marql · inventory · playbook 01
Free the working capital sitting in excess stock.
marql finds stock above target cover per SKU and location, prices the excess from your own cost data, and turns it into a plan with an owner, a deadline and a review date.
- ranked in €
- computed from source data
- effect only after measurement
Found
€2,728
cost of the excess
Screens from the Maison Marché demo tenant. The interface is shown in English in every market; the figures in it are generated.
01 · Situation
It is selling. The stock is still excessive.
At Maison Marché the mineral water is not dead stock: the last sale was today. But cover reached 110 days against a 7-day target. marql computed the excess against target stock, priced the capital tied up in it, and put the options on the table.
- 110
- days of cover
- 7
- target cover
- €2,728
- cost of the excess
- 35
- days deviating
02 · The cycle
From a deviation to a measured effect.
Signal, diagnosis, management action and result assessment are one process — with no gap between the analysis and the thing somebody actually does.
Excess stock is visible before the period closes.
A daily run over SKU × location finds where days of cover exceed the target and ranks the deviations by what the excess costs.
Automatic monitoring · SKU × location
It shows the cause, and the ways to act on it.
marql separates dead stock from a selling SKU with too much cover, reading sell-through, the weather factor and availability in the other locations. Then it offers: pause replenishment, accelerate sell-through with a promotion, transfer between locations, or change the threshold.
7 factors read · arithmetic checked against the data
The recommended quantity comes from an inventory model.
ShelfSense reads stock on hand, average daily usage, lead time and the target buffer. The AI explains the reasoning; the excess quantity and its cost are computed deterministically.
On hand 4,625.6 · forecast 39.2 units/day
An effect counts only after the control period.
Once an action is confirmed, marql fixes the baseline period and the measurement window. The Impact Ledger keeps «found», «measuring», «no effect», «not enough data» and «proven» apart — with no retroactive inflation of the result.
Baseline · control where available · change history
03 · Questions for the data
Start with the one that matters
Which SKUs are tying up working capital without turning fast enough?
- 01Where is the excess stock concentrated — by SKU, category and location?
- 02Is this dead stock, a slow-turning SKU, or a temporary surplus?
- 03Where do we pause replenishment, mark down, promote, or transfer?
- 04Which locations can take the SKU, given their demand and their own stock?
- 05How much working capital can be released without pushing up out-of-stocks?
- 06Draft the plan: action, owner, deadline and review date.
04 · Minimum data
What it takes to price the excess.
Sales by SKU × location
Quantity, revenue, date of the last sale, location and category.
Stock on hand + cost price
Without cost prices the excess quantity can still be counted, but the cost of the excess stock cannot be computed correctly — so the money column stays empty rather than estimated.
Lead time + stock on order
Supplier, open purchase orders, distribution centre, shelf life, and which transfers between locations are actually possible.
05 · The line proof stops at
Releasable potential
≠ a proven effect.
€2,728 is the computed cost of stock above the target level. It is not a realised saving and it is not marql's ROI.
A proven effect is recorded only after the action is executed, the baseline period is set and the control window closes. Where the data is insufficient or an outside factor distorted the result, the Impact Ledger marks the effect unproven.
Where these screens live
The four surfaces this decision passed through
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