Journal of Forecasting, Vol. 3, 229-234 (1984)

The Teachers/Practitioners Corner Forecasting in Business Schools: A Survey

JOHN H A N K E Eastern Washington University, U.S.A.

ABSTRACT The study of forecasting techniques has received increased attention in recent years. How to incorporate this topic into the business school curriculum is a frequent subject of discussion. The purpose of this study was to determine whether forecasting is being taught in business schools and how it is incorporated into the curriculum. The survey instrument was sent to 622 member institutions of the American Assembly of Collegiate schools of Business. The importance of teaching forecasting techniques at both the undergraduate and graduate level was investigated.

KEY WORDS Forecasting Business schools

The past decade has witnessed several important developments in the area of business forecasting. Both the theory and practice of forecasting have been advanced by the advent of the computer and the increasing complexity and competitiveness of the business environment. Firms of all sizes find it essential to predict variables that directly affect decision making and performance (Hanke and Reitsch, 198 1). How are business schools educating students in the area of forecasting? A review of related literature produces few answers.

The purpose of this article is to report the results of a survey to determine whether forecasting is being taught in business schools; and, if so, how it is incorporated into the curriculum. One objective was to determine how important the faculty members responsible for the forecasting area felt it was for business undergraduate and graduate students to understand techniques used in forecasting.

A second objective was to determine if formal forecasting courses are offered in business schools and by which departments. Whether forecasting courses are taught is an indicator of the importance of the topic. Where forecasting is taught should indicate what concepts are emphasized.

The third objective was to determine how formal forecasting courses are designed. A number of surveys have looked at the general area of which techniques are used in business (Sales Management, 1967; Conference Board, 1970, 1971 ; Wheelwright and Clarke, 1976). Mentzer and Cox (1983) found that regression, subjective, exponential smoothing and moving average techniques were fairly well known and often used by the sales forecasting community. They found a high degree of unfamiliarity and dissatisfaction with Box-Jenkins time series analysis, whereas Fildes and Lusk (1 983) who surveyed forecasting practitioners found basically opposite results. 0277-6693/84/020229-06$01 .OO Received April 1983 '0 1984 by John Wiley & Sons, Ltd. Revised September 1983

230 Journal of’ Forcxasting Vol. 3, Iss. N o . 2

However, very little is known about what is taught in forecasting courses and how it is approached. Is there a relationship between what business is using and what forecasting instructors feel is important?

A final objective was to determine why business schools which do not offer forecasting do not offer formal forecasting courses. Is forecasting considered unimportant, or do schools simply lack the resources to teach i t ? If a forecasting course is not offered. in which courses are forecasting techniques taught?

METHODOLOGY

A questionnaire’ was constructed to be sent to faculty members who are responsible for the forecasting area at their particular institution. The survey instrument was sent to all 620 (571 U.S.A. and 49 International) member institutions of the American Assembly of Collegiate Schools of Business. The questionnaire was mailed to the Dean of the School of Business who was instructed to‘Please ask a qualified faculty member in either business statistics or economics to complete this brief instrument’. The questionnaire was returned by a faculty member from 324 institutions or 51.5 per cent of the schools sampled.

To determine how important it is for business undergraduate and graduate students to understand techniques used in forecasting two questions were asked of all respondents.’ The first question was “How important is it for business undergraduate students to understand techniques used in forecasting?“ Exhibit 1 shows a breakdown of the responses to this question. The respondents felt regression analysis and multiple regression were ‘very important’ techniques (75 per cent and 64 per cent. respectively). Over 99 per cent of the faculty members indicated that regression analysis was ’important or very important‘ and multiple regression was considered ‘important or very important‘ by 92 per cent. Moving averages, times series: decomposition method and exponential smoothing were also selected as ‘important or very important’ techniques by over 77 per cent of the respondents. Over half of the faculty members indicated that autoregressive models. adaptive filtering, Box --Jenkins and Census 11 were ‘not important’ techniques for undergraduates.

The second question was “How important is i t for business graduate students to understand techniques used in forecasting?” Exhibit 1 indicates the responses to this question. Regression analysis was considered ‘very important’ by 94 per cent of the responding instructors, and 89 per cent indicated that multiple regression was ‘very important’. Fewer than 1 per cent of the respondents considered regression analysis or multiple regression ‘not important’ for business graduate students. Close to 90 per cent of the faculty members indicated that time series: decomposition method. moving averages, exponential smoothing and autocorrelation were ‘important or very important’ techniques. Over 75 per cent of the instructors felt that autoregressive models and Box-Jenkins were ‘important or very important’ concepts. Adaptive filtering was considered ’not important’ by 46 per cent of the respondents and Census 11 had a 48 per cent ‘not important‘ proportion.

