Document Type : Research Paper
Authors
1 Associate Professor, Department of Management, Faculty of Management and Economics, University of Guilan, Rasht, Ir
2 Master of Student of Industrial Management (Production and Operation), Department of Management, Faculty of Management and Economics, University of Guilan, Rasht, Iran.
3 Associate Professor, Department of Management, Faculty of Industrial Engineering and Management, Shahrood University of Technology, Semnan, Iran.
Abstract
Digital transformation and Industry 4.0 have emerged as key drivers for enhancing competitiveness and improving product quality across various industries, particularly in the automotive parts sector. This research focuses on Guilan Province, examining the extent of Industry 4.0 technologies’ implementation aimed at increasing product longevity. Initially, through qualitative content analysis of 35 articles published between 2016 and 2024, 16 sub-criteria were identified within four main groups. Subsequently, using the novel OPLO-POCOD method (Opportunity Lost Assessment Based on Distance in Polar Coordinate Space) and surveying 14 experts from 10 parts manufacturing companies, the performance of these companies was analyzed. The results indicated that criteria such as automated warehousing systems, inventory management automation, and blockchain-based tracking had the highest impact on increasing product lifespan, with the lowest opportunity loss values of 0.0231, 0.0242, and 0.0253, respectively. On the other hand, the physical-information integration of the supply chain using cloud computing is still in the early stages of implementation. This research uniquely combines qualitative analyses with the innovative OPLO-POCOD method, enabling precise ranking of companies and identification of execution gaps. The findings emphasize the importance of focusing on smart technologies to achieve more sustainable and competitive production, assisting managers and policymakers in prioritizing Industry 4.0 strategies. Overall, while automotive parts industries in Guilan have made progress in areas such as automated warehousing, there is a need to accelerate the implementation of new technologies like cloud computing to fully realize the benefits of Industry 4.0 and complete the digital transformation process.
Introduction
In the contemporary competitive landscape, enhancing production quality and adopting sustainable supply chain management strategies—particularly with an emphasis on extending product lifespan—have evolved into strategic imperatives. Increasing product longevity not only alleviates pressure on natural resources and mitigates environmental impacts but also significantly enhances the economic value of products. Industry 4.0, as a transformative paradigm, leverages smart technologies such as additive manufacturing, the Internet of Things (IoT), robotics, and artificial intelligence to provide unprecedented potential for achieving these objectives. This digital transformation, enabling real-time tracking throughout the entire product lifecycle, predictive maintenance optimization, and production personalization, directly contributes to extending the useful life of products. The integration of this concept with Industry 4.0 smart technologies substantially enhances the capacity to realize these goals. This is particularly critical in complex and capital-intensive industries such as automotive and auto parts manufacturing, where production quality is directly linked to safety and competitiveness. However, despite prevailing assertions regarding the role of Industry 4.0 in sustainable development, few studies have specifically examined the impact of smart technologies on product lifespan extension. Aiming to address this research gap, this study focuses on the auto parts manufacturing industry to identify key components for enhancing product longevity within the Industry 4.0 framework. Employing an innovative methodology—the Lost Opportunity Technique based on distance in polar coordinate space—it investigates the extent of implementation of these factors within the industry. The findings of this research are poised to provide industrial managers and policymakers with a strategic roadmap for developing more sustainable and competitive products.
Methodology
The present study is applied in purpose and descriptive-survey in terms of data collection, adopting a multiple case study approach. This research was conducted using a mixed-methods (qualitative-quantitative) approach in two phases. In the qualitative phase, content analysis and a systematic review of library sources and reputable databases from 2016 to 2024 were employed to identify the most influential factors affecting product lifespan extension, with an emphasis on smart technologies within Industry 4.0. In the quantitative phase, a researcher-developed questionnaire based on a ten-point scale and the novel “Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD)” was used to collect field data from 10 active companies in the automotive parts industry in Guilan Province. Sampling was performed using targeted and snowball sampling methods, and the questionnaires were completed by 14 experts (production managers, IT managers, and production line supervisors) with at least five years of professional experience and familiarity with Industry 4.0 concepts. The reliability of the questionnaire was confirmed with a Cohen’s kappa coefficient of 0.743, and its validity was endorsed by specialists. Finally, the collected data were analyzed using the OPLO-POCOD technique, and the companies under study were ranked based on the identified criteria.
Findings
The findings of this study, conducted using a mixed-methods approach (qualitative content analysis and the OPLO-POCOD technique), reveal that the adoption of Industry 4.0 technologies plays a significant role in enhancing product longevity in the automotive parts manufacturing industry. In the qualitative phase, which involved the analysis of 35 studies published between 2016 and 2024, 16 key concepts were identified across four main criteria: additive manufacturing, the Internet of Things (IoT), robotics, and smart supply chain management. The reliability of this analysis was confirmed with a Cohen’s kappa coefficient of 0.743. In the quantitative phase, employing the novel OPLO-POCOD technique and surveys of 14 experts across 10 automotive parts manufacturing companies, the studied companies were ranked based on their level of adoption of Industry 4.0 technologies. The results indicated that Companies 4, 3, and 5 achieved the highest percentages of opportunity gained (94.4%, 94.3%, and 93.9%, respectively) and the lowest levels of lost opportunity (below 0.061), securing the top ranks. In contrast, Companies 7, 2, and 8, with the highest levels of lost opportunity (between 0.131 and 0.174), demonstrated the weakest performance in adopting these technologies. These findings underscore the direct impact of implementing Industry 4.0 technologies—particularly in additive manufacturing, the Internet of Things, and smart supply chain management—on extending product lifespan.
Discussion and conclusion
The present study aimed to assess the level of Industry 4.0 implementation in automotive parts manufacturing companies in Guilan Province. The findings revealed that three criteria—automated warehouse systems, inventory management automation, and blockchain-based traceability, all falling within the domain of data transparency and tracking—were identified as the most effective factors in enhancing product longevity. These results indicate that in Iran’s industrial context, the most fundamental layers of digitalization serve as essential prerequisites for achieving higher-level objectives such as the circular economy. On the other hand, the findings demonstrate that despite relative progress in certain foundational technologies, significant challenges persist in smart supply chain management, rooted in infrastructural limitations and weaknesses in strategic coordination among supply chain actors. This study contributes to the academic discourse in several ways, including the development of a prioritized operational framework (OPLO-POCOD) for assessing industrial readiness, the identification of specific mechanisms effective within each criterion, and the adaptation of global findings to Iran’s specific industrial context. By bridging theoretical literature with practical industry requirements, this research provides a valuable roadmap for industrial managers and policymakers. Based on the research findings, it is recommended that industrial managers allocate resources toward improving smart supply chain management. Furthermore, policymakers are advised to facilitate the successful implementation of Industry 4.0 by developing national cloud platforms for supply chain integration, providing targeted incentives to companies committed to national standards, and establishing mandatory data exchange standards. For future research, it is suggested that emerging criteria such as human-robot collaboration be examined, the study be replicated in other national industries, fuzzy logic-based methods be employed, and causal methods be utilized to analyze relationships among the identified criteria.
Keywords
Main Subjects
- Ammar, M., Haleem, A., Javaid, M., Wail, R., & Bah, S. (2021). Improving material quality management and manufacturing organizations system through Industry 4.0 technologies. Materials Today: Proceedings, vol. 45, pp. 5089–5096. https://doi.org/10.1016/j.matpr.2021.01.585
- Aparisi, T.A., & Van Ewijk, S. (2015). Analysing impacts of product life extension through material flow analysis: the case of EEE and paper. In Plate Product Lifetimes and the Environment Conference, Vol. 2015, pp. 1-12.
- Bakker, C., Wang, F., Huisman, J., & Den Hollander, M. (2014a). Products that go round: exploring product life extension through design. Journal of Cleaner Production, vol. 69, pp. 10–16. https://doi.org/10.1016/j.jclepro.2014.01.028
- Bakker, C., Den Hollander, M.C., Van Hinte, E., & Zijlstra, Y. (2014b). Products that last: Product Design for Circular Business Models. Delft, The Netherlands: Delft Library/Marcel den Hollander IDRC, 112p.
