecasts of unstable sales series to a greater extent than does use of quantitative techniques. Subjects asked to make judgmental forecasts about a stable/ unstable series and with/without scenarios.

4. Combining forecasts

Aggregation of ,subjective forecasts made by executives. Data were short-run forecasts of advertising sales for TIME magazine. 5 weighting methods used to combine forecasts.

Used Dept. of Energy's econometric model to forecast gaso- line consumption. Also used a regression model and an ARIMA time-series model. Weights for combining these 3 models not constrained to add to one.

5. Descriptive versus predictive validity

Testing of a procedure for measuring and estimating consumer preferences under uncertainty. Procedure derived from measurement of yon Neumann Morgestem utility functions and development of individual models of consumer preference using conjoint analysis. Sample of MBA students used in prediction of preferred jobs.

Examines published empirical evidence about the predictive performance of econometric market share models and uses data for 15 brands from 3 markets to examine the predictive ability in more detail. Analysis limited to market where few brands hold the majority of the market share and for which product class sales are relatively stable.

MAJOR FINDING(S)

The forecasting models which work for manufacturers also provide quite accurate forecasts for retail sales. Results show (1) time series methods are better than judgment or econometric models at forecasting retail sales, (2) exponential smoothing is usually a better time series method than Box-Jenkins in forecasting depart- ment store sales, and (3) it is very important to have data that represent the underlying process and not distorted by atypical events or accounting practices.

Nature of the data must be taken into account in determining the most reasonable model for forecasting.If only aggregate data are available, the relative shares (regression) model appears better. The logit model appears to dominate when dis aggregate data are available.

Sales forecasters presented with scenarios no less sur- prised at outcomes than those who received only a graph of historical data. Those presentedwith scenarios were more likely to be confident of their forecasts. No evidence that multiple scenarios improved the accuracy of judgmental sales forcasts.

Aggregating a small number of subjective forecasts found to be more accurate than the individuals' forecasts that comprised the aggregates. This occurred regardless of weighting method employed.

The 2-model combination with the highest R 2 is the econometric - time-series one. This 2-model combination contained as much information as the 3-model one. Combining a simple model with a complex one pro- duces a better forecast than the complex one generates alone.

Both statistical and modified statistical approaches sur- passed the algebraic approach on the predictive accuracy criterion. These approaches also seemed to be easier to implement than the algebraic approach.

Econometric market share models not consistently more accurate than simple extrapolation (time series) methods for short-term forecasting. Market share models did not usually capture enough of the important features of the market to be used by themselves as "stand alone" forecasting instruments. The face validity of the esti-

SOURCE

Geurts and Kelly (1986)

Berkowitz and Haines (1984)

Schnaars and Topoi (1987)

Ashton and Ashton (1985)

Bopp (1985)

Currim and Satin 0983)

Brodie and de Kluyver (1987)

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EMERGING ISSUES IN SALES FORECASTING AND DECISION sUPPORT SYSTEMS MAHMOUD, RICE & MALHOTRA

TABLE 1 (continued) Selected Recent Research Studies in Sales Forecasting and Decision Support Systems

TOPIC

B. Implementational Issues

1. Data for sales forecasting

Discussion of concepts and systems for forecasting for services. Description of unique aspects of services and how to forecast demand given these conditions. Survey of 17 financial and academic organizations in Indiana.

2. Bias in forecasting

Study of the behavioral process of sales forecasting. Consid- ers post-decision audit bias, advocacy bias and optimism bias. Three experiments to test hypotheses about planning and bias in forecasts.

Presentation of a set of procedures that incoporate performance measures for guiding improvements in forecasting when management judgement is .used. Empirical test of management judgment forecasts and multiple time series for sales of a household regulator device.

Investigation of the relationship between various measures of accuracy (square, absolute, and percentage errors) and the correction of decisions based on the forecasts. Study of interest rates and foreign exchange rates with respect to borrowing and purchasing decisions. Use of ex-post knowledge to judge ex-ante forecasting methods and their usefulness in decision support is analyzed.

3. People's acceptance of forecasting

Survey of sales forecasting practices in U.S. firms.

Exploration of practical issues in the use of quantitative forecasting models. Analysis of data from survey of 10 Au~ralian organizations and experimental data using stu- dent subjects.

