Innovative management of autonomous stores
The retail industry is undergoing a significant transformation with the development of autonomous stores. Our client, one of the leading autonomous convenience store operators in Europe, wanted to be at the forefront of this change.
Forecasts indicated that the global retail automation market could exceed $23 billion by 2026, which is why the focus on fully autonomous convenience stores has steadily grown. Located in strategic locations such as train stations, these stores were supposed to offer the possibility of shopping 24/7 while reducing operating costs, especially staff costs. However, the challenge remained to ensure smooth handling of human processes in such an automated environment.
The Challenge
Scaling was a significant challenge. Store managers were overloaded with the amount of work. Problems arose when monitoring critical systems such as refrigerators and Point Of Sale (POS) systems. The goal was to ensure that the autonomous technology would synchronize with periodic employee visits without the need to involve many technical specialists.
Commhaven's Solution
In response to this challenge, Commhaven created a mobile app connected to all the store's key systems, also supporting employee visit scheduling. The solution was not only to manage operations, but also to scale them. Thanks to cloud data processing and a mobile application interface, we have ensured fast, centralized data processing and the ability to seamlessly integrate any number of new systems.
Project Process
Start of cooperation and development of MVP
Creation of the first version of the application, focused on key functionalities.
Integration of key systems
Connection with the most important store systems and planning of employee visits.
Data processing and mobile interface
Cloud data processing with mobile app as main interface.
Gradual expansion of functionality
Constantly adding new functionalities to improve the operation of stores.
Implementing Predictive Maintenance
Introduction of intelligent predictive maintenance algorithms for inventory control and systems.
Outcome
The mobile app has changed the way customer stores operate. Employees no longer had to contact multiple people on a single visit. Remote monitoring of various store systems has reduced the number of unnecessary visits.
The application made it possible to immediately report failures requiring the intervention of a technician. In addition, the implementation of intelligent predictive maintenance algorithms made it possible to predict bottlenecks and optimize the cost of visits.
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