A Knowledge System for Integrated Production Waste Elimination in Support of Organisational Decision Making

This paper discusses a knowledge system for organisational decision making on waste elimination to achieve lean production. The system is named Production Waste Elimination Knowledge System (ProWEKS). An empirical study was undertaken to obtain production engineers and managers’ empirical knowledge and expertise. A knowledge acquisition matrix has been designed for the knowledge elicitation activity. A waste elimination knowledge model is proposed which captures the inter-relationships between different knowledge components across four knowledge layers including know-what, know-how, know-why and know-with. A knowledge base has been developed based on the knowledge model through constructing a decision tree. The system is demonstrated and evaluated using a quality control decision case from the electronics industry. The main contribution of the paper is that it proposes a new knowledge architecture which comprehensively captures not only the know-what and know-how, but also the know-why and know-with of the waste elimination knowledge for lean production.

[1]  Balasubramaniam Ramesh,et al.  Traceability-based knowledge integration in group decision and negotiation activities , 2007, Decis. Support Syst..

[2]  Robin Mann.  Revisiting a TQM research project: The quality improvement activities of TQM , 2008 .

[3]  Philip E. T. Lewis,et al.  Research Methods for Business Students (5th edn) , 2007 .

[4]  Yuh-Jen Chen,et al.  Development of a method for ontology-based empirical knowledge representation and reasoning , 2010, Decis. Support Syst..

[5]  Jan Riezebos,et al.  Lean Production and information technology: Connection or contradiction? , 2009, Comput. Ind..

[6]  Kauko Leiviskä,et al.  Knowledge acquisition for decision support systems on an electronic assembly line , 2009, Expert Syst. Appl..

[7]  Duško Pavletić,et al.  Quality Improvement - PDCA Cycle vs. DMAIC and DFSS , 2007 .

[8]  Shaofeng Liu,et al.  Towards the Realization of an Integrated Decision Support Environment for Organizational Decision Making , 2009, Int. J. Decis. Support Syst. Technol..

[9]  Philip E. T. Lewis,et al.  Research Methods for Business Students , 2006 .

[10]  Luis G. Vargas,et al.  Decision Making with the Analytic Network Process: Economic, Political, Social and Technological Applications with Benefits, Opportunities, Costs and Risks , 2013 .

[11]  Chien-wen Shen,et al.  Business process re-engineering in the logistics industry: a study of implementation, success factors, and performance , 2010, Enterp. Inf. Syst..

[12]  Ibrahim Rawabdeh,et al.  A model for the assessment of waste in job shop environments , 2005 .

[13]  Yasutaka Kainuma,et al.  A multiple attribute utility theory approach to lean and green supply chain management , 2006 .

[14]  Khalid Hafeez,et al.  A Framework for TQM to Achieve Business Excellence , 2006 .

[15]  Daniel Roos,et al.  The machine that changed the world : the story of lean production , 1991 .

[16]  Josefa Mula,et al.  A collaborative knowledge management framework for supply chains: a UML-based model approach , 2008 .

[17]  C. Spenser.  Drawing on your knowledge with VisiRule , 2007, IEEE Potentials.

[18]  M. Lynne Markus,et al.  Toward A Theory of Knowledge Reuse : Types of Knowledge Reuse Situations and Factors in Reuse Success , 2022 .

[19]  Shaofeng Liu,et al.  Integration of decision support systems to improve decision support performance , 2010, Knowledge and Information Systems.

[20]  Donald R. Cooper,et al.  Business Research Methods , 1980 .

[21]  Xingxing Zu,et al.  The evolving theory of quality management: The role of Six Sigma , 2008 .

[22]  Thomas L. Saaty,et al.  Decision making with the analytic network process , 2013 .

[23]  Efraim Turban,et al.  Decision Support and Business Intelligence Systems (8th Edition) , 2006 .

[24]  Matthias Holweg,et al.  The genealogy of lean production , 2007 .