Cam study analysis of Xerox Corporation's experience with implementing its Copy Volume Forecasting System.

MAJOR FINDING(S)

mated models did not appear to be a good indicator of forecasting accuracy.

No single technique can be applied universally to the forecastingneeds of service organizations. Database re- qnirements vary substantially. Forecasts are used for a variety of functions related to sales. Organizational commitment (e.g. centralized vs decentralized) has no pattern.

Results support the hypothesis that planning induces optimism (e.g. subjects who were allowed to decide the level of the marketing budget and persons who actively engaged in preparing a marketing plan were more opti- mistic).

Managers are relatively more adept at tracking the pattern of sales than predicting average sales levels. The design features help to track improvement or deteriora- tion in performance over time. In the empirical example, management judgment forecasts improve through time in the absence of correction, and with the assistance of correction are competitive (as measured by Theirs U) with the systematic forecasting alternative.

If forecasting methods are evaluated according to forecast accuracy alone, then incorrect management decisions can result, leading to financial losses. The forecasting method that is most accurate is not necessarily the best in terms of planning and decision support.

Most popular techniques axe the sales force composite and jury of executive opinion. Standard procedure is to provide point estimates. The average firm used 2.7 forecasting techniques on a regular basis. 64% of the respondents always or frequently use computers, As computer usage increased, forecasting errors declined.

Few organizations using computer based short-term forecasting techniques. The reason appears not to be lack of exposure to the techniques, but rather lack of success with implementation. Perceived accuracy improvement is not associated with the use of quantitative techniques.

Four key factors supported a successfulimplementation (1) carefully managing key decision makers, (2) gaining commitment of those affectedby the system, (3) de,sign- ing the system so it met needs of stake-holders, (4) ensuring adaptation of system to unforeseeable environmental changes.

SOURCE

Mabert and Showalter (1987)

Tyebjee (1987)

Moriarty (1985)

Wright el al. (1986)

Dalrymple (1987)

Lawrence (1983)

]V~ller (1985)

JAMS 52 FALL, 1988


stages of the cycle. Causal models should be used with caution, however. Such models are typically based on the analysis of covariance structures. Cross-sectional covariance data, collected at one point in time cannot be used to demonstrate a time-order relationship between variables, whether manifest or latent. It cannot be determined whether it is A which "causes" B or B which "causes" A. With the addition of a theoretical or logical justification, however, it might be possible to eliminate one of the A/B alternatives and thus settle on the other as the plausible relationship. There are, for example, factors which logi- cally precede sales. Having data on these factors could enable the testing of the fit of models which include direc- tional links between these factors and sales. The combination of the good (and non-trivial) fit of the proposed model to the data and the logical exclusion of other competing models might then mean that important "causal" relation- ships have been identified.

Mullick et al. (1987) note that there is the least amount of forecasting effort at the phasing out or decline stage of the product life Cycle; forecasts for phasing out products with short life cycles are particularly crucial, however. Marketing managers could use "tracking" tools such as Census Bureau X-I 1.

Statistical versus judgmental forecasts

Statistical forecasting techniques are not widely used for certain sales forecasting applications. In industrial marketing, the cornerstone of the forecasting process is the individual salesperson's experience and insight and therefore much of industrial sales forecasting is intuitive, complex and political (Weinstein 1987). Dalrymple's (1987) survey of a wide cross-section of firms showed that the two most popular methods for sales forecasting were the sales force composite and the jury of executive opinion. Arm- strong (1985) indicates, however, that objective methods are typically more accurate, especially the further one goes into the future. The question of why there is a discrepancy between practice and the evidence from research comparing forecasting method performance can be answered by examining the organizational and implementational issues in forecasting. These will be dealt with in the next section. There are also methodological reasons for the lack of use of quantitative sales forecasting methods. In particular, Le- wandowski (1987) argues that forecasting methods often suffer from three major deficiencies. Firstly, they are too complex to be understood by the average user. Secondly, they involve a number of unrealistic assumptions that greatly hinder their use by practitioners. Thirdly, they do not integrate into a single model both extrapolative and explicative variables. To overcome such problems, Lewan- dowski has developed realistic systems for use by practitioners. His approach, widely used in Europe, focuses on the analysis of S-shaped curves and the causal factors that influence certain events. For example, the FORSYS system for sales forecasting (Lewandowski 1982) enables the user to include explanatory variables which might influence the forecasts, allows for automatic adjustments in the parameters of the model and also permits the user to incor- porate special events such as promotions and price changes.

