Context
Forecast variance caused overstock in low-demand locations and shortages in high-demand stores, impacting margin and service levels.
Supply Chain Case Study 2: Demand Sensing Engine
Innovation
Deployed AI demand sensing using sales, weather, and promotion signals integrated with ERP planning cycles and replenishment triggers.
Outcome
Forecast accuracy improved by 21%, carrying costs declined, and store-level stock availability became more stable.
Executive Analysis
Senior Research Review
Evidence shows blended signal quality was the primary value driver, especially where promotion metadata was normalized weekly.
- Method: signal ablation and store-cluster comparison
- Key KPI shift: lower forecast error in volatile categories
- Recommendation: institutionalize data quality scorecards per category
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Domain: Supply Chain
Reference: Supply Chain Case Study 2: Demand Sensing Engine
- Delivery model: Senior-only architecture and implementation team
- Security baseline: AES-256, Zero-Trust IAM, VPC isolation
- SLA baseline: 4-hour critical response pathway