supply-chain-analytics

To calculate supply chain KPIs, use the provided Python code examples to compute metrics across procurement, inventory, warehouse, and transportation. This yields specific performance indicators like On-Time Delivery, Inventory Turns, and Cost per Order for your analysis.

You can build a logistics dashboard using the provided example code for Plotly and Streamlit. This generates executive and real-time monitoring dashboards that visualize your calculated supply chain metrics and track operational performance.

ABC-XYZ segmentation is an advanced analytics technique that categorizes inventory based on value and demand variability. It is used in supply chain analytics to identify critical stock items and tailor inventory management strategies to reduce holding costs.

Yes, you can perform cost-to-serve analysis using the advanced Python techniques included in the skill. This calculates the total cost associated with serving specific customers or channels, helping identify profitability gaps and optimize logistics operations.

Yes, the analytics approach covers descriptive, diagnostic, predictive, and prescriptive insights. It provides Python code examples to analyze historical SCOR metrics and generate forward-looking logistics recommendations, enabling comprehensive end-to-end supply chain visibility.