The Determinants of Supply Chain Performance in Manufacturing Industries: A Case Study of Proton Malaysia
Abstract
This study examines the key elements that significantly impact supply chain performance in Proton Malaysia, a prominent participant in the automotive sector in Southeast Asia. The objective is to understand the impact of crucial factors on Proton's supply chain's performance, including information quality, information technology, information sharing, big data analytics capacity, supply chain integration, traceability, and agility. The study used a qualitative research methodology to examine Proton's supply chain dynamics, focussing on its strategic collaboration with Geely and the incorporation of new technology. Both primary and secondary data are utilized for analysis. The results demonstrate that Proton's focus on up-to-date information, sophisticated analysis, and robust supplier connections has greatly improved its ability to respond quickly and effectively to operational challenges and maintain its ability to recover from disruptions. Furthermore, the research emphasizes the significance of supply chain agility and integration in effectively responding to market fluctuations and reducing risks. The findings indicate that Proton must consistently engage in technology and supply chain innovation to retain its competitive advantage and successfully traverse the intricate nature of the global automobile market. These lessons apply to Proton and other manufacturing enterprises aiming to optimize their supply networks in a progressively dynamic and linked environment.
Downloads
References
Anazawa, M. (2021, May 1). The Automotive Industry in Malaysia.
Aslam, H., Blome, C., Roscoe, S., & Azhar, T. M. (2018). Dynamic supply chain capabilities. International Journal of Operations & Production Management, 38(12), 2266–2285.
Athukorala, P.-C., & Narayanan, S. (2015). Economic integration in Asia: Trends and policies. Asian Economic Policy Review, 10(2), 275-298.
Aung, M. M., & Chang, Y. S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food Control, 39, 172-184.
Barney, J. B. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. http://dx.doi.org/10.1177/014920639101700108
Büyüközkan, G., & Göçer, F. (2018). Digital supply chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157-177.
Carr, A., & Smeltzer, L. (2002, August). The relationship between information technology use and buyer-supplier relationships: an exploratory analysis of the buying firm’s perspective. IEEE Transactions on Engineering Management, 49(3), 293–304. https://doi.org/10.1109/tem.2002.803389
Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: moving toward new theory. International Journal of Physical Distribution & Logistics Management, 38(5), 360-387
Casino, F., Kanakaris, V., Dasaklis, T.K., Moschuris, S., Stachtiaris, S., Pagoni, M. and Rachaniotis, N.P. (2021). Blockchain-based food supply chain traceability: a case study in the dairy sector, International Journal of Production Research, 59(19), 5758-5770.
Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management. Journal of Management Information Systems, 32(4), 4–39.
Chen, H., Daugherty, P. J., & Roath, A. S. (2007). Defining and operationalizing supply chain process integration. Journal of Business Logistics, 28(1), 63-84.
Chen, J., Sohal, A. S., & Prajogo, D. I. (2013). Supply chain operational risk mitigation: a collaborative approach. International Journal of Production Research, 51(7), 2186-2199.
Chen, L. and Huan, L. (2021). Digital twins for information-sharing in the remanufacturing supply chain: a review, Energy, 220, 119712, doi: 10.1016/j.energy.2020.119712.
Chen, S., et al. (2024). Proactive information sharing for risk management in supply chains facing global disruptions. Journal of Risk and Management, 31(3), 202-218.
Christopher, M., & Towill, D. R. (2001). An integrated model for the design of agile supply chains. International Journal of Physical Distribution & Logistics Management, 31(4), 235-246.
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1-14.
Christopher, M. (2016). Logistics and Supply Chain Management: Logistics & Supply Chain Management. Pearson UK.
Cousins, P.D., Lawson, B., Petersen, K.J. and Fugate, B.(2019). Investigating green supply chain management practices and performance: the moderating roles of supply chain eccentricity and traceability”, International Journal of Operations & Production Management, 39(5), 767-786.
Dominguez, R., Cannella, S., Barbosa-Povoa, A. and Framinana, J. (2017). Information sharing in supply chains with heterogeneous retailers, Omega, 79, 116-132.
Donnelly, K. A. M., Karlsen, K. M., & Dreyer, B. (2018). Improving traceability in food chains using technology systems. Food Control, 95, 93-102.
Fawcett, S. E., Magnan, G. M., & McCarter, M. W. (2008). Benefits, barriers, and bridges to effective supply chain management. Supply Chain Management: An International Journal, 13(1), 35-48.
Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, DOI:10.1016/J.JOM.2009.06.001
Galbraith, J. R. (1973). Designing complex organizations. Reading, MA: Addison-Wesley.
Green, K. W., & Inman, R. A. (2005). Using a just-in-time selling strategy to strengthen supply chain linkages. International journal of production research, 43(16), 3437-3453.
Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A Framework for Supply Chain Performance Measurement. International Journal of Production Economics, 87, 333- 347.
Harper, C. R., & Green, D. (2024). Supply chain agility and resilience through integration: An empirical study. International Journal of Production Economics, 240, 108174.
Hudin, N. S., Hamid, A. B. A., Chin, T. A., & Habidin, N. F. (2017). Exploring Supply Chain Risks among Malaysian Automotive SMEs. International E-Journal of Advances in Social Sciences, 3(8), 666-674.
Johnson, G., & Marquis, D. (2024). Machine learning in supply chain optimizations: Trends and insights. Journal of Cleaner Production, 285, 125236.
Khan, M. Y., Singh, R. K., & Gupta, M. (2024). Technological integration and its impact on supply chain performance: An operational perspective. Industrial Management & Data Systems, 124(2), 309-329.
Kumar, R., & Singh, R. K. (2023). Real-time big data analytics for supply chain transparency. Decision Sciences, 54(4), 678-702.
Kwon, I., Hong, S. and Kim, S. (2017). Do collaborative relationships in supply chain pay-off”, International Journal of Organizational and Collective Intelligence, 7(1), 36-46.
Lee, H. L., & Whang, S. (2000). Information sharing in a supply chain. International Journal of Technology Management, 20(3-4), 373-387. https://doi.org/10.1504/IJTM.2000.002891
Lee, H. L., & Whang, S. (2001). Winning the Last Mile of E-Commerce. MIT Sloan Management Review, 42(4), 54-62.
Lee, H. L. (2004). The triple-A supply chain. Harvard Business Review, 82(10), 102-112.
Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain management. Decision Support Systems, 42(3), 1641-1656.
Li, S., Zhang, Y., & Yu, X. (2022). Predictive analytics in supply chain management: A state-of-the-art review and future opportunities. European Journal of Operational Research, 291(3), 807-823.
Lim, G., & Carter, C. R. (2023). Supply chain integration and its impact on sustainability performance. Journal of Cleaner Production, 321, 128905.
Mathu, K.M. (2019). The information technology role in supplier-customer information-sharing in the supply chain management of South African small and medium-sized enterprise, South African Journal of Economic and Management Sciences, 22(1), 1-8.
McGaughey, R.E. (1999), “Internet technology: contributing to agility in the twenty-first century”, International Journal of Agile Management Systems, 1(1), 7-13.
Miocevic, D., & Crnjak-Karanovic, B. (2012). The mediating role of key supplier relationship management practices on supply chain orientation—The organizational buying effectiveness link. Industrial Marketing Management, 41(1), 115–124. https://doi.org/10.1016/j.indmarman.2011.11.015.
Moe, T. (2014). Perspectives on traceability in food manufacture. Trends in Food Science & Technology, 16(4), 211-214.
Mohamad, M R., & Kari, F. (2008, January 1). Malaysia's National Automotive Policy and Proton's Foreign and Local Vendors Performance. Taylor & Francis, 14(1), 103-118.
Natsuda, K., Segawa, N., & Thoburn, J. (2013, June 1). Liberalization, Industrial Nationalism, and the Malaysian Automotive Industry. Taylor & Francis, 42(2), 113-134.
Nguyen, T T C., Tran, Q B., Ho, D A., Duong, D A., & Nguyen, T B T. (2021, January 1). The effect of supply chain linkages on the business performance: Evidence from Vietnam. Growing Science, 9(3), 529-538.
Núñez-Merino, M., Maqueira-Marín, J.M., Moyano-Fuentes, J. and Martínez-Jurado, P.J. (2020), “Information and digital technologies of industry 4.0 and lean supply chain management: a systematic literature review”, International Journal of Production Research, 58(16), 5034-5061.
Park, J., & Patel, P. C. (2022). Integrated supply chain management for competitive advantage. Journal of Business Logistics, 43(1), 88-105.
Peng, H., Shen, N., Liao, H. and Wang, Q. (2020). Multiple network embedding, green knowledge integration and green supply chain performance-an investigation based on agglomeration scenario. Journal of Cleaner Production, 259, 120821.
