The Impact of AI in Supply Chain Resilience: A Systematic Mapping Review
DOI:
https://doi.org/10.61856/nrcn3x58Abstract
Supply chain resilience has gained significant attention in recent years due to the increasing frequency and severity of disruptions such as natural disasters, disruption, conflict, and pandemic. Supply chain resilience enhancement through Artificial Intelligence (AI) is an emerging research area. This paper presents a systematic mapping review to considerate the existing body research on the impact of AI technologies in improving supply chain resilience. Through a structure analysis of relevant studies, this review categorizes AI application in various supply chain function and identifies trends, challenges, and gaps in the literature. The findings reveal that AI, practically machine learning, predictive analytics, and automation, has a notable impact on enhancing flexibility, agility, and responsiveness in supply chains. However, there is limited research on the integration of AI with human decision-making processes and the long-term robustness of AI driven supply chains. The paper concludes by identifying future research directions and practical implications for business aiming to enhance supply chain resilience using AI.
References
Ali, A., & Khan, S. (2023), “Building resilience in supply chains: Strategies for the future”, Journal of Supply Chain Management, 45(2), 123-145.
Arksey, H., & O'Malley, L. (2005),”Scoping studies: towards a methodological framework”, International journal of social research methodology, 8(1), 19-32.
Akbari, M., & Do, T. N. A. (2021), “A systematic review of machine learning in logistics and supply chain management: current trends and future directions”, Benchmarking: An International Journal, 28(10), 2977-3005.
Akhtar, Pervaiz, Arsalan Mujahid Ghouri, Haseeb Ur Rehman Khan, Mirza Amin ul Haq, Usama Awan, Nadia Zahoor, Zaheer Khan, and Aniqa Ashraf (2023), "Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions." Annals of operations research 327, no. 2: 633-657.
Al Bashar, M., Taher, M. A., Islam, M. K., & Ahmed, H. (2024), “The Impact of Advanced Robotics And Automation On Supply Chain Efficiency In Industrial Manufacturing: A Comparative Analysis Between The Us And Bangladesh”, Global Mainstream Journal of Business, Economics, Development & Project Management, 3(03), 28-41.
Aslam, F., & Calghan, J. (2023), “Using NLP to Enhance Supply Chain Management Systems”, Journal of Engineering Research and Reports, 25(9), 211-219.
Agarwal, A., & Jayant, A. (2019),”Machine Learning and Natural Language Processing in Supply Chain Management: A Comprehensive Review and Future Research Directions”, International Journal of Business Insights & Transformation, 13(1).
Agarwal, U., Rishiwal, V., Tanwar, S., Chaudhary, R., Sharma, G., Bokoro, P. N., & Sharma, R. (2022), “Blockchain technology for secure supply chain management: A comprehensive review”, Ieee Access, 10, 85493-85517.
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), 2179-2202.
Beck, J., Birkel, H., Spieske, A., & Gebhardt, M. (2023), “Will the blockchain solve the supply chain resilience challenges? Insights from a systematic literature review”, Computers & Industrial Engineering, 109623.
Belhadi, A., Kamble, S., Fosso Wamba, S., & Queiroz, M. M. (2022), “Building supply-chain resilience: an artificial intelligence-based technique and decision-making framework”, International Journal of Production Research, 60(14), 4487-4507.
Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024), “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, 333(2), 627-652.
Büyüközkan, G., & Göçer, F. (2018), “Digital Supply Chain: Literature review and a proposed framework for future research”, Computers in industry, 97, 157-177.
Christopher, M. and Peck, H. (2004), “Building the resilient supply chain”, International Journal of Logistics Management, Vol. 15 No. 2, pp. 1-14.
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), 96.
Choi, J., Gu, B., Chin, S., & Lee, J. S. (2020), “Machine learning predictive model based on national data for fatal accidents of construction workers”, Automation in Construction, 110, 102974.
Chaudhari, N. (2019), “Impact of automation technology on logistics and supply chain management”, American Journal of Theoretical and Applied Business, 5(3), 53-58.
Choi, T. M., Kumar, S., Yue, X., & Chan, H. L. (2022), “Disruptive technologies and operations management in the Industry 4.0 era and beyond”, Production and Operations Management, 31(1), 9-31.
Choudhury, A., Behl, A., Sheorey, P. A., & Pal, A. (2021), “Digital supply chain to unlock new agility: a TISM approach”, Benchmarking: an international journal, 28(6), 2075-2109.
