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Forecast errors and inventory performance under forecast information sharing

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  • Ali, Mohammad M.
  • Boylan, John E.
  • Syntetos, Aris A.
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    Abstract

    Previous research has shown that the forecast accuracy is to be distinguished from the performance of the forecasts when utility measures are employed. This is particularly true in an inventory management context, where the interactions between forecasting and stock control are not yet fully understood. In this paper, the relationship between the forecasting performance and inventory implications is explored under an ARIMA representation of the demand process. Two distinct scenarios are incorporated in our analysis: Forecast Information Sharing (FIS) and No Information Sharing (NIS) in a two-stage supply chain. We approach the problem analytically and by means of simulation. The validity of the theoretical results is assessed on a real sales dataset from a major European superstore. The results indicate that the gain in accuracy from Forecast Information Sharing depends on the demand process. The translation to inventory savings then depends on the magnitude of the forecast accuracy improvement, regardless of the demand process. Insights into pertinent managerial issues are also offered, and our paper concludes with an agenda for further research in this area.

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    Bibliographic Info

    Article provided by Elsevier in its journal International Journal of Forecasting.

    Volume (Year): 28 (2012)
    Issue (Month): 4 ()
    Pages: 830-841

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    Handle: RePEc:eee:intfor:v:28:y:2012:i:4:p:830-841

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    Web page: http://www.elsevier.com/locate/ijforecast

    Related research

    Keywords: Sales forecasting; Forecasting accuracy; Supply chain; Inventory forecasting; Forecast information sharing;

    References

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    1. Syntetos, Aris A. & Boylan, John E., 2006. "On the stock control performance of intermittent demand estimators," International Journal of Production Economics, Elsevier, vol. 103(1), pages 36-47, September.
    2. Mohammad M. Ali & John E. Boylan, 2010. "The Value of Forecast Information Sharing in Supply Chains," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 18, pages 14-18, Summer.
    3. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
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    7. Timmermann, Allan & Granger, Clive W. J., 2004. "Efficient market hypothesis and forecasting," International Journal of Forecasting, Elsevier, vol. 20(1), pages 15-27.
    8. Stephen C. Graves, 1999. "Addendum to "A Single-Item Inventory Model for a Nonstationary Demand Process"," Manufacturing & Service Operations Management, INFORMS, vol. 1(2), pages 174-174.
    9. Kim, Jeon G. & Chatfield, Dean & Harrison, Terry P. & Hayya, Jack C., 2006. "Quantifying the bullwhip effect in a supply chain with stochastic lead time," European Journal of Operational Research, Elsevier, vol. 173(2), pages 617-636, September.
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    11. John Boylan & Aris Syntetos, 2006. "Accuracy and Accuracy Implication Metrics for Intermittent Demand," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 4, pages 39-42, June.
    12. Zhang, Xiaolong, 2004. "The impact of forecasting methods on the bullwhip effect," International Journal of Production Economics, Elsevier, vol. 88(1), pages 15-27, March.
    13. Lee, TS & Cooper, FW & Adam, EE, 1993. "The effects of forecasting errors on the total cost of operations," Omega, Elsevier, vol. 21(5), pages 541-550, September.
    14. Duc, Truong Ton Hien & Luong, Huynh Trung & Kim, Yeong-Dae, 2008. "A measure of bullwhip effect in supply chains with a mixed autoregressive-moving average demand process," European Journal of Operational Research, Elsevier, vol. 187(1), pages 243-256, May.
    15. Paul Goodwin, 2009. "Taking Stock: Assessing the True Cost of Forecast Errors," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 15, pages 8-11, Fall.
    16. Sanders, Nada R. & Graman, Gregory A., 2009. "Quantifying costs of forecast errors: A case study of the warehouse environment," Omega, Elsevier, vol. 37(1), pages 116-125, February.
    17. Tonya Boone & Ram Ganeshan, 2008. "The Value of Information Sharing in the Retail Supply Chain: Two Case Studies," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 9, pages 12-17, Spring.
    18. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    19. Syntetos, Aris A. & Nikolopoulos, Konstantinos & Boylan, John E., 2010. "Judging the judges through accuracy-implication metrics: The case of inventory forecasting," International Journal of Forecasting, Elsevier, vol. 26(1), pages 134-143, January.
    20. Luong, Huynh Trung & Phien, Nguyen Huu, 2007. "Measure of bullwhip effect in supply chains: The case of high order autoregressive demand process," European Journal of Operational Research, Elsevier, vol. 183(1), pages 197-209, November.
    21. Hau L. Lee & Kut C. So & Christopher S. Tang, 2000. "The Value of Information Sharing in a Two-Level Supply Chain," Management Science, INFORMS, vol. 46(5), pages 626-643, May.
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    Cited by:
    1. Kim, T.Y. & Dekker, R. & Heij, C., 2013. "The impact of forecasting errors on warehouse labor efficiency: A case study in consumer electronics," Econometric Institute Research Papers EI2013-15, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Bivin, David, 2013. "Production chains and aggregate output volatility," International Journal of Production Economics, Elsevier, vol. 145(2), pages 807-816.

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