Supply Chain Case Study 2: Demand Sensing Engine

Warehouse operations with palletized inventory and demand planning activity

Context

Forecast variance caused overstock in low-demand locations and shortages in high-demand stores, impacting margin and service levels.

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

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