In addition,the system assumes a trend factor, a seasonal factor and the influence of extraordinary calendar events (working days, temperature, etc.).

Combining forecasts

Because of the difficulties of selecting an accurate, un- biased forecasting method that will apply to various situations, there has been, and continues, a trend towards combining forecasts. In a comprehensive review of accuracy in forecasting, Mahmoud (1984) concluded that forecasting accuracy can be improved by combining techniques but that much more theoretical and empirical research is required to determine the best approach for doing so. Makri- dakis and Winkler (1983) also showed that combining of forecasts substantially improves forecasting accuracy. More specifically, for example, Lawrence, Edmundson and O'Conner (1986) demonstrated that combined judgmental and statistical forecasts are more accurate than either one alone. Moriarty and Adams (1984) used a composite model as a standard for evaluating constituent sales forecasting method altematives and found that this procedure was superior to comparing one method directly with another.

There are various ways of combining forecasts. The simple averaging approach is easy and robust (Winkler 1984). Used in empirical studies, it outperforms more complicated procedures. Another way of combining is to apply a weighted average instead. More complex combining rules are also available.

In a survey of sales forecasting practices in the U.S., Dalrymple (1987) found that respondents used an average of 2.7 forecasting methods on a regular basis. Clearly, managers recognize the difficulties of applying one technique to different situations. They do not rely on a single method, but compare forecasts from several techniques or use different techniques in different situations. However, about 61 percent of Dalrymple's respondents either did not combine forecasts or did so only occasionally.

Descriptive versus predictive validity

The final methodological issue to be considered is descriptive versus predictive validity in marketing models. Wittink (1987) writes that there is a substantial literature on econometric modeling in marketing. Many articles emphasize methodological issues such as a superior parameter estimation procedure. Wittink notes that researchers have not shown special concem for the predictive validity of the estimated models. Exceptions include Naert and Wever- bergh (1981; 1985), Brodie and de Kluyver (1984; 1987), Ghosh, Neslin and Shoemaker (1984) and Leeflang and Reuyl (1984). For example, Brodie and de Kluyver (1987; see Table 1) found that econometric market share models were not consistently more accurate than simple extrapolation (time series) methods for short-term forecasting. (Note that market share modeling is an indirect approach to forecasting sales). Bass (1987) explains the results of Brodie and de Kluyver as follows: "It is the information content of models that is important. Missing information may, in certain circumstances, be accounted for in naive models about as well as it is in incomplete models." Wittink (1987) notes that Brodie and de Kluyver may have used

JAMS 53 FALL, 1988


models which lacked descriptive validity. In this context, however, it is important to mention that improving model fit does not necessarily improve forecasts (for example, see Koehler 1985).

Despite conceptualizations and findings concerning marketing models' predictive validity, it is important to remember that the accuracy of econometric models generally improves with the number of observations. On the other hand, the accuracy of the naive model will not improve under such conditions (Hagerty 1987). Certainly econometric models are important to help marketing decision making. As with any forecasting technique, their selection as sales forecasting tools must be based on careful analysis of the forecasting situation (the decision-type, data available, time horizon, etc.). It was noted earlier in this paper that the integration of forecasting and decision-making tasks is essential. As an example, econometric features have been used in end-use models for electric utility forecasting. These features capture consumer response to price changes (Faruqui 1987) and the impact on sales of owner- ship levels of appliances, efficiency improvements, technological change and innovation (Fischler and Nelson 1986).

Summary To summarize the foregoing analysis of methodological

issues in sales forecasting: (1) No one sales forecasting method is appropriate for every

situation.

(2) Different techniques should be used over the different stages of the product life cycle.

(3) Statistical forecasting techniques for sales forecasting are not widely used in practice. Judgmental techniques dominate especially in industrial sales forecasting. Methodological problems preventing widespread use of statistical forecasts persist, but advances in system implementation are being made (see Lewandowski 1982). Computer-based techniques are now common for new product sales forecasting.

(4) Combining forecasting techniques improves accuracy.

