Predicting the performance of business partners, using issue data of the iSense system : mapping a perception to data using machine learning

Nedap retail helps retailers in their diverse needs in loss prevention, stock management and store monitoring. Retailers use Nedap’s products to protect themselves against loss prevention, to manage their stocks and to monitor their stores. Nedap’s policy is to outsource specific activities like installation and maintenance of their devices to local business partners. Nedap does not have to enrol employees abroad and business partners are familiar with local legislation. iSense is a new alarm pedestal that detects and identifies goods passing the entrance of a retail store. Currently, the market is making a shift to the new iSense system. The research question is how to use emerging big data analysis to extract business partner performance information from the iSense messages. To answer this question, we use supervised machine learning with a continuous output. We used a questionnaire to obtain input labels and interviews with various experts to obtain the candidate features. Our model does predict the perception of the experts, however insights in the data show that these perceptions are not always correct. Using the opinions of the relevant parties as input values for a machine learning algorithm proved valuable to address problems and obtain insights.