ML Pipeline FMCG Logistics NGO Operations

Supply Chain Demand Forecasting

This sandbox helps planners test how demand history, stock levels, lead time and service targets affect replenishment decisions. It turns SKU-level data into forecasts, risk bands and reorder recommendations that a supply team can act on.

34%
MAPE Reduction
120+
SKUs Forecasted
24
Hourly Pipeline
95%
Inventory Accuracy

Problem Statement

Supply teams are often asked to balance two risks at the same time: stockouts that disrupt service and overstock that locks up working capital. This project demonstrates how a simple forecasting layer can make that trade-off visible by SKU, warehouse and region.

Business impact: Better forecasting can cut emergency replenishment costs and reduce stockouts for essential goods across distribution networks.

Pipeline Architecture

A planning flow from demand history and inventory data to forecasts, stock-cover checks, reorder points and an action queue.

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Data Consolidation
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Feature Engineering
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Model Ensemble
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Bias Calibration
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Dashboard & Alerts
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Operational Handoff

Use Cases & Applicability

This forecasting framework supports any organisation that needs accurate inventory or distribution planning across multiple SKUs.

Retail
Shelf-Level Replenishment
Predict demand for fast-moving products to reduce shelf stockouts and avoid excess seasonal inventory.
Logistics
Transport Planning
Use demand forecasts to optimise truck loads, reduce lead-time risk, and schedule just-in-time deliveries.
NGO Operations
Field Supply Allocation
Ensure medical supplies and food parcels are pre-positioned accurately for donor-led distribution campaigns.
๐Ÿ“„ Full Documentation ๐Ÿ”ฌ Methodology Deep-Dive ๐Ÿงช Try the Sandbox โ†’