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Demand forecasting for supply processes in consideration of pricing and market information

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  • Reiner, Gerald
  • Fichtinger, Johannes

Abstract

We develop a dynamic model that can be used to evaluate supply chain process improvements, e.g. different forecast methods. In particular we use for evaluation a bullwhip effect measure, the service level (fill rate) and the average on hold inventory. We define and apply a robustness criterion to enable the comparison of different process alternatives, i.e. the range of observation periods above a certain service level. This criterion can help managers to reduce risks and furthermore variability by applying robust process improvements. Furthermore we are able to demonstrate with our research results that the bullwhip effect is an important but not the only performance measure that should be used to evaluate process improvements.

Suggested Citation

  • Reiner, Gerald & Fichtinger, Johannes, 2009. "Demand forecasting for supply processes in consideration of pricing and market information," International Journal of Production Economics, Elsevier, vol. 118(1), pages 55-62, March.
    • Handle: RePEc:eee:proeco:v:118:y:2009:i:1:p:55-62
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    1. Pastore, Erica & Alfieri, Arianna & Zotteri, Giulio, 2019. "An empirical investigation on the antecedents of the bullwhip effect: Evidence from the spare parts industry," International Journal of Production Economics, Elsevier, vol. 209(C), pages 121-133.
    2. A A Syntetos & N C Georgantzas & J E Boylan & B C Dangerfield, 2011. "Judgement and supply chain dynamics," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1138-1158, June.
    3. Zhu, Xiaowei & Mukhopadhyay, Samar K. & Yue, Xiaohang, 2011. "Role of forecast effort on supply chain profitability under various information sharing scenarios," International Journal of Production Economics, Elsevier, vol. 129(2), pages 284-291, February.
    4. Lu, Chi-Jie & Wang, Yen-Wen, 2010. "Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting," International Journal of Production Economics, Elsevier, vol. 128(2), pages 603-613, December.

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