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Use Case · Inventory Optimisation

Allocate Stock From Demand Signals, Not Spreadsheet Estimates.

Overstock destroys margin. Stockouts destroy relationships. Both happen when allocation is based on quarterly averages instead of real-time velocity data. FIRE captures structured demand signals from every buyer interaction and turns them into inventory intelligence that compounds with every cycle.

30%average overstock reduction with velocity-based allocation
3xfaster stockout detection vs quarterly review cycles
10+connected products feeding demand signals
3cycles to AI-powered allocation recommendations
The Problem

Your Allocation Model Runs on Guesswork Disguised as Data

Overstock Burns Margin

Producing what someone guessed the market needs — instead of what velocity data shows it needs — creates overstock that requires markdowns, storage, and write-offs. Every unit produced without demand evidence is a margin risk.

Stockouts Kill Relationships

When a buyer wants to reorder and the product is unavailable, they do not wait — they switch. One stockout damages trust. Two stockouts lose the listing. By the time the quarterly report shows the problem, the buyer has already moved on.

Planning on Monthly Averages

Monthly sales averages hide the signals that matter. They mask channel-specific velocity differences, seasonal acceleration patterns, and promotional demand spikes. Allocating inventory from averages is like driving using only the rear-view mirror.

The FIRE Approach

Balance Supply and Demand With Real Signals

Click each scenario to see how structured data changes the allocation decision.

Without Data
After Cycle 1
After Cycle 2
After Cycle 3 + AI
Overstock Risk
Balance
Demand Alignment

Allocation Based on Guesswork

Without structured demand data, inventory allocation relies on last year's numbers adjusted by gut feel. Overstock averages 30-40% on new launches. Stockouts appear weeks after the damage is done. Channel-specific demand differences are invisible.

~35%Overstock Rate
WeeksStockout Detection
0Demand Signals Used
Key Capabilities

How FIRE Makes Inventory Allocation Intelligent

Velocity-Based Allocation

Allocate stock based on real rotation velocity per SKU, per channel, per region. High-velocity products in high-frequency channels get more stock. Slow movers get flagged before they become overstock.

Stockout Early Warning

When reorder velocity accelerates beyond forecast, FIRE flags stockout risk in real time — not in next month's report. Your operations team intervenes before buyers experience unavailability.

Channel-Specific Planning

Different channels consume inventory at different rates. FIRE reveals these channel-specific velocity patterns so you can allocate by actual demand per channel — not split evenly across a spreadsheet.

Seasonal Demand Prediction

Two cycles of structured data creates year-over-year comparison. Three cycles enables seasonal prediction. FIRE shows how demand shifts by season, by channel, and by product configuration — evidence for production planning.

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