Historical Evolution of Supply Chain Management in The VUCA Age: Sustainable, LARG and Digital Supply Chain Managements

Year 2025, Volume: 24 Issue: 1, 273 - 293, 28.01.2025

Nuri Özgür Doğan , Serkan Derici

https://doi.org/10.21547/jss.1473810

Abstract

The increasing impact of technology is differentiating the business processes from the classical meaning. In this study, based on the fact that business processes in the world now include components such as Volatility, Uncertainty, Complexity, Ambiguity (VUCA) under the influence of different factors, new approaches in supply chains are discussed under the concept of VUCA as a flow process. Different important developments affecting the world such as political developments, digital transformation, epidemics and natural disasters make the classical supply chain dysfunctional. A review of the literature shows that the historical development of lean, agile, flexible and green (LARG) supply chains and digital supply chains under the VUCA era are not addressed in relation to each other. In today's studies on supply chains, the lack of knowledge of the new components of supply chains causes studies to be carried out in the classical chain understanding and this prevents applications that reflect the real world. In this respect, within the scope of the study, the historical development of supply chain management, industrial revolutions and their interaction with each other were revealed. Thus, it will contribute to new studies to understand the development of supply chain management from past to present and to focus on the right issues. The historical integrity has been preserved and developments have been obtained by reviewing the literature on supply chain management and industrial revolutions and these developments have been associated with each other and the historical development process has been presented in tables. In addition, all concepts are explained with the support of the literature and each development is expressed as a temporal flow in stages in this direction. It has been determined that there is a lack of theoretical explanation of this evolutionary development in the literature. The information presented in the article reveals the development of supply chain management as a whole with industrial revolutions and will contribute to the development of a professional perspective.

Keywords

Digital Supply Chain Management, Sustainability, LARG, VUCA Age

Historical Evolution of Supply Chain Management in The VUCA Age: Sustainable, LARG and Digital Supply Chain Managements

Abstract

Teknolojinin giderek etkisinin artması ile beraber iş süreçleri klasik anlamdan farklılaşmaktadır. Bu çalışmada, dünyadaki iş süreçlerinin artık farklı faktörlerin etkisi altında Volatilite, Belirsizlik, Karmaşıklık, Belirsizlik (VUCA) gibi bileşenleri içerdiği gerçeğinden hareketle, tedarik zincirlerindeki yeni yaklaşımlar bir akış süreci olarak VUCA kavramı altında ele alınmaktadır. Politik gelişmeler, dijital dönüşüm, salgın hastalıklar ve doğal afetler gibi dünyayı etkileyen farklı önemli gelişmeler klasik tedarik zincirini işlevsiz hâle getirmektedir. Literatür incelendiğinde yalın, çevik, esnek ve yeşil (LARG) tedarik zincirlerinin tarihsel gelişimi ile VUCA dönemi altında dijital tedarik zincirlerinin birbiriyle ilişkili olarak ele alınmadığı görülmektedir. Günümüz şartlarında tedarik zincirlerinin ele alındığı çalışmalarda, tedarik zincirlerinin sahip olduğu yeni bileşenlerin bilinmemesi klasik zincir anlayışında çalışmalar yapılmasına neden olmakta ve bu durumda gerçek dünyayı yansıtan uygulamaların yapılmasına engel olmaktadır. Bu bakımdan çalışma kapsamında tedarik zinciri yönetiminin tarihsel gelişimi, endüstriyel devrimler ve bunların birbirleriyle olan etkileşimi ortaya konulmuştur. Böylece yeni çalışmaların geçmişten günümüze tedarik zinciri yönetiminin gelişimini anlaması ve doğru konulara odaklanmasına katkıda bulunulacaktır. Tarihsel bütünlük korunarak tedarik zinciri yönetimi ve sanayi devrimleri ile ilgili literatür taraması yapılarak gelişmeler elde edilmiş ve bu gelişmeler birbirleri ile ilişkilendirilerek tarihsel gelişim süreci tablolar hâlinde sunulmuştur. Ayrıca tüm kavramlar literatür desteği ile açıklanmış ve her bir gelişme bu doğrultuda aşama aşama zamansal bir akış olarak ifade edilmiştir. Bu evrimsel gelişimin teorik olarak açıklanmasında literatürde bir eksiklik olduğu tespit edilmiştir. Makalede sunulan bilgiler, tedarik zinciri yönetiminin endüstriyel devrimlerle birlikte bir bütün olarak gelişimini ortaya koymaktadır ve profesyonel bir bakış açısının gelişmesine katkı sağlayacaktır.

