industry 4.0
artificial intelligence
supply chain management
bibliometric analysis

How to Cite



Industry 4.0 concepts and technologies, which focus on interconnectivity, digitalization, and automation, are critical to the long-term success of both micro and macroeconomic entities. Artificial Intelligence (AI) has emerged as a critical enabler for effective Supply Chain Management (SCM) within this framework. This research study conducts a thorough examination of the current literature to investigate the role of AI in SCM. The study attempts to identify research trends, appraise the present state of knowledge, and provide insights on management implications through a systematic review and the use of bibliometric analytic methodologies. The management implications of this study provide light on the potential benefits and possibilities that AI may provide to SCM operations. The research findings provide firms with the means to improve their supply chain operations, elevate decision-making processes, and achieve a competitive advantage in the changing business landscape by properly using the potential of AI.


Aamer, A., Eka Yani, L., Alan Priyatna, I. (2020). Data analytics in the supply chain management: Review of machine learning applications in demand forecasting. Operations and Supply Chain Management: An “International Journal”, 14(1). DOI: 10.31387/oscm0440281.

Aburto, L., Weber, R. (2007). Improved supply chain management based on hybrid demand forecasts. “Applied Soft Computing”, 7(1). DOI: 10.1016/j.asoc.2005.06.001.

Altiparmak, F., Gen, M., Lin, L., Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain network design. “Computers & industrial engineering”, 56(2). DOI: 10.1016/j.cie.2007.05.012.

Aria, M., Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. “Journal of Informetrics”, 11(4). DOI: 10.1016/j.joi.2017.08.007.

Bag, S., Pretorius, J.H.C., Gupta, S., Dwivedi, Y.K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. “Technological Forecasting and Social Change”, 163. DOI: 10.1016/j.techfore.2020.120420.

Baryannis, G., Papadopoulos, T., Manthou, V. (2019). Artificial neural networks in supply chain management: A comprehensive review. “Applied Sciences”, 9(10). DOI: 10.3390/app9102149.

Baryannis, G., Validi, S., Dani, S., Antoniou, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. “International Journal of Production Research”, 57(7). DOI: 10.1080/00207543.2018.1530476.

Belhadi, A., Mani, V., Kamble, S.S., Khan, S.A.R., Verma, S. (2021). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: an empirical investigation. “Annals of Operations Research”. DOI: 10.1007/s10479-021-03956-x.

Benzidia, S., Makaoui, N., Bentahar, O. (2021). The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. “Technological forecasting and social change”, 165. DOI: 10.1016/j.techfore.2020.120557.

Chauhan, S., Singh, R., Gehlot, A., Akram, S.V., Twala, B., Priyadarshi, N. (2022). Digitalization of Supply Chain Management with Industry 4.0 Enabling Technologies: A Sustainable Perspective. “Processes”, 11(1). DOI: 10.3390/pr11010096.

Di Vaio, A., Boccia, F., Landriani, L., Palladino, R. (2020). Artificial intelligence in the agri-food system: Rethinking sustainable business models in the COVID-19 scenario. “Sustainability”, 12(12). DOI: 10.3390/su12124851.

Dubey, R., Bryde, D.J., Blome, C., Roubaud, D., Giannakis, M. (2021). Facilitating artificial intelligence powered supply chain analytics through alliance management during the pandemic crises in the B2B context. “Industrial Marketing Management”, 96. DOI: 10.1016/j.indmarman.2021.05.003.

Foo, P.Y., Lee, V.H., Tan, G.W.H., Ooi, K.B. (2018). A gateway to realising sustainability performance via green supply chain management practices: A PLS–ANN approach. “Expert Systems with Applications”, 107. DOI: 10.1016/j.eswa.2018.04.013.

Ghadge, A., Er Kara, M., Moradlou, H., Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. “Journal of Manufacturing Technology Management”, 31(4). DOI: 10.1108/jmtm-10-2019-0368.

Grover, P., Kar, A.K., Dwivedi, Y.K. (2022). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. “Annals of Operations Research”, 308(1–2). DOI: 10.1007/s10479-020-03683-9.

Hiassat, A., Diabat, A., Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. “Journal of manufacturing systems”, 42. DOI: 10.1016/j.jmsy.2016.10.004.

Jain, M., Sharma, D.K., Sharma, N. (2022). Artificial Intelligence Computing and Nature-Inspired Optimization Techniques for Effective Supply Chain Management [In:] Data Analytics and Artificial Intelligence for Inventory and Supply Chain Management (pp. 63–80). Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-19-6337-7_4.

Kaur, M., Kang, S. (2016). Market Basket Analysis: Identify the changing trends of market data using association rule mining. “Procedia computer science”, 85. DOI: 10.1016/j.procs.2016.05.180.

Kusiak, A., Smith, M. (2007). Data mining in design of products and production systems. “Annual Reviews in Control”, 31(1). DOI: 10.1016/j.arcontrol.2007.03.003.

Lau, H.C., Chan, T.M., Tsui, W.T., Pang, W.K. (2009). Application of genetic algorithms to solve the multidepot vehicle routing problem. “IEEE transactions on automation science and engineering”, 7(2). DOI: 10.1109/tase.2009.2019265.

Lee, I., Mangalaraj, G. (2022). Big data analytics in supply chain management: A systematic literature review and research directions. “Big data and cognitive computing”, 6(1). DOI: 10.3390/bdcc6010017.