(5) The predictive validity of econometric marketing models is questionable, but is known to improve over the longer term.

both external and intemal sources. Goslar and Brown (1986) indicate that it is the emphasis on flexibility which differentiates the DSS from the marketing information system. Flexibility refers specifically to features such as "what it" question alternatives, goal-seeking and optimizing techniques. Such features provide the system with the decision support characteristics.

Forecasting is a decision-specific task and therefore forecasting has to be tailored to the specific setting and linked into other organizational activities like marketing plans and the data collection system. This is stressed by Mabert and Showalter (1987) in their evaluation of sales forecasting for service organizations. The reason that this decision-specificity must be emphasized is because people prefer making forecasts judgmentally (Makridakis 1987) and hence the links between forecasting and the decision- making function are weak (Fildes 1987). There is a need to formalize the forecasting process for sales and market forecasting and to develop and apply systematically decision rules (Makridakis 1987). Even in the case of forecasting for long range planning activities where the decision context is more general, the forecasting still takes place in a specific environment.

In the light of these observations, the implementational issues discussed in this section of the paper concern data for sales forecasting, bias in forecasting, and people's acceptance of the forecasting function. In conjunction with the last issue, some empirical results are provided from a survey of forecasting practice.

Data for Sales Forecasting

An appropriate database must be built to improve forecasting accuracy (Makridakis 1987). The nature of the data must be taken into account in determining the most reasonable model for forecasting. For example, in an application forecasting Canadian residential heating demand, Berkowitz and Haines (1984) show that a regression model appears better if only aggregate data are available. How- ever, a multinomial logit model appears to dominate when disaggregated data are used. In a retail sales forecasting situation, Geurts and Kelly (1986) stress that is necessary to obtain data that are not distorted by accounting practices and methods. Rice and Mahmoud (1984) and Davidson and Prusak (1987) discuss in detail the database requirements for sales forecasting; the former study is in an international context, the latter, in more general terms.

IMPLEMENTATIONAL ISSUES IN SALES FORECASTING AND DECISION SUPPORT SYSTEMS

Organizational aspects of forecasting have received little attention but seem to have the potential of contribut- ing most to the improvement of forecasting (Fildes 1987). According to Goslar and Brown (1986) the effectiveness of a DSS hinges on: (1) flexibility, which includes model- building capabilities; (2) availability of a range of statistical tools; (3) a convenient interactive communication mode; and (4) a database containing data collected from

Bias in Sales Forecasting

Weinstein (1987) reports that studies in the U.S., Eu- rope and South America show that in industrial sales forecasting, the subordinate-superior interface is characterized by the following process: (1) a benchmark forecast preparation which may be based

on formal system inputs, raw disaggregate forecasts, information search, and past forecasting experience;

(2) individual bias behavior, either "income-seeking" or "approval seeking;"

JAMS 54 FALL, 1988


(3) superior counter bias behavior;

(4) resolution;

(5) authorization and adjustment.

In particular, Tyebjee (1987) observes that new product forecasts are not merely the result of a technical forecasting process but rather are the result of a complex, behavioral process influenced by the values, goals and roles of many members of the organization. Also, for various reasons, sales forecasts tend to be upwardly biased. For example, one such type of bias is "advocacy bias" (Tyebjee 1987). From the new product planner's perspective, a forecast is not made merely to reduce uncertainty about the future but rather to influence the resource allocation process to support the proposal. The view of Hogarth and Makridakis (1981 ) that the "illusion of control" arising from participat- ing in the planning process can lead to optimism in forecasting supports Tyebjee's (1987) experimental results.

Forecasting biases can be corrected to some extent by the superior evaluating the forecast, as discussed by Wein- stein (1987). Armstrong (1987) suggests the creation of a central data bank for use by all managers in order to overcome the biased use of data. He notes that it should not be large, otherwise managers can find irrelevant data to confirm their beliefs. In a search for ways to reduce bias, Moriarty (1985) proposes three design features for forecasting systems. These features are intended to detect bias in current forecasts and reduce bias in subsequent forecasts by direct communication or by analytical correction. Moriarty's system assumes that there is no interaction between the manager making the forecast and the correction procedures. For example, he acknowledges that his features will he difficult to implement if the sales manager is inflating his sales forecast by an amount to compensate for the reduction he has learned the system will apply. Yet Tyebjee (1987) and Weinstein (1987) suggest that this is exactly what occurs in judgmental sales forecasting situations. Clearly more research is needed in this area.

