For Supply Chain Analysts ·
What you'll accomplish
By the end of this guide, you'll be able to write Python analytics code for supply chain tasks — ABC inventory analysis, safety stock calculations, demand forecast models — using GitHub Copilot to generate and explain the code, even if you're not a programmer. You'll get real analytical work done without knowing Python fluently.
What you'll need
What you should see: A Copilot icon in the VS Code status bar, and a chat panel available in the sidebar.
Click the Copilot icon (speech bubble with a sparkle) in the VS Code sidebar. This opens the Copilot Chat panel — a conversation interface where you can describe what you want to build.
In the Copilot Chat panel, describe the analysis you want to perform in plain English. Be specific about:
Example: "I have a CSV file called inventory.csv with columns: SKU, Description, Annual_Units_Sold, Unit_Cost, Current_Stock, Avg_Lead_Time_Days. Write Python code to perform an ABC-XYZ analysis. ABC: A items = top 20% of SKUs by annual revenue (Units × Cost), B = next 30%, C = bottom 50%. XYZ: X = items with forecast CV less than 0.5 (stable demand), Y = CV 0.5-1.0, Z = CV greater than 1.0 (high variability). I'll need the CV data computed from a second file called demand_history.csv with columns SKU, Week, Units_Sold."
Copilot will write the Python code in the chat. Click Insert to add it to a new Python file. Review it for obvious errors. Press F5 (or click Run) to execute.
If there are errors, paste the error message back into the Copilot Chat and say "Fix this error." Copilot will diagnose and correct the code.
Safety stock calculation: "Write Python to calculate safety stock for each SKU. Formula: safety stock = Z-score × standard deviation of demand × square root of lead time. I have CSV with: SKU, Avg_Daily_Demand, Demand_StdDev, Lead_Time_Days. Use Z=1.65 for 95% service level."
Demand trend analysis: "Write Python to analyze demand trends. I have weekly demand data CSV: SKU, Week, Units_Sold. For each SKU, calculate: mean demand, standard deviation, coefficient of variation, and whether demand is trending up/flat/down based on linear regression slope."
Supplier on-time performance: "Write Python to calculate supplier OTIF from a CSV with columns: PO_Number, Supplier, Promised_Date, Actual_Delivery_Date, Ordered_Qty, Received_Qty. Calculate OTIF by supplier (on time = within 0 days late; in full = received >= 95% of ordered) and export a scorecard CSV."
Inventory turnover report: "Write Python to calculate inventory turns and days of supply from: inventory_value CSV and cost_of_goods_sold CSV. Output: by SKU and by category. Flag items with turns below 4x annually."