This page documents where the forecasting sandbox fits in real planning work: SKU demand, stock cover, reorder points and replenishment priorities across regions, warehouses and categories.
The examples below show where this project can be used, what decision it supports, and how a user can test the scenario in the sandbox.
Where it fits: Forecast fast-moving SKUs by region and warehouse to reduce empty shelves and overstock.
Decision supported: Decide reorder quantities, transfer stock and set service levels.
How to test it: Load sample data, filter by category/warehouse, run a promo-spike scenario and review the action queue.
Where it fits: Estimate food, shelter, WASH or medical-kit demand before distributions in remote field locations.
Decision supported: Plan procurement and pre-positioning before access constraints or seasonal peaks.
How to test it: Filter by region and SKU, then compare stock cover and demand trend visuals.
Where it fits: Track demand for medicines, lab consumables or emergency stock against lead time and service requirements.
Decision supported: Reduce stockouts of critical items and justify safety-stock levels.
How to test it: Change service level settings and review reorder points for critical SKUs.
Where it fits: Forecast component consumption to avoid production delays while limiting excess raw-material inventory.
Decision supported: Align procurement with production schedules and supplier lead times.
How to test it: Upload a manufacturing-style CSV and map demand, stock and lead-time fields.
Where it fits: Forecast seed, fertiliser or equipment demand around planting and harvest cycles.
Decision supported: Support seasonal procurement, warehouse positioning and dealer allocation.
How to test it: Use seasonal sample rows or uploaded data and inspect trend/forecast visuals.
Where it fits: Compare stock cover across warehouses to identify transfer opportunities before urgent purchasing.
Decision supported: Reduce emergency freight and improve fulfilment SLAs.
How to test it: Open the action queue and sort P1/P2 escalations.
The examples above were revised against current industry and research references on how this class of analytics solution is used in practice.