Knowledge Promotes Quality Management: A Case Study of Quality Problem-Solving in Two Automotive Plants
Authore(s) : Yanzhong Dang || Institute of System EngineeringDalian University of Technology
Volume : (11), Issue : (7), August - 2021
Abstract : Quality management is a vital link to ensure product quality in the automobile production process. This paper investigates problem-solving of two automotive plants and explores the factors that influence the quality improvement in the organisation. The case studies analyse the status quo and problems in the quality management organisation, problem-solving process, and team, as well as quality management information system with particular emphasis on how each plant uses data, information, and knowledge to solve quality problems from the perspective of knowledge management. The result shows that there is a lack of utilisation of data, information, and knowledge in problem-solving. Based on the analysis result and the demands of plants for improving the efficiency and effectiveness of problem-solving, we propose a knowledge management based intelligent problem-solving system (IPSS). At the same time, a five-tier environment construction for the successful implementation of IPSS is proposed. The main shortcomings identified are common to many other plants and companies worldwide. The suggestions and proposals put forward are of great significance for manufacturing enterprises to improve the efficiency and effectiveness of quality problem-solving.
Keywords :Quality Management, Knowledge Management, Automotive Industry, Problem-Solving, Case Study
Article: Download PDF Journal DOI : 132/348
Cite This Article:
A Case Study of Quality Problem-Solving in Two Automotive Plants
Vol.I (11), Issue.I (7)
Article No : 102234
Number of Downloads : 105
References :
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Aslan, B., Ekinci, Y., & Toy, A. Ö. (2016). Special Control Charts Using Intelligent Techniques: EWMA Control Charts. In C. Kahraman, & S.... More
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https://doi.org/10.1007/s10845-019-01466-z
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- Asan, U., & Soyer, A. (2016). Failure Mode and Effects Analysis under Uncertainty: A Literature Review and Tutorial. In C. Kahraman, & S. Yanik (Eds), Intelligent Decision Making in Quality Management: Theory and Applications (pp. 265-325). Springer International Publishing.
- Aslan, B., Ekinci, Y., & Toy, A. Ö. (2016). Special Control Charts Using Intelligent Techniques: EWMA Control Charts. In C. Kahraman, & S. Yanik (Eds), Intelligent Decision Making in Quality Management: Theory and Applications (pp. 101-125). Springer International Publishing.
- Berends, H. (2005). Exploring Knowledge Sharing: Moves, Problem Solving and Justification. Knowledge Management Research & Practice, 3, 97-105.
- Ch, F. A., Khobreh, M., Nasiri, S., & Fathi, M. (2009). Knowledge Management Support for Quality Management to Achieve Higher Customer Satisfaction. Processing of 2009 IEEE International Conference on Electro/Information Technology, Windsor, 7-9 June 2009, 78-83.
- Chen, F., Deng, P., Wan, J., Zhang, D., Vasilakos, A. V., & Rong, X. (2015). Data Mining for the Internet of Things: Literature Review and Challenges. International Journal of Distributed Sensor Networks, 2015, Article ID: 431047. https://doi.org/10.1155/2015/431047
- Choo, A. S., Linderman, K. W., & Schroeder, R. G. (2007). Method and Context Perspectives on Learning and Knowledge Creation in Quality Management. Journal of Operations Management, 25, 918-931. https://doi.org/10.1016/j.jom.2006.08.002
- Erginel, N., & Şentürk, S. (2015). Intelligent Systems in Total Quality Management. Intelligent Systems Reference Library, 87, 407-430. https://doi.org/10.1007/978-3-319-17906-3_16
- Fine, C. H. (1986). Quality Improvement and Learning in Productive Systems. Management Science, 32, 1301-1315. https://doi.org/10.1287/mnsc.32.10.1301
- Hanaysha, J., Hilman, H., & Abdul-Ghani N. H. (2014). Direct and Indirect Effects of Product Innovation and Product Quality on Brand Image: Empirical Evidence Automotive Industry. FEBS Letters, 388, 213-225.
- Herrmann, A., Henneberg, S. C., & Landwehr, J. (2010). Squaring Customer Demands, Brand Strength, and Production Requirements: A Case Example of an Integrated Product and Branding Strategy. Total Quality Management & Business Excellence, 21, 1017-1031. https://doi.org/10.1080/14783363.2010.487706
- Ibrahim, S. B., & Heng, L. H. (2013). Learning and Knowledge Management: Learning as an Integrative Role for Knowledge Creation. International Conference on Informatics and Creative Multimedia, Kuala Lumpur, 4-6 September 2013, 209-214.
