Getting sell-out data via EDI: SLSRPT, INVRPT and what to do with them
Sales and inventory reports from retail partners show brands what actually sells, store by store and size by size. How the two EDI messages work and how merchandising teams can use them for re-orders.
KEY TAKEAWAYS Summary by the editors
- SLSRPT is the EANCOM sales data report: GS1 defines it as a message for reporting basic sales data with location, time period, product identification, pricing and quantity.
- INVRPT is the EANCOM inventory report, covering actual stock as well as planned or target levels such as minimum, maximum and reorder point stock.
- GS1 states that SLSRPT should never replace business transactions such as purchase orders or inventory reports.
- Sell-out and stock data at store, GTIN and week level allow brands to spot size gaps, propose re-orders and plan the next season's buy with evidence.
- The value depends on data agreements and quality: which stores, how often, how returns are treated and whether product identifiers match the brand's master data.
Sell-out data via EDI usually arrives in two EANCOM messages: SLSRPT, the sales data report, and INVRPT, the inventory report. Together they tell a brand how many units of each GTIN a retailer sold in each location and period, and how much stock remains. For merchandising and wholesale teams, that is the basis for evidence-based re-orders, replenishment proposals and better assortment decisions in the next buy.
What is the SLSRPT message?
GS1's EANCOM manual defines SLSRPT as a message enabling companies to exchange or report electronically basic sales data related to products or services, including the corresponding location, time period, product identification, pricing and quantity information. It is used between sellers and their suppliers, or from distribution centres to third parties for statistical purposes. Because data volumes are high, GS1 emphasises coded identification of products and locations. It also sets a clear limit: the message should never be used to replace business transactions such as purchase orders, delivery schedules or inventory reports.
Fashion-specific profiles exist. GS1 Austria publishes a SLSRPT profile for the fashion sector, recommended by the FashionAustria initiative, in which stores are identified by 13-digit Global Location Numbers, products by GTIN and sales quantities are reported per period.
What is the INVRPT message?
GS1 defines INVRPT as a message specifying information related to held inventories and planned or targeted inventories, usable in either direction between trading partners. Quantities can represent opening stock, actual stock, stock held for quality control, damaged stock and goods movements, and also planned levels such as model or target stock, minimum and maximum stock and reorder point. Data can be broken down by product, location and other classifications.
| SLSRPT (sales data report) | INVRPT (inventory report) | |
|---|---|---|
| Answers | What sold, where and when | What is in stock, where, and what the target is |
| Key data | GTIN, location, period, quantity, price | GTIN, location, stock quantity by type, target levels |
| Typical sender | Retailer to brand | Either direction |
| Main use for brands | Sell-through, size curves, re-order signals | Stock cover, replenishment, avoiding stock-outs |
| Not a substitute for | Purchase orders and inventory reports | Purchase orders |
How can merchandising teams use sell-out data?
Once sales and stock arrive per store, GTIN and period, several analyses become routine:
- Sell-through by style and colour, showing early winners and slow movers within the first weeks on the floor.
- Size curves per retailer or region, revealing where the ordered size mix differs from what customers buy.
- Stock cover, combining sales rate and remaining stock to show where a store will run out before the season ends.
- Re-order and replenishment proposals for never-out-of-stock items and in-season bestsellers, sent to the retailer for approval.
- Pre-order planning, using actual sell-out rather than sell-in to guide the next season's range and quantity recommendations.
This is where AI is most useful. Forecasting models can combine sell-out history with stock levels, calendar effects and the brand's own data to suggest re-order quantities per store and size. A brand can then present the retailer with a proposal grounded in the retailer's own data, which is a stronger basis for a conversation than a sales rep's impression.
What data quality problems should brands expect?
Sell-out data inherits every master data problem in the relationship. If the retailer's article numbers do not map to the brand's GTINs, sales cannot be attributed to styles and sizes. If store codes change or new stores are missing from the brand's location list, sales disappear from regional analyses. Other common issues are reports that net returns against sales without saying so, missing weeks, late files and inconsistent units. A first step is a reconciliation: compare the reported stock and sales with the brand's own deliveries to that retailer and investigate large gaps.
Timing is a further issue. Weekly reports that arrive days late lose much of their value for in-season decisions, and gaps in the series distort forecasts. Monitoring should therefore cover not just content but completeness: did every expected report arrive, for every agreed store, on time? An automated check that alerts the account team when a report is missing is simple to build and prevents decisions based on partial data.
How do you set up sell-out reporting with a retailer?
- Ask whether the retailer already sends SLSRPT and INVRPT to other suppliers, and request its implementation guide.
- Agree scope, frequency and data rules, ideally starting with a pilot group of stores.
- Map retailer identifiers to your GTINs and location list, and set up an exception report for unknown codes.
- Load the data into a reporting or planning environment linked to your sell-in and delivery data.
- Define the decisions the data should support, such as weekly re-order proposals, and share results with the retailer.
Is sell-out data via EDI worth the effort?
For brands with a small number of large wholesale accounts, it is often the only systematic view of end-customer demand in those doors. The effort lies less in the EDI connection than in mapping, data agreements and the discipline to act on the numbers. Where retailers cannot send EDI reports, portals or file exports can provide similar data, but the same rules on scope, identifiers and quality apply.
The data also changes the sales conversation. When a brand can show a retailer which sizes sold out first in which stores, and propose a re-order that fills those gaps, the discussion moves from opinion to evidence. That benefits both sides, provided the brand uses the data within the agreed terms and shares its analysis rather than keeping it to itself.
Frequently asked questions
What does SLSRPT stand for in EDI?
SLSRPT is the EANCOM and UN/EDIFACT sales data report message. It carries sales quantities, prices, locations and periods per product, usually from a retailer to a supplier.
What is the difference between SLSRPT and INVRPT?
SLSRPT reports what was sold in a period and location; INVRPT reports stock held and, optionally, target levels such as minimum, maximum or reorder point. Used together, they show sales rate and remaining stock cover.
Will retailers share sell-out data with fashion brands?
Many larger retailers do for key suppliers, but terms vary. Brands should agree scope, frequency, data rules and permitted use in writing before relying on the data.
How can AI use sell-out data for re-orders?
Forecasting models can combine sales history, current stock and calendar effects per store and size to propose re-order quantities. The proposals are only as good as the identifiers and completeness of the reports, so data checks come first.
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