The session brought together leadership from Lindex, Kappahl, Nilson Group, Fjällräven, Lager 157 and Nudie Jeans, in conversation with Jeroen Struycken, Head of Strategy & Alliances at Nedap Retail, and Peter Groenland, Enterprise Account Executive at Nedap Retail. Following our luncheon in Stockholm in June, the conversation went deeper into the practical side: what it actually takes to move from uncertain stock data to a foundation the whole business can trust, and what that foundation unlocks. Below are key insights that emerged from the conversation.
Key takeaways
1. The business case is the top line, not the time saved.
Several brands entered their pilots expecting efficiency in stock counts to carry the investment. The numbers told a different story. Better replenishment, driven by better data, is what moves the business case, and by a wide margin.
2. It is a change project before it is a technology project.
First attempts tend to stall when a single function owns them. The projects that succeed are backed by management, involve everyone from production offices and labelling to warehouse and store staff, and treat the education of store teams as decisive rather than as an afterthought.
3. Engaged store teams keep the accuracy high.
When store staff understand what the data is for and see the results, such as knowing exactly what to refill each morning, accuracy becomes something they take ownership of. Without that buy-in, the technology alone does not hold.
4. The ERP stays. What is missing is the real-time layer.
The room agreed that the ERP remains the system of record for stock and prices. The gap is the layer in between: real-time, item-level data with context, able to serve different needs with different levels of certainty, from financial reporting to store replenishment to promising a product online.
5. The foundation comes before AI.
Inaccurate inventory data has always been a problem. AI and agentic commerce simply make it impossible to ignore. Any tool that recommends, promises or orders on behalf of a customer is only as good as the stock data underneath it.
