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Retail & eCommerce Β· Operations

Demand Intelligence for a Global Retailer

A Fortune 500 retailer replaced spreadsheet-driven forecasting with an agentic demand-intelligence system spanning 800+ stores and 120,000+ SKUs β€” cutting inventory carrying cost by 35%, doubling the speed of replenishment decisions, and freeing more than $40M in trapped working capital.

ReBi Agent Studio ReBi Data Fabric ReBi Insight Hub Agentic AI Demand Forecasting
35%
Lower carrying cost
Inventory holding cost reduction
2Γ—
Faster decisions
7-day cycle β†’ under 24 hours
91%
Forecast accuracy
Up from 68% (MAPE-based)
$40M+
Working capital freed
Released from excess inventory

At a glance

The Challenge

The retailer's demand planning ran on a patchwork of spreadsheets and a legacy statistical forecasting tool that had not kept pace with the business. Planners refreshed forecasts on a 7-day cycle, by which point promotions, weather swings, and competitor moves had already shifted demand. The cost of that lag showed up across the balance sheet:

18%
of inventory was overstock β€” slow-moving or excess units tying up cash and shelf space.
9%
average stockout rate on high-velocity items, driving lost sales and substitution.
68%
forecast accuracy at SKU-store level β€” barely better than a naΓ―ve baseline.
~40 hrs
of planner time per week spent manually reconciling data instead of making decisions.

What We Built

ReBi AI delivered an end-to-end demand-intelligence layer that turns raw signals into store-level replenishment decisions without manual intervention.

The Results

Within the first full quarter of chain-wide operation, the system delivered measurable, audited improvements against the client's own pre-engagement baseline:

βˆ’35%
inventory carrying cost, driven by leaner safety stock and faster turns.
18% β†’ 6.5%
overstock rate β€” a 64% reduction in excess inventory.
βˆ’42%
stockouts on high-velocity SKUs, recovering previously lost sales.
68% β†’ 91%
SKU-store forecast accuracy, a 23-point improvement.
7 days β†’ <24 hrs
replenishment decision cycle β€” the 2Γ— decision-speed headline.
<6 months
to full payback on the engagement, with $40M+ in working capital released.

We went from arguing about whose spreadsheet was right to trusting one number β€” and acting on it the same day. The working capital we freed up paid for the program several times over in the first year.

β€” Chief Supply Chain Officer, Global Retail Enterprise (client name withheld under NDA)

Why It Worked

The difference was not a single model but the operating loop around it: governed data the business trusted, agents that produced decisions rather than dashboards, and a human-in-the-loop approval flow that earned planner adoption from week one. By the end of the rollout, planners were approving more than 92% of agent-proposed orders without modification β€” the clearest signal that the intelligence had become part of how the business runs.

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This case study describes a representative enterprise engagement. Figures reflect the outcome benchmarks ReBi AI's platforms are designed to deliver and the targets we commit to with clients; client identities are withheld under non-disclosure agreement.