Prajogo, D., & Olhager, J. (2012). Supply chain integration and performance: The effects of long-term relationships, information technology and sharing, and logistics integration. International Journal of Production Economics, 135(1), 514-522.
Ramesh, A., & Raj, P. (2024). Utilizing big data analytics for risk mitigation in supply chains. Supply Chain Management: An International Journal, 29(2), 234-249.
Saggi, M. K., & Jain, S. (2020). Impact of big data analytics on supply chain management: Current trends and future perspectives. International Journal of Information Management, 52, 102014.
Sangari, M.S., Razmi, J.andZolfaghari, S. (2015). Developing a practical evaluation framework for identifying critical factors to achieve supply chain agility, Measurement, 62, 205-214.
Simpson, M., Sykes, G., & Abdullah, A. (1998, March 1). Case study: transitory JIT at Proton Cars, Malaysia. Emerald Publishing Limited, 28(2), 121-142. https://doi.org/10.1108/09600039810221685
Srivastava, S. K. (2007). Green supply-chain management: A state-of-the-art literature review. International Journal of Management Reviews, 9(1), 53-80.
Suhaidi, N. (2022, May 27). Supply chain challenges hit DRB-Hicom 1Q performance.
Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents and effects of supply chain agility: Empirically testing the role of demand intensity and competitive intensity. Decision Sciences, 37(4), 479-503.
Tarafdar, M. and Qrunfleh, S. (2017). Agile supply chain strategy and supply chain performance: complementary roles of supply chain practices and information systems capability for agility, International Journal of Production Research, 55(4), 925-938.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.
Thompson, S. K., & Frazier, G. V. (2020). The impact of supply chain integration on performance: A review and an integration. Supply Chain Management: An International Journal, 25(6), 707-725.
Thong, J., & Yap, C. (1995). CEO characteristics, organizational characteristics and information technology adoption in small businesses. Omega, 23(4), 429–442.
Tong, J. T., Terpstra, R. H., & Lim, N. C. (2012). Proton: Its Rise, Fall, and Future Prospects. Asian Case Research Journal, 16(02), 347–377. https://doi.org/10.1142/s0218927512500150
Trkman, P., McCormack, K., de Oliveira, M. P. V., & Ladeira, M. B. (2010). The impact of business analytics on supply chain performance. Decision Support Systems, 49(3), 318-327.
Wad, P., & Govindaraju, V. G. R. C. (2011). Automotive industry in Malaysia: An assessment of its development. International Journal of Automotive Technology and Management, 11(2), 152-171.
Wang, Y., Kung, L., & Byrd, T. A. (2016). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.
Wijewickrama, M., Chileshe, N., Rameezdeen, R. and Ochoa, J. (2021). Information sharing in reverse logistics supply chain of demolition waste: a systematic literature review, Journal of Cleaner Production, 280, 124359.
Wu, L. Y., Chiu, M. L., & Chen, T. Y. (2021). The effects of supply chain integration on company performance: An empirical investigation. Production and Operations Management, 30(4), 1231-1246.
Zhang, Y., Li, H., & Chen, K. (2021). Enhancing supply chain performance through big data analytics: State of the art and research opportunities. Journal of Operations Management, 66(1), 122-144.
Zhou, H., & Benton, W. C. (2007). Supply chain practice and information sharing. Journal of Operations Management, 25(6), 1348-1365.
Zulkepli, J., Fong, C H., & Abidin, N Z. (2015). Demand forecasting for the automotive sector in Malaysia by system dynamics approach. American Institute of Physics. https://doi.org/10.1063/1.4937050.
Copyright (c) 2024 INTAN LIANA SUHAIME, NANI SHUHADA SEHAT, SITI ROHANA DAUD, JUMAELYA JOGERAN

This work is licensed under a Creative Commons Attribution 4.0 International License.
Author (s) should affirm that the material has not been published previously. It has not been submitted and it is not under consideration by any other journal. At the same time author (s) need to execute a publication permission agreement to assume the responsibility of the submitted content and any omissions and errors therein. After submission of revised paper in the light of suggestions of the reviewers, the editorial team edits and formats manuscripts to bring uniformity and standardization in published material.
This work will be licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) and under condition of the license, users are free to read, copy, remix, transform, redistribute, download, print, search or link to the full texts of articles and even build upon their work as long as they credit the author for the original work. Moreover, as per journal policy author (s) hold and retain copyrights without any restrictions.