Automated Store Ordering to improve a supermarket’s inventory management

Where supply chain management and data science meet, interesting questions arise. In our Data2Move Research Stories, you’ll find out how students have managed to answer them.

This time: Bob van Beuningen’s master thesis on Automated Store Ordering versus Manual Store Ordering at Jumbo Supermarkten.

Ever since the Dutch retail market entered a fierce price war in 2003, retailers have continuously been looking for ways to save costs while maintaining the high service level that is demanded by customers.

That’s where, for instance, an Automated Store Ordering system comes in. This can reduce food waste, reduce stock outs, and can save employees a significant amount of work.

Where it started

Every one of Jumbo’s supermarkets relies on such an ASO system to predict the amount of goods that should be in stock on any given day. A challenge, however, is in the fact that 9% of the generated orders are manually adapted by store managers.

Jumbo wanted to find out why they make these adjustments. By finding out, the system could be changed in order to create a so-called hands-off policy, meaning adjustments would never be necessary.

Bob focuses on this in his thesis – and also delivers recommendations for Jumbo to judge the ‘correctness’ of the adjusted orders (i.e. whether the orders add more value or whether the adjustment only costs more money).


Bob conducted interviews and performed a logistic regression analysis. Three main reasons were found that actively cause managers to adjust the orders:

  • The product is on promotion
  • The product is on second placing (i.e. store managers have allocated extra shelf space, typically at the head of an aisle)
  • The inventory in the ASO system was incorrect

Additionally, it was tested if these order adjustments added value, meaning they were good for the company. First it turned out 75% of the adjustments meant a bigger order and 25% meant a smaller one. Results were that only 15% of ‘upwards adaptations’ added value and 65% of downward adaptations added value. What seemed to contribute to the latter was the question whether or not a product was perishable. For instance, downward adjustments in perishable products are more likely to add value.

“Store managers are more likely to add value for perishable products than for non-perishable products,” Bob wrote.

Further advice

In order to get better results, Bob advises Jumbo to change some of their Key Performance Indicators: “It is recommended to use the KPIs ‘process trustworthiness’, ‘added value of order adaptions’, and ‘order acceptance’ to move to a hands-off situation. It is important to use these KPIs to find out whether a specific store is able to move to a hands-off policy or not.”

“If an adaptation was correct, and thus added value, then the system should be able to recognize this and make the adaptation itself in the future. By means of these KPIs, Jumbo should be able to get more insights in what needs to be changed in the system in order to achieve this goal.”