People's Acceptance of the Forecasting Function

Schultz (1984) summarizes the most constant predictors of system success or failure as management support, user involvement, and conduct of the implementation process itself. He also provides details of twelve implementation variables and how they may influence the implementation of forecasting in an organization. With respect to user involvement, Lawrence (1983) states that "without the ac- tive and enthusiastic support of [these] users a forecasting system cannot succeed." Indeed, he emphasizes that the forecasting system must be viewed by users (in the case of this paper, marketing managers) as a support to their job of forecasting. In a study of Australian organizations' implementation of forecasting methods, Lawrence (1983) found that the common factors in the companies who had abandoned forecasting were organizational factors, not methodological ones. Jobber and Watts (1986) also found organizational or behavioral aspects to be significant in implementing marketing information systems in British firms. They concluded that when developing systems it is impor-

tant to stress to users the increase in personal prestige which is likely to arise from implementation in the use of the system.

To improve the record of forecasting and therefore, im- plicitly, managers' acceptance of this function, several writers have emphasized a focus on the forecasting process, rather than the forecast itself (for example, see Armstrong 1987; Levenbach and Cleary 1984). In this way, the process can be improved over time, resulting in increased accuracy and reduced costs (Armstrong 1987). Perceived accuracy of forecasts is a primary determinant of their acceptance (Lawrence 1983).

A number of surveys have examined the use of forecasting techniques by managers (for example, see Dal- rymple 1975, 1987; Mentzer and Cox 1984; Sparkes and McHugh 1984). One of this paper's authors (Mahmoud) conducted a survey of forecasting practice in 1986. A questionnaire, with a cover letter requesting routing to the person most familiar with forecasting, mailed to a random selection of 200 of the firms on the Fortune 500 list resulted in a 33.5 percent response rate. Of these firms, 85 percent used forecasting for sales and marketing decisions. There- fore, the results in general can be assumed to have rele- vance for a discussion of sales forecasting.

In Table 2 we show the usage and perceived accuracy of qualitative and quantitative forecasting methods. Thurstone's Case V analysis (Green and Tull 1978; Mahmoud and Malhotra 1986) is used to indicate the relative perceived accuracy of the different methods (see Fig- ure 2). Case V presents the results in a clear and simple way. The Case V analysis takes as input, individual-level data and develops a group-level interval scale in which the stimuli are assigned a value ranging from 0 to 1. In relative terms, the method perceived to be most accurate is assigned a value of 1.00 and the one perceived to be least accurate is assigned a value of 0.00. It should be emphasized that a factor assigned a value of 0.00 should not be interpreted as having no importance. The correct interpretation is that this factor is relatively the least important. The most commonly used forecasting methods were qualitative (see Table 2). These results are consistent with the results of Dalrymple (1987). It is surprising that Mahmoud's results show such a high use of scenario development. Perhaps this method is widely used on an informal basis but tends not to be applied in a systematic way. The results of Schnaars and Topoi (1987) suggest that heavy reliance on scenario development may be unwise. Their experimental data showed that there was no evidence that the use of multiple scenarios improved the accuracy of judgmental forecasts.

The most popular quantitative methods were econometric and regression methods (see Table 2). Extrapolation methods were also used by a good portion of the respondents, lending support to Dalrymple (1987) and Mentzer and Cox (1984). Note, that, as shown in Table 2, while qualitative techniques are more popular, several quantitative techniques are perceived by the respondents to he more accurate. Although accuracy is the most important factor considered in the selection of a forecasting method (see Table 3 and Figure 3), clearly implementational issues such as ease of use and data requirements play a significant role.

