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A prediction-based supply chain recovery strategy under disruption risks.

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Title: A prediction-based supply chain recovery strategy under disruption risks.
Authors: Yang, Yi1 (AUTHOR), Peng, Chen1 (AUTHOR) c.peng@shu.edu.cn
Source: International Journal of Production Research. Nov2023, Vol. 61 Issue 22, p7670-7684. 15p. 1 Color Photograph, 5 Charts, 4 Graphs.
Subjects: Supply chains, Product recovery, Product costing, Integer programming, Reverse logistics, Heuristic
Abstract: This paper proposes a prediction-based product change recovery strategy for the SC (supply chain) under long-term disruptions. A real-world case composed of multi-period planning and dynamic customer demand is considered. First, to forecast dynamic customer demand, a data-based demand predictive method with feedback errors is designed. Second, to schedule procurement and production in advance, based on the predicted demand, the selection of the supply portfolio is transformed into a bi-objective mixed integer programming problem incorporating product change. Furthermore, goods allocation and customer order fulfillment strategy is also designed to finish the transportation of goods and delivery of customer orders. To systematically synthesise and address the problems aforementioned, a three-stage heuristic method is further developed. Finally, a case study is presented to substantiate the reliability of the proposed strategy via an actual SC model of Dongsheng Electronics Co., Ltd. Based on the results obtained after one month, the proposed disruption recovery strategy can reduce the unit product cost and improve the service level, which outperforms the original method adopted by Dongsheng. Additionally, sensitivity analysis of unit product change cost is conducted to reveal the effect of different unit product change costs on SC performance. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
  Group: Ti
  Data: A prediction-based supply chain recovery strategy under disruption risks.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Yi%22">Yang, Yi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Peng%2C+Chen%22">Peng, Chen</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> c.peng@shu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Nov2023, Vol. 61 Issue 22, p7670-7684. 15p. 1 Color Photograph, 5 Charts, 4 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Supply+chains%22">Supply chains</searchLink><br /><searchLink fieldCode="DE" term="%22Product+recovery%22">Product recovery</searchLink><br /><searchLink fieldCode="DE" term="%22Product+costing%22">Product costing</searchLink><br /><searchLink fieldCode="DE" term="%22Integer+programming%22">Integer programming</searchLink><br /><searchLink fieldCode="DE" term="%22Reverse+logistics%22">Reverse logistics</searchLink><br /><searchLink fieldCode="DE" term="%22Heuristic%22">Heuristic</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper proposes a prediction-based product change recovery strategy for the SC (supply chain) under long-term disruptions. A real-world case composed of multi-period planning and dynamic customer demand is considered. First, to forecast dynamic customer demand, a data-based demand predictive method with feedback errors is designed. Second, to schedule procurement and production in advance, based on the predicted demand, the selection of the supply portfolio is transformed into a bi-objective mixed integer programming problem incorporating product change. Furthermore, goods allocation and customer order fulfillment strategy is also designed to finish the transportation of goods and delivery of customer orders. To systematically synthesise and address the problems aforementioned, a three-stage heuristic method is further developed. Finally, a case study is presented to substantiate the reliability of the proposed strategy via an actual SC model of Dongsheng Electronics Co., Ltd. Based on the results obtained after one month, the proposed disruption recovery strategy can reduce the unit product cost and improve the service level, which outperforms the original method adopted by Dongsheng. Additionally, sensitivity analysis of unit product change cost is conducted to reveal the effect of different unit product change costs on SC performance. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/00207543.2022.2161022
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 7670
    Subjects:
      – SubjectFull: Supply chains
        Type: general
      – SubjectFull: Product recovery
        Type: general
      – SubjectFull: Product costing
        Type: general
      – SubjectFull: Integer programming
        Type: general
      – SubjectFull: Reverse logistics
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      – SubjectFull: Heuristic
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              Text: Nov2023
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              Y: 2023
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