Every season meant a fresh size-and-color matrix in spreadsheets, and three channels could oversell the same stock. With a major retailer requiring EDI, this distributor described the season it runs, styles in a matrix, per-account wholesale pricing, one available-to-sell number, and the AI built it into a system they own.
Every collection meant a fresh style-color-size matrix, built in spreadsheets at the start of each season. Shopify sold DTC, boutiques ordered wholesale by email and PDF, and a marketplace pulled from the same stock, so a size run could oversell across channels before anyone noticed.
Then a major retailer signed on and required EDI, with chargebacks for missed ship windows and broken size runs. Opening that account on the old stack meant another multi-week spreadsheet rebuild before a single compliant order could ship.
Styles in a style-color-size matrix with prepacks and ratio packs, per-account wholesale pricing and ship windows, one available-to-sell number across DTC and wholesale, and next season opened as a copy of this one.
Opser's AI built that flow into their own system the same week as a described change, not a custom-development project, with the data in their database. EDI for the new retail account shipped with it.
Opening a season went from a multi-week spreadsheet rebuild to an afternoon, and mid-season changes shipped in hours instead of waiting on a vendor.
A style-color-size matrix with one available-to-sell number across DTC, wholesale, and marketplace, so a size run cannot oversell.
Linesheets and B2B orders in-system with per-account pricing and ship windows, plus EDI for retail accounts, so email and PDF re-keying and missed-window chargebacks are gone.
Buys planned against variant-level demand and prepack ratios, not a flat guess.
Orders flow into invoicing and the ledger, ending the re-key into QuickBooks.