Task

With the development of e-commerce, the demand for convenient delivery methods is growing. My Postamat wanted to optimise the location of its postamats in order to be closer to its customers. This required a tool that would analