Integrating Artificial Intelligence and Data Analytics for Supply Chain Optimization in the Pharmaceutical Industry

This inquire about examines the integration of Artificial Intelligence (AI) and information analytics to optimize supply chain forms within the pharmaceutical industry. Through tests and writing audits, the ponder investigates the adequacy of AI calculations counting Linear Regression, Random Forest Regression, K-Means Clustering, and Deep Learning Neural Systems over request estimating, stock optimization, generation planning, and coordination optimization. Results appear that Random Forest Relapse beats Direct Relapse in request determining with RMSE of 80.20, MAE of 60.75, R-2 of 0.90, and MAPE of 6.50%. K-Means Clustering recognizes five clusters for stock optimization. Profound Learning Neural Systems accomplish RMSE of 75.10, MAE of 55.30, R-2 of 0.92, and MAPE of 5.80% for generation planning. In coordination's optimization, Genetic Algorithm accomplishes a add up to fetched of $150,000 and conveyance time of 5 days compared to Mimicked Strengthening with $160,000 and 6 days. The research contributes to understanding the part of AI and information analytics in improving supply chain effectiveness, decreasing costs, and guaranteeing maintainability within the pharmaceutical segment.