Demand Forecasting Problems in Production Planning

  • Jonathan R. M. Hosking
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 151)


A recent survey of 247 senior finance executives (CFO Research Services, 2003) found that “accurately forecasting demand” was the most commonly occurring problem in their companies’ supply chain management. Forecasting is recognized as a hard problem. “It is difficult to predict, especially the future,” according to a quotation attributed to Niels Bohr (among many others).


Lead Time Forecast Error Mean Absolute Percentage Error Product Family Forecast Accuracy 
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  1. Armstrong JS (1985) Long-range forecasting, 2nd edn. Wiley, New York.Google Scholar
  2. Armstrong JS (ed.) (2001) Principles of forecasting: a handbook for researchers and practitioners. Kluwer Academic Publishers, Boston, Mass.Google Scholar
  3. Armstrong JS (2006) Findings from evidence-based forecasting methods for reducing forecast error. Int J Forecast 22:583–598.CrossRefGoogle Scholar
  4. Armstrong JS, Adya M, Collopy F (2001) Rule-based forecasting: using judgment in time-series extrapolation. In Armstrong (2001), pp. 259–282.Google Scholar
  5. Armstrong JS, Collopy F (1992) Error measures for generalizing about forecasting methods: empirical comparisons. Int J Forecast 8:69–80.CrossRefGoogle Scholar
  6. Aviv Y, Federgruen A (2001) Design for postponement: a comprehensive characterization of its benefits under unknown demand distributions. Oper Res 49:578–598.CrossRefGoogle Scholar
  7. Blattberg RC, Levin A (1987) Modelling the effectiveness and profitability of trade promotions. Mark Sci 6:124–146.CrossRefGoogle Scholar
  8. CFO Research Services (2003) CFOs and the supply chain. CFO Publishing Corp., Boston, Mass.
  9. Clemen RT (1989) Combining forecasts: a review and annotated bibliography. Int J Forecast 5:559–583.CrossRefGoogle Scholar
  10. Collopy F, Armstrong JS (1992) Rule-based forecasting: development and validation of an expert systems approach to combining time series extrapolations. Manage Sci 38:1394–1414.CrossRefGoogle Scholar
  11. Ding X, Puterman ML, Bisi A (2002) The censored newsvendor and the optimal acquisition of information. Oper Res 50: 517–527.CrossRefGoogle Scholar
  12. Gardner ES, Jr (1990) Evaluating forecast performance in an inventory control system. Manage Sci 36:490–499.CrossRefGoogle Scholar
  13. Gebhardt J, Detmer H, Madsen AL (2003) Predicting parts demand in the automotive industry: an application of probabilistic graphical models. In Uncertainty in Artificial Intelligence: Proceedings of the 19th Conference (UAI-2003). Morgan-Kaufman, San Francisco.Google Scholar
  14. Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 102:359–378.CrossRefGoogle Scholar
  15. Hahn G, Meeker W (1991) Statistical intervals: a guide for practitioners. Wiley, New York.Google Scholar
  16. Heath DC, Jackson PL. (1994). Modeling the evolution of demand forecasts with application to safety stock analysis in production/distribution systems. IIE Trans 26:17–30.CrossRefGoogle Scholar
  17. Heching AR, Hosking JRM, Leung YT (2004) Forecasting demand for IBM semiconductor products. Research Report RC23074, IBM Research Division, Yorktown Heights, New York.Google Scholar
  18. Hyndman RJ, Koehler AB. (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679–688.CrossRefGoogle Scholar
  19. Kopalle PK, Mela CF, Marsh L (1999) The dynamic effect of discounting on sales: empirical analysis and normative pricing implications. Mark Sci 18:317–332.CrossRefGoogle Scholar
  20. Makridakis SG, Wheelwright SC, Hyndman RJ. (1998) Forecasting: Methods and Applications 3rd edn. Wiley, New York.Google Scholar
  21. Price DHR, Sharp JA (1986) A comparison of the performance of different univariate forecasting methods in a model of capacity acquisition in UK electricity supply. Int J Forecast 2:333–348.CrossRefGoogle Scholar
  22. Schmittlein DC, Kim J, Morrison DG. (1990) Combining forecasts: operational adjustments to theoretically optimal rules. Manage Sci 36:1044–1056.CrossRefGoogle Scholar
  23. Tan B, Karabati S (2004) Can the desired service level be achieved when the demand and lost sales are unobserved? IIE Trans 36:345–358.CrossRefGoogle Scholar
  24. Toktay LB, Wein LM (2001) Analysis of a forecasting-production-inventory system with stationary demand. Manage Sci 47:1268–1281.CrossRefGoogle Scholar
  25. Vardeman SB (1992) What about the other intervals? Am Stat 46:193–197.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.IBM Research DivisionT. J. Watson Research CenterYorktown HeightsUSA

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