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Stein-Rule Combination Forecasting on RFID Based Supply Chain

Author

Listed:
  • WenJie Wang

    (Glorious Sun School of Business and Management, Donghua University, 1882 Yan An Xi Lu, Shanghai 200051, P. R. China)

  • Qi Xu

    (Glorious Sun School of Business and Management, Donghua University, 1882 Yan An Xi Lu, Shanghai 200051, P. R. China)

  • Dandan Fan

    (Glorious Sun School of Business and Management, Donghua University, 1882 Yan An Xi Lu, Shanghai 200051, P. R. China)

Abstract

Radio frequency identification technology has been applied in many fields, especially in logistics operations and supply chain management. Supply chain coordination among partners, which is the core part of supply chain management, can be more practical and effective through sharing real-time product data along the supply chain tracked by RFID technology. This paper focused on the study of the supply chain collaborative forecasting process by sharing RFID real-time data. The collaborative forecasting process among supply chain partners based on the sharing RFID product data is discussed for product demand decision in the paper at first. Then, a Stein-rule combination-forecasting model is proposed to integrate the forecasting knowledge and coordinate forecasting process between the retailers and manufactures shared the RFID data in the supply chain. Moreover, in order to enhance collaborative forecasting precision an error correction combination-forecasting model is discussed. Finally, the outcomes of mathematics simulation verify that the forecast combinations with Stein-rule estimation rules and error correction algorithms are effective to improve forecast precision and coordinate RFID-based supply chain.

Suggested Citation

  • WenJie Wang & Qi Xu & Dandan Fan, 2018. "Stein-Rule Combination Forecasting on RFID Based Supply Chain," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 35(02), pages 1-13, April.
    • Handle: RePEc:wsi:apjorx:v:35:y:2018:i:02:n:s0217595918400018
    DOI: 10.1142/S0217595918400018
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    References listed on IDEAS

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    1. Barrow, Devon K. & Crone, Sven F., 2016. "A comparison of AdaBoost algorithms for time series forecast combination," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1103-1119.
    2. Seungjin Whang, 2010. "Timing of RFID Adoption in a Supply Chain," Management Science, INFORMS, vol. 56(2), pages 343-355, February.
    3. Michael Ketzenberg & Jacqueline Bloemhof & Gary Gaukler, 2015. "Managing Perishables with Time and Temperature History," Production and Operations Management, Production and Operations Management Society, vol. 24(1), pages 54-70, January.
    4. Rodrigues, Bruno Dore & Stevenson, Maxwell J., 2013. "Takeover prediction using forecast combinations," International Journal of Forecasting, Elsevier, vol. 29(4), pages 628-641.
    5. Genre, Véronique & Kenny, Geoff & Meyler, Aidan & Timmermann, Allan, 2013. "Combining expert forecasts: Can anything beat the simple average?," International Journal of Forecasting, Elsevier, vol. 29(1), pages 108-121.
    6. Tian, Jing & Anderson, Heather M., 2014. "Forecast combinations under structural break uncertainty," International Journal of Forecasting, Elsevier, vol. 30(1), pages 161-175.
    7. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.
    8. Nicole DeHoratius & Ananth Raman, 2008. "Inventory Record Inaccuracy: An Empirical Analysis," Management Science, INFORMS, vol. 54(4), pages 627-641, April.
    9. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    10. Graefe, Andreas & Armstrong, J. Scott & Jones, Randall J. & Cuzán, Alfred G., 2014. "Combining forecasts: An application to elections," International Journal of Forecasting, Elsevier, vol. 30(1), pages 43-54.
    11. Mincong Tang & Yinan Qi & Min Zhang, 2017. "Impact of Product Modularity on Mass Customization Capability: An Exploratory Study of Contextual Factors," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 939-959, July.
    12. Samuels, Jon D. & Sekkel, Rodrigo M., 2017. "Model Confidence Sets and forecast combination," International Journal of Forecasting, Elsevier, vol. 33(1), pages 48-60.
    13. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251.
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