Davis, J., K. Mengersen, S. Bennett, and L. Mazerolle. 2014. “Viewing Systematic Reviews and Meta-analysis in Social Research Through Different Lenses.” SpringerPlus 3 (1): 511–519.
Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024), “Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small-and medium-sized enterprises”, International Journal of Production Research, 62(15), 5417-5456.
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, 135-146.
Ghadge, A., Er, M., Ivanov, D., & Chaudhuri, A. (2022), “Visualisation of ripple effect in supply chains under long-term, simultaneous disruptions: a system dynamics approach”, International Journal of Production Research, 60(20), 6173-6186.
Grant, M. J., & Booth, A. (2009), “A typology of reviews: an analysis of 14 review types and associated methodologies”, Health information & libraries journal, 26(2), 91-108.
Ghazal, T. M., & Alzoubi, H. M. (2022),”Fusion-based supply chain collaboration using machine learning techniques”, Intelligent Automation & Soft Computing, 31(3), 1671-1687.
Guan, Y., Huang, Y., & Qin, H. (2022), “Inventory Management Optimization of Green Supply Chain Using IPSO‐BPNN Algorithm under the Artificial Intelligence”, Wireless Communications and Mobile Computing, 2022(1), 8428964.
Ghobakhloo, M. (2020), “Determinants of information and digital technology implementation for smart manufacturing”, International Journal of Production Research, 58(8), 2384-2405.
Ghobakhloo, M., Iranmanesh, M., Foroughi, B., Tseng, M. L., Nikbin, D., & Khanfar, A. A. (2023), “Industry 4.0 digital transformation and opportunities for supply chain resilience: a comprehensive review and a strategic roadmap”, Production Planning & Control, 1-31.
Hemsley-Brown, J., and I. Oplatka. (2015), “University Choice: What Do We Know, What Don’t We Know and What Do We Still Need to Find Out?” International Journal of Educational Management 29 (3): 254–274.
Hirsch, K., Niemann, W., & Swart, B. (2024), “Artificial intelligence and information systems capabilities for supply chain resilience: A study in the South African fast-moving consumer goods industry”, Journal of Transport and Supply Chain Management, 18, 1025.
Hohenstein, N.O., Feisel, E., Hartmann, E. and Giunipero, L. (2015), “Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation”, International Journal of Physical Distribution and Logistics Management, Vol. 45 Nos 1-2, pp. 90-117.
Hübner, A., Holzapfel, A., & Kuhn, H. (2016), “Distribution systems in omni-channel retailing.”, Business Research, 9, 255-296.
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. (2020), “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case”, Transportation Research Part E: Logistics and Transportation Review, 136, 101922.
Ivanov, D. (2021), “Supply chain viability and the COVID-19 pandemic: a conceptual and formal generalisation of four major adaptation strategies”, International Journal of Production Research, 59(12), 3535-3552.
Jain, M., Soni, G., Verma, D., Baraiya, R., & Ramtiyal, B. (2023), “Selection of technology acceptance model for adoption of industry 4.0 technologies in agri-fresh supply chain”, Sustainability, 15(6), 4821.
Janjua, N. K., Nawaz, F., & Prior, D. D. (2023), “A fuzzy supply chain risk assessment approach using real-time disruption event data from Twitter”, Enterprise Information Systems, 17(4), 1959652.
Khan, S., Zehra, F. T., & Khan, S. (2024), “Optimizing Organizational Agility: The Symbiotic Impact of AI-Enhanced Supply Chain Collaboration and Risk Management on Performance and Flexibility”, Engineering Proceedings, 76(1), 68.
Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2022), “Enhancing Supply Chain Resilience through AI: Leveraging Deep Reinforcement Learning and Predictive Analytics”, International Journal of AI and ML, 3(9).
Kamble, S. S., Gunasekaran, A., & Sharma, R. (2020), “Modeling the blockchain enabled traceability in agriculture supply chain”, International Journal of Information Management, 52, 101967.
Kitchenham, B., and S. Charters. (2007), "Guidelines for Performing Systematic Literature Reviews in Software Engineering.",Technical Report, Ver. 2.3 EBSE Technical Report, EBSE, SN.
Kondapaka, K. K. (2021), “Advanced Artificial Intelligence Techniques for Demand Forecasting in Retail Supply Chains: Models, Applications, and Real-World Case Studies”, African Journal of Artificial Intelligence and Sustainable Development, 1(1), 180-218.
Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2022),”Enhancing Supply Chain Resilience through AI: Leveraging Deep Reinforcement Learning and Predictive Analytics”, International Journal of AI and ML, 3(9).
Kumar, D., Soni, G., Joshi, R., Jain, V., & Sohal, A. (2022), “Modelling supply chain viability during COVID-19 disruption: A case of an Indian automobile manufacturing supply chain”, Operations Management Research, 15(3), 1224-1240.
Lin, H., Lin, J., & Wang, F. (2022),”An innovative machine learning model for supply chain management”, Journal of Innovation & Knowledge, 7(4), 100276.
Ma, L., & Chang, R. (2024),”How big data analytics and artificial intelligence facilitate digital supply chain transformation: the role of integration and agility”, Management Decision.
Martinez, P. (2023), “Integration of Robotic Process Automation (RPA) and Internet of Things (IoT) in Supply Chain Management: Enhancing Visibility and Efficiency”, Journal of Innovative Technologies, 6(1), 1-8.
Mariappan, M. B., Devi, K., Venkataraman, Y., Lim, M. K., & Theivendren, P. (2023), “Using AI and ML to predict shipment times of therapeutics, diagnostics and vaccines in e-pharmacy supply chains during COVID-19 pandemic”, The International Journal of Logistics Management, 34(2), 390-416.
Mueller, D., & Schmitt, T. V. (2020),”Production planning in autonomous and matrix-structured assembly systems: Effects of similarity of precedence graphs on order release sequencing”, Procedia CIRP, 93, 1358-1363.
Modgil, S., Singh, R. K., & Hannibal, C. (2022), “Artificial intelligence for supply chain resilience: learning from Covid-19”, The International Journal of Logistics Management, 33(4), 1246-1268.
Muthukalyani, A. R. (2023),”Unlocking Accurate Demand Forecasting in Retail Supply Chains with AI-driven Predictive Analytics”, Information Technology and Management, 14(2), 48-57.
Nitsche, B. (2021), “Exploring the Potentials of automation in logistics and supply chain management: Paving the way for autonomous supply chains”, Logistics, 5(3), 51.
Oliveira, E. E., & Pereira, T. (2023), “A new generation? a discussion on deep generative models in supply chains”, In IFIP International Conference on Advances in Production Management Systems (pp. 444-457).
Palmatier, R. W., M. B. Houston, and J. Hulland. 2018. “Review Articles: Purpose, Process, and Structure.” Journal of the Academy of Marketing Science 46: 1–5.
Papachristos, V., Antonopoulos, C., Rachaniotis, N. P., Spontas, D., & Dasaklis, T. K. (2023),”The Potential of ICT Adoption in Promoting Sustainable and Resilient Supply Chains: Evidence from Greek Logistics Firms”, Sustainability, 15(22), 15854.
Paul, S. K., & Chowdhury, P. (2021),”A production recovery plan in manufacturing supply chains for a high-demand item during COVID-19”, International Journal of Physical Distribution & Logistics Management, 51(2), 104-125.
Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015), “Guidelines for conducting systematic mapping studies in software engineering: An update”, Information and software technology, 64, 1-18.
Petersen, K., Feldt, R., Mujtaba, S., & Mattsson, M. (2008), “Systematic mapping studies in software engineering”, In 12th international conference on evaluation and assessment in software engineering (EASE). BCS Learning & Development.
Ponomarov, S.Y. and Holcomb, M.C. (2009), “Understanding the concept of supply chain resilience”, International Journal of Logistics Management, Vol. 20 No. 1, pp. 124-143.
Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021), “Artificial intelligence applications in supply chain management”, International Journal of Production Economics, 241, 108250.
Rahman, T., Paul, S. K., Shukla, N., Agarwal, R., & Taghikhah, F. (2022), “Supply chain resilience initiatives and strategies: A systematic review”, Computers & Industrial Engineering, 170, 108317.
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, 114702.
Sheldon, T., & Chalmers, I. (1994), “The UK Cochrane Centre and the NHS Centre for reviews and dissemination: respective roles within the information systems strategy of the NHS R&D programme, coordination and principles underlying collaboration.”, Health economics, 3(3), 201-203.
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), 7527-7550.
Singh, S., Kumar, R., Panchal, R., & Tiwari, M. K. (2021), “Impact of COVID-19 on logistics systems and disruptions in food supply chain”, International journal of production research, 59(7), 1993-2008.
