Treffer: A multi-agent based big data analytics system for viable supplier selection.

The world is characterized by volatility, uncertainty, complexity, and ambiguity (VUCA). In such an environment, the viability in terms of digitalization, resilience, and sustainability capabilities has gained worldwide attention in supply chain management. Therefore, it is crucial to give special consideration to these paradigms when selecting suppliers. Moreover, the availability of data in digital supply chain systems can aid in supplier selection by using Artificial Intelligence techniques to identify viable suppliers. This approach can streamline the supplier selection process and lead to more efficient and effective manufacturing operations. Thus, it is necessary to have a big data analytics infrastructure in today's data-driven world. In this context, this paper aims to design a multi-agent system that belongs to the theory of Distributed Artificial Intelligence based on big data analytics to give a strong tool for finding the best viable suppliers based on a thorough and data-driven evaluation. To do so, designing a multi-agent-based big data analytics system model necessitates identifying the multiple criteria needed for selecting viable suppliers in real-time decision-making. To this end, through a literature review, this paper analyzes more than 140 publications and identifies the main criteria needed for viable suppliers' selection in the VUCA world. Therefore, the proposed system can be used as an intelligent viable supplier selection that improves the quality of the process and controls it while considering different capabilities. It presents a comprehensive model for viable supplier selection, consisting of four main layers: decision-making system, data resources, supplier selection, and big data analytics. The model incorporates six types of agents: Suppliers agent, Resource Agent, Knowledge Management Agent, Pilot Agent, Analyst Agent, and Decision-Making Agent. The integration of these layers and agents enables real-time data-driven decision-making, contributing to the selection of viable suppliers in a volatile and uncertain environment. The proposed model enhances supply chain performance in the digital era, offering a robust tool for both academics and practitioners to improve the quality of supplier selection. [ABSTRACT FROM AUTHOR]

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