So you stock what will sell, not what sold, for every SKU in every store.
We tell you how many units to order and the size mix quantity every store needs.
Two days. We measure the potential upside and plan how many of each SKU size every store needs, and when.
You reshuffle. SKU sizes move from the stores where they sit unsold into the stores that are short of them.
Ten days. We report the actual increase in units sold, and its financial impact, in absolute units against a baseline.
One sales extract. No system access, no integration, no personal data.
Talk to usA customer finds the SKU she wants. Her size is not there. Your staff go looking, in the stockroom, in another store, online. Often she buys nothing anyway. None of it reaches your data, because sales show what was sold, not what was wanted, so the next order repeats last time's quantity and the same size runs out again.
A US specialty apparel retailer: 4 SKUs across sizes 0 to 5, 23 stores, one 21-day selling cycle. We identified which sizes each store was missing. The retailer supplied them in full from elsewhere in its network of around 2,000 stores. The units sold. No additional buying was required.
On the SKUs in the pilot. Where you land depends mostly on how much of the right stock is available elsewhere in your network to move.
It records every size you sold. It cannot record the customer who wanted a 12, found none, and left. That is the number your planners have never had.
The units already exist somewhere in your network. The whole result comes from putting them where the demand is.
Three things separate this from a forecasting tool, and none of them is about better mathematics.
A sales record can only show what was available to buy. We rebuild the demand that was never recorded, because by then the size had gone.
You can forecast a style perfectly and still send the wrong sizes to the wrong stores. Every number we produce is for one SKU, in one size, in one store.
Instead of a single prediction to hit or miss, you get the range of demand that could occur, and you decide how much of it your stock should satisfy.
Four steps, and only two of them are yours. There is no system to replace, no integration to build and no new team to hire. What arrives is a list: this store, this SKU, these sizes, these quantities.
SKU, store, date and units sold, at the most granular level you hold. One file. No system access, no integration, no personal data.
For every SKU, store and size, we work out what would have sold if the size had been on the shelf, not only what did sell while it was there.
A plain list of quantities by store, SKU and size, ready to hand to your allocation team. No new screens to learn unless you want them.
You supply the sizes from wherever you hold them. We keep measuring units sold against the baseline, season after season.
This runs alongside whatever you already use. If your team already produces a forecast, we can build on theirs rather than replace it.
Every recommendation shows the demand estimate behind it, including the stores and sizes where we found nothing wrong.
Your planners keep the decisions. What changes is that they are working from demand rather than from a sales record that never saw it.
The same two business days, from the data side. No charge. Before we put a number on your business we look at what you hold. What comes back is a written statement of what we will commit to.
SKU, store, date and units sold, at the most granular level you hold, with size as a separate field if your SKU code does not already carry it. Point-of-sale data is ideal. Price if you can share it, and it is fine if you cannot.
No system access. No integration. No personal data. No inventory file. A single extract is enough to start.
Confirm what the file contains and at which level it resolves, then reconcile it against totals you already know, so we both know the data is complete before anyone relies on it.
The potential upside on your own data, and the plan, how many of each SKU size every store needs and when, with what we will commit to and what we will measure. If your data will not support a commitment, we tell you that before you spend anything.
Three steps, and you carry no cost until the results are in. The commercial terms are agreed in writing before the pilot begins, so there is nothing left to negotiate afterwards. One number decides it, and it is agreed before we begin.
You send one sales extract. We report, for every SKU, store and size in scope, what you sold against what you could have sold had the size been on the shelf, including where nothing is missing. You get the plan with it, how many of each SKU size every store needs and when.
2 business days · no chargeThe trigger, the commercial terms and the term go into one document, signed before you move a single unit. Nothing is owed unless the trigger is met.
Signed, not invoicedYou work the plan, moving sizes from the stores where they sit unsold into the stores that are short of them. Ten days after the reshuffle we report the actual increase in units sold, and its financial impact.
10 selling days · no new inventoryOn the SKUs in the pilot. That figure goes into the agreement before anything starts. We set it well below what we expect, so that agreeing to it costs you nothing. If a 10% increase would not change your decision, tell us now and we will not waste your time.
Absolute units, for each SKU, store and size in the pilot, over the ten selling days that follow the reshuffle, measured in the same stores against comparable SKUs agreed before we start. No averaging by store, no averaging by SKU.
Out-of-stock events at SKU and size level, before and after the reshuffle.
The pilot scope, the baseline, and the rule that any size you cannot supply is excluded from measurement, recorded at the time rather than afterwards.
The pilot covers a few SKUs in a few stores. Three products take it from there, one for moving stock, one for markdowns, one for planning the new season buy.
TDI Allocate sets how many units of each SKU size every store should hold, the same run as the pilot, applied to every store and every category you choose. A single correction drifts back within a few weeks, so we keep the mix right every week the season runs.
TDI Markdown watches every SKU size quantity in every store against its own demand estimate. When one is not selling to plan, it says when to discount and by how much, while the season can still recover the margin.
TDI Order plans how many units of each SKU size every store needs next season. Added up, that is the quantity to order. Broken out, that is what each store gets. One plan, so the buy and the allocation cannot disagree.
