Research note

PeterMaurice Catt1
1School of Computing and Information Technology, Unitec New Zealand, Auckland, New Zealand

Tóm tắt

Purpose

The purpose of this article is to provide a critique of SAP's enterprise resource planning (ERP) (release ECC 6.0) forecasting functionality and offer guidance to SAP practitioners on overcoming some identified limitations.

Design/methodology/approach

The SAP ERP forecasting functionality is reviewed against prior seminal empirical business forecasting research.

Findings

The SAP ERP system contains robust forecasting methods (exponential smoothing), but could be substantially improved by incorporating simultaneous forecast comparisons, prediction intervals, seasonal plots and/or autocorrelation charts, linear regressions lines for trend analysis, and event management based on structured judgmental forecasting or intervention analysis.

Practical implications

The findings provide guidance to SAP forecasting practitioners for improving forecast accuracy via important forecasting steps outside of the system.

Originality/value

The paper contributes to the need for studies of widely adopted ERP systems to critique vendor claims and validate functionality through prior empirical research, while offering insights and guidance to SAP's 12 million+ worldwide enterprise system practitioners.

Từ khóa


Tài liệu tham khảo

Armstrong, J.S. (1984), “Forecasting by extrapolation: conclusions from 25 years of research”, Interfaces, No. 14, pp. 52‐66.

Armstrong, J.S. (1989), “Combining forecasts: the end of the beginning or the beginning of the end?”, International Journal of Forecasting, Vol. 5 No. 4, pp. 585‐8.

Armstrong, J.S. (Ed.) (2001), Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishers, Norwell, MA.

Chatfield, C. (2001), “Prediction intervals for time‐series forecasting”, in Armstrong, E. (Ed.), Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishers, Norwell, MA.

Clemen, R.T. (1989), “Combining forecasts: a review and annotated bibliography”, International Journal of Forecasting, Vol. 5 No. 4, pp. 559‐83.

Fildes, R., Goodwin, P. and Lawrence, M. (2003), “The design features of forecasting support systems and their effectiveness”, Lancaster University Management School Working Paper, Lancaster.

Fliedner, G. (2001), “Hierarchical forecasting: issues and use guidelines”, Industrial Management & Data Systems, Vol. 101 No. 1, pp. 5‐12.

Gardner, E.S. (1985), “Exponential smoothing: the state of the art”, Journal of Forecasting, Vol. 4, pp. 1‐28.

Gardner, E.S. (2006), “Exponential smoothing: the state of the art – Part II”, available at: www.bauer.uh.edu/gardner/Exponential%20Smoothing.pdf (accessed June 21, 2006).

Gardner, E.S. and McKenzie, E. (1985), “Forecasting trends in time series”, Management Science, Vol. 31, p. 1237.

Lee, T., Cooper, F. and Adam, J. (1993), “The effects of forecasting errors on the total cost of operations”, Omega, Vol. 21 No. 5, pp. 541‐50.

Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., Newton, J., Parzen, E. and Winkler, R. (1982), “The accuracy of extrapolation (time series) methods: results of a forecasting competition”, Journal of Forecasting, Vol. 1, pp. 111‐53.

Makridakis, S., Chatfield, C., Hibon, M., Lawrence, M., Mills, T., Ord, K. and Simmons, L.F. (1993), “The M2‐competition: a real‐time judgmentally based forecasting study”, International Journal of Forecasting, Vol. 9 No. 1, pp. 5‐22.

Makridakis, S. and Hibon, M. (1979), “Accuracy of forecasting: an empirical investigation”, Journal of the Royal Statistical Society, Vol. 142, pp. 97‐145.

Makridakis, S. and Hibon, M. (2000), “The M3‐competition: results, conclusions and implications”, International Journal of Forecasting, Vol. 16 No. 4, pp. 451‐76.

Mentzer, J.T. and Moon, M.A. (2005), Sales Forecasting Management: A Demand Management Approach, 2nd ed., Sage Publications, Thousand Oaks, CA.

O'Leary, D. (2002), Enterprise Resource Planning Systems: Systems, Life Cycle, Electronic Commerce, and Risk, Cambridge University Press, Cambridge.

Pegels, C.C. (1969), “Exponential forecasting: some new variations”, Management Science, Vol. 15 No. 5, pp. 311‐5.

SAP (2006), SAP Annual Report 2006, SAP, Walldorf.

SAP (2007), “SAP ERP central component (Release 6.0 SR1), Sales and operations planning (LO‐LIS‐PLN)”, available at: http://help.sap.com (accessed July 29, 2007).

Sengupta, S. and Turnball, T. (1996), “Seamless optimization of the entire supply chain”, IIE Solutions, October, pp. 28‐33.

Tashman, L.J. and Hoover, J. (2001), “Diffusion of forecasting principles: an assessment of forecasting software programs”, in Armstrong, E. (Ed.), Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishers, Norwell, MA.

Taylor, J.W. (2003), “Exponential smoothing with a damped multiplicative trend”, International Journal of Forecasting, Vol. 19 No. 4, pp. 715‐25.

Vega, R.R. (2001), “The impact of enterprise resource planning systems on forecasting”, unpublished PhD Thesis, Texas A&M University, College Station, TX.

Vollmann, T.E., Berry, W.L. and Whybark, D.C. (1992), Manufacturing Planning and Control Systems, 3rd ed., Irwin, Boston, MA.