Singh, Nitin Kumar, Manish Yadav, Vijai Singh, Hirendrasinh Padhiyar, Vinod Kumar, Shashi Kant Bhatia, and Pau-Loke Show (2023), "Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems.", Bioresource technology 369: 128486.
Soni, G., Jain, V., Chan, F. T., Niu, B., & Prakash, S. (2019),”Swarm intelligence approaches in supply chain management: potentials, challenges and future research directions”, Supply Chain Management: An International Journal, 24(1), 107-123.
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, 2021(1), 1476043.
Tukamuhabwa, B.R., Stevenson, M., Busby, J. and Zorzini, M. (2015), “Supply chain resilience: definition, review and theoretical foundations for further study”, International Journal of Production Research, Vol. 53 No. 18, p. 5592.
Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017), “What does industry 4.0 mean to supply chain?”, Procedia manufacturing, 13, 1175-1182.
Tranfield, D., Denyer, D., & Smart, P. (2003), “Towards a methodology for developing evidence‐informed management knowledge by means of systematic review”, British journal of management, 14(3), 207-222.
Wang, M., Wu, Y., Chen, B., & Evans, M. (2020),” Blockchain and supply chain management: a new paradigm for supply chain integration and collaboration”, Operations and Supply Chain Management: An International Journal, 14(1), 111-122.
Wamba, Samuel Fosso, Ransome Epie Bawack, Cameron Guthrie, Maciel M Queiroz, and Kevin Daniel André Carillo. (2021), “Are we Preparing for a Good AI Society? A Bibliometric Review and Research Agenda.” Technological Forecasting and Social Change 164: 120482.
Wang, H., Tao, J., Peng, T., Brintrup, A., Kosasih, E. E., Lu, Y., ... & Hu, L. (2022), “Dynamic inventory replenishment strategy for aerospace manufacturing supply chain: combining reinforcement learning and multi-agent simulation”, International Journal of Production Research, 60(13), 4117-4136.
Wamba, S. F. (2022), “Impact of artificial intelligence assimilation on firm performance: The mediating effects of organizational agility and customer agility”, International Journal of Information Management, 67, 102544.
Wang, J., Zhao, M., Huang, X., Song, Z., & Sun, D. (2024), “Supply chain diffusion mechanisms for AI applications: A perspective on audit pricing. International Review of Financial Analysis, 93, 103113
Waller, M. A., & Fawcett, S. E. (2013), “Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management”, Journal of Business logistics, 34(2), 77-84.
Wang, H., Xie, F., Duan, Q., & Li, J. (2022), “Federated learning for supply chain demand forecasting”, Mathematical Problems in Engineering, 2022(1), 4109070.
Wang, M., & Pan, X. (2022), “Drivers of artificial intelligence and their effects on supply chain resilience and performance: an empirical analysis on an emerging market”, Sustainability, 14(24), 16836.
Wong, L. W., Tan, G. W. H., Ooi, K. B., Lin, B., & Dwivedi, Y. K. (2024), “Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis”, International Journal of Production Research, 62(15), 5535-5555.
Wong, L. W., Tan, G. W. H., Lee, V. H., Ooi, K. B., & Sohal, A. (2021),”Psychological and system-related barriers to adopting blockchain for operations management: an artificial neural network approach”, IEEE Transactions on Engineering Management, 70(1), 67-81.
Yang, Z., Guo, X., Sun, J., Zhang, Y., & Wang, Y. (2022),”What does not kill you makes you stronger: Supply chain resilience and corporate sustainability through emerging IT capability”, IEEE Transactions on Engineering Management.
Younis, H., Sundarakani, B., & Alsharairi, M. (2022), “Applications of artificial intelligence and machine learning within supply chains: systematic review and future research directions” Journal of Modelling in Management, 17(3), 916-940.
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023), “Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review”, Annals of Operations Research, 327(2), 605-632.
Zhou, R., Awasthi, A., & Stal-Le Cardinal, J. (2021), “The main trends for multi-tier supply chain in Industry 4.0 based on Natural Language Processing”, Computers in Industry, 125, 103369.
Zhu, Q., Kouhizadeh, M., & Sarkis, J. (2022), “Formalising product deletion across the supply chain: blockchain technology as a relational governance mechanism”, International Journal of Production Research, 60(1), 92-110.
Downloads
Published
Issue
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
