Case Study · Supply Chain & Manufacturing

Advanced Weekly Depot-Level Sales Forecasting

Distribution · Seasonal SKUs · ML Forecasting · Depot-Level Analytics

The challenge

Unreliable, coarse forecasts.

Inaccurate demand forecasting

Predicting accurate sales volumes across multiple depots was unreliable — leading to overstock and stockout exposure.

Complex seasonal demand signals

Seasonality, regional events, and festival cycles created a complex, multi-variable demand environment.

No depot-level forecast granularity

Diverse datasets could not be integrated — no reliable weekly forecasts at individual depot level.

Static, outdated planning models

No regular model refresh cycle — forecasts quickly became stale and misaligned with actual market conditions.

What we built

Depot-level ML forecasting.

Depot-level forecasting model

Forecasting model built on historical sales and inventory data delivering reliable weekly demand signals per depot.

Customized forecasting logic

Forecasting logic tailored to different business areas — accounting for seasonality, festivals, and regional holidays.

Monthly model refresh cycle

Regular monthly updates ensuring ongoing accuracy, relevance, and alignment with shifting market conditions.

Data-centric planning transformation

Decision-making transformed from intuition to data — monthly forecasts now drive procurement and distribution strategy.

Results

Quantified outcomes.

Refined

Forecast accuracy across depots.

Market-Ready

Demand and supply chain preparedness assured.

Aligned

Production and marketing strategy with market demand.

Data-Driven

Monthly forecasts replacing gut-feel planning.

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