Keywords

Dijital Tedarik Zinciri Yönetimi, Sürdürülebilirlik, LARG, VUCA Çağı

References

• Abdel-Basset M., Manogaran G., Mohamed M. (2018). Internet of Things (IoT) and its impact on supply chain: A framework for building smart, secure and efficient systems. Future Generation Computer Systems, 86(9), 614-628.
• Abidi, N., Bandyopadhayay, A., Gupta, V. (2017). Sustainable supply chain management: A three dimensional framework and performance metric for Indian IT product companies. International Journal of Information Systems and Supply Chain Management (IJISSCM), 10(1), 29-52.
• Ageron B., Bentahar O., Gunasekaran A. (2020). Digital supply chain: challenges and future directions. In Supply Chain Forum: An International Journal (Vol. 21, No. 3, pp. 133-138). Taylor & Francis.
• Agrawal, P., Narain, R. (2018). Digital supply chain management: An Overview. In IOP Conference Series: Materials Science and Engineering (Vol. 455, No. 1, p. 012074). IOP Publishing.
• Ahi, P., Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of cleaner production, 52, 329-341.
• Akbari, M., Hopkins, J. L. (2022). Digital technologies as enablers of supply chain sustainability in an emerging economy. Operations Management Research, 15(3-4), 689-710.
• Alshehri, S. M. A., Jun, W. X., Shah, S. A. A., Solangi, Y. A. (2022). Analysis of core risk factors and potential policy options for sustainable supply chain: an MCDM analysis of Saudi Arabia’s manufacturing industry. Environmental Science and Pollution Research, 1-31.
• Athmaja, S., Hanumanthappa, M., Kavitha, V. (2017, March). A survey of machine learning algorithms for big data analytics. In 2017 International conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1-4). IEEE.
• Ballou RH (1978). Basic business logistics: transportation, materials management, physical distribution (No. 2). Prentice Hall.
• Balon, V. (2020). Green supply chain management: Pressures, practices, and performance-An integrative literature review. Business Strategy & Development, 3(2), 226-244.
• Baramichai, M., Zimmers Jr, E. W., Marangos, C. A. (2007). Agile supply chain transformation matrix: an integrated tool for creating an agile enterprise. Supply Chain Management: An International Journal, 12(5), 334-348.
• Battineni, G., Chintalapudi, N., Amenta, F. (2019). Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM). Informatics in Medicine Unlocked, 16, 100200. Beltagui, A., Nunes, B., Gold, S. (2022). Sustainability and the digital supply chain. In The Digital Supply Chain (pp. 397-417). Elsevier. Bhargava B, Ranchal R., Othmane LB (2013). Secure information sharing in digital supply chains. In 2013 3rd IEEE international advance computing conference (IACC) (pp. 1636-1640). IEEE.
• Bousqaoui, H., Achchab, S., Tikito, K. (2017). Machine learning applications in supply chains: An emphasis on neural network applications. In 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) (pp. 1-7). IEEE.
• Bubicz, M. E., Barbosa-Póvoa, A. P. F. D., Carvalho, A. (2019). Incorporating social aspects in sustainable supply chains: Trends and future directions. Journal of Cleaner Production, 237, 117500.
• Burbidge, R., Trotter, M., Buxton, B., Holden, S. (2001). Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers & chemistry, 26(1), 5-14.
• Büyüközkan, G., Göçer, F. (2018). Digital Supply Chain: Literature review and a proposed framework for future research. Computers in Industry, 97, 157-177.
• Cabral, I., Grilo, A., Cruz-Machado, V. (2012). A decision-making model for lean, agile, resilient and green supply chain management. International Journal of Production Research, 50(17), 4830-4845.
• Carvalho, H., Cruz-Machado, V. (2011). Integrating lean, agile, resilience and green paradigms in supply chain management (LARG_SCM). Supply chain management.
• Carvalho, H., Duarte, S., Machado, V. C. (2011). Lean, agile, resilient and green: divergencies and synergies. International Journal of Lean Six Sigma, 2(2), 151-179.
• Carvalho, H., Azevedo, S. G., Cruz-Machado, V. (2012). Agile and resilient approaches to supply chain management: influence on performance and competitiveness. Logistics Research, 4, 49-62.