]A_MS 55 FALL, 1988

EMERGING ISSUES IN SAI.ES FORECASTING AND DECISION SUPPORT SYSTEMS MAHMOUD, RICE & MALHOTRA

TABLE 2 Usage and Perceived Accuracy of

Forecasting Methods

Method Mean Firms using method: Perceived No.% (n--=67) Accuracy I

Qualitative

Individual subjective probability assessment 3.53 62 93%

Jury of executive opinion 3.32 60 90

Scenario development 3.33 62 93 Formal .survey 3.22 40 60 Sales force composite 2.81 61 91 Delphi method 2.73 27 40

Quantitative

Holt" s two parameter 4.00 20 30 Winters' three paranaeter 3.90 25 37 Inventory models 3.81 35 52 Box-Jenkins 3.75 15 22 Leading indicators 3.50 39 58 Classical decomposition 3.50 10 15 Econometric methods 3.48 42 63 Regression 3.40 41 61 Input/output 3.30 35 52 Brown's linear exponential 3.30 20 30 Naive method adjusted for sea.~nality 3.30 22 33 Single exponential smoothing 3.20 17 25 Moving average 2.92 28 42 Mean 2.53 23 34 Naive method 2.34 18 27 Brown' s quadratic 2.00 3 4

JPerceived accuracy was measured on a scale of 1 to 5 where I = not accurate and 5 = very accurate.

'Respondents used these methods for various forecasting tasks and the results do not differentiate the methods used specifically for sales forecasting.

FIGURE 2 Normalized Scale Values of Relative Perceived

Accuracy of Forecasting Methods

1.00 Holt's Two Parameter (mean score 4.00)

0.950 Winters' Three Parameter

0.905 Inventory Models 0.875 Box-Jenkins

0.765 0.750 0.740

Individual Subjective Probability Assessment Leading Indicators, Classical Decomposition Econometric Methods

0.700 Regression

0.665 0.660 0.650

0.610 0.600

Scenario Development Jury of Executive Opinion Input/Output, Brown's Linear Exponential, Naive Method Adjusted Formal Survey Single Exponential Smoothing

0.460 Moving Average

0.405 Sales Force Composite

0.365 Delphi Method

0.265 Mean

0.170 Naive Method

0.000 Brown's Quadratic (mean score 2.00)

"Ease of use" was also rated second after accuracy in Mentzer and Cox (1984). Dal rymple (1987) comments that business organizat ions may tend to choose methods that are famil iar and easy to use. M a h m o u d ' s survey results show that while Box-Jenkins received a relat ively good perceived accuracy mean score of 3.75, this method was used by only 22 percent of respondents. Both Sparkes and McHugh

(1984) and Mentzer and Cox (1984) poin ted out that Box- Jenkins was unfamil iar to many respondents . The repor ted use of this method was even less in the Dal rymple (1987) study than in the Mahmoud study.

The use o f different forecast ing methods for different t ime horizons is shown in Table 4. Note that this table includes and compares the results o f three studies. Wi th

JAMS 56 FALL, 1988


TABLE 3 Relative Importance of Factors Considered in the

Selection of a Forecasting Method

FIGURE 3 Normalized Scale Values of Relative Importance of

Factors Considered in the Selection of a Forecasting Method

Factor Mean No. of Importance Score I Responses

Accuracy 4.64 67

Ease of use 4.13 66

Data requirements 3.90 67

Time horizon 3.43 61

Data pattern 3.24 59

Number of items to be forecasted 3.13 58

Availability of software 3.00 58

'Respondents were asked to rate the relative importance of each factor on a scale of I to 5 where I represented "not important" and 5 represented "very important."

respect to qualitative methods, the jury of executive opinion shows consistent use over all time horizons. The individual subjective assessment method is most used for a medium time horizon, and the sales force composite for the near and medium time horizons. Moriarty and Adams 0984), however, found improved judgmental forecasting when the time between the judgment decision and the feedback result is short. Therefore, some of the respondents in the studies may be inappropriately using some judgmental or qualitative techniques. The reasons though may be linked to implementational issues such as "ease of use" as discussed above.

Scenario development is used much more for longer term forecasting, as would be expected. The use of regression and econometric models also is greater as the time horizon increases, with the exception of less use in the long term (over one year) in the Dalrymple study. This latter result is difficult to understand as for the most part the findings on the regression/econometric models are consis-

1.00 Accuracy (mean score 4.64)

0.689 Ease of Use

0.549 Data Requirements

0.262 Time Horizon

0.146 Data Pattern

0.079 Number of Items to be Forecasted

0.000 Availability of Software (mean score 3.00)

JAMS 57 FALL, 1988


TABLE 4 Percentage of Respondents Using Techniques for Different Time Horizons *

METHODS TIME HORIZONS

Mahmoud Mentzer & Dalrymple Mahmoud Mentzer Dalrymple up to Cox up to Short 3 Mo. &Cox Medium 3 Mo. 3 Mo. 1-3 Mo. -2Yrs. 3 Mo.-2 Yrs. 4 Mo.-IYr.