Li, S.G., Kuo, X. (2008). The inventory management system for automobile spare parts in a central warehouse. “Expert Systems with Applications”, 34(2). DOI: 10.1016/j.eswa.2006.12.003.

Lim, A.F., Lee, V.H., Foo, P.Y., Ooi, K.B., Wei–Han Tan, G. (2022). Unfolding the impact of supply chain quality management practices on sustainability performance: an artificial neural network approach. “Supply Chain Management: An International Journal”, 27(5). DOI: 10.1108/scm-03-2021-0129.

McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. “AI magazine”, 27(4). DOI: 10.1609/aimag.v33i2.2417.

Min, H. (2010). Artificial intelligence in supply chain management: theory and applications. “International Journal of Logistics: Research and Applications”, 13(1). DOI: 10.1080/13675560902736537.

Naso, D., Surico, M., Turchiano, B., Kaymak, U. (2007). Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete. “European Journal of Operational Research”, 177(3). DOI: 10.1016/j.ejor.2005.12.019.

Ordoobadi, S.M. (2009). Development of a supplier selection model using fuzzy logic. “Supply chain management: An international journal”, 14(4).

Pech, G., Delgado, C., Sorella, S.P. (2022). Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures: An application in Physics. “Journal of the Association for Information Science and Technology”, 73(11). DOI: 10.1002/asi.24655.

Pournader, M., Ghaderi, H., Hassanzadegan, A., Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. “International Journal of Production Economics”, 241. DOI: 10.1016/j.ijpe.2021.108250.

Rajput, S., Singh, S.P. (2019). Connecting circular economy and industry 4.0. “International Journal of Information Management”, 49. DOI: 10.1108/13598540910970144.

Riahi, Y., Saikouk, T., Gunasekaran, A., Badraoui, I. (2021). Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions. “Expert Systems with Applications”, 173. DOI: 10.1016/j.eswa.2021.114702.

Rodríguez-Espíndola, O., Chowdhury, S., Beltagui, A., Albores, P. (2020). The potential of emergent disruptive technologies for humanitarian supply chains: The integration of blockchain, artificial intelligence and 3D printing. “International Journal of Production Research”, 58(15). DOI: 10.1080/00207543.2020.1761565.

Rosendorff, A., Hodes, A., Fabian, B. (2021). Artificial intelligence for last-mile logistics-Procedures and architecture. “The Online Journal of Applied Knowledge Management (OJAKM)”, 9(1). DOI: 10.36965/ojakm.2021.9(1)46-61.

Sharma, R., Shishodia, A., Gunasekaran, A., Min, H., Munim, Z. H. (2022). The role of artificial intelligence in supply chain management: mapping the territory. “International Journal of Production Research”, 60(24). DOI: 10.1080/00207543.2022.2029611.

Shore, B., Venkatachalam, A.R. (2003). Evaluating the information sharing capabilities of supply chain partners: A fuzzy logic model. “International Journal of Physical Distribution & Logistics Management”, 33(9). DOI: 10.1108/09600030310503343.

Silva, N., Ferreira, L.M.D., Silva, C., Magalhães, V., Neto, P. (2017). Improving supply chain visibility with artificial neural networks. “Procedia Manufacturing”, 11. DOI: 10.1016/j.promfg.2017.07.329.

Stock, J.R., Boyer, S.L. (2009). Developing a consensus definition of supply chain management: a qualitative study. „International Journal of Physical Distribution & Logistics Management”, 39(8). DOI: 10.1108/09600030910996323.

Tirkolaee, E.B., Sadeghi, S., Mooseloo, F.M., Vandchali, H.R., Aeini, S. (2021). Application of machine learning in supply chain management: a comprehensive overview of the main areas. “Mathematical problems in engineering”. DOI: 10.1155/2021/1476043.

Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., Fischl, M. (2021). Artificial intelligence in supply chain management: A systematic literature review. “Journal of Business Research”, 122. DOI: 10.1016/j.jbusres.2020.09.009.

Tsiptsis, K.K., Chorianopoulos, A. (2011). Data mining techniques in CRM: inside customer segmentation. John Wiley & Sons. DOI: 10.1002/9780470685815.

Vercellis, C. (2011). Business intelligence: data mining and optimization for decision making. John Wiley & Sons. DOI: 10.1002/9780470753866.

Yeh, W.C., Chuang, M.C. (2011). Using multi-objective genetic algorithm for partner selection in green supply chain problems. “Expert Systems with applications”, 38(4). DOI: 10.1016/j.eswa.2010.09.091

Yue, L., Yafeng, Y., Junjun, G., Chongli, T. (2007, August). Demand forecasting by using support vector machine. In Third International Conference on Natural Computation (ICNC 2007) (Vol. 3). IEEE. DOI: 10.1109/icnc.2007.324.

Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., Duan, Z., 2016. Comparing keywords plus of WOS and author keywords: A case study of patient adherence research: Comparing Keywords Plus of WOS and Author Keywords. “Journal of the Association for Information Science and Technology”, 67(4). DOI: 10.1002/asi.23437.

Zhou, G., Min, H., Gen, M. (2002). The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach. “Computers & Industrial Engineering”, 43(1–2). DOI: 10.1016/s0360-8352(02)00067-0.

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