Cohort: 3

Members: Linfeng Sun, Shuaizhou Wang, Jinyi Wang (Xuchang Pang Donglai), Richard Wong, Liang Tang, Feibai Zhu, Jingquan Yu, Shelley Hofer (Mettler Toledo) 

Problem: Supermarkets run up to three disconnected manual workflows within each station that do not talk to each other. In the back room, packaging machines require constant readjustment for every product, wasting film and increasing food-safety risks. At the fresh food counter, staff memorize thousands of products. At checkout, queues build up while cashiers look items up by hand. The result is higher costs, slower service, and low customer satisfaction.

Approach: A cloud-edge AI system spans all three stations. AI-powered auto-tuning packaging machines, counter scales and checkouts each recognize products with 99% accuracy by combining camera images, weight readings and dimensional data, based on the algorithm purposely designed to resist contamination. A central layer monitors every device, and the cloud continuously retrains the shared model across all stores while filtering out faulty data from individual devices.

Result: Checkout time per item dropped 60%, film waste dropped 20%, packaging pass rates rose 18 percentage points, and pre-pack machine downtime fell 30-60 minutes per store daily.