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Business and Social Sciences

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

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