Supply chain modelling using a multi‐agent system

Abstract

PurposeThe purpose of this paper is to focus on a supply chain (SC) simulation of all its management processes by means of a multi‐agent system (MAS).Design/methodology/approachNowadays, the company must develop its activity in an environment characterized by: globalization, hard competitiveness, the necessity of flexibility and of answering dynamically to a changing demand. Thus, a distributed, autonomous approach, strong enough to face changes is necessary, which is what MASs contribute to. An agent can represent each of the components that form the SC. Then the resulting agent system will own similar characteristics to the ones in the studied SC: autonomy, social abilities, reactivity, pro‐activity.FindingsWhen analysing the demand for each SC member (from manufacturer to final consumer), one can observe that while consumer demand is a relatively stable feature, the upper link in the chain (the manufacturer), presents a very pronounced variability. This is known as the “bullwhip effect” or “forrester effect” and is mainly due to the fact that the SC members' strategies are not considered as a whole but as a sum of individual strategies. In the proposed system, each agent will be communicated with other “agents” who will be the only responsible for making forecasts based on information provided to it by all components of the chain. The ultimate goal is for each SC echelon to satisfy its own objectives, while at the same time meet the local and external constraints.Research limitations/implicationsIn this work a standard SC is proposed (one manufacturer – one distributor – one wholesaler – one retailer) although it could easily be modified to incorporate a bigger number of members in each echelon within the SC.Originality/valueThe paper shows the benefits of using artificial intelligence in the SC management.

Multi Agent Approach for Environmental Customer Collaboration: Study Case in Automotive Spare Parts Sector

“…The agents interaction has different levels and it can go from information exchange to cooperation, coordination and negotiation to manage the different activities [25]. The "agentbased modeling is the most appropriate tool for systems with high degree of location and distribution and dominated by discrete decision such as Supply Chain [26]. In fact, Multiagent systems aim to deal Supply Chain complexity such as bullwhip effects, poor communication results, and poor coordination between members of the supply chain [27].…”