💊 Pharmaceutical Supply Chain: Inventory Risk & ROI Optimization

Balancing Patient Health and Business Profitability with Machine Learning

📝 Project Overview

In the pharmaceutical industry, inventory management is a high-stakes balancing act. This project uses Machine Learning to solve the dual challenge of Overstocking (which leads to expired drugs and financial loss) and Understocking (which impacts patient health).

🎯 Project Objectives

• Exploratory Data Analysis (EDA): Visualizing the gap between market demand and stock levels.
• Business Risk Engine: Feature engineering to instantly flag "Stock-out" or "Overstock" risks.
• Automated Strategy: A classification model that recommends the best Restocking_Strategy based on real-time metrics.

🛠️ Key Technical Features

• Risk Scoring: Custom algorithms to quantify supply chain vulnerabilities.
• ROI Focused: Models optimized not just for accuracy, but for financial impact.
• Actionable Insights: Turning complex data into clear procurement decisions.

💻 Tech Stack

• Language: Python
• Libraries: Pandas, Scikit-Learn, Seaborn, Matplotlib
• Environment: Jupyter Notebook / Kaggle

📊 Business Outcome

By implementing this model, pharmaceutical distributors can reduce operational waste by predicting demand patterns and automating the replenishment cycle, ensuring that the right medicine reaches the right patient at the right time.