Mahmoud Mentzer Dalrymple over & Cox over

2Yrs. over2Yrs. IYr. Qualitative

Individual Subjective Probabifity Assessments 15% 48% 12%

Jury of Executive Opinion 22 37% 18.7% 22 42% 29.1% 37 38% 6.7%

Scenario Development 9 15 45

Sales Force Composite 19 37 23.1 30 36 34.3 18 8 5.2

Formal Survey 13 25 18.6 16 24 26.9 28 12 15.9

Delphi Method 22 15

Quantitative

Regression/Econometric Models 12% 18% 12.6% 27% 45% 29.8% 48% 38% 15%

Leading Indicators 6 3.7 54 20.1 10 7.6

Input-Output 45 16

Box-Jenkins 6 5 6 12 6 3.7 4 2 2.2

Naive Method 4 34.3 16 17.9 0.7

Naive Adjusted for Seasonality 10 37 7 -

Moving Average 9 24 17.9 27 22 12.7 5 2.2

Mean 12 30

Single Exponential Smoothing 13 10

Holt's Two Parameter 6 21

Census 11 4 12 4

Adaptive Smoothing 7 1 3

Kalman Filter 3 3

Exponential Smoothing 24 9.7 17 9.0 6 6.7

* Based on an idea from Dalrymple (1987) and adapted from Dalrymple (1987) and Mentzer and Cox (1984). NOTE: Mahmoud's results apply to forecasting for all management functions and not only sales forecasting.

tent with methodological research. This research implies that regression is more suitable for longer term forecasts and when more data are likely to be available.

Exponential smoothing models of various types and other extrapolation models are used most often for the near and medium time horizons. The naive method adjusted for seasonality is used by over a third of respondents for the medium term and, by a few respondents, even in the long term. Such use of the naive method would be supported by methodological research (for example: Makridakis et al. 1982; Schnaars 1984). This research implies that using a naive method to forecast is as accurate as using complex, sophisticated methods.

Summary

The above analysis of implementational issues in sales forecasting and decision support systems can be summa- rized as follows:

(1) Particular attention must be paid to matching the sales forecasting technique to the decision-task. Important variables in this process include the data available and the time horizon. Ease of use of the technique has been shown in surveys to be an important selection criterion. Accuracy is not sufficient for a forecasting technique to be implemented.

JAMS 58 FALL, 1988


(2) Judgmental forecasting is popular among managers and this leads to the problem of bias. While some techniques have been proposed to reduce this bias, much research needs to be done.

(3) Sales forecasting can be integrated into the marketing decision support system only with the support of the forecast user.

FORECASTING PAPERS IN THIS ISSUE

The special section in this issue of JAMS contains five papers on sales forecasting and decision support systems. All are concerned in some way with the improvement of forecasting accuracy for marketing decision-making. The first paper by John T. Mentzer entitled "Forecasting with Adaptive Extended Exponential Smoothing" acknowledges the difficulties of forecasting in marketing and presents a new technique to deal with some of the problems. Adaptive Extended Exponential Smoothing (AEES) assumes that the data contain level, trend, seasonality and noise and consid- ers all these components in producing a forecast. It also provides adaptive capabilities for cz . Mentzer reviews some time series techniques commonly used for short-term sales forecasting. In an empirical test using 14 test conditions incorporating a variety of data characteristics, he dem- onstrates the superiority of AEES over five other time series techniques.

In "A Framework for the Combination of Forecasts," Benito E. Flores and Edna M. White argue that there is a need to combine forecasts for marketing. They present a systematic analysis of the literature by classifying research on the combination of forecasts along two dimensions. These dimensions are the selection of the forecasts to be combined, and the selection of the method of combining the base forecasts. From a decision support standpoint, Flores and White conclude that researchers and managers must consider user satisfaction, user IrusI and ease of understanding when comparing combining versus selecting one forecast.

The paper by Zoher E. Shipchandier and James S. Moore, "Examining the Effects of Regression Procedures on the Temporal Stability of Parameter Estimates in Mar- keting Models" addresses an issue important for longer term marketing planning. Shipchandler and Moore focus on the problem of multicollinearity, a common feature of marketing data. Using two empirical examples, a furniture sales model (discussed in detail) and a home pricing model (mentioned briefly), they show that the temporal stability of parameter estimates obtained through ridge regression is better than that of estimates achieved through ordinary least squares and latent root regression.