- Carvalho, A.V., Enrique, D.V., Chouchene, A., & Charrua-Santos, F. (2021). Quality 4.0: an overview. Procedia Computer Science, vol.181, 341-346. https://doi.org/10.1016/j.procs.2021.01.176
- Cox, J., Griffith, S., Giorgi, S., & King, G. (2013). Consumer understanding of product lifetimes. Resources, Conservation and Recycling, vol. 79, pp. 21-29. https://doi.org/10.1016/j.resconrec.2013.05.003
- Dalenogare, L.S., Benitez, G.B., Ayala, N.F. & Frank, A.G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, vol.204, 383-394. https://doi.org/10.1016/j.ijpe.2018.08.019
- Den Hollander, M.C., Bakker, C.A., & Hultink, E.J. (2017). Product design in a circular economy:Development of a typology of key concepts and terms. Journal of Industrial Ecology, 21(3), 517-525. https://onlinelibrary.wiley.com/doi/10.1111/jiec.12610
- Deryabina, G., & Trubnikova, N. (2021). The Impact of Digital Transformation in Automotive Industry on Changing Industry Business Model. International Scientific and Ptactical Conference, No. 45, pp. 1–7. https://doi.org/10.1145/3487757.3490886
- Du, G., Zhang, P., Mai, J., & Li, Z. (2012). Markerless kinect-based hand tracking for robot teleoperation. International Journal of Advanced Robotic Systems, 9(2), 1-36. http://dx.doi.org/10.5772/50093
- Ertz, M & Gasteau, F. (2023). Role of smart technologies for implementing Industry 4.0 environment in product lifetime extension towards circular economy. A qualitative research,Heliyon, 9(6), e16762. https://doi.org/10.1016/j.heliyon.2023.e16762
- Ertz, M., & Patrick, K. (2019). The future of sustainable healthcare: extending product lifecycles. Resources, Conservation and Recycling, vol. 153, 104589. https://doi.org/10.1016/j.resconrec.2019.104589
- Esmaeilian, B., Sarkis, J., Lewis, K., & Behdad, S. (2020). Blockchain for the future of sustainable supply chain management in Industry 4.0. Resources Conservation and Recycling, vol. 163, https://doi.org/10.1016/j.resconrec.2020.105064
- Formentini, M., & Taticchi, P. (2016). Corporate sustainability approaches and governance mechanisms in sustainable supply chain management. Journal of Cleaer. Productoin, 112 (3), 1920-1933. https://doi.org/10.1016/j.jclepro.2014.12.072
- Frank, A.G., Dalenogare, L.S., & Ayala, N.F. (2019). Industry 4.0 technologies: implementation patterns in manufacturing companies. International Journal of Production Economics, vol. 210, pp, 15-26. https://doi.org/10.1016/j.ijpe.2019.01.004
- Graneheim, U., & Lundman, B. (2004). Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness. Nurse Education Today, 24(2), 105-112. https://doi.org/10.1016/j.nedt.2003.10.001
- Gray, J., & Depcik, C. (2020). Review of Additive Manufacturing for Internal Combustion Engine Components. SAE International Journal of Engines, 13(5), 617-632. http://dx.doi.org/10.4271/03-13-05-0039
- Griffiths, M., Howarth, C.A., De Almeida-Rowbotham, J., Rees, A., & Kerton, R. (2016). A design of experiments approach for the optimization of energy and waste during the production of parts manufactured by 3D printing. Journal of cleaner production, vol. 139, pp. 74-85. https://doi.org/10.1016/j.jclepro.2016.07.182
- Ivanov, D., Dolgui, A., Sokolov, B., Werner, F., & Ivanova, M. (2016). A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0. International Journal of Production Research, 54(2), 386-402. http://dx.doi.org/10.1080/00207543.2014.999958
- Jabbour, C.J.C., Jabbour, A.B.L.D.S., Sarkis, J., & Filho, M.G. (2018). Unlocking the circular economy through new business models based on large-scale data: an integrative framework and research agenda. Technological Forecasting and Social Change, vol.144, 546–552. https://doi.org/10.1016/j.techfore.2017.09.010
- Javid, M., Haleem, A., Singh, R., & Suman, R. (2022a). Artificial intelligence applications for industry 4.0: a literature-based study. Journal of Industrial Integration and Management, 1–29. https://doi.org/10.1142/S2424862221300040
- Javid, M., Haleem, A., Pratap Singh, R., & Suman, R. (2022b). Enabling flexible manufacturing system (FMS) through the applications of industry 4.0 Technologies. Internet of Things and Cyber-Physical Systems, vol. 2, pp. 49-62. https://doi.org/10.1016/j.iotcps.2022.05.005
- Kagermann, H., Wahlster, W., & Helbig, J. (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. Acatech, Forschungsunion, 112p.
