Predictive Forecasting for a Global Consumer Goods Manufacturer

Demand signals breaking down.

A global consumer goods manufacturer was operating in a category where demand patterns had become harder to read. Post-pandemic shifts in consumption, channel migration, promotional volatility, and new entrants had eroded the predictive power of the client's existing forecasting models.

Stock-outs in fast-moving SKUs were running alongside overstock in others, with margin and working capital both under pressure.

What was at stake.

Annual production planning, trade promotion investment, and inventory commitments across multiple markets all depended on a forecast the business no longer trusted. Leadership needed a more reliable view of demand across SKUs, channels, and geographies, and they needed it in time for the next planning cycle.

A predictive forecasting program built on multiple data layers.

SIS International Research built a predictive forecasting solution that combined the client's internal data with external market signals to produce SKU-level demand projections across priority markets.

An integrated data architecture.

We unified historical shipment data, retail point-of-sale feeds, pricing and promotion calendars, and category share data into a single modeling environment. Macroeconomic indicators, weather data, and consumer sentiment series were layered on top to capture external drivers.

A layered modeling approach.

The solution combined several techniques to balance accuracy with explainability:

1. Time-series decomposition to isolate trend, seasonality, and promotional lift
2. Machine learning models for SKU-level demand projection
3. Scenario modeling for price, promotion, and distribution changes
4. Cross-validation against held-out historical periods
5. Confidence intervals to flag SKUs and markets needing analyst review

Outputs the planning team could act on.

Forecasts were delivered through an interactive dashboard with rolling updates, SKU-level drilldowns, and scenario toggles, supported by a quarterly analyst review with the client's demand planning team.

A forecast the business could plan against.

Forecast accuracy improved materially across priority SKUs and markets, with the largest gains in promoted and seasonal items where the prior model had been weakest.

Decisions that moved.

The client used the new forecast to:

1. Rebalance production allocation across plants and SKUs
2. Refine trade promotion investment toward higher-lift activities
3. Reduce safety stock in stable SKUs and raise it where volatility warranted
4. Align S&OP conversations around a single, defensible demand view

An embedded planning capability.

The forecasting solution became a standing input into the client's S&OP process, with quarterly recalibration. Working capital tied up in inventory came down over the first year, and stock-out incidents in priority SKUs fell meaningfully without an offsetting rise in overstock.