مطالعه تطبیقی شناسایی و اولویت‌بندی ابعاد فرصت‌ها و چالش‌های مدیریت کیفیت در صنعت 4 با استفاده از تکنیک BWM
محورهای موضوعی :
مدیریت صنعتی
hassanali aghajani
1
,
Fatemeh Zahra Rajabi kafshgar
2
1 - Prof. of Industrial Management, Mazandaran University, Babolsar, Iran
2 - MSc. in Industrial Management, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran
تاریخ دریافت : 1398/05/15
تاریخ پذیرش : 1398/10/11
تاریخ انتشار : 1399/02/23
کلید واژه:
BWM,
مدیریت کیفیت,
صنعت نسل4,
چکیده مقاله :
رشد بهره‌وری صنعتی همیشه تحت تأثیر رشد تکنولوژی‌ها بوده است. این ادعا را می‌توان بر اساس انقلاب صنعتی که اولین بار با استفاده از موتور بخار در کارخانه تولید آغاز شد، نشان داد. در حال حاضر، انقلاب صنعتی چهارم حتی قبل از اینکه به‌طور کامل اجرا شود، به تصویر تبدیل شد و بسیاری از متخصصان و سازمان‌ها به‌شدت تلاش می‌کنند تا مفهوم انقلابی را پیاده‌سازی کنند. مفاهیم کارخانه هوشمند، سیستم فیزیکی سایبری و اینترنت اشیا و خدمات، فرصت‌های بسیار توانا و همچنین چالش‌های پیشرو در مدیریت کیفیت در بخش‌های تولید را ارائه می‌دهد. بنابراین، در این مقاله، به بررسی فرصت‌ها و چالش‌ها در زمینه اجرای صنعت 4 برای مدیریت کیفیت پرداخته‌شده است. بدین منظور، ابتدا شاخص‌های فرصت‌ها و چالش‌های مدیریت کیفیت در صنعت 4 شناسایی شدند. سپس با استفاده از روش تصمیم‌گیری چندشاخصه بهترین- بدترین که به‌عنوان یکی از نوین‌ترین روش‌های وزن‌دهی در ادبیات تصمیم‌گیری چندشاخصه مطرح است، این شاخص‌ها با نظرات خبرگان وزن‌دهی شدند. نتایج پژوهش نشان‌دهنده این بود که شاخص‌های کاهش هزینه‌ها و زمان تولید، افزایش خدمات‌رسانی و رضایت مشتری و افزایش مهارت و شایستگی‌ها به ترتیب به‌عنوان مهم‌ترین فرصت‌ها و شاخص‌های ادغام عمودی، ادغام افقی و تجربه و متخصصان به ترتیب به‌عنوان مهم‌ترین چالش‌ها معرفی شدند. در انتها بر اساس نتایج پژوهش، پیشنهاد‌ها اجرایی و پژوهشی ارائه گردید.
چکیده انگلیسی:
Industrial productivity growth has always been influenced by the growth of technology. This claim could be show based on the industrial revolution that began the first time using a steam engine in the production plant. At the moment, the fourth industrial revolution turned into an image, even before it was fully implemented, and many experts and organizations are working hard to implement the revolutionary concept. The concepts of the smart factory, the physical cyber system and the Internet of Things and Services, offer great opportunities, as well as the leading challenges in quality management in the manufacturing sectors. Therefore, in this article, we examine the opportunities and challenges in implementing industry 4.0 for quality management. To this end, attributes of opportunities and quality management challenges were identified in industry 4.0. Then, using the Best Worst Multi criteria decision making method, which is considered as one of the most innovative weighting methods in the Multi criteria decision making literature, was weighted by expert opinions. The results of the study indicated that the attributes of cost reduction and production time, increase in services and customer satisfaction, and increase the skills and competencies respectively, introduced as the most important opportunities and attributes of vertical integration, horizontal integration and experience, and specialists respectively introduced as The main challenges. Finally, based on the research results, practical and research suggestions were presented.
منابع و مأخذ:
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Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: implementation patterns in manufacturing companies. International Journal of Production Economics.