- Kandade, K., Samara, G., Parada, M.J., & Dawson, A. (2021). From family successors to successful business leaders: A qualitative study of how high quality relationships develop in family businesses. Journal of Family Business Strategy, 12(2), 100334. https://doi.org/10.1016/j.jfbs.2019.100334
- Karabegović. I., Karabegović, E., Mahmić, M., Husak, E. & Mahmic, M. (2022). How the Core Technologies of Industry 4.0 are Changing the Automotive Industry in the World, with a Focus on China, IOP Conference Series:Materials Science and Engineering, Vol.1271, 012017. https://doi.org/10.1088/1757-899X/1271/1/012017
- Lee, C.K.M., Lv, Y., Ng, K.K.H., Ho, W., & Choy, K.L. (2017). Design and application of internet of things-based warehouse management system for smart logistics. International Journal of Production Research, 56(8), 2753–2768. http://dx.doi.org/10.1080/00207543 .2017.1394592
- Liboni, L.B., Cezarino, L.O., Jabbour, C.J.C., Oliveira, B.G., & Stefanelli, N.O. (2019). Smart industry and the pathways to HRM 4.0: implications for SCM. Supply Chain Management: An International Journal, 24(1), 124-146. http://dx.doi.org/10.1108/SCM-03-2018-0150
- Lienig, J., & Bruemmer, H. (2017). Recycling Requirements and Design for Environmental Compliance In Fundamentals of Electronic Systems Design. Springer International Publishibe, pp. 193-218. http://dx.doi.org/10.1007/978-3-319-55840-0_7
- Mamad, M. (2018). Challenges and Benefits of Industry 4.0. International Journal of Supply and Operations Management, 5(3), 256-265. http://dx.doi.org/10.22034/2018.3.7
- Murakami, S., Oguchi, M., Tasaki, T., Daigo, I., & Hashimoto, S. (2010). Lifespan of commodities, part 1. The creation of a database and its review. Journal of Industrial Ecology. 14(4), 598–612. https://doi.org/10.1111/j.1530-9290.2010.00250
- Nuryakin & Priyo, J.S. (2018). Service quality, trust and customer loyalty: The role of customer satisfaction at the hotel services industry in Indonesia. Scholary Journal, 19(166), 50-55.
- Olsen, T.L., & Tomlin, B. (2020). Industry 4.0: Opportunities and Challenges for Operations Management. Manufacturing & Service Operations Management, 22(1), pp. 1-122. https://doi.org/10.1287/msom.2019.0796
- Özcan Avşar, N., Sevinc, A., Gur, S., Özcan, E., & Eren, T. (2020). Evaluation of the Transition Process of Industry 4.0 in Automotive Spplier Industry. Başkent Üniversitesi Ticari Bilimler Fakültesi Dergisi, 4(2), pp. 1-18. http://dergipark.gov.tr/jcsci
- Peter, O., Pradhan, A., & Mbohwa, C. (2023). Industry 4.0 concepts within the sub Saharan African SME manufacturing sector. Procedia Computer Science, vol. 217, pp. 846-855. https://doi.org/10.1016/j.procs.2022.12.281
- Piron, M., Wu, J., Fedele, A., & Manzardo, A. (2024). Industry 4.0 and life cycle assessment: Evaluation of the technology applications as an asset for the life cycle inventory. Science of the Total Environment, vol. 916, 170263. https://doi.org/10.1016/j.scitotenv.2024.170263
- Radziwill, N.M. (2018). Quality 4.0: Let's Get Digital-The many ways the fourth industrial revolution is reshaping the way we think about quality. ArXiv, Quality Progress, pp. 24-29. http://dx.doi.org/10.48550/arXiv.1810.07829
- Renda, A., Schwaag Serger, S., Tataj, D. (2022). Industry 5.0, a Transformative Vision for Europe: Governing Systemic Transformations towards a Sustainable Industry. Directorate- General for Research and Innovation (European Commiddion), No, 3. https://data.europa.eu/doi/10.2777/17322
