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Management of supply chain: an alternative modelling technique for forecasting

Author

Listed:
  • S Datta

    (Massachusetts Institute of Technology)

  • C W J Granger

    (University of California at San Diego)

  • M Barari

    (Missouri State University)

  • T Gibbs

    (Intel Corporation)

Abstract

Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view of the great strides made by research and the increasing abundance of data made possible by automatic identification technologies, such as radio frequency identification (RFID). The relationship of various parameters that may change and impact decisions are so abundant that any credible attempt to drive meaningful associations are in demand to deliver the value from acquired data. This paper proposes some modifications to adapt an advanced forecasting technique (GARCH) with the aim to develop it as a decision support tool applicable to a wide variety of operations including supply chain management (SCM). We have made an attempt to coalesce a few different ideas toward a ‘solutions’ approach aimed to model volatility and in the process, perhaps, better manage risk. It is possible that industry, governments, corporations, businesses, security organizations, consulting firms and academics with deep knowledge in one or more fields, may spend the next few decades striving to synthesize one or more models of effective modus operandi to combine these ideas with other emerging concepts, tools, technologies and standards to collectively better understand, analyse and respond to uncertainty. However, the inclination to reject deep-rooted ideas based on inconclusive results from pilot projects is a detrimental trend and begs to ask the question whether one can aspire to build an elephant using mouse as a model.

Suggested Citation

  • S Datta & C W J Granger & M Barari & T Gibbs, 2007. "Management of supply chain: an alternative modelling technique for forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(11), pages 1459-1469, November.
  • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:11:d:10.1057_palgrave.jors.2602419
    DOI: 10.1057/palgrave.jors.2602419
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    References listed on IDEAS

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    Cited by:

    1. Joanna Bruzda, 2020. "Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 309-336, March.
    2. Y Barlas & B Gunduz, 2011. "Demand forecasting and sharing strategies to reduce fluctuations and the bullwhip effect in supply chains," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 458-473, March.
    3. Chen, Yenming J. & Sheu, Jiuh-Biing & Lirn, Taih-Cherng, 2012. "Fault tolerance modeling for an e-waste recycling supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 897-906.
    4. Trapero, Juan R. & Cardós, Manuel & Kourentzes, Nikolaos, 2019. "Empirical safety stock estimation based on kernel and GARCH models," Omega, Elsevier, vol. 84(C), pages 199-211.
    5. Tuncer Yılmaz & Bülent Yıldız, 2022. "Yatırımcıların Risk İştahı Endeksi İle Korku Endeksleri Arasındaki İlişki: Türkiye’de ARDL İle Ampirik Bir Uygulama," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, vol. 7(3), pages 646-676.
    6. Jin Sung Rha, 2020. "Trends of Research on Supply Chain Resilience: A Systematic Review Using Network Analysis," Sustainability, MDPI, vol. 12(11), pages 1-27, May.
    7. Bruzda, Joanna, 2019. "Quantile smoothing in supply chain and logistics forecasting," International Journal of Production Economics, Elsevier, vol. 208(C), pages 122-139.
    8. J W Taylor, 2011. "Multi-item sales forecasting with total and split exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 555-563, March.

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