Fundin, A., Bergquist, B., Eriksson, H., & Gremyr, I. (2018). Challenges and propositions for research in quality management. International Journal of Production Economics, 199, 125–137.
Gattullo, M., Scurati, G. W., Fiorentino, M., Uva, A. E., Ferrise, F., & Bordegoni, M. (2019). Towards augmented reality manuals for industry 4.0: A methodology. Robotics and Computer-Integrated Manufacturing, 56, 276–286.
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Kumar, P., Maiti, J., & Gunasekaran, A. (2018). Impact of quality management systems on firm performance. International Journal of Quality & Reliability Management, 35(5), 1034–1059.
Ladewski, B. J., & Al-Bayati, A. J. (2019). Quality and safety management practices: The theory of quality management approach. Journal of Safety Research.
Lee, J., Kao, H.-A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp, 16, 3–8.
Li, D., Zhao, Y., Zhang, L., Chen, X., & Cao, C. (2018). Impact of quality management on green innovation. Journal of Cleaner Production, 170, 462–470.
Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179.
Magdalena, G. (2016). Industry 4.0 and sustainability impacts: Critical discussion of sustainability aspects with a special focus on future of work and ecological consequences.
Manavalan, E., & Jayakrishna, K. (2018). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering.
Mayer, F., & Pantförder, D. (2014). Unterstützung des Menschen in Cyber-Physical-Production-Systems. In Industrie 4.0 in Produktion, Automatisierung und Logistik (pp. 481–491). Springer.
Mohammadi, M., & Rezaei, J. (2019). Bayesian Best-Worst Method: A Probabilistic Group Decision Making Model. Omega.
Moktadir, M. A., Ali, S. M., Kusi-Sarpong, S., & Shaikh, M. A. A. (2018). Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection.
Mrugalska, B., & Wyrwicka, M. K. (2017). Towards lean production in industry 4.0. Procedia Engineering, 182, 466–473.
Oettmeier, K., & Hofmann, E. (2017). Additive manufacturing technology adoption: an empirical analysis of general and supply chain-related determinants. Journal of Business Economics, 87(1), 97–124.
Olszewska, A. M. (2017). Research issues undertaken within quality management--overview of selected literature and a knowledge map. Procedia Engineering, 182, 518–523.
Rauch, E., Linder, C., & Dallasega, P. (2019). Anthropocentric Perspective of Production before and within Industry 4.0. Computers & Industrial Engineering, 0–49. https://doi.org/10.1016/j.cie.2019.01.018
Saberi, S., & Yusuff, R. M. (2011). Advanced manufacturing technology implementation performance: Towards a strategic framework. In Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia (pp. 145–150).
Safari, S., Shirzad, S., & others. (2012). Quality Management Structure Supported by Information Technology (A Survey in Central Insurance of Iran). Journal of Information Technology Management, 4(12), 113–134.
Sahoo, S., & Yadav, S. (2018). Total quality management in Indian manufacturing SMEs. Procedia Manufacturing, 21, 541–548.
Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., & Prote, J.-P. (2014). Collaboration Mechanisms to increase Productivity in the Context of Industrie 4.0. Procedia CIRP, 19, 51–56.
Sung, T. K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting and Social Change, 132, 40–45.
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Zeng, J., Zhang, W., Matsui, Y., & Zhao, X. (2017). The impact of organizational context on hard and soft quality management and innovation performance. International Journal of Production Economics, 185, 240–251.
Zhou, M., Liu, X.-B., Chen, Y.-W., & Yang, J.-B. (2018). Evidential reasoning rule for MADM with both weights and reliabilities in group decision making. Knowledge-Based Systems, 143, 142–161.
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Arnold, C., Kiel, D., & Voigt, K.-I. (2017). The Driving Role of the Industrial Internet of Things for Strategic Change: The Case of Electronic Engineering Business Models. In Proceedings of the 24th Innovation and Product Development Management Conference (IPDMC), Reykjavik, Iceland (pp. 11–13).
Bodi, S., Popescu, S., Drageanu, C., Popescu, D., & others. (2015). Virtual Quality Management elements in optimized new product development using genetic algorithms. In Joint International Conference: Managing Intellectual Capital and Innovation for Sustainable and Inclusive Society-Management, Knowledge and Learning-Technology, Innovation and Industrial Management, Bari, Italy (pp. 633–642).
