Supplier quality improvement: the value of information under uncertainty

John Quigley, Lesley Walls, Güven Demirel, Bart McCarthy, Mahdi Parsa

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.
LanguageEnglish
Pages932-947
Number of pages16
JournalEuropean Journal of Operational Research
Volume264
Issue number3
Early online date26 May 2017
DOIs
Publication statusPublished - 1 Feb 2018

Fingerprint

Value of Information
Quality Improvement
Epistemic Uncertainty
Uncertainty
Critical Threshold
Optimal Investment
Model
Supply Chain
Monotonic
Supply chains
Industry
Siméon Denis Poisson
Managers
Planning
Value of information
Quality improvement
Suppliers
Decrease
Approximation
Modeling

Keywords

  • decision analysis
  • manufacturing
  • Supply Chain Management
  • risk analysis
  • uncertainty modelling

Cite this

Quigley, John ; Walls, Lesley ; Demirel, Güven ; McCarthy, Bart ; Parsa, Mahdi. / Supplier quality improvement : the value of information under uncertainty. In: European Journal of Operational Research. 2018 ; Vol. 264, No. 3. pp. 932-947.
@article{c47467b5f91a4c9894e33cb82436635e,
title = "Supplier quality improvement: the value of information under uncertainty",
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.",
keywords = "decision analysis, manufacturing, Supply Chain Management, risk analysis, uncertainty modelling",
author = "John Quigley and Lesley Walls and G{\"u}ven Demirel and Bart McCarthy and Mahdi Parsa",
year = "2018",
month = "2",
day = "1",
doi = "10.1016/j.ejor.2017.05.044",
language = "English",
volume = "264",
pages = "932--947",
journal = "European Journal of Operational Research",
issn = "0377-2217",
number = "3",

}

Supplier quality improvement : the value of information under uncertainty. / Quigley, John; Walls, Lesley; Demirel, Güven; McCarthy, Bart; Parsa, Mahdi.

In: European Journal of Operational Research, Vol. 264, No. 3, 01.02.2018, p. 932-947.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Supplier quality improvement

T2 - European Journal of Operational Research

AU - Quigley, John

AU - Walls, Lesley

AU - Demirel, Güven

AU - McCarthy, Bart

AU - Parsa, Mahdi

PY - 2018/2/1

Y1 - 2018/2/1

N2 - 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.

AB - 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.

KW - decision analysis

KW - manufacturing

KW - Supply Chain Management

KW - risk analysis

KW - uncertainty modelling

UR - http://www.sciencedirect.com/science/journal/03772217?sdc=1

U2 - 10.1016/j.ejor.2017.05.044

DO - 10.1016/j.ejor.2017.05.044

M3 - Article

VL - 264

SP - 932

EP - 947

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -