Samsung and Best Buy: collaborative retail replenishment

Executive summary

Samsung’s supply chain was reacting to episodic retail purchase orders rather than underlying consumer demand, generating bullwhip-effect amplification, post-promotion excess inventory, and stockouts during high-velocity product cycles.

To close the gap between factory shipments and retail sell-through, Samsung deployed a Collaborative Planning, Forecasting and Replenishment (CPFR) framework with Best Buy in partnership with RoadMap Technologies’ supply chain and inventory planning practice. Anchoring forecasts directly in downstream point-of-sale (POS) consumption data and instituting rigorous Forecast Value Added (FVA) override governance improved SKU-location replenishment precision, eliminated systematic forecast bias, and strengthened the strategic partnership between Samsung and Best Buy.

The challenge

For Samsung, tier-one retail partners such as Best Buy represent concentrated revenue channels where supply chain misalignment carries immediate financial and commercial risk. Across fast-moving consumer electronics categories characterized by rapid product introductions, steep holiday peaks, and aggressive promotional calendars, legacy planning processes struggled to keep supply synchronized with store-level demand:

• Purchase-order distortion and bullwhip amplification: Samsung’s upstream supply plans reacted to Best Buy purchase orders rather than actual register sell-through, amplifying order swings across factories and regional distribution centers.
• Divergence between factory shipments and POS velocity: Without a unified view of downstream store and distribution center (DC) inventory, factory shipment forecasts frequently decoupled from real consumer pull.
• Unmeasured manual override bias: Commercial and account teams applied manual forecast adjustments without empirical validation, compounding forecast error ahead of major promotional events.
• Margin compression and stockout exposure: Inventory imbalances between DCs and retail stores led simultaneously to lost sales during peak promotional windows, costly air-freight expediting, and post-event markdown exposure on aging models.

The solution

Samsung and RoadMap Technologies implemented a structured CPFR framework with Best Buy as the shared demand planning and replenishment decision workflow. Instead of forecasting from historical factory sell-in, the model ingested downstream retail POS sell-through, promotional calendars, and on-hand inventory by DC and store into a unified statistical baseline.

CPFR Architecture Layer	What RoadMap Delivered	Operational & Commercial Impact
1. Consumption-Anchored Statistical Baselines	Integrated Best Buy POS sell-through, store/DC inventory positions, and promotional calendars into automated statistical models capturing seasonality, promotional lift, and short product lifecycles	Grounded the entire replenishment chain in real consumer demand rather than lagged purchase orders
2. Multi-Echelon SKU-DC-Store Reconciliation	Generated synchronized forecasts across item, store, distribution center, and national channel levels with automated sell-in to sell-through reconciliation	Connected Best Buy store shelf availability directly to DC replenishment and Samsung factory allocation
3. SKU-Location FVA & Override Governance	Measured Forecast Value Added (FVA) at the SKU-location level, auditing every manual commercial or planner adjustment against the statistical baseline	Eliminated systematic optimism bias and ensured manual overrides only persisted where they demonstrably improved accuracy
4. Exception-Driven Weekly CPFR Cadence	Automated variance alerts flagged high-deviation SKUs and upcoming promotional items for weekly joint business planning reviews between Samsung and Best Buy	Focused collaborative planning sessions on high-impact exceptions and strategic revenue categories

Core capabilities delivered

• Multi-echelon hierarchical forecasting: Synchronized statistical models operating across SKU, store, distribution center, and account channel levels.
• Seasonality, lift, and lifecycle decomposition: Automated separation of baseline velocity, holiday seasonality, promotional lift, and rapid electronics product transitions.
• POS consumption-based demand modeling: Direct ingestion of retail sell-through and sell-through-to-inventory ratios as the primary forecasting signal.
• Sell-in to sell-through reconciliation: Closed-loop mapping between downstream POS consumption, retailer DC inventory targets, and upstream factory shipments.
• Granular FVA measurement: Automated tracking of Forecast Value Added at SKU-location granularity to govern manual overrides.
• Weekly exception-based CPFR workflows: Structured collaborative review queues highlighting high-variance SKUs and promotional readiness risks.

Results & business impact

Anchoring the Samsung and Best Buy CPFR partnership in consumption-based statistical forecasting transformed joint replenishment performance across both organizations:

• Tighter sell-in to sell-through alignment: Synchronized Samsung factory shipment schedules directly with Best Buy store and ecommerce sell-through velocity.
• Reduced forecast bias: Statistical baselines paired with SKU-location FVA tracking eliminated recurring manual override bias across account teams.
• Higher promotional SKU-location accuracy: Econometric lift and seasonality decomposition improved forecast precision across major holiday and promotional cycles.
• Fewer promotional stockouts: Proactive visibility into store and DC inventory positions protected on-shelf availability during peak promotional windows.
• Optimized high-velocity safety stock: More reliable consumption signals allowed planners to right-size safety stock buffers on flagship SKUs without risking service levels.
• Faster Best Buy DC inventory turns: Balancing inventory across regional distribution centers and stores improved overall retail inventory velocity.
• Lower post-promotion overstock risk: Modeling both promotional lift and post-event demand tapering prevented excess inventory buildup as models transitioned.
• Reduced expediting and stronger working capital efficiency: Smoother replenishment schedules cut rush logistics costs and improved working capital deployment for Samsung.