Bolatan, G. I. S., Gozlu, S., Alpkan, L., & Zaim, S. (2016). The impact of technology transfer performance on total quality management and quality performance. Procedia-Social and Behavioral Sciences, 235, 746–755.
Brunelli, M., & Rezaei, J. (2019). A multiplicative best--worst method for multi-criteria decision making. Operations Research Letters, 47(1), 12–15.
Chen, Y. (2017). Integrated and intelligent manufacturing: Perspectives and enablers. Engineering, 3(5), 588–595.
Chua, C. K., Wong, C. H., & Yeong, W. Y. (2017). Chapter Nine-Quality Management Framework in Additive Manufacturing. Standards, Quality Control, and Measurement Sciences in 3D Printing and~….
Colledani, M., Tolio, T., Fischer, A., Iung, B., Lanza, G., Schmitt, R., & Váncza, J. (2014). Design and management of manufacturing systems for production quality. CIRP Annals, 63(2), 773–796.
Diez-Olivan, A., Del Ser, J., Galar, D., & Sierra, B. (2019). Data fusion and machine learning for industrial prognosis: Trends and perspectives towards Industry 4.0. Information Fusion, 50, 92–111.
Dorian, U., & Gaspar, M. (2018). Quality Management in Cloud Services using Remote Assistance--a Literature Review. Procedia-Social and Behavioral Sciences, 238, 607–614.
Escrig-Tena, A. B., Segarra-Ciprés, M., Garc\’\ia-Juan, B., & Beltrán-Mart\’\in, I. (2018). The impact of hard and soft quality management and proactive behaviour in determining innovation performance. International Journal of Production Economics, 200, 1–14.
Faheem, M., Shah, S. B. H., Butt, R. A., Raza, B., Anwar, M., Ashraf, M. W., … Gungor, V. C. (2018). Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges. Computer Science Review, 30, 1–30.
Foidl, H., & Felderer, M. (2015). Research challenges of industry 4.0 for quality management. In International Conference on Enterprise Resource Planning Systems (pp. 121–137).
Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: implementation patterns in manufacturing companies. International Journal of Production Economics.
Fundin, A., Bergquist, B., Eriksson, H., & Gremyr, I. (2018). Challenges and propositions for research in quality management. International Journal of Production Economics, 199, 125–137.
Gattullo, M., Scurati, G. W., Fiorentino, M., Uva, A. E., Ferrise, F., & Bordegoni, M. (2019). Towards augmented reality manuals for industry 4.0: A methodology. Robotics and Computer-Integrated Manufacturing, 56, 276–286.
Glass, R., Meissner, A., Gebauer, C., Stürmer, S., & Metternich, J. (2018). Identifying the barriers to Industrie 4.0. Procedia CIRP, 72, 985–988. https://doi.org/https://doi.org/10.1016/j.procir.2018.03.187
Gunasekaran, A., Subramanian, N., & Ngai, E. (2019). Quality management in the 21st century enterprises: Research pathway towards Industry 4.0. Elsevier.
Gutierrez-Gutierrez, L. J., Barrales-Molina, V., & Kaynak, H. (2018). The role of human resource-related quality management practices in new product development: A dynamic capability perspective. International Journal of Operations & Production Management, 38(1), 43–66.
Hamdoun, M., Jabbour, C. J. C., & Othman, H. Ben. (2018). Knowledge transfer and organizational innovation: Impacts of quality and environmental management. Journal of Cleaner Production, 193, 759–770.
Hirsch-Kreinsen, H. (2014). Smart production systems. A new type of industrial process innovation. In DRUID Society Conference (pp. 16–18).
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion.
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2018). Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry. Computers in Industry, 101, 107–119.
Kannan, V. R., Tan, K.-C., Handheld, R. B., & Ghosh, S. (1999). Tools and techniques of quality management: an empirical investigation of their impact on performance. Quality Management Journal, 6(3), 34–49.
Kheybari, S., Kazemi, M., & Rezaei, J. (2019). Bioethanol facility location selection using best-worst method. Applied Energy, 242, 612–623.
