A Multi-Agent Based System with Big Data Processing for Enhanced Supply Chain Agility
Abstract
Purpose: Decision support systems have become an indispensable tool for managing complex supply chains. This paper develops a multi-agent based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored.
Design/methodology/approach: For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled.
Findings: Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility.
Research limitations/implications: The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems.
Practical implications: The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis.
Originality/value. A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.
Origin : Files produced by the author(s)
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