• Manya
• Dr. Pramod Kumar

This paper reviews the use of data mining algorithms to optimize the pharmaceutical supply chain (PSC), focusing on improving lead time prediction, inventory control, and demand forecasting. Techniques such as Random Forest and Artificial Neural Networks are compared based on accuracy, scalability, and computational efficiency. Results show that ensemble and deep learning methods outperform traditional models in predictive accuracy, though at higher computational cost. The study provides a practical framework for selecting suitable algorithms in pharmaceutical logistics, balancing performance with efficiency to enhance overall supply chain decision-making.

"Data Mining Algorithms For Pharmaceutical Supply Chain Optimisation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.d897-d902, July-2025, Available :http://www.jetir.org/papers/JETIR2507404.pdf

"Data Mining Algorithms For Pharmaceutical Supply Chain Optimisation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppd897-d902, July-2025, Available at : http://www.jetir.org/papers/JETIR2507404.pdf