• Cavalcante IM, Frazzon EM, Forcellini FA, Ivanov D (2019). A supervised machine learning approach to data-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86-97.
• Chen, M., Challita, U., Saad, W., Yin, C., Debbah, M. (2017). Machine learning for wireless networks with artificial intelligence: A tutorial on neural networks. arXiv preprint arXiv:1710.02913, 9.
• Chen, M., Challita, U., Saad, W., Yin, C., Debbah, M. (2019). Artificial neural networks-based machine learning for wireless networks: A tutorial. IEEE Communications Surveys & Tutorials, 21(4), 3039-3071.
• Cherrafi, A., Garza-Reyes, J. A., Kumar, V., Mishra, N., Ghobadian, A., Elfezazi, S. (2018). Lean, green practices and process innovation: A model for green supply chain performance. International Journal of Production Economics, 206, 79-92.
• Chowdhary, C. L. (2022). Agile Supply Chain: Framework for Digitization. In Innovative Supply Chain Management via Digitalization and Artificial Intelligence (pp. 73-85). Singapore: Springer Singapore.
• Christopher, M. (1992). Logistics and supply chain management, FT Prentice Hall, London.
• Clift, R. (2003). Metrics for supply chain sustainability. Clean Technologies and Environmental Policy, 5, 240-247.
• Ciccullo, F., Pero, M., Caridi, M., Gosling, J., Purvis, L. (2018). Integrating the environmental and social sustainability pillars into the lean and agile supply chain management paradigms: A literature review and future research directions. Journal of cleaner production, 172, 2336-2350.
• Digalwar, A., Raut, R. D., Yadav, V. S., Narkhede, B., Gardas, B. B., Gotmare, A. (2020). Evaluation of critical constructs for measurement of sustainable supply chain practices in lean‐agile firms of Indian origin: A hybrid ISM‐ANP approach. Business Strategy and the Environment, 29(3), 1575-1596.
• Dossou, P. E. (2018). Impact of Sustainability on the supply chain 4.0 performance. Procedia Manufacturing, 17, 452-459.
• Duarte, S., Cabrita, M. D. R. D. M. F., Machado, V. A. C. (2011). Exploring lean and green supply chain performance using balanced scorecard perspective. In Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management (IEOM) (pp. 520-525). IEOM Research Solutions Pty Ltd.
• Farajpour, F., Hassanzadeh, A., Elahi, S., Ghazanfari, M. (2022). Digital supply chain blueprint via a systematic literature review. Technological Forecasting and Social Change, 184, 121976.
• Frazzon EM, Rodriguez CMT, Pereira MM, Pires MC, Uhlmann I. (2019) Towards supply chain management 4.0. Brazilian Journal of Operations & Production Management, 16(2), 180-191.
• Ghotbabadi, A. R., Gandaee, S., Gandaee, M. T. (2016). Making LARG supply chain management smart and identification of its conditions with management tools of SWOT, BI, and RFID technology. International Journal of Academic Research in Business and Social Sciences, 6(9), 321-333.
• Galal, N. M., Moneim, A. F. A. (2016). Developing sustainable supply chains in developing countries. Procedia Cirp, 48, 419-424.
• Garay-Rondero, C. L., Martinez-Flores, J. L., Smith, N. R., Caballero Morales, S. O., Aldrette-Malacara, A. (2020). Digital supply chain model in Industry 4.0. Journal of Manufacturing Technology Management, 31(5), 887-933.
• Garcia-Buendia, N., Moyano-Fuentes, J., Maqueira-Marín, J. M. (2022). A bibliometric study of lean supply chain management research: 1996–2020. Total Quality Management & Business Excellence, 33(15-16), 1872-1895.
• Goldstein, B. A., Navar, A. M., Carter, R. E. (2017). Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges. European Heart Journal, 38(23), 1805-1814.
• Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S. F., Childe, S. J., Hazen, B., Akter, S. (2017). Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317.
• Habib MM, Jungthirapanich C (2008). An integrated framework for research and education supply chain for the universities. In 2008 4th IEEE International Conference on Management of Innovation and Technology (pp. 1027-1032). IEEE.
• Habib, M. (2014). Supply chain management (SCM): Its future implications. Open Journal of Social Sciences, 2(09), 238.
• Hamdani, F. E., Quintero, I. A. Q., Enjolras, M., Camargo, M., Monticolo, D., Lelong, C. (2022). Agile supply chain analytic approach: a case study combining agile and CRISP-DM in an end-to-end supply chain. In Supply Chain Forum: An International Journal (pp. 1-15). Taylor & Francis.
