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Assessing end-of-supply risk of spare parts using the proportional hazard model

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Assessing end-of-supply risk of spare parts using the proportional hazard model. / Li, Xishu; Dekker, Rommert; Heij, Christiaan et al.
In: Decision Sciences, Vol. 47, No. 2, 30.04.2016, p. 373-394.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Li, X, Dekker, R, Heij, C & Hekimoglu, M 2016, 'Assessing end-of-supply risk of spare parts using the proportional hazard model', Decision Sciences, vol. 47, no. 2, pp. 373-394. https://doi.org/10.1111/deci.12192

APA

Li, X., Dekker, R., Heij, C., & Hekimoglu, M. (2016). Assessing end-of-supply risk of spare parts using the proportional hazard model. Decision Sciences, 47(2), 373-394. https://doi.org/10.1111/deci.12192

Vancouver

Li X, Dekker R, Heij C, Hekimoglu M. Assessing end-of-supply risk of spare parts using the proportional hazard model. Decision Sciences. 2016 Apr 30;47(2):373-394. Epub 2016 Jan 20. doi: 10.1111/deci.12192

Author

Li, Xishu ; Dekker, Rommert ; Heij, Christiaan et al. / Assessing end-of-supply risk of spare parts using the proportional hazard model. In: Decision Sciences. 2016 ; Vol. 47, No. 2. pp. 373-394.

Bibtex

@article{e66538c3a0304d25a396f960522636e6,
title = "Assessing end-of-supply risk of spare parts using the proportional hazard model",
abstract = "Operators of long field-life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end-of-supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end-of-supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end-of-supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross-validation results and out-of-sample risk assessments show good performance of the method to identify spare parts with high end-of-supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high-risk spare parts.",
author = "Xishu Li and Rommert Dekker and Christiaan Heij and Mustafa Hekimoglu",
year = "2016",
month = apr,
day = "30",
doi = "10.1111/deci.12192",
language = "English",
volume = "47",
pages = "373--394",
journal = "Decision Sciences",
issn = "0011-7315",
publisher = "Wiley-Blackwell",
number = "2",

}

RIS

TY - JOUR

T1 - Assessing end-of-supply risk of spare parts using the proportional hazard model

AU - Li, Xishu

AU - Dekker, Rommert

AU - Heij, Christiaan

AU - Hekimoglu, Mustafa

PY - 2016/4/30

Y1 - 2016/4/30

N2 - Operators of long field-life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end-of-supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end-of-supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end-of-supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross-validation results and out-of-sample risk assessments show good performance of the method to identify spare parts with high end-of-supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high-risk spare parts.

AB - Operators of long field-life systems like airplanes are faced with hazards in the supply of spare parts. If the original manufacturers or suppliers of parts end their supply, this may have large impacts on operating costs of firms needing these parts. Existing end-of-supply evaluation methods are focused mostly on the downstream supply chain, which is of interest mainly to spare part manufacturers. Firms that purchase spare parts have limited information on parts sales, and indicators of end-of-supply risk can also be found in the upstream supply chain. This article proposes a methodology for firms purchasing spare parts to manage end-of-supply risk by utilizing proportional hazard models in terms of supply chain conditions of the parts. The considered risk indicators fall into four main categories, of which two are related to supply (price and lead time) and two others are related to demand (cycle time and throughput). The methodology is demonstrated using data on about 2,000 spare parts collected from a maintenance repair organization in the aviation industry. Cross-validation results and out-of-sample risk assessments show good performance of the method to identify spare parts with high end-of-supply risk. Further validation is provided by survey results obtained from the maintenance repair organization, which show strong agreement between the firm's and the model's identification of high-risk spare parts.

U2 - 10.1111/deci.12192

DO - 10.1111/deci.12192

M3 - Journal article

VL - 47

SP - 373

EP - 394

JO - Decision Sciences

JF - Decision Sciences

SN - 0011-7315

IS - 2

ER -