A second objective was to determine if formal forecasting courses are offered in business schools, and by which departments. Of the 324 institutions who responded, 188, or 58 per cent, offered a formal forecasting course. Of the 188 institutions which offered a forecasting course, 100 taught i t in the business area. 52 taught it in the economics area, and 36 taught a

’ Copies of the questionnaire can be obtained from the author or the editor of the Journul of Forerusting ’ The scale choices were: very important. important. not important and not covered.

J . Hanke Forecasting in Busincss Schools 23 1

Technique Undergraduates Graduates

Very Important Not Very Important Not important important important important

Regression analysis Multiple regression Time series:

decomposition method

Moving averages Exponential smoothing Autocorrelation Autoregressive models Box-Jenkins Adaptive filtering Census I1

‘;< 75 64

38 32 25 20 14 12 5 5

0, / I >

25 28

45 56 53 44 34 25 23 24

:4 0 8

17 12 22 36 52 63 72 71

0 / 0

94 89

53 45 44 45 36 31 18 15

0, ,‘” 6

10

37 47 48 44 43 43 36 31

0 0

0 I

10 a 8

I I 21 26 46 48

Exhibit I . Important techniques for business undergraduates and graduate students. n = 324

forecasting course in both areas. When the respondents were asked “What department or functional area teaches your forecasting course?’ 52 replied economics, 30 indicated business economics, 41 said quantitative methods or management science, 18 responded decision science or statistics, and the rest were scattered throughout the management area.

The respondents were asked “Is your forecasting course required or optional?’ Of the 148 institutions that replied to this question 109, or 74 per cent, indicated that their forecasting course was optional whereas 26 per cent said it was required. Forecasting was required by 1 1 economics programmes, eight M BA programmes and seven finance departments.

Institutions offering forecasting were also asked “At what level is forecasting offered?’ Forty- one per cent taught forecasting at both the undergraduate and graduate levels, 33 per cent taught it only at the graduate level and 26 per cent taught forecasting only at the undergraduate level.

The forecasting instructors were also asked “Are students assigned a practical forecasting project as a requirement for the forecasting course?’ Over 83 per cent indicated that they assigned a practical forecasting project as a requirement for their course. Exhibit 2 indicates the techniques emphasized in the project assignment. Regression analysis was mentioned by 62 instructors.

Technique Times mentioned (number of responses)

Regression analysis 62 Box-Jenkins 28 Time series 25 Exponential smoothing 16 ARlMA 9 Moving averages Model selection Autocorrelation Census I1

Exhibit 2. Techniques emphasized in forecasting project

232 Journal of' Forecasting Vol. 3, Iss. No. 2

Box--Jenkins techniques. and time series analysis were mentioned by 28 and 25 faculty members, respectively. An examination of Exhibit 2 shows an absence of judgemental methods. "What percentage of class time is spent on the project ?" was another question asked of the respondents. Evidently, the project is considered important, because approximately 57 per cent of the faculty spend between 10 and 30 per cent of their class time on it and another 20 per cent spend more.

Forecasting instructors were also asked "How is the computer used in the forecasting courses?' Exhibit 3 shows that a majority of instructors use some type of forecasting package or canned programs in the forecasting course. Some faculty members indicated that they use some combination of both approaches.

Category Times mentioned (number of responses)

Computer forecasting package Individual canned programs Output provided Not at all

105 47 28

7

Exhibit 3 . Computer usage

Finally, the instructors were asked "Which textbook or textbooks are used in your forecasting class'?'' Forecasting Methods.for Management by Wheelwright and Makridakis was mentioned by 51 instructors. Other texts used by between 10 and 20 faculties were Business Forecasting by Hanke and Reitsch. Forecasting in Business and Economics by Granger, Business Fluctuations by Bails and Pepper, Forecasting and Time Series by Bowerman and OConnell, and Econometric Models und Economic Forecasts by Pindyck and Rubinfeld. Several instructors used more than one textbook and a few indicated that they used materials they had developed. It was of interest that an extremely large variety of books were mentioned in one context or another.

The final objective was to determine why business schools not offering a formal forecasting course have chosen this course of action. Exhibit 4 indicates that 'lack of faculty' was mentioned by 40 institutions. Other reasons given for not offering a forecasting course were 'not enough room in the curriculum' indicated by 19 schools and 'forecasting is included in other courses' mentioned by 17 respondents. Responses to the question "Do you plan to offer a formal forecasting course within the next two years?' are shown in Exhibit 5. Seventy per cent of those institutions who do.not offer

Reason Percentage

Lack of faculty No room in curriculum Included in other courses No student interest Not considered crucial Lack of funds School too small Thrown out earlier N o degree in statistics

31 18 16 9 8 5 5 2 1

Exhibit 4. casting. n = 107

Reasons for not offering fore-

J . Hanke Forecasting in Business Schools 233

Plan Percentage

Yes 25 No 6 Not sure 70

Exhibit 5 . casting. n = 122

Plan to offer fore-

a formal forecasting course responded ‘not sure’ whereas 25 per cent indicated that they planned to offer forecasting within the next 2 years.

