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Volume 1 Number 1 2023

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 1 (1)

AI-Driven Supply Chain Resilience: Integrating Reinforcement Learning and Predictive Analytics for Proactive Disruption Management

Ismoth Zerine 1, Md Saiful Islam 1, Md Yousuf Ahmad 1, Md Mainul Islam 1, Younis Ali Biswas 2

+ Author Affiliations

Business and Social Sciences 1 (1) 1-12 https://doi.org/10.25163/business.1110343

Submitted: 04 July 2023 Revised: 23 September 2023  Published: 25 September 2023 

Abstract

The world experienced the most severe disruptions to global supply chains due to pandemics, geopolitics, and climate emergencies showing the fatal flaws of classic, efficiency-driven supply chains. Although the use of artificial intelligence (AI) solutions was already in the spotlight, current studies were lacking thorough designs in terms of the successful integration of reinforcement learning (RL) and predictive analytics on supply chain resilience in a wholesome manner. This disparity has been ongoing whereby organizations have been using reactive strategies and stagnant forecasting tools, which were inefficient in dynamic environments of operation. This research paper created and tested an inclusive AI model that would streamline inventory and reduce the impact of unforeseen events with the iterative utilization of RL and predictive analytics. The study adopted a mixed-methods survey, where five multinational corporations were used to analyze a quantitative response and the opinion of 100 professionals in the supply chain. Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) reinforcement learning models were trained based on past inventorying data and predictive analytics models like ARIMA and LSTM neural networks were used in demand forecasting and disruption forecasting. The outcomes proved to be very constructive in terms of supply chain performance. The RL models decreased stock outs by 32.4 percent (p < 0.001) using the dynamic strategies of replenishing the inventory. In disruptive prediction, predictive analytics had an average underestimate and overestimate of 12.3 percent, which is 15.2 percent better than standard exponential smoothing techniques (p = 0.008). Organizations that are using the integrated framework recorded the decision-making process 50 percent quicker during the disruptive period and 65.3 percent more optimization successes than those that have not adopted ( 2 = 18.7, p < 0.001). These results produced empirical support that the implementation of RL together with predictive analytics may result in the conversion of supply chain functions into proactive systems. The paper made a contribution to academic learning and can be used by practitioners in the field to implement the AI-driven resilience framework, which was validated in the study. The findings supported the need of workforce development to capitalize on the power of AI in supply chain management, and future study ought to be conducted investigating human-AI relations in operational settings.

Keywords: Supply chain resilience; Reinforcement learning; Predictive analytics; Inventory optimization; Disruption management

References

Abdulraheem, A.?O. (2018). Just-in-time manufacturing for improving global supply chain resilience. International Journal of Engineering Technology Research & Management, 2(11), 58.

Almaiah, M.?A., Alfaisal, R., Salloum, S.?A., Hajjej, F., Shishakly, R., Lutfi, A., … Al-Maroof, R.?S. (2022). Measuring institutions’ adoption of artificial intelligence applications in online learning environments: Integrating the innovation diffusion theory with technology adoption rate. Electronics, 11(20), 3291. https://doi.org/10.3390/electronics11203291

Alves, J.?C., & Mateus, G.?R. (2022). Multi-echelon supply chains with uncertain seasonal demands and lead times using deep reinforcement learning. arXiv preprint arXiv:2201.04651. https://arxiv.org/abs/2201.04651

Balachandra, K., Perera, H.?N., & Thibbotuwawa, A. (2020, February). Human factor in forecasting and behavioral inventory decisions: A system dynamics perspective. In International Conference on Dynamics in Logistics (pp. 516–526). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-44783-0_48

Baldwin, R., & Freeman, R. (2022). Risks and global supply chains: What we know and what we need to know. Annual Review of Economics, 14(1), 153–180. https://doi.org/10.1146/annurev-economics-051420-113737

Boppiniti, S.?T. (2019). Machine learning for predictive analytics: Enhancing data-driven decision-making across industries. International Journal of Sustainable Development in Computing Science, 1(3).

Cheng, L., Varshney, K.?R., & Liu, H. (2021). Socially responsible AI algorithms: Issues, purposes, and challenges. Journal of Artificial Intelligence Research, 71, 1137–1181. https://doi.org/10.1613/jair.1.12814

Choi, K., Yi, J., Park, C., & Yoon, S. (2021). Deep learning for anomaly detection in time-series data: Review, analysis, and guidelines. IEEE Access, 9, 120043–120065. https://doi.org/10.1109/ACCESS.2021.3107975

Chouhan, M., & Srivastava, D.?K. (2022). COVID-19 outbreaks challenges to global supply chain management and demand forecasting on SCM using autoregressive models. In Innovative Supply Chain Management via Digitalization and Artificial Intelligence (pp. 99–117). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-19-0240-6_7

Czapaj, R., Kaminski, J., & Soltysik, M. (2022). A review of auto-regressive methods applications to short-term demand forecasting in power systems. Energies, 15(18), 6729. https://doi.org/10.3390/en15186729

Dahlberg, E.?A., & Vangdal, E. (2022). The impact of COVID-19, the Suez Canal blockage and the War in Ukraine on the container shipping industry and an analysis of how to prepare for disruptions in the future (Master’s thesis, Handelshøyskolen BI).

Dassanayake, W. (2022). Critical comparison of statistical and deep learning models applied to the New Zealand Stock Market Index (Doctoral dissertation, UNITEC Institute of Technology).