- Rojko, A. (2017). Industry 4.0 concept: Background and overview. International. Journal of Interactive Mobile Technologies(iJIM), 11(5), pp. 77-90. http://dx.doi.org/10.3991/ijim.v11i5.7072
- Saberi, S., Kouhizadeh, M., Sarkis, J., & Shen, L. (2019). Blockchain technology and its relationships to sustainable supply chain management. International Journal of Production Research, 57(7), pp. 2117–2135. https://doi.org/10.1080/00207543.2018.1533261
- Sauerwein, M., Doubrovski, E., Balkenende, R., & Bakker, C. (2019). Exploring the potential of additive manufacturing for product design in a circular economy. Journal of Cleaner Production, vol. 226, pp. 1138-1149. https://doi.org/10.1016/j.jclepro.2019.04.108
- Saw, H.S., Bin Azmi, A.A., Chew, K.W., & Show, P.L. (2021). Sustainability and development of industry 5.0, in: The Prospect of Industry 5.0 in Biomanufacturing. CRC Press, pp. 287–304. http://dx.doi.org/10.1201/9781003080671-13-13
- Sheikh, R., & Senfi, S. (2023). Rapid Assessment of Customers and their Classification with the OPportunity LOsses-Based POlar COordinate Distance Sort (OPLO-POCOD SORT) and Net Promoter Score (NPS). Journal of Business Management Perspective, 22(55), 82-112. https://doi.org/10.48308/2024/234864.1581
- Sheikh, R., & Senfi, S. (2024). A Novel Opportunity Losses-Based Polar Coordinate Distance (OPLO-POCOD) Approach to Multiple Criteria Decision-Making. Journal of Mathematics, 2024. https://doi.org/10.1155/2024/8845886
- Show, P.L., Chew, K.W., & Ling, T.C. (2021). The Prospect of Industry 5.0 in Biomanufacturing. CRC Press, 2021, 326p. http://dx.doi.org/10.1201/9781003080671
- Tajabadi, A., Ahmadi, F., Sadooghi Asl, A., & Vaismoradi, M. (2020). Unsafe nursing documentation: A qualitative content analysis. Nursing Ethics, 27(5), 1213-1224. https://doi.org/10.1177/ 0969733019871682
- Talaie, H., Ziaeian, M., & Malekinejad, P. (2022). Designing the establishment and implementation model of quality 4.0 with the integrated approach of interpretive structural modeling and structural equation modeling. Journal of Quality Engineering and Management, 12(1),51-68. https://dor.isc.ac/dor/20.1001.1.23221305.1401.12.1.4.4
- Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018a). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(4), pp. 3563-3576. https://link.springer.com/article/ 10.1007%2Fs00170-017-0233-1
- Tao, F., Qi, Q., Liu, A., & Kusiak, A. (2018b). Data-driven smart manufacturing. Journal of Manufacturing Systems, vol. 48, pp. 157-169. https://doi.org/10.1016/j.jmsy.2018.01.006
- Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, Ch., Lundholm, Th., & Lennartson, B. (2015). An event-driven manufacturing information system architecture. IFAC-Papers Online, 48(3), pp. 547-554. https://doi.org/10.1016/j.ifacol.2015.06.138
- Van Nes, N., & Cramer, J. (2003). Design strategies for the lifetime optimisation of products. The Journal of Sustainable Product Design, 3(3-4), pp. 101-107. http://dx.doi.org/10.1007/s10970-005-2144-8
- Van Nes, N., & Cramer, J. (2006). Product lifetime optimization: a challenging strategy towards more sustainable consumption patterns. Journal of Cleaner Production, 14(15-16), pp. 1307-1318. https://doi.org/10.1016/j.jclepro.2005.04.006
- Walker, j., Childe, s., Wang, y. (2019). Analysing manufacturing enterprises to identify opportunities for automation and guide implementation – a review. IFAC Papers Online, 52(13), pp. 2273-2278. https://doi.org/10.1016/j.ifacol.2019.11.544
- Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical systems in manufacturing. Journal of Manufacturing Systems, 37(2), 517-527. https://doi.org/10.1016/j.jmsy.2015.04.008
