Abstract
The development of a sales forecasting system involves three major steps. The first step is to obtain prior sales data and to identify the model that will best forecast the patterns that exist in the data. The second step is to estimate parameter values for the selected model by analyzing the prior sales data. The third step is to test the accuracy of the model by use of the prior sales data. Each of the steps requires use of prior data.
In all three steps, there is a basic assumption that the past data represent some underlying process that can be identified and modeled. In some cases the past data may not represent the underlying process, and the forecasting process is seriously distorted. Some frequent causes of distorted data are 1) accounting methods that are used to record or collect the data, 2) marketing tactics such as promotions which that create outliers, 3) limits on production capacity that cause stockouts.
This paper looks at events and actions that may distort data used for sales forecasting and at the resulting impact the events and actions may have on forecasting accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Armstrong, J. Scott. 1978.Long Range Forecasting From Crystal Ball to Computer. New York: Wiley.
Box, George E. P. and Gwilym M. Jenkins. 1976.Time Series Analysis Forecasting and Control. (Rev. ed.) San Francisco: Holden-Day.
Chambers, J. C., S. K. Mullick and D. D. Smith. 1974.An Executive’s Guide to Forecasting. New York, N.Y.: John Wiley and Sons.
Cleveland, W. S., D. M. Dunn and I. J. Terpenning. 1979. “SABL—A Resistant Seasonal Analysis of Economic Time Series.” Ed. A. Zellner. Washington, D.C.: U.S. Government Printing Office.
Coopersmith, Lewis W., 1983. “Forecasting Time Series Which are Inherently Discontinuous.”Journal of Forecasting Vol. 2:255–235.
Dagum, E. B., 1978. “Modeling, Forecasting, and Seasonally Adjusting Economic Time Series With the X-11 ARIMA Method.”The Statistician 27: 203–16.
Day, George. 1981. “The Product Life Cycle: Analysis and Applications Issues.”Journal of Marketing Vol. 45, No. 4: 60–67.
Fisher, Franklin M., 1966.The Identification Problem in Econometrics. New York: McGraw-Hill.
Huber, P. J., 1964. “Robust Estimation of a Location Parameter.”Annuals of Mathematical Statistics 35: 73–101.
Kallek, S., 1978. “An Overview of the Objectives and Framework of Seasonal Adjustment.” InSeasonal Analysis of Economic Time Series. Ed. A. Zellner, Washington, D.C.: U.S. Government Printing Office.
Krueger, Russell, 1980. “Seasonal Adjustment of Irregular Time Series: U.S. Merchandise Trade.” Unpublished manuscript—presented at the third International Time Series Meeting, Houston, Texas.
Levenbach, Hans and James P. Cleary. 1981.The Beginning Forecaster. Belmont, California: Lifetime Learning Publications.
Mahmoud, E., 1984. “Accuracy in Forecasting.”Journal of Forecasting 2: 139–159.
Makridakis, S., A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen and R. Winkler. 1982. “The Accuracy of Extrapolation (Time Series) Methods.”Journal of Forecasting 2: 111–153.
Makridakis, Spyros, Steven C. Wheelwright and Victor E. McGee. 1983.Forecasting Methods and Applications. Second edition. New York: John Wiley and Sons.
McKeown, James C. and Kenneth S. Lorek. 1978. “A Comparative Analysis of the Predictive Ability of Adaptive Forecasting, Re-Estimation and Re-Identification Using Box-Jenkins Time Series Analysis of Quarterly Earnings Data.”Decision Science 9 (4): 673–87.
Tukey, John W., 1977.Exploratory Data Analysis. Reading, Massachusetts: Addison-Wesley.
Wasson, Chester R., 1978.Dynamic Competitive Strategy and Product Life Cycles. Austin, Texas: Lone Star Publishers, Inc.
Winters, P. R., 1960. “Forecasting Sales by Exponentially Weighted Moving Averages.”Management Science 6 (April): 324–342.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Geurts, M.D. The impact of misrepresentative data patterns on sales forecasting accuracy. JAMS 16, 88–94 (1988). https://doi.org/10.1007/BF02723364
Issue Date:
DOI: https://doi.org/10.1007/BF02723364