• Handfield RB, Nıcholas EL (1999). Introduction to Supply Chain Management, Prentice-Hall, Inc., New Jersey. Hervani, A. A., Helms, M. M., Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking: An international journal, 12(4), 330-353.
• Heskett JL, Glaskowsky NA, Ivie RM (1973). Business logistics: Physical distribution and materials management (Vol. 1). New York: Ronald Press Company.
• Hofmann E., Rüsch M (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34.
• Hung CC, Hsieh CC (2017). Big data management on wireless sensor networks. In big data analytics for sensor-network collected intelligence (pp. 99-116). Academic Press.
• Iddris, F. (2018). Digital supply chain: survey of the literature. International Journal of Business Research and Management, 9(1), 47-61.
• Ivanov, D., Dolgui, A., Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846.
• Ivanov, D., Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. Production Planning & Control, 32(9), 775-788.
• Jabbour, C. J. C., Fiorini, P. D. C., Ndubisi, N. O., Queiroz, M. M., Piato, É. L. (2020). Digitally-enabled sustainable supply chains in the 21st century: A review and a research agenda. Science of the total environment, 725, 138177.
• Jagielski, M., Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., Li, B. (2018). Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. In 2018 IEEE symposium on security and privacy (SP) (pp. 19-35). IEEE.
• Jasti, N. V. K., Kodali, R. (2015). A critical review of lean supply chain management frameworks: Proposed framework. Production Planning & Control, 26(13), 1051-1068.
• Kagermann, H., Lukas, W., Wahlster, W. (2011). Industrie 4.0 –Mit dem Internet der Dinge auf dem Weg zur 4. Industriellen Revolution. VDI Nachrichten, Berlin . Kawa, A., Maryniak, A. (2019). Lean and agile supply chains of e-commerce: empirical research. Journal of Information and Telecommunication, 3(2), 235-247.
• Ki Fiona Cheung, Y., Rowlinson, S. (2011). Supply chain sustainability: a relationship management approach. International journal of managing projects in business, 4(3), 480-497.
• Kim, J. S., Merrill, R. K., Arvind, V., Kaji, D., Pasik, S. D., Nwachukwu, C. C., ... Cho, S. K. (2018). Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine, 43(12), 853.
• Kumar, D., Rahman, Z., Chan, F. T. (2017). A fuzzy AHP and fuzzy multi-objective linear programming model for order allocation in a sustainable supply chain: A case study. International Journal of Computer Integrated Manufacturing, 30(6), 535-551.
• Kumar DP, Amgoth T, Annavarapu CSR (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1-25.
• Kumar Sharma, S., Bhat, A. (2014). Modelling supply chain agility enablers using ISM. Journal of modelling in management, 9(2), 200-214.
• La Londe BJ (1997). Supply Chain Management: Myth or Reality?. Supply Chain Management Review, Vol. 1, Spring, pp. 6-7.
• Lamming, R. (1996). Squaring lean supply with supply chain management. International Journal of Operations & Production Management.
• Liu, Y., Zhu, Q., Seuring, S. (2020). New technologies in operations and supply chains: Implications for sustainability. International journal of production economics, 229, 107889.
• Luthra S, Mangla SK (2018). Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process Safety and Environmental Protection, 117, 168-179.
• Mack, O., Khare, A., Krämer, A., Burgartz, T. (Eds.). (2015). Managing in a VUCA World. Springer.
• Mangan, J., Lalwani, C. (2016). Global logistics and supply chain management. John Wiley & Sons.
• Manthou, V., Vlachopoulou, M., Folinas, D. (2014). Virtual e-Chain (VeC) model for supply chain collaboration. International Journal of Production Economics, 87(3), 241-250.
• Martin-Löf, P. (1982). Constructive mathematics and computer programming. In Studies in Logic and the Foundations of Mathematics (Vol. 104, pp. 153-175). Elsevier.
• Matić, B., Jovanović, S., Das, D. K., Zavadskas, E. K., Stević, Ž., Sremac, S., Marinković, M. (2019). A new hybrid MCDM model: Sustainable supplier selection in a construction company. Symmetry, 11(3), 353.
• Maulud, D., Abdulazeez, A. M. (2020). A review on linear regression comprehensive in machine learning. Journal of Applied Science and Technology Trends, 1(4), 140-147.
