Targeted evidence collection for uncertain supplier selection

Edward Abel*, Julio César Cortés Ríos, Norman W. Paton, John A. Keane, Alvaro A.A. Fernandes

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Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Abstract

The problem of selecting which suppliers, and how much of different items to order from each, involves multiple, often conflicting, criteria such as costs and delivery times. Within real world multi-criteria supplier selection problems there is inherent uncertainty involved, and consideration of its impacts and mitigation is a current and important research direction going forward within the field of supplier selection. Uncertainty within multi-criteria supplier selection may be in relation to (i) a decision maker's ambiguous preferences, such as the importance between criteria, (ii) the suppliers’ supply capacities of, and demand for, different items, and (iii) known information about suppliers with respect to the set of criteria, such as each supplier's delivery times or their average defect ratios. Whilst previous work has explored the first two of these, less work has explored uncertainty pertaining to information about suppliers in terms of the criteria and, specifically, how it could be efficiently reduced. Such uncertainty is an important problem to address, as it may have a large impact upon an order regarding its perceived quality compared to its realised quality, so reducing such uncertainty can have a significant impact. This paper presents a Targeted Evidence Collection (TEC) approach for efficient reduction of uncertainty, pertaining to suppliers, by looking to efficiently collect additional evidence. The approach looks to utilise and gather evidence intelligently and dynamically – by considering both the likelihood that each supplier will be part of a solution, along with a decision maker's preferences between criteria – to reduce the uncertainty and efficaciously move towards the most appropriate solution given no uncertainty. The approach is able to handle scenarios for which there are both certain and uncertain criteria present, and can take into account any number of criteria. The TEC strategy is evaluated against alternative approaches, including an active learning based approach, for varying numbers of uncertain criteria, numbers of suppliers, and variations in a decision maker's preferences. The experimentation highlights how TEC efficiently reduces uncertainty, relating to information about suppliers with respect to the set of criteria, requiring up to three times less evidence than its competitors. In this way, TEC helps to effectively mitigate the uncertainty's adverse effects, and reduce the risks inherent within a supplier selection problem.

OriginalsprogEngelsk
Artikelnummer113583
TidsskriftExpert Systems with Applications
Vol/bind159
Antal sider15
ISSN0957-4174
DOI
StatusUdgivet - nov. 2020
Udgivet eksterntJa

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