Integrating Traditional and Industry 4.0 Approaches in Quality Management: The Case of Wärtsilä Marine and Energy Systems
Keywords:
Total quality management, Industry 4.0, Lean production, Industrial IoTAbstract
This research explores the integration of traditional Quality Management (QM) techniques such as TQM, Lean Manufacturing, Six Sigma, and ISO 9001 with Fourth Industrial Revolution technologies like IoT, AI, and Digital Twins. Focusing on Wärtsilä, a multinational company in marine and energy solutions, the study examines how it addresses sustainability, efficiency, and customer value challenges in advanced manufacturing. Through a descriptive case study, including participatory observation, interviews with quality control managers and engineers, and employee questionnaires, the research highlights the positive impact of combining traditional QM practices with Industry 4.0 technologies. Key benefits include improved product quality, operational efficiency, and environmental conservation, achieved through digital twins and predictive maintenance, allowing for real-time monitoring and reduction of defects and downtime. The study also discusses how Wärtsilä proactively manages training and infrastructure costs with strategic investments in human capital. It offers recommendations for industries aiming to adopt similar integrations to stay competitive in the global market.
References
Deming, W. E. (1986). Principles for transformation. MIT Press.
Pande, P., Neuman, R., & Cavanagh, R. (2000). The six sigma way, chapter 12-identifying core processes and key customers. McGraw Hill Professional.
Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0. , National Academy of Science and Engineering.
Saraph, J. V, Benson, P. G., & Schroeder, R. G. (1989). An instrument for measuring the critical factors of quality management. Decision sciences, 20(4), 810–829. https://doi.org/10.1111/j.1540-5915.1989.tb01421.x
Flynn, B. B., Schroeder, R. G., & Sakakibara, S. (1994). A framework for quality management research and an associated measurement instrument. Journal of operations management, 11(4), 339–366. https://doi.org/10.1016/S0272-6963(97)90004-8
Black, S. A., & Porter, L. J. (1996). Identification of the critical factors of TQM. Decision sciences, 27(1), 1–21. https://doi.org/10.1111/j.1540-5915.1996.tb00841.x
Benson, P. G., Saraph, J. V, & Schroeder, R. G. (1991). The effects of organizational context on quality management: an empirical investigation. Management science, 37(9), 1107–1124. https://doi.org/10.1287/mnsc.37.9.1107
Ahire, S. L., Golhar, D. Y., & Waller, M. A. (1996). Development and validation of TQM implementation constructs. Decision sciences, 27(1), 23–56. https://doi.org/10.1111/j.1540-5915.1996.tb00842.x
Anderson, R. D., Jerman, R. E., & Crum, M. R. (1998). Quality management influences on logistics performance. Transportation research part E: Logistics and transportation review, 34(2), 137–148. https://doi.org/10.1016/S1366-5545(98)00008-8
Sitkin, S. B., Sutcliffe, K. M., & Schroeder, R. G. (1994). Distinguishing control from learning in total quality management: a contingency perspective. Academy of management review, 19(3), 537–564. https://doi.org/10.5465/amr.1994.9412271813
Shah, R., & Ward, P. T. (2007). Defining and developing measures of lean production. Journal of operations management, 25(4), 785–805. https://doi.org/10.1016/j.jom.2007.01.019
Boiral, O. (2012). ISO 9000 and organizational effectiveness: A systematic review. Quality management journal, 19(3), 16–37. https://doi.org/10.1080/10686967.2012.11918071
Yusuf, Y., Gunasekaran, A., & Dan, G. (2007). Implementation of TQM in China and organisation performance: an empirical investigation. Total quality management, 18(5), 509–530. https://doi.org/10.1080/14783360701239982
Sila, I., & Ebrahimpour, M. (2002). An investigation of the total quality management survey based research published between 1989 and 2000: A literature review. International journal of quality & reliability management, 19(7), 902–970. https://doi.org/10.1108/02656710210434801
Simpson, D. F., & Power, D. J. (2005). Use the supply relationship to develop lean and green suppliers. Supply chain management: An international journal, 10(1), 60–68. https://doi.org/10.1108/13598540510578388
Hines, P., Holweg, M., & Rich, N. (2004). Learning to evolve: a review of contemporary lean thinking. International journal of operations & production management, 24(10), 994–1011. https://doi.org/10.1108/01443570410558049
Antony, J. (2004). Some pros and cons of six sigma: An academic perspective. The tqm magazine, 16(4), 303–306. https://doi.org/10.1108/09544780410541945
Naveh, E., & Marcus, A. (2005). Achieving competitive advantage through implementing a replicable management standard: Installing and using ISO 9000. Journal of operations management, 24(1), 1–26. https://doi.org/10.1016/j.jom.2005.01.004
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: a review. Engineering, 3(5), 616–630. https://doi.org/10.1016/J.ENG.2017.05.015
McAdam, R., & Lafferty, B. (2004). A multilevel case study critique of six sigma: statistical control or strategic change? International journal of operations & production management, 24(5), 530–549. https://doi.org/10.1108/01443570410532579
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of manufacturing systems, 49, 194–214. https://doi.org/10.1016/j.jmsy.2018.10.005
Cohen, L., Manion, L., & Morrison, K. (2002). Research methods in education. Routledge. https://doi.org/10.4324/9780203224342
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