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Retail, FMCG & E-commerce
Overstock and stockout are the same failure wearing different clothes.
Both are forecast errors, and both are expensive: one ties up working capital and ends in markdown, the other hands the sale to a competitor. Retail value concentrates in forecasting, pricing and recovering the demand that abandoned you mid-purchase.
−31%
stockoutsOn the top-selling SKU cohort
+23%
recovered basketsAbandoned carts converted
−22%
working capitalInventory held without service loss
The best-selling product that was out of stock every third week.
A distribution client's top SKU was unavailable roughly a third of the time. Their forecast was a rolling three-month average, which is a reasonable method for stable demand and a poor one for a product with promotional spikes, seasonal swing and a supplier whose lead time varied by eleven days.
Meanwhile their warehouse held nine months of cover on slower lines nobody had reviewed since launch. The same planning process was producing both problems, and the working capital tied up in the slow lines was roughly what the fast lines were losing in missed sales.
Forecasting at SKU, store and day level with promotion, seasonality and lead-time variability as features cut stockouts on the top cohort by 31% and released 22% of inventory value. Neither number required a single additional warehouse or supplier.
We had a stockout problem and an overstock problem, and it turned out they were one problem.
Six retail value pools worth measuring first.
High volume and thin margin make retail unusually responsive to small percentage improvements in forecasting and pricing.
Demand forecasting & replenishment
Forecast at SKU, store and day level with promotion, seasonality, weather and lead-time variability, feeding automated replenishment proposals.
Pricing & markdown optimisation
Price and markdown decisions optimised against margin and elasticity by segment, with brand and competitive guardrails respected.
Personalisation & basket growth
Recommendations and offers chosen on expected margin and repeat behaviour rather than on popularity, applied across web, app and till.
Cart & enquiry recovery
Detect abandonment intent and recover it through the right channel and message, including voice for high-value baskets and trade orders.
Store & footfall analytics
Traffic, dwell, queue length and conversion measured from existing cameras so staffing and layout decisions rest on evidence.
Assortment & space planning
Identify lines that consume space and capital without earning either, and quantify the margin opportunity in reallocating both.
What changes in a retail operation.
Each of these is written into the engagement as a number with an owner, a baseline and a review date.
OUTCOME 01
Working capital is released
Better forecasting reduces cover requirements on slow lines while improving availability on fast ones. The cash released is usually the fastest-realised value in the model.
OUTCOME 02
Margin improves without price increases
Optimising markdown timing, assortment and basket composition raises margin without a headline price change that customers would notice and resent.
OUTCOME 03
Demand stops escaping mid-purchase
Recovering abandoned baskets and unanswered trade enquiries converts demand you have already paid to acquire, which is why it consistently pays back fastest.
What a retail engagement delivers.
- Forecasting models at SKU, store and day level with promotion features
- Replenishment proposals integrated into your ERP or planning system
- Pricing and markdown recommendations with guardrails and test design
- Recovery journeys across email, messaging and voice for high-value baskets
- Store analytics from existing camera infrastructure
- Monthly value reporting on availability, inventory, margin and recovery
The engineering underneath.
Numbers from work already in production.
Measured against the baseline agreed with the client before the engagement started.
−31%
Stockouts on top cohortSKU-level forecasting with lead-time variability
−22%
Inventory value heldWorking capital released without service loss
+23%
Recovered basketsAbandonment detection and recovery
What retail and supply chain leaders ask.
- 01. Our data is messy. Can you still forecast?Usually yes. Retail data is reliably messy and the modelling accounts for it. What matters more is whether promotional and price history exists, since without it the largest driver of demand variation is invisible.
- 02. Will this replace our planning team?No. It changes what they spend time on — from producing forecasts to reviewing exceptions and making commercial judgements. Planner acceptance is the constraint on value, so we design for it explicitly.
- 03. How do you prove the margin improvement?Store or cohort holdouts. A comparable group keeps the existing approach so the improvement can be attributed rather than assumed, which matters in a sector where seasonality flatters everything.
- 04. Do you work with FMCG manufacturers as well as retailers?Yes. On the manufacturing and distribution side the value tends to concentrate in demand planning, trade promotion effectiveness and route-to-market execution.
Where to go next.
Give us two years of sales history. We will show you the capital sitting in your warehouse.
The assessment reviews forecast accuracy, availability and inventory cover, then quantifies the working capital and margin recoverable.
