Founded in 1934, Meijer is the pioneer of the modern supercenter with 242 stores across the Midwest.

Every year, an estimated $10-30 million in assets are lost due to organized shoplifting. Meijer has identified behavior strongly associated with shoplifting, including short or long dwell times in high risk areas, leaving the store without passing through a point of sale, as well as leaving the store using employee or emergency exits.

However, Meijer stores do not have the manpower to watch and monitor every shopper who comes through their doors.

Our Reducing Shoplifting Using Machine Learning project automatically tracks Meijer shoppers throughout the store to identify suspicious behavior to prevent shoplifting.

Meijer has installed Mist wireless access points throughout their stores, which gives them the ability to track the general location of shoppers during their time in the store.

Our system tracks, in real time, the paths various shoppers take. It then uses machine learning to determine the probability that a given shopper is engaged in illegal shoplifting behavior.

If any suspicious activity is identified, the Meijer Asset Protection team receives an alert on their smartphone regarding the incident. The employee then uses that information to review the incident using the store surveillance system integrated into our desktop app. If an incident is confirmed to be shoplifting, the device number associated with the shoplifter is stored for future alerts.

Whenever a device that has previously engaged in shoplifting reenters a Meijer store, employees are notified and action can be taken to prevent further acts of shoplifting.

Our desktop app and mobile apps are written in C#. Our database is on Azure SQL. Our machine learning algorithm is written in Python and devices are tracked via Wi-Fi and Bluetooth using Mist access points.