"The Impact of Misrepresentative Data Patterns on Sales Forecasting Accuracy" by Michael D. Geurts deals with improving forecasting accuracy by analyzing and improving the data on which the forecasts are based. Geurts examines the nature of sales data, discussing problems such as outliers, the characteristics of the product life cycle, and accounting induced distortions. He suggests several ways of coping with data problems when forecasting; included are discussions of filtering and data modification.

The goal of the final paper, "Decision Support Oriented Sales Forecasting Methods" by David J. Wright, is to show thai the appropriate method of evaluation is critically dependent on the purpose for which management requires the forecast. Wright argues that bias in forecasting may not necessarily be undesirable and that the emphasis should not be only on forecast accuracy but on the overall management situation. He uses two examples to support his argu- ment: the comparison of forecast accuracy to inventory management costs, and forecasting for market share control. He also discusses the organizational implications of his approach to selecting a good forecasting method.

CONCLUSIONS

This paper has stressed the need to integrate sales forecasting into the decision support system. The link between the forecasting function and the DSS was clarified. The discussion of methodological and implementational issues suggests several directions for future research. The theory of sales forecasting must address more specifically the organizational and implementational impacts of the forecasting process. More research needs to focus on the applied aspects of forecasting. Researchers could develop integrated systems that would be useful for a particular industry context or organization. The situation-specific nature of sales forecasting is clear. Surveys could be undertaken to identify more closely forecasting implementational problems and procedures in the selection of forecasting software. On the methodological side, researchers should con- tinue to focus on the accuracy and combining of forecasts. Nevertheless, they should not lose sight of the practical implications. For example, studies are needed to test Lewandowski's (1982, 1987) systems and to develop his ideas further. We hope that the papers in forecasting presented in this issue will encourage research along these lines.

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ACKNOWLEDGEMENT

The authors are grateful to Irene Lange for her helpful comments on an earlier version of this paper.

ABOUTTHEAUTHORS

ESSAM MAHMOUD is Associate Professor of Man- agement Science at the University of North Texas. He has published widely, in journals such as the Journal of Forecasting, Technological Forecasting and Social Change, Management International Review, Journal of Business Forecasting, The American Statistician, Journal of the Academy of Marketing Science and INFOR Informa- tion Systems and Operational Research. His research focuses on forecasting accuracy and forecasting software evaluation. He is an Associate Editor of the International Journal of Forecasting, serves on the Editorial Review Boards of JAMS, and Information & Management and as a reviewer for other major journals such as Decision Sciences and Journal of Marketing Research.

GILLIAN RICE obtained her Ph.D from the University of Bradford (UK) in 1982. She has taught at the University of Michigan - Flint, West Virginia University and Concor- dia University (Montreal) and she is now a freelance writer and consultant. Her areas of interest are political risk forecasting, the implementation of forecasting, international marketing, and export promotion. Her articles have appeared in such journals as Journal of Forecasting, Man- agement International Review, Management Decision, Food Marketing and Journal of Business Forecasting. She is a book review editor for the Journal of Global Marketing and has co-edited a special issue of International Market- ing Review.

NARESH MALHOTRA is Associate Professor and Co- ordinator of Marketing at Georgia Institute of Technology. Recently, he has been appointed to the Centennial Distin- guished Professorship. He has published extensively in major journals including the Journal of Marketing Research, Journal of Consumer Research, Marketing Science, Journal of Marketing, Journal of Retailing, Jour- nal of Health Care Marketing, Journal of the Academy of Marketing Science, a n d l e a d i n g j o u r n a l s in s ta t i s t ics , m a n - a g e m e n t s c i e n c e , and p s y c h o l o g y . H e s e r v e s as an Assoc i - ate E d i t o r o f Decision Sciences and as S e c t i o n Edi to r , H e a l t h Ca re M a r k e t i n g A b s t r a c t s , Journal of Health Care Marketing. Also , he s e r v e s o n the ed i to r i a l b o a r d s o f e i g h t journals.

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Documents

Emerging issues in sales forecasting and decision support systems