Abstract: Contemporary firms rely heavily on the effectiveness of their supply chain management. Modern supply chains are complicated and unpredictable, and traditional methods frequently find it difficult to adjust to these factors. Increasing supply chain efficiency through improved supplier performance, demand prediction, inventory optimisation, and streamlined logistics processes may be achieved by utilising sophisticated data analytics and machine learning approaches. In order to improve supply chain management efficiency, this study suggests a unique data-driven strategy that makes use of Deep Q-Learning (DQL). The goal is to create optimisation frameworks and prediction models that can support well-informed decision-making and supply chain operational excellence. The deep Q learning technique is thoroughly integrated into supply chain management in this study, which makes it innovative. The suggested framework gives a comprehensive method for tackling the difficulties of contemporary supply chain management by integrating cutting-edge methodologies including demand forecasting, inventory optimisation, supplier performance prediction, and logistics optimisation. Predictive modelling, performance assessment, and data preparation are three of the proposed framework's essential elements. Cleansing and converting raw data to make it easier to analyse is known as data preparation. To create machine learning frameworks for applications like demand forecasting and logistics optimization, predictive modelling uses DQL. The method's efficacy in raising supply chain efficiency is evaluated through performance evaluation and acquired 98.9% accuracy while implementation. Findings show that the suggested DQL-based strategy is beneficial. Demand is precisely predicted using predictive models, which improves inventory control and lowers stockouts. Supply chain efficiencies brought about by DQL-based optimisation algorithms include lower costs and better service quality. Performance assessment measures show notable gains above baseline methods, highlighting the importance of DQL in supply chain management. This study demonstrates how Deep Q-Learning has the ability to completely change supply chain management procedures. In today's dynamic environment, organisations may gain competitive advantage and sustainable development through supply chain operations that are more efficient, agile, and resilient thanks to the incorporation of modern analytical methodologies and data-driven insights.

Shamrao Parashram Ghodake, Vinod Ramchandra Malkar, Kathari Santosh, L. Jabasheela, Shokhjakhon Abdufattokhov and Adapa Gopi, “Enhancing Supply Chain Management Efficiency: A Data-Driven Approach using Predictive Analytics and Machine Learning Algorithms” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150469

@article{Ghodake2024,
title = {Enhancing Supply Chain Management Efficiency: A Data-Driven Approach using Predictive Analytics and Machine Learning Algorithms},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150469},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150469},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Shamrao Parashram Ghodake and Vinod Ramchandra Malkar and Kathari Santosh and L. Jabasheela and Shokhjakhon Abdufattokhov and Adapa Gopi}
}