Kiel, D., Müller, J. M., Arnold, C., & Voigt, K.-I. (2017). Sustainable industrial value creation: Benefits and challenges of industry 4.0. International Journal of Innovation Management, 21(08), 1740015.
Kovacs, O. (2018). The dark corners of industry 4.0--Grounding economic governance 2.0. Technology in Society, 55, 140–145.
Kumar, P., Maiti, J., & Gunasekaran, A. (2018). Impact of quality management systems on firm performance. International Journal of Quality & Reliability Management, 35(5), 1034–1059.
Ladewski, B. J., & Al-Bayati, A. J. (2019). Quality and safety management practices: The theory of quality management approach. Journal of Safety Research.
Lee, J., Kao, H.-A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia Cirp, 16, 3–8.
Li, D., Zhao, Y., Zhang, L., Chen, X., & Cao, C. (2018). Impact of quality management on green innovation. Journal of Cleaner Production, 170, 462–470.
Luthra, S., & Mangla, S. K. (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168–179.
Magdalena, G. (2016). Industry 4.0 and sustainability impacts: Critical discussion of sustainability aspects with a special focus on future of work and ecological consequences.
Manavalan, E., & Jayakrishna, K. (2018). A review of Internet of Things (IoT) embedded sustainable supply chain for industry 4.0 requirements. Computers & Industrial Engineering.
Mayer, F., & Pantförder, D. (2014). Unterstützung des Menschen in Cyber-Physical-Production-Systems. In Industrie 4.0 in Produktion, Automatisierung und Logistik (pp. 481–491). Springer.
Mohammadi, M., & Rezaei, J. (2019). Bayesian Best-Worst Method: A Probabilistic Group Decision Making Model. Omega.
Moktadir, M. A., Ali, S. M., Kusi-Sarpong, S., & Shaikh, M. A. A. (2018). Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection. Process Safety and Environmental Protection.
Mrugalska, B., & Wyrwicka, M. K. (2017). Towards lean production in industry 4.0. Procedia Engineering, 182, 466–473.
Oettmeier, K., & Hofmann, E. (2017). Additive manufacturing technology adoption: an empirical analysis of general and supply chain-related determinants. Journal of Business Economics, 87(1), 97–124.
Olszewska, A. M. (2017). Research issues undertaken within quality management--overview of selected literature and a knowledge map. Procedia Engineering, 182, 518–523.
Rauch, E., Linder, C., & Dallasega, P. (2019). Anthropocentric Perspective of Production before and within Industry 4.0. Computers & Industrial Engineering, 0–49. https://doi.org/10.1016/j.cie.2019.01.018
Saberi, S., & Yusuff, R. M. (2011). Advanced manufacturing technology implementation performance: Towards a strategic framework. In Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, Kuala Lumpur, Malaysia (pp. 145–150).
Safari, S., Shirzad, S., & others. (2012). Quality Management Structure Supported by Information Technology (A Survey in Central Insurance of Iran). Journal of Information Technology Management, 4(12), 113–134.
Sahoo, S., & Yadav, S. (2018). Total quality management in Indian manufacturing SMEs. Procedia Manufacturing, 21, 541–548.
Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. R., & Prote, J.-P. (2014). Collaboration Mechanisms to increase Productivity in the Context of Industrie 4.0. Procedia CIRP, 19, 51–56.
Sung, T. K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting and Social Change, 132, 40–45.
Vaidyaa, S., Ambadb, P., & Bhoslec, S. (2018). Industry 4.0--a glimpse. Design Engineering, 2351, 9789.
Zaidin, N. H. M., Diah, M. N. M., Yee, P. H., & Sorooshian, S. (2018). Quality Management in Industry 4.0 Era, 4(3).
Zeng, J., Zhang, W., Matsui, Y., & Zhao, X. (2017). The impact of organizational context on hard and soft quality management and innovation performance. International Journal of Production Economics, 185, 240–251.
Zhou, M., Liu, X.-B., Chen, Y.-W., & Yang, J.-B. (2018). Evidential reasoning rule for MADM with both weights and reliabilities in group decision making. Knowledge-Based Systems, 143, 142–161.
