Title: An Online Analytical Processing based predictive system for better process quality in the supply chain network

Authors: G.T.S. Ho, H.C.W. Lau, T.M. Chan, C. Tang, Y.K. Tse

Addresses: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong. ' Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong. ' Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong. ' Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong. ' Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

Abstract: Today, enterprises are under pressure to improve process performance while still remaining customer oriented. The problem of workflow and process quality is a very important issue in supply chain management. Successful improvement of the logistics process has to be viewed as one way of making improvements to the integrated supply chain network. This article describes the application of an Online Analytical Processing (OLAP) based neural ensembles strategy to the acquisition of process knowledge during the supply chain operations. It demonstrates the capabilities of the proposed approach to analyse and predict the quality of the finished product under different process operation parameters. The simulation results indicate that the proposed model is generally superior to the traditional approach by providing real-time prediction and better decision support functionality.

Keywords: intelligent systems; online analytical processing; OLAP; process quality; supply chain management; SCM; neural ensembles strategies; process operations; predictive systems; knowledge acquisition.

DOI: 10.1504/IJSTM.2010.032882

International Journal of Services Technology and Management, 2010 Vol.14 No.1, pp.17 - 25

Available online: 05 May 2010 *

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