Model Based on Bayesian Networks for Monitoring Events in a Supply Chain

ERICA FERNANDEZ; ENRIQUE SALOMONE; OMAR CHIOTTI

Bordeaux – Francia

Congreso; APMS2009, International Conference on Advances in Production Management Systems; 2009

IFIP Working Group 5.7 on Integrated Production Management

The execution of supply process orders in a supply chain is conditioned by different types of disruptive events that must be detected and solved in real time. This requires the ability to proactively monitor, analyze and notify disruptive events. In this work we present a model that captures this functionality and was used as the foundation to design a software agent. A reactive-deliberative hybrid architecture provides the ability to proactively detect, analyze and notify disruptive events that take place in a supply chain. For the deliberative performance of the agent, a cause-effect relation model based on a Bayesian network with decision nodes is proposed