• McIvor R, Humphreys P (2004). Early supplier involvement in the design process: lessons from the electronics industry. Omega, 32(3), 179-199.
• Mensah, P., Merkuryev, Y. (2014). Developing a resilient supply chain. Procedia-Social and behavioral sciences, 110, 309-319.
• Mohanta, B., Nanda, P., Patnaik, S. (2020). Management of VUCA (Volatility, Uncertainty, Complexity and Ambiguity) Using machine learning techniques in industry 4.0 paradigm. New Paradigm of Industry 4.0: Internet of Things, Big Data & Cyber Physical Systems, 1-24.
• Mukhamedjanova, K. A. (2020). Concept of supply chain management. Journal of Critical Reviews, 7(2), 759-766.
• Murugan, S., Rajavel, S., Aggarwal, A. K., Singh, A. (2020). Volatility, uncertainty, complexity and ambiguity (VUCA) in context of the COVID-19 pandemic: challenges and way forward. International Journal of Health Systems and Implementation Research, 4(2), 10-16.
• Lee, S. M., Rha, J. S. (2016). Ambidextrous supply chain as a dynamic capability: building a resilient supply chain. Management Decision.
• Oh, J., Jeong, B. (2019). Tactical supply planning in smart manufacturing supply chain. Robotics and Computer-Integrated Manufacturing, 55, 217-233.
• Oliveira-Dias, D., Maqueira, J. M., Moyano-Fuentes, J. (2022). The link between information and digital technologies of industry 4.0 and agile supply chain: Mapping current research and establishing new research avenues. Computers & Industrial Engineering, 108000.
• Oliver RK, Webber MD (1992). Supply chain management: Logistic catches up with strategy. 1982. Outlook cit. Christopher, M., Logistics, the Strategic Issues.
• Power, D. J., Sohal, A. S., Rahman, S. U. (2001). Critical success factors in agile supply chain management‐An empirical study. International journal of physical distribution & logistics management.
• Purvis, L., Spall, S., Naim, M., Spiegler, V. (2016). Developing a resilient supply chain strategy during ‘boom’and ‘bust’. Production Planning & Control, 27(7-8), 579-590.
• Qrunfleh, S., Tarafdar, M. (2013). Lean and agile supply chain strategies and supply chain responsiveness: the role of strategic supplier partnership and postponement. Supply Chain Management: An International Journal, 18(6), 571-582.
• Radivojević G., Milosavljević L. (2019). The concept of logistics 4.0. In 4th Logistics International Conference (pp. 23-25).
• Raj A, Dwivedi G, Sharm, A, de Sousa Jabbour ABL, Rajak S (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546.
• Rajesh, R. (2016). Forecasting supply chain resilience performance using grey prediction. Electronic Commerce Research and Applications, 20, 42-58.
• Rodrigue JP (2020). The geography of transport systems. Routledge.
• Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M. J. O. G. R. (2015). Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71, 804-818.
• Rosen, W. (2012). The most powerful idea in the world: A story of steam, industry, and invention. University of Chicago Press.
• Saravanan, R., Sujatha, P. (2018). A state of art techniques on machine learning algorithms: a perspective of supervised learning approaches in data classification. In 2018 Second international conference on intelligent computing and control systems (ICICCS) (pp. 945-949). IEEE.
• Sauer, P. C., Seuring, S. (2018). A three-dimensional framework for multi-tier sustainable supply chain management. Supply Chain Management: An International Journal, 23(6), 560-572.
• Scmdojo (2023) Evolution of Supply Chain Management and Logistics https://www.scmdojo.com/history-and-evolution-of-supply-chain-and-logistics/
• Scuotto, V., Caputo, F., Villasalero, M., Del Giudice, M. (2017). A multiple buyer–supplier relationship in the context of SMEs’ digital supply chain management. Production Planning & Control, 28(16), 1378-1388.
• Seuring, S., Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of cleaner production, 16(15), 1699-1710.
• Sharma, V., Raut, R. D., Mangla, S. K., Narkhede, B. E., Luthra, S., Gokhale, R. (2021). A systematic literature review to integrate lean, agile, resilient, green and sustainable paradigms in the supply chain management. Business Strategy and the Environment, 30(2), 1191-1212.
