Case Study / 01 Predictive Inventory
$9.3 Million EBITDA Recovered.
A 31-month chronological backtest proving the impact of our Predictive Inventory module across 6,185 SKUs (35,389 product variants). Stop over-buying. Keep best-sellers in stock.
The Pain: Manual Planning Traps Capital.
Manual sheets lock up your cash. For a brand operating 6,185 SKUs (35,389 product variants) and generating $35.3 Million in annual revenue, legacy sheets could not keep up. Over-buying slow SKUs led to heavy markdowns and high logistics costs. Meanwhile, under-buying best-sellers meant constant stockouts across channels.
Static spreadsheet replenishment could not handle the complexity of Myntra, Ajio, Amazon, and direct retail. The brand needed a system to align supply chain orders with real-world sales.
The Fix: Continuous Demand Forecasting.
The brand deployed our AI Growth Engine to autonomously align inventory orders with live market demand. Moving away from static averages, the AI segmented SKUs into three dynamic velocity tiers:
- 01Tier A (High-Velocity): Maximizes gross margin by forecasting channel sales as trends take off.
- 02Tier B (Mid-Velocity): Balances stockout risk with holding costs to keep shelves full without over-committing capital.
- 03Tier C (Slow-Velocity): Prevents dead stock. If a SKU's sales probability is low, the engine scales future orders to zero, freeing up upfront cash.
The Results: $9.3 Million Saved.
A 31-month chronological backtest (October 2020 – April 2023) compared actual spreadsheet orders against Grainline's recommendations.
Key Performance Milestones:
• Net Margin Saved: $9.3 Million
• Monthly Lift: $300k / month
• Recovered Revenue: $9.7 Million in lost sales recaptured
• Return on Spend: Tier A stock adjustments yielded a 25x return on inventory spend.
Operational Deep Dive: The Static Data Fallacy
1. Stockout Masking
Static Data FallacyPlanners cap future demand at stock on hand. When a SKU sells out, static tools see zero sales, freezing replenishment and locking in stockouts.
Grainline SolutionGrainline automatically identifies stockout periods to reconstruct your actual demand, prompting orders before shelves go empty.
2. Multi-Dimensional Forecasting
Static Data FallacySpreadsheets rely on flat 3-month rolling averages, lagging behind seasonal spikes and rapid demand shifts.
Grainline SolutionGrainline tracks active data—from past sales and markdowns to size curves and channel-mix dynamics—to forecast proactively.
3. SKU-Level Adjustments
Static Data FallacyManual sheets apply category-level multipliers, diluting hot items and over-buying slow variants.
Grainline SolutionGrainline analyzes each SKU individually. Slow SKUs scale down to zero, saving upfront capital.
Performance Comparison
(M = Million, k = Thousand; Grainline metrics highlighted vs Legacy Spreadsheet baseline)
Stop leaving your margins to chance.
Deploy our AI Growth Engine to:
- Predict demand to eliminate stockouts.
- Calibrate PLA ad spend for maximum ROAS.
- Anticipate market shifts with real-time intelligence.