AI for the fashion industry

AI in fashion pays off where decisions are repetitive and data already exists: demand forecasting for new collections, size and colour curves, allocation and replenishment, clienteling and markdown planning. The prerequisite is not the model but the data: clean master data, sell-out history and reliable stock. Ocra Group implements these use cases on top of SAP and cloud data platforms, with on-shore delivery from Italy.

• Collection forecasting with no sales history relies on attribute similarity, not time series alone.
• Size and colour curves per store are usually worth more than a better aggregate forecast.
• Clienteling only works when store, e-commerce and customer care share one customer record.
• Markdown and pricing decisions need margin floors and rules, not a raw model output.
• Every use case must land inside SAP or the store systems, otherwise nobody uses it.