Dora, M., Kumar, A., Mangla, S.?K., Pant, A., & Kamal, M.?M. (2022). Critical success factors influencing artificial intelligence adoption in food supply chains. International Journal of Production Research, 60(14), 4621–4640. https://doi.org/10.1080/00207543.2021.1959665

Elsaraiti, M., & Merabet, A. (2021). A comparative analysis of the ARIMA and LSTM predictive models and their effectiveness for predicting wind speed. Energies, 14(20), 6782. https://doi.org/10.3390/en14206782

Feix, T., & Feix, T. (2020). Embedded M&A strategy. In End-to-End M&A Process Design: Resilient Business Model Innovation (pp. 31–107). https://doi.org/10.1007/978-3-658-30289-4_2

Francis Onotole, E., Ogunyankinnu, T., Adeoye, Y., Osunkanmibi, A.?A., Aipoh, G., & Egbemhenghe, J. (2022). The role of generative AI in developing new supply chain strategies: Future trends and innovations. International Journal of Modern Research in Engineering, 3(1), 638–646. https://doi.org/10.54660/.IJMRGE.2022.3.1-638-646

Hangl, J., Behrens, V.?J., & Krause, S. (2022). Barriers, drivers, and social considerations for AI adoption in supply chain management: A tertiary study. Logistics, 6(3), 63. https://doi.org/10.3390/logistics6030063

 

Kalusivalingam, A.?K., Sharma, A., Patel, N., & Singh, V. (2020). Optimizing industrial systems through deep Q-networks and proximal policy optimization in reinforcement learning. International Journal of AI and ML, 1(3).

Kalusivalingam, A.?K., Sharma, A., Patel, N., & Singh, V. (2020). Enhancing supply chain visibility through AI: Implementing neural networks and reinforcement learning algorithms. International Journal of AI and ML, 1(2).

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).

Khalifa, N., Abd Elghany, M., & Abd Elghany, M. (2021). Exploratory research on digitalization transformation practices within supply chain management context in developing countries specifically Egypt in the MENA region. Cogent Business & Management, 8(1), 1965459. https://doi.org/10.1080/23311975.2021.1965459

Khan, R.?S., Sirazy, M.?R.?M., Das, R., & Rahman, S. (2022). An AI and ML-enabled framework for proactive risk mitigation and resilience optimization in global supply chains during national emergencies. Sage Science Review of Applied Machine Learning, 5(2), 127–144.

Khanidahaj, Z. (2018). Deep learning and reinforcement learning for inventory control. Ecole Polytechnique, Montreal, Canada.

Li, L., Wang, Z., Ye, F., Chen, L., & Zhan, Y. (2022). Digital technology deployment and firm resilience: Evidence from the COVID-19 pandemic. Industrial Marketing Management, 105, 190–199. https://doi.org/10.1016/j.indmarman.2022.06.002

Mao, Y., Wang, D., Muller, M., Varshney, K.?R., Baldini, I., Dugan, C., & Mojsilovic, A. (2019). How data scientists work together with domain experts in scientific collaborations: To find the right answer or to ask the right question? Proceedings of the ACM on Human-Computer Interaction, 3(GROUP), 1–23. https://doi.org/10.1145/3361118

Modgil, S., Gupta, S., Stekelorum, R., & Laguir, I. (2022). AI technologies and their impact on supply chain resilience during COVID-19. International Journal of Physical Distribution & Logistics Management, 52(2), 130–149. https://doi.org/10.1108/IJPDLM-12-2020-0434

Rimita, K.?N. (2019). Leader readiness in a volatile, uncertain, complex, and ambiguous (VUCA) business environment (Doctoral dissertation, Walden University). https://doi.org/10.5590/JOSC.2020.12.1.02

Scheibe, K.?P., & Blackhurst, J. (2018). Supply chain disruption propagation: A systemic risk and normal accident theory perspective. International Journal of Production Research, 56(1–2), 43–59. https://doi.org/10.1080/00207543.2017.1355123

Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2021). Accelerating retail supply chain performance against pandemic disruption: Adopting resilient strategies to mitigate the long-term effects. Journal of Enterprise Information Management, 34(6), 1844–1873. https://doi.org/10.1108/JEIM-07-2020-0286

Shea, T.?M. (2019). Dynamic decisions. University of California, Merced.

Shneiderman, B. (2020). Bridging the gap between ethics and practice: Guidelines for reliable, safe, and trustworthy human-centered AI systems. ACM Transactions on Interactive Intelligent Systems (TiiS), 10(4), 1–31. https://doi.org/10.1145/3419764

Tarigan, Z.?J.?H., Siagian, H., & Jie, F. (2021). Impact of internal integration, supply chain partnership, supply chain agility, and supply chain resilience on sustainable advantage. Sustainability, 13(10), 5460. https://doi.org/10.3390/su13105460

Wang, C., Wang, J., Shen, Y., & Zhang, X. (2019). Autonomous navigation of UAVs in large-scale complex environments: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 68(3), 2124–2136. https://doi.org/10.1109/TVT.2018.2890773

Yan, Y., Chow, A.?H., Ho, C.?P., Kuo, Y.?H., Wu, Q., & Ying, C. (2022). Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities. Transportation Research Part E: Logistics and Transportation Review, 162, 102712. https://doi.org/10.1016/j.tre.2022.102712

Zeng, Y., & Klabjan, D. (2019). Online adaptive machine learning based algorithm for implied volatility surface modeling. Knowledge-Based Systems, 163, 376–391. https://doi.org/10.1016/j.knosys.2018.08.039


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