- Xu, L.D., Xu, E.L. & Li, L. (2018). Industry 4.0: state of the art and future trends. International Journal of Production Research, 56(8), 2941-2962. https://doi.org/10.1080/00207543.2018.1444806
- Zonnenshain, A. & Kenett, R.S. (2020). Quality 4.0—the challenging future of quality engineering. Quality Engineering, 32(4), pp. 614-626. https://doi.org/10.1080/08982112.2019.1706744
- Amini, S., Ramzani, M., Beykzad, J., & Sangi Noorpour, A. (2022). Designing a Sustainable Development Model in the Iranian Automotive Industry Using the Fourth Industrial Revolution Approach. Quarterly Journal of Strategic Management in Industrial Systems, 17 (61), 140-156. .
- Arjmandi, R., Fathi, M., Manteghi, M., & Shahbazi, M. (2023). Providing a model of technological transition to the fourth generation of the industrial revolution in the automobile industry. Quarterly Journal of Industrial Technology Development, 21(52), 80-96. .
- Eskini, A., & Ahangari, F. (2022). Review of the parts Manufacturing Industry. Monthly Journal of the Stock Exchange, 12(2), 45-58..
- Farahbakhsh, M., Modiri, M., Khatami Firouzabadi, M., & Puorebrahimi, A. (2022). Power industru's life cycle simulation using agent based modeling. Industrial Management Perspective, 12(4), 9-35..
- Jafari, T., Zarei, A., Azar, A., & Moghadam, A. (2022). Designing a Model for the impact of Business intelligence on supply chain performance with an emphasis on integration and agility. Industrial Management Perspective, 12(47), 279-315. .
- Khoshsepehr, Z., Alimohammadlou, M., Mohammadi, A., & Allah Ranaei Kordshouli, H. (2022). Scientometrics and Analysis of the Research trends in the fourth Industrial revolution field and Quality 4.0. Sciences and Techniques of Information Management, 9(2), 133-166. .
- Koohi Aghdam, A., Koohi Aghdam, E., Javan Amani, V., & Mashhadi Mohammadi, A. (2020). The impact of technology management on service quality with emphasis on the mediator role of organizational agility (Case Study: Irtoya Compani). Quarterly Journal of Industrial Technology Development, 17(38), 55-65. .
- Mohaghar, A., Asgharizadeh, E., Ghodsypour, H., & Samarrokhi, A. (2021). Presenting a conseptual model delineating the effect of production and operations strategies on sustainable competitive advantage in Iranian Automotive Industry: The case of Tehran automobile manufacturing companies. Journal of Productivity Management, 15(56), 163-187. .
- Mohammadi, A., & Babaei, S. (2021). Model for identifying and absorbing key technologies in small to medium enterprises (SME). Journal of New Research Approaches in Management and Accounting, 5(66), 84-102. .
- Mollaei, M., & Banihashemi, S. (2021). Identify the role of virtual production and new technology on organizational agility and profitability in competitive markets. Science Journal- Management & Sustainable Development Studies, 1(2), 155-190. .
- Morovati Sharif-Abadi, A., Ziaeian, M., Mirfakhradini, H., & Zanjirchi, M. (2022). Investigating the role of Industry 4.0 in the quality of products and services (Case Study: Home appliance industry). Journal of Quality Engineering and Management, 12(3), 319-342. .
- Rahmanikhalili, A., Fallah, M., Gholami Jamkarani, R., & Jahangirnia, H. (2022). Design and validation of a model for the use of Blockchain technology and cryptocurrencies under sanctions of Islamic of republic of Iran. Iranian Journal of Political Socioligy, 5(10), 1540-1568..
 
						
						