Supplier quality improvement: the value of information under uncertainty
We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty...
| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier
2018
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| Online Access: | https://eprints.nottingham.ac.uk/50669/ |
| _version_ | 1848798310300647424 |
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| author | Quigley, John Walls, Lesley Demirel, Güven MacCarthy, Bart L. Parsa, Mahdi |
| author_facet | Quigley, John Walls, Lesley Demirel, Güven MacCarthy, Bart L. Parsa, Mahdi |
| author_sort | Quigley, John |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability. |
| first_indexed | 2025-11-14T20:17:44Z |
| format | Article |
| id | nottingham-50669 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:17:44Z |
| publishDate | 2018 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-506692020-05-04T19:30:28Z https://eprints.nottingham.ac.uk/50669/ Supplier quality improvement: the value of information under uncertainty Quigley, John Walls, Lesley Demirel, Güven MacCarthy, Bart L. Parsa, Mahdi We consider supplier development decisions for prime manufacturers with extensive supply bases producing complex, highly engineered products. We propose a novel modelling approach to support supply chain managers decide the optimal level of investment to improve quality performance under uncertainty. We develop a Poisson–Gamma model within a Bayesian framework, representing both the epistemic and aleatory uncertainties in non-conformance rates. Estimates are obtained to value a supplier quality improvement activity and assess if it is worth gaining more information to reduce epistemic uncertainty. The theoretical properties of our model provide new insights about the relationship between the degree of epistemic uncertainty, the effectiveness of development programmes, and the levels of investment. We find that the optimal level of investment does not have a monotonic relationship with the rate of effectiveness. If investment is deferred until epistemic uncertainty is removed then the expected optimal investment monotonically decreases as prior variance increases but only if the prior mean is above a critical threshold. We develop methods to facilitate practical application of the model to industrial decisions by a) enabling use of the model with typical data available to major companies and b) developing computationally efficient approximations that can be implemented easily. Application to a real industry context illustrates the use of the model to support practical planning decisions to learn more about supplier quality and to invest in improving supplier capability. Elsevier 2018-02-01 Article PeerReviewed Quigley, John, Walls, Lesley, Demirel, Güven, MacCarthy, Bart L. and Parsa, Mahdi (2018) Supplier quality improvement: the value of information under uncertainty. European Journal of Operational Research, 264 (3). pp. 932-947. ISSN 0377-2217 Supply chain management; Risk analysis; Uncertainty modelling; Decision analysis; Manufacturing https://www.sciencedirect.com/science/article/pii/S0377221717304915 doi:10.1016/j.ejor.2017.05.044 doi:10.1016/j.ejor.2017.05.044 |
| spellingShingle | Supply chain management; Risk analysis; Uncertainty modelling; Decision analysis; Manufacturing Quigley, John Walls, Lesley Demirel, Güven MacCarthy, Bart L. Parsa, Mahdi Supplier quality improvement: the value of information under uncertainty |
| title | Supplier quality improvement: the value of information under uncertainty |
| title_full | Supplier quality improvement: the value of information under uncertainty |
| title_fullStr | Supplier quality improvement: the value of information under uncertainty |
| title_full_unstemmed | Supplier quality improvement: the value of information under uncertainty |
| title_short | Supplier quality improvement: the value of information under uncertainty |
| title_sort | supplier quality improvement: the value of information under uncertainty |
| topic | Supply chain management; Risk analysis; Uncertainty modelling; Decision analysis; Manufacturing |
| url | https://eprints.nottingham.ac.uk/50669/ https://eprints.nottingham.ac.uk/50669/ https://eprints.nottingham.ac.uk/50669/ |