- Lari, A. (2004). A Decision Support System for Solving Quality Problems Using Case-Based Reasoning. Total Quality Management & Business Excellence, 14, 733-745.
- Liang, K., & Zhang, Q. (2010). Study on the Organizational Structured Problem Solving on Total Quality Management. International Journal of Business & Management, 5, 178-183. https://doi.org/10.5539/ijbm.v5n10p178
- Linderman, K., Schroeder, R. G., Zaheer, S., Liedtke, C., & Choo, A. S. (2004). Integrating Quality Management Practices with Knowledge Creation Processes. Journal of Operations Management, 22, 589-607. https://doi.org/10.1016/j.jom.2004.07.001
- Liu, H. C., Liu, L., & Li, P. (2014). Failure Mode and Effects Analysis Using Intuitionistic Fuzzy Hybrid Weighted Euclidean Distance Operator. International Journal of Systems Science, 45, 1-19. https://doi.org/10.1080/00207721.2012.760669
- Liu, H. C., Liu, L., & Lin, Q. L. (2013a). Fuzzy Failure Mode and Effects Analysis Using Fuzzy Evidential Reasoning and Belief Rule-Based Methodology. IEEE Transactions on Reliability, 62, 23-36.
- Liu, H. C., Liu, L., & Liu, N. (2013b). Risk Evaluation Approaches in Failure Mode and Effects Analysis: A Literature Review. Expert Systems with Applications, 40, 828-838.
- Liu, H. C., Liu, L., Liu, N., & Mao, L.-X. (2012). Risk Evaluation in Failure Mode and Effects Analysis with Extended VIKOR Method under Fuzzy Environment. Expert Systems with Applications, 39, 12926-12934. https://doi.org/10.1016/j.eswa.2012.05.031
- Macduffie, J. P. (1997). The Road to “Root Cause”: Shop-Floor Problem-Solving at Three Auto Assembly Plants. Management Science, 43, 479-502. https://doi.org/10.1287/mnsc.43.4.479
- Mons, B., Van, H. H., Chichester, C., Hoen, P. B., den Dunnen, J. T., van, O. G., & Schultes, E. (2011). The Value of Data. Nature Genetics, 43, 281-283. https://doi.org/10.1038/ng0411-281
- Peachey, T. A., & Hall, D. J. (2006). Supporting Complex Problems: An Examination of Churchman’s Inquirers as a Knowledge Management Foundation. Knowledge Management Research & Practice, 4, 197-206. https://doi.org/10.1057/palgrave.kmrp.8500100
- Postrel, S. (2002). Islands of Shared Knowledge: Specialization and Mutual Understanding in Problem-Solving Teams. Organization Science, 13, 303-320. https://doi.org/10.1287/orsc.13.3.303.2773
- Ruikar, K., Anumba, C. J., & Egbu, C. (2007). Integrated Use of Technologies and Techniques for Construction Knowledge Management. Knowledge Management Research & Practice, 5, 297-311.
- Srikanth, K., Harish, A., Heymaraju, C. H., & Ashok Kumar, N. (2010). Intelligent Quality Management Expert System Using PA-AKD in large Databases. International Journal of Engineering Science and Technology, 2, 632-636.
- Stanleigh, M. (2013). Future Trends in Quality Management. Project Management Articles for Project Managers. https://pmhut.com/future-trends-in-quality-management
- Wang, X. (2009). Intelligent Quality Management Using Knowledge Discovery in Databases. International Conference on Computational Intelligence and Software Engineering, Wuhan, 11-13 December 2009, 1-4.
- Xu, Z., & Dang, Y. (2020). Automated Digital Cause-and-Effect Diagrams to Assist Causal Analysis in Problem-Solving: A Data-Driven Approach. International Journal of Production Research, 58, 5359-5379. https://doi.org/10.1080/00207543.2020.1727043
- Xu, Z., Dang, Y., & Munro, P. (2018). Knowledge-Driven Intelligent Quality Problem Solving System in the Automotive Industry. Advanced Engineering Informatics, 38, 441-457. https://doi.org/10.1016/j.aei.2018.08.013
- Xu, Z., Dang, Y., Munro, P., & Wang, Y. (2020). A Data-Driven Approach for Constructing the Component-Failure Mode Matrix for FMEA. Journal of Intelligent Manufacturing, 31, 249-265. https://doi.org/10.1007/s10845-019-01466-z