Supply Chain Analytics using Python Objective: Leverage Python's data analytics capabilities to analyze supply chain data, extracting valuable insights on transit times, inventory, and costs.
Analytics and Visualization: Utilize Python for data extraction from MySQL, calculating average transit times, tracking inventory trends, and assessing cost distributions. Visualize findings using Matplotlib and Seaborn.
Impact: Python-driven insights empower decision-makers to optimize routes, manage inventory smartly, and allocate resources efficiently, resulting in a streamlined and cost-effective supply chain.