Samsung and Best Buy: collaborative retail replenishment

Case study · Consumer electronics and retail

Samsung implemented a Collaborative Planning, Forecasting and Replenishment (CPFR) framework with Best Buy using the RoadMap GPS Planning Suite, shifting from shipment-based forecasting to consumption-based planning and reducing supply chain volatility across high-revenue retail accounts.

The challenge

For Samsung, major retailers like Best Buy represent concentrated revenue channels where misalignment carries significant financial risk. Retail order volatility masked true consumer demand, and manual forecast overrides introduced bias. Inventory imbalanced between distribution centers and stores, factory shipment forecasts diverged from POS demand, and replenishment delays compounded during peak promotional periods.

Without structured collaboration, Samsung’s supply chain reacted to purchase orders rather than consumption signals. This created bullwhip-effect amplification, excess inventory in slow periods, lost sales during high-velocity cycles, and margin compression from expediting and mark-down costs.

The solution

Samsung implemented a structured CPFR framework with Best Buy using the RoadMap GPS Planning Suite as the centralized demand planning and supply chain decision layer. The system integrated retail POS data, promotion calendars, inventory position by DC and store, and sell-through metrics into a single statistical baseline. Forecasts were anchored in downstream consumption data rather than historical shipments.

A joint business planning cadence brought weekly forecast alignment reviews, exception flagging for high-variance SKUs, and executive-level alignment on high-revenue categories. Override governance tracked Forecast Value Added impact, ensuring manual adjustments improved rather than degraded accuracy.

• Multi-level forecasting: SKU, DC, store, and channel
• Statistical baseline generation with seasonality decomposition, promotional lift, and lifecycle analysis
• Consumption-based forecasting anchored to POS sell-through data
• Shipment-to-consumption reconciliation with structured override governance
• FVA measurement at SKU-location granularity
• Weekly collaborative alignment process with exception-based management

Results

• Improved retail forecast alignment between Samsung factory shipments and Best Buy sell-through
• Reduced forecast bias through statistical baselines and override governance
• Improved forecast accuracy at SKU-location level across promotional cycles
• Reduced stockouts during promotional windows
• Optimized safety stock buffers in high-velocity categories
• Improved inventory turns across Best Buy distribution centers
• More reliable in-stock performance for Best Buy, reducing overstocks post-promotion
• Reduced expediting costs and improved working capital efficiency for Samsung

Key takeaways

• Forecast consumption, not just shipments. Anchoring supply plans to POS data removes the lag and distortion that purchase orders introduce.
• CPFR is a risk mitigation framework, not an operational nice-to-have. When a single retail partner drives concentrated revenue, forecast misalignment is a strategic risk.
• Override governance must be measured. Tracking FVA at SKU-location level turns override discipline from a policy into a performance metric.
• Weekly collaborative alignment at the exception level, not the full SKU list, is what makes joint planning sustainable at scale.