Mitigation strategies against supply disruption risk: a case study at the Ford Motor Company
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作者:
Sanci, Ece
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Univ Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USA
Univ Bath, Sch Management, Bath, Avon, EnglandUniv Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USA
Sanci, Ece
[1
,2
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Daskin, Mark S.
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Univ Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USAUniv Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USA
Daskin, Mark S.
[1
]
Hong, Young-Chae
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机构:
Ford Motor Co, Dearborn, MI 48121 USAUniv Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USA
Hong, Young-Chae
[3
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Roesch, Steve
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机构:
Ford Motor Co, Dearborn, MI 48121 USAUniv Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USA
Roesch, Steve
[3
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Zhang, Don
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机构:
Ford Motor Co, Dearborn, MI 48121 USAUniv Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USA
Zhang, Don
[3
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机构:
[1] Univ Michigan, Ind & Operat Engn, Ann Arbor, MI 48109 USA
[2] Univ Bath, Sch Management, Bath, Avon, England
Supply chains are exposed to different risks, which can be mitigated by various strategies based on the characteristics and needs of companies. In collaboration with Ford, we develop a decision support framework to choose the best mitigation strategy against supply disruption risk, especially for companies operating with a small supplier base and low inventory levels. Our framework is based on a multistage stochastic programming model which incorporates a variety of plausible strategies, including reserving backup capacity from the primary supplier, reserving capacity from a secondary supplier, and holding backup inventory. We reflect disruption risk into the framework through decision makers' input on the time to recover and the disruption probability. Our results demonstrate that relying on the strategy which is optimal when there is no disruption risk can increase the expected total cost substantially in the presence of disruption risk. However, this increase can be reduced significantly by investing in the mitigation strategy recommended by our framework. Our results also show that this framework removes the burden of estimating the time to recover and the disruption probability precisely since there is often a small loss associated with using another strategy that is optimal in the neighbourhood of the estimated values.
机构:
Univ Gdansk, Fac Econ, Chair Transport Econ, PL-81824 Sopot, PolandUniv Gdansk, Fac Econ, Chair Transport Econ, PL-81824 Sopot, Poland
Cirella, Giuseppe T.
Iyalomhe, Felix O.
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机构:
Natl Open Univ Nigeria, Dept Environm Sci & Nat Resource Management, Abuja 900211, Nigeria
Polo Ctr Sustainabil, I-18100 Imperia, ItalyUniv Gdansk, Fac Econ, Chair Transport Econ, PL-81824 Sopot, Poland
Iyalomhe, Felix O.
Adekola, Paul O.
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Polo Ctr Sustainabil, I-18100 Imperia, Italy
Covenant Univ, Demog & Social Stat, Ota 112233, NigeriaUniv Gdansk, Fac Econ, Chair Transport Econ, PL-81824 Sopot, Poland
机构:
Siksha O Anusandhan Univ, Fac Management Studies, Inst Business & Comp Studies, Bhubaneswar, IndiaSiksha O Anusandhan Univ, Fac Management Studies, Inst Business & Comp Studies, Bhubaneswar, India
Pattanayak, Amiya Kumar
Prakash, Anand
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机构:
Natl Inst Construct Management & Res, Sch Gen Management, Pune, Maharashtra, IndiaSiksha O Anusandhan Univ, Fac Management Studies, Inst Business & Comp Studies, Bhubaneswar, India
Prakash, Anand
Mohanty, R. P.
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Siksha O Anusandhan Univ, Khandagiri Sq, Bhubaneswar 751030, IndiaSiksha O Anusandhan Univ, Fac Management Studies, Inst Business & Comp Studies, Bhubaneswar, India