Quantitative Method

Yeecon Minimart plans to set up a chain of self-help mini-convenience stores which are 100% digitalised with no attendants or cashiers, and open at all times. Products are tagged with Radio Frequency Identification (RFID) chips and are placed in clusters according to categories such as beverages, toiletries, sundries, canned food, etc. There will be no perishables.

Customers will use a mobile app with their unique ID to check into the store and to pay for products that they have checked out from the store. There is no checkout counter. Instead, a network of strategically located RFID scanners and cameras driven by Artificial Intelligence (AI) will seamlessly track products checked out by customers. Inspired by Pick & GO (Singapore’s first AI unmanned convenience store, see https://pickngo.com.sg/stores/Links to an external site.), Yeecon’s self-help unmanned stores will leverage on its existing supply chain and local community knowledge to create an inviting experience for heart landers.

At a recent project meeting to discuss the features of the app that customers will use, Alex Ang, a software engineer with Yeecon, proposed adding a visual map that will show the locations of all product clusters linked by a user-friendly route drawn using the Minimum Spanning Tree algorithm. This will make it more convenient and efficient for customers to shop at the stores, as it will minimise the distance that they need to move around in the store. Many of the project team members nodded excitedly in agreement. Mr Yee, the boss of Yeecon Minimart, who was also at the meeting, looked doubtful as he pondered deeply over yet another “creative” idea from his young and IT-savvy employees.

This question requires you to think critically about how network models can be used to solve real-world problems. In that pursuit, please:

Discuss critically whether you agree with Alex Ang’s proposal to add a visual map using the Minimum Spanning Tree (MST) algorithm as mentioned in the above scenario.