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Intelligent Decision Support Systems in Supply Chain Management

Validi, Sahar (2016) Intelligent Decision Support Systems in Supply Chain Management. In: 28th European Conference on Operational Research, 3-6 July 2016, Poznan, Poland. (Unpublished)

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Abstract

Increasingly complex supply chains have to adapt to the uncertain and dynamic environment in which they operate. Efficient decision-making in such an environment is a necessity and affects the supply chain performance significantly. Research shows that conventional approaches to decision-making are no-longer an efficient way of dealing with problems in supply chains. Artificial Intelligence or Knowledge-Based techniques are used increasingly as efficient alternatives to more conventional techniques to decision making.

Use of Decision Support Systems and Artificial Intelligent techniques has a long history in management of Information Systems, yet literature review reveals limited use of AI techniques in decision making and managing supply chains. AI techniques are recognised as complex and dynamic approaches through which complicated situations can be dealt with. Ideally, a Knowledge-based decision support system within the supply chain should behave like a smart (human) consultant; gather and analyse data, identify problems throughout the supply chain, find and evaluate the solutions and propose and monitor actions.

This paper is based on an ongoing interdisciplinary research on the applications of Artificial Intelligence techniques in Supply Chain Management. The focus of this paper is specifically on Decision Support Systems and the contribution of AI in this field to efficient management of supply chains. It reviews the existing literature on research and publications in AI, Decision Support Systems and IDSS to date. Through the content analysis, research gaps in the subject area are identified and proposed for future research.

Item Type: Conference or Workshop Item (Paper)
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Schools: Huddersfield Business School
Related URLs:
Depositing User: Sahar Validi
Date Deposited: 28 Nov 2016 10:32
Last Modified: 28 Nov 2016 10:32
URI: http://eprints.hud.ac.uk/id/eprint/28981

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