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Supply Chain Uncertainty Under ARIMA Demand Process

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Business Process Management Workshops (BPM 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 171))

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Abstract

This paper discusses a typical supply chain system based on Auto-Regressive Integrated Moving Average (ARIMA) demand process. Minimum Mean Square Error principle and stochastic optimal control theory are introduced to build a new framework for supply chain uncertainty study under general ARIMA demand process. After formulating the order and inventory quantity at time period t, this paper analyzes the optimal order policy as to decrease the bullwhip effect and stock fluctuations under non-stationary demand. The theoretical analysis reveals that a reasonable order quantity can reduce the bullwhip effect generated by demand uncertainty. We also show the negative correlation between the bullwhip effect and inventory stability in the discussed supply chain model.

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Author information

Authors and Affiliations

  1. The State Key Laboratory of Industrial Control Technology, Institute of Cyber-systems and Control, Zhejiang University, Hangzhou, China

    Mi Pan & Weimin Wu

Authors
  1. Mi Pan
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  2. Weimin Wu
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Corresponding author

Correspondence to Weimin Wu .

Editor information

Editors and Affiliations

  1. University of Rostock, Rostock, Germany

    Niels Lohmann

  2. Ulsan National Institute of Science and Technology, Ulsan, Korea, Republic of (South Korea)

    Minseok Song

  3. Stockholm University, Stockholm, Sweden

    Petia Wohed

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Pan, M., Wu, W. (2014). Supply Chain Uncertainty Under ARIMA Demand Process. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_29

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  • DOI: https://doi.org/10.1007/978-3-319-06257-0_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06256-3

  • Online ISBN: 978-3-319-06257-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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