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Demand Planning


Demand Planning plays a dominant role in the Business Plan and Strategy of every organization. Demand planning is complex and unique for every company.

Understocking (lost sales, OOS in FMCG!) and overstocking (dead inventory) are an inevitable part of every business. P&L Managers put all their efforts into minimizing both these costs.

It is more crucial for organizations dealing with products that have “Expiry Date”! The ballooning cost of “expiry date” linked unsellable inventory creates a scare amongst Product Managers as well as Supply Chain Managers. To liquidate “Inventory at Risk” based on Expiry Date becomes a nightmare for Sales Team.

We have developed Excel-based complex Demand Planning Models for several organizations. To give an idea of complexity, we share the column names for one such sheet.

  1. Country
  2. SKU
  3. Maximum Inventory Coverage (Months)
  4. Minimum Inventory Coverage (Months)
  5. Lead + Transit Time (Days)
  6. Minimum Order Quantity
  7. Pallet Size
  8. Model Month
  9. Model Month Opening Stock
  10. Model Month Trend Sales
  11. Model Month Closing Stock (without Stock in Transit)
  12. Stock Arriving during Model Month
  13. Stock Available for Next Month Sales
  14. Inventory Coverage Months starting Next Month
  15. Likely Date by Which Inventory will fall below Min Level
  16. Order Date so that Stock reach in time to maintain Minimum Inventory
  17. Likely Order Quantity
  18. Final Order Quantity based on Minimum Order Quantity and Pallet Size
  19. Likely Arrival Date

These columns are from one sheet only. A usual Model has 6 to 8 such complex sheets. Add to that the Master Tables.

To add to complexity, we have Market Dynamics at play. Numbers change based on

  1. Rolling Sales Forecast

  2. Windfall Institutional Order

  3. Extra Demand from Distributor inside the “No Change Allowed – Freeze” period.


Such solutions take several weeks to complete. In summary, our solutions:

1. Automate: Our solution automates the complete journey from inputs like Opening stocks, Goods-in-Transit, etc. to outcomes like Suggested Orders, Risky Inventory, Inventory Coverage, etc.

2. Complex Calculations: Implements intricate cumulative calculations in Excel for accurate projections.

3. Historical Data: If available and provided by the client, we use historical sales data, transit time, etc. to make the model more realistic.

4. Lean Model Approach: Data is such projects is voluminous. Similarly, formulas are complex and long. Both these factors increase Model file size, slows the Model, and at time “Times Out” the model! We have developed our own practice of Lean Model – we retain only the logic in the model file.

Please Email or WhatsApp us if you would you like to see a demo of this solution.