Abstract
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).
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References
Armstrong JS (1985) Long-range forecasting, 2nd edn. Wiley, New York.
Armstrong JS (ed.) (2001) Principles of forecasting: a handbook for researchers and practitioners. Kluwer Academic Publishers, Boston, Mass.
Armstrong JS (2006) Findings from evidence-based forecasting methods for reducing forecast error. Int J Forecast 22:583–598.
Armstrong JS, Adya M, Collopy F (2001) Rule-based forecasting: using judgment in time-series extrapolation. In Armstrong (2001), pp. 259–282.
Armstrong JS, Collopy F (1992) Error measures for generalizing about forecasting methods: empirical comparisons. Int J Forecast 8:69–80.
Aviv Y, Federgruen A (2001) Design for postponement: a comprehensive characterization of its benefits under unknown demand distributions. Oper Res 49:578–598.
Blattberg RC, Levin A (1987) Modelling the effectiveness and profitability of trade promotions. Mark Sci 6:124–146.
CFO Research Services (2003) CFOs and the supply chain. CFO Publishing Corp., Boston, Mass. http://www.ups-scs.com/solutions/documents/research_CFO_OnlinePDF.pdf.
Clemen RT (1989) Combining forecasts: a review and annotated bibliography. Int J Forecast 5:559–583.
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.
Ding X, Puterman ML, Bisi A (2002) The censored newsvendor and the optimal acquisition of information. Oper Res 50: 517–527.
Gardner ES, Jr (1990) Evaluating forecast performance in an inventory control system. Manage Sci 36:490–499.
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.
Gneiting T, Raftery AE (2007) Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 102:359–378.
Hahn G, Meeker W (1991) Statistical intervals: a guide for practitioners. Wiley, New York.
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.
Heching AR, Hosking JRM, Leung YT (2004) Forecasting demand for IBM semiconductor products. Research Report RC23074, IBM Research Division, Yorktown Heights, New York.
Hyndman RJ, Koehler AB. (2006) Another look at measures of forecast accuracy. Int J Forecast 22:679–688.
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.
Makridakis SG, Wheelwright SC, Hyndman RJ. (1998) Forecasting: Methods and Applications 3rd edn. Wiley, New York.
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.
Schmittlein DC, Kim J, Morrison DG. (1990) Combining forecasts: operational adjustments to theoretically optimal rules. Manage Sci 36:1044–1056.
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.
Toktay LB, Wein LM (2001) Analysis of a forecasting-production-inventory system with stationary demand. Manage Sci 47:1268–1281.
Vardeman SB (1992) What about the other intervals? Am Stat 46:193–197.
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Hosking, J.R.M. (2011). Demand Forecasting Problems in Production Planning. In: Kempf, K., Keskinocak, P., Uzsoy, R. (eds) Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, vol 151. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6485-4_6
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DOI: https://doi.org/10.1007/978-1-4419-6485-4_6
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