The potential impact of suppliers' delivery reliability issues in many industries requires a proper decision support system (DSS) that allows decision makers to analyze and reduce the delay's detrimental effects. Despite the relevance of the topic, companies are often confronted with the lack of historical, quantitative data and knowledge about a supplier's performance (i.e., when selecting a new supplier). In this paper, we address the problem of the scarcity of quantitative data by considering and extending the human-in-the-loop DSS concept, which accounts for an expert's knowledge and experience. In our concept, a human expert is involved in making and revising data provided by a computational model, with the aim of supporting companies in making decisions when dealing with unreliable suppliers, in order to minimize the costs related to external discontinuities. To deal with scant quantitative data, we developed a distribution-free model. Our findings positively support the distribution-free approach as an effective tool to be used when only a limited and perhaps unstructured base of data is available. The presented computational model aims at creating a solid foundation for developing a comprehensive human-in-the-loop decision support system.

Managing supplier delivery reliability risk under limited information: Foundations for a human-in-the-loop DSS

TAISCH, MARCO
2013

Abstract

The potential impact of suppliers' delivery reliability issues in many industries requires a proper decision support system (DSS) that allows decision makers to analyze and reduce the delay's detrimental effects. Despite the relevance of the topic, companies are often confronted with the lack of historical, quantitative data and knowledge about a supplier's performance (i.e., when selecting a new supplier). In this paper, we address the problem of the scarcity of quantitative data by considering and extending the human-in-the-loop DSS concept, which accounts for an expert's knowledge and experience. In our concept, a human expert is involved in making and revising data provided by a computational model, with the aim of supporting companies in making decisions when dealing with unreliable suppliers, in order to minimize the costs related to external discontinuities. To deal with scant quantitative data, we developed a distribution-free model. Our findings positively support the distribution-free approach as an effective tool to be used when only a limited and perhaps unstructured base of data is available. The presented computational model aims at creating a solid foundation for developing a comprehensive human-in-the-loop decision support system.
Supply Chain Risk Management; Decision Support Systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/707734
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