• Shokouhifar, M., Ranjbarimesan, M. (2022). Multivariate time-series blood donation/demand forecasting for resilient supply chain management during COVID-19 pandemic. Cleaner Logistics and Supply Chain, 5, 100078.
• Song, D. W., Panayides, P. (2012). Maritime logistics: a complete guide to effective shipping and port management. Kogan Page Publishers.
• Soto-Acosta, P. (2020). COVID-19 pandemic: Shifting digital transformation to a high-speed gear. Information Systems Management, 37(4), 260-266.
• Southern RN (2011). Historical perspective of the logistics and supply chain management discipline. Transportation Journal, 50(1), 53-64.
• Srivastava, S. K. (2007). Green supply‐chain management: a state‐of‐the‐art literature review. International journal of management reviews, 9(1), 53-80.
• Stroumpoulis, A., Kopanaki, E. (2022). Theoretical perspectives on sustainable supply chain management and digital transformation: a literature review and a conceptual framework. Sustainability, 14(8), 4862.
• Smykay EW, Bowersox DJ & Mossman FH (1961). Physical DistributionManagement, New York: Macmillan Publishing Co, 1961.
• Tarafdar, M., 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.
• Taseer, M. I., Ahmed, A. (2022). The most effective leadership style in pursuit of resilient supply chain: Sequential mediation of flexibility and agile supply chain. Pakistan Journal of Commerce and Social Sciences (PJCSS), 16(3), 387-423.
• Tortorella, G. L., Miorando, R., Marodin, G. (2017). Lean supply chain management: Empirical research on practices, contexts and performance. International Journal of Production Economics, 193, 98-112.
• Tseng, M. L., Islam, M. S., Karia, N., Fauzi, F. A., Afrin, S. (2019). A literature review on green supply chain management: Trends and future challenges. Resources, Conservation and Recycling, 141, 145-162.
• Vairagade, N., Logofatu, D., Leon, F., Muharemi, F. (2019). Demand forecasting using random forest and artificial neural network for supply chain management. In Computational Collective Intelligence: 11th International Conference, ICCCI 2019, Hendaye, France, September 4–6, 2019, Proceedings, Part I 11 (pp. 328-339). Springer International Publishing.
• Vermeulen, W. J., Seuring, S. (2009). Sustainability through the market-the impacts of sustainable supply chain management: introduction. Sustainable Development, 17(5), 269-273.
• Yang, H., Kumara, S., Bukkapatnam, S. T., Tsung, F. (2019). The internet of things for smart manufacturing: A review. IISE transactions, 51(11), 1190-1216.
• Yildiz Çankaya, S., Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. Journal of Manufacturing Technology Management, 30(1), 98-121.
• Yusuf, Y., Menhat, M. S., Abubakar, T., Ogbuke, N. J. (2020). Agile capabilities as necessary conditions for maximising sustainable supply chain performance: An empirical investigation. International Journal of Production Economics, 222, 107501.
• Womack JP, Jones DT, Roos D (1990) The Machine that Changed the World, Mandarin Books, London.
• Wu, L., Yue, X., Jin, A., Yen, D. C. (2016). Smart supply chain management: a review and implications for future research. The International Journal of Logistics Management.
• Zekhnini, K., Cherrafi, A., Bouhaddou, I., Benghabrit, Y., Garza-Reyes, J. A. (2020). Supply chain management 4.0: a literature review and research framework. Benchmarking: An International Journal, 28(2), 465-501.
• Zekhnini, K., Cherrafi, A., Bouhaddou, I., Chaouni Benabdellah, A., Bag, S. (2022). A model integrating lean and green practices for viable, sustainable, and digital supply chain performance. International Journal of Production Research, 60(21), 6529-6555.
• Zhao, J., Ji, M., Feng, B. (2020). Smarter supply chain: A literature review and practices. Journal of Data, Information and Management, 2, 95-110.
• Zhu, Q., Sarkis, J., Lai, K. H. (2012). Examining the effects of green supply chain management practices and their mediations on performance improvements. International journal of production research, 50(5), 1377-1394.
• Zijm H., Klumpp M., Heragu S., Regattieri A. (2019). Operations, logistics and supply chain management: definitions and objectives. Operations, logistics and supply chain management, 27-42.