Cohort: 2 

Location: Beijing, China 

Application Title: Enterprise retail AI for pricing, loss prevention and energy management 

Team Members

Wumart: Hongyang Wei, Deyu Liu,  

Dmall: Nan Song (Nathan), Chaonan Qu (Grace), Xiyu Du (Troy), Pinzhu Chen, Chenyang Zhang (Cheny), Mengdi Li 

The challenge 

Modern retail operations depend on thousands of small decisions made continuously across stores. Pricing adjustments, inventory handling, store inspections and energy use all affect margins and customer experience. In many supermarket chains, however, these decisions are still supported by disconnected systems and manual routines. 

Markdown pricing is often applied uniformly across stores, regardless of local demand or remaining shelf life. Store audits tend to be reactive, relying on staff checks after issues have already occurred. Energy systems frequently run on fixed schedules, even as occupancy and refrigeration needs change throughout the day. These practices can lead to waste, higher operating costs and uneven execution across locations. 

As store networks expand, coordinating these activities consistently becomes more difficult without real-time visibility and integrated decision support. 

The solution 

A unified operating platform for daily retail decisions 

Wumart partnered with Dmall, a retail digitalisation company, to build a unified operating platform that connects sales data, inventory systems, cameras and in-store sensors into a single workflow. 

The platform replaces static rules with event-driven processes that adjust throughout the day. Instead of treating pricing and store monitoring as separate functions, the system links them through shared data and coordinated responses. 

Dmall provides the digital infrastructure and AI capabilities, while Wumart integrates the platform into its store operations and decision-making processes. This allows insights generated centrally to be applied consistently at the store level.

How it works 

Linking data to action in real time 

For pricing, the system analyses sales velocity, remaining shelf life and current inventory to calculate store-specific discount curves for fresh goods. Prices are adjusted dynamically rather than according to fixed markdown schedules.  

For in-store operations, computer vision models monitor video feeds to identify operational issues. These include empty shelves, checkout errors or safety-related incidents. Detected events are converted into guided tasks, which are then verified through follow-up checks or additional sensor data before being closed. 

For energy management, IoT gateways and virtual meters collect data from refrigeration units, lighting systems and HVAC equipment. The platform forecasts demand and adjusts controls in near real time, balancing energy use with food safety and customer comfort requirements. 

The system runs on a hybrid architecture, combining cloud coordination with edge execution inside stores. This supports fast response times while maintaining local control over data. 

How it differs from conventional approaches 

Moving beyond manual coordination 

Traditional retail operations rely on predefined rules and periodic reviews. Decisions are often made in isolation by different teams, with limited feedback between pricing, store execution and energy management. 

Wumart and Dmall’s approach coordinates these activities through a shared operating layer. Decisions that were previously manual or scheduled are triggered by real-time events. This reduces delays and helps ensure more consistent execution across stores. 

Rather than adding new tools for staff to manage, the platform integrates into existing workflows and translates insights into concrete tasks. 

Impact 

• Pilot deployments across approximately 100 stores, supporting pricing and operations teams 
• Reduction in deep markdowns, with 20% of stores no longer requiring 50% discounts and improved full-price sell-through 
• 85% reduction in self-checkout shrink, alongside lower labour costs and faster issue resolution 
• Approximately 26% reduction in daily energy use, saving around 1.3 million kWh annually 
• Avoidance of roughly 85,600 tonnes of CO₂ emissions per year across pilot sites 

Why it matters 

Supporting consistent retail execution at scale 

Retail performance depends on how well daily decisions are coordinated across stores. The collaboration between Wumart and Dmall shows how integrated data and AI-supported workflows can help reduce waste, improve margins and manage energy use more effectively. 

By connecting pricing, operations and energy management within a single system, the platform supports more consistent execution without increasing operational complexity. This approach becomes increasingly relevant as retail networks grow and operating conditions become more dynamic.