Stock Analytics — E-Commerce
Merging sales and stock data to surface dead inventory, prioritise discounting, and turn warehouse holdings back into cash.
Typical engagement
Across SKU, channel, season
Achieved in recent work
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The Situation
Cash is locked in the warehouse, and nobody is sure how much. The ERP holds stock data. The e-commerce platform holds sales data. The buyers operate from gut feel and last-season patterns. The CFO knows that inventory is too high; nobody can say which lines are the problem.
Some SKUs haven’t moved in 200 days and are still being reordered. Some lines are quietly tying up six-figure positions because they were bought in volume during a forecast that didn’t play out. Discounting decisions are made by category, not by SKU, so good stock gets discounted alongside the dead stock.
Meanwhile the working capital line on the cash forecast is a flat assumption. The CFO knows it’s wrong but doesn’t have the data to make it right.
What we build
An analytics layer that joins the ERP’s stock view with the e-commerce platform’s sales view, at SKU level, and turns it into decisions.
Every SKU gets a velocity profile, an age profile, and a cash-recovery profile. We identify dead stock (SKUs that haven’t moved in 90, 180, 365 days), slow movers, seasonal mismatches, and over-ordered lines. We model the cash that’s recoverable at different discount levels and prioritise the SKUs where discounting releases the most cash for the smallest margin sacrifice.
The output isn’t a one-off report. It’s a live dashboard the buying team and the CFO work from — with weekly updates on stock age, recommended discount actions, and the cash impact of last week’s decisions.
Where the buying process itself is creating the dead stock, we flag the policy fixes — reorder rules, forecasting cadence, sign-off thresholds.
What you get
Stock value reduced materially. Recent work has cut stock by around 40%, with the released cash visible on the balance sheet within the engagement.
Better turnover. The stock that’s left turns faster, because the dead weight has been cleared and the buying patterns have been corrected.
Margin protected, not sacrificed. Targeted discounting on the right SKUs releases more cash and protects more margin than blanket category-level discounting.
A working capital line on the cash forecast that’s grounded in data, not in an assumption.
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