Institutions who do not have formal forecasting courses were also asked to indicate in which courses forecasting techniques were taught. Statistics was the course mentioned most frequently, followed by econometrics, production or operations management, quantitative methods and marketing research. Regression analysis, multiple regression, and time series analysis were usually taught in some type of statistics course. Autocorrelation, autoregressive models, and Box-Jenkins techniques were usually taught in an econometrics or statistics course. Exponential smoothing and moving averages were taught in either production or operations management or statistics courses.

CONCLUSIONS

This article has presented the results of a survey of member institutions of the American Assembly of Collegiate Schools of Business concerning whether forecasting courses are being taught, and if so how. It was found that regression analysis and multiple regression were the techniques the respondents felt most important for business undergraduate and graduate students to understand. Moving averages, exponential smoothing, and time series: decomposition method were other techniques selected as important by respondents.

Formal forecasting courses were offered by 58 per cent of the responding institutions. Approximately half of the respondents indicated that a course was offered in the economics or business economics area. Otherwise, there was a large selection of departments responsible for the course. The forecasting course was required by about 25 per cent of the respondents. It was offered at the graduate level by 74 per cent of the institutions and at the undergraduate level by 67 per cent.

Regression analysis and multiple regression were the most important techniques in the design of a forecasting course. Time series: decomposition method was considered the next most important subject. Moving averages, exponential smoothing, autocorrelation, autoregressive models and Box-Jenkins techniques were felt to be important topics by a majority of the respondents who taught a forecasting course. Faculty members who teach forecasting courses assign practical projects as a requirement for the course. Regression analysis, multiple regression, Box-Jenkins and time series analysis are the techniques emphasized in the project. Most instructors spend between 10 and 30 per cent of their class time on the project. A majority of the faculty members use either a computer forecasting package or individual canned computer programs as a part of their approach to teaching forecasting. Forecasting Methods for Munagement by Wheelwright and Makridakis is the textbook that the largest number of instructors use as a required text. A large variety of forecasting textbooks were mentioned.

The most frequently mentioned reason why business schools do not offer formal forecasting courses was ‘lack of faculty’. Other reasons given were ‘not enough room in the curriculum’ and ‘forecasting topics are included in other courses’. Approximately 25 per cent of these institutions plan to offer a forecasting course within the next 2 years.

234 Journal of Forecasting Vol. 3, Iss. No. 2

If a forecasting course were not offered at an institution, statistics was the course where forecasting techniques were most frequently taught. Econometrics and production or operations management were also courses mentioned as containing forecasting techniques.

It is apparent that the interest in forecasting techniques is growing. Research is needed to determine whether business schools are meeting the needs of business and industry in the forecasting area. Are business graduates adequately educated to become either forecasters o r more important intelligent users of forecasts?

REFERENCES

Conference Board. Sales Forecasting. New York, 1970. Conference Board. Planning and Forecastitig in the Smaller C‘ompanr, New York, 1971. Fildes, R. and Lusk, E. J.. ‘Prior knowledge and the forecasting competition’, Inti~rnutionul Symposium on

Hanke, John E. and Reitsch, A. G.. Business Foriwusting, Boston: Allyn and Bacon, 1981. Mentzer, John T. and Cox. J . E. Jr . . ‘Familiarity. application. and performance of sales forecasting

Sales Munagement, ‘Sales forecasting: is five percent error good enough?’, December 15, 1967, pp. 41 -48. Wheelwright. Steven C. and Clarke. D. G. ‘Corporate forecasting: promise and reality’, Hurvard Bzisine.ss

Forecasting, Philadelphia, June 1983.

techniques’. lniernational Symposium on Forecasting. Philadelphia, June 1983.

R~L%w. 54 (November-December 1976). 40-42.

A ut ho r’s hiographj,: John Hanke is Professor of Management Information Systems at Eastern Washington University. He received a Bachelor’s degree from Northern Illinois University (Accounting) and a Ph.D. from the University of Northern Colorado (Research and Statistical Methodology). He has authored several articles in the education. psychology and business areas, and served as a consultant in these fields. He has co-authored Business Forecusting (Allyn and Bacon, I98 I ) and Sioti.stic,al Decision Model.s,fbr Management (Allyn and Bacon, 1983).

Aufhor’s address: John Hanke, School of Business, Eastern Washington University. Cheney. WA 99004, U.S.A