Using neural networks to monitor supply chain behaviour

Authors:
Reinaldo Moraga;Luis Rabelo;Albert Jones;Joaquin Vila
Affiliations:
Department of Industrial and Systems Engineering, Northern Illinois University, 590 Garden Rd. DeKalb, IL 60115, USA.;Industrial Engineering and Management Systems Department, University of Central Florida, 4000 Central Florida Blvd. Orlando, FL 32816, USA.;National Institute of Standards and Technology, Manufacturing Systems Integration Division, Gaithersburg, MD 20899, USA.;School of Information Technologies, Illinois State University, Normal, IL 4307, USA
Venue:
International Journal of Computer Applications in Technology
Year:
2011

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Abstract

Intelligent agents are expected to play an increasingly important role in Supply Chain Management (SCM) by automating event-tracking, trend-prediction and decision-making functions. In this paper, we proposed a new trend-prediction methodology that recognises behavioural patterns and predicts future performance based on those patterns. We used fuzzy Adaptive Resonance Theory (ART) Neural Networks (NNs) to build the patterns and BackPropagation NNs (BPNNs) to make the predictions. We based this methodology on System Dynamics (SD) models, which were used to train the NNs. We believe that our approach could be incorporated easily into a number of software agents. These agents could improve dramatically the capabilities of current dashboard-monitoring systems.