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Supplier quality improvement: the value of information under uncertainty

Quigley, John; Walls, Lesley; Demirel, Güven; MacCarthy, Bart L.; Parsa, Mahdi

Authors

John Quigley

Lesley Walls

Güven Demirel

Mahdi Parsa



Abstract

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.

Citation

Quigley, J., Walls, L., Demirel, G., MacCarthy, B. L., & Parsa, M. (2018). Supplier quality improvement: the value of information under uncertainty. European Journal of Operational Research, 264(3), https://doi.org/10.1016/j.ejor.2017.05.044

Journal Article Type Article
Acceptance Date May 21, 2017
Online Publication Date May 26, 2017
Publication Date Feb 1, 2018
Deposit Date Mar 26, 2018
Publicly Available Date Mar 26, 2018
Journal European Journal of Operational Research
Print ISSN 0377-2217
Electronic ISSN 0377-2217
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 264
Issue 3
DOI https://doi.org/10.1016/j.ejor.2017.05.044
Keywords Supply chain management; Risk analysis; Uncertainty modelling; Decision analysis; Manufacturing
Public URL http://eprints.nottingham.ac.uk/id/eprint/50669
Publisher URL https://www.sciencedirect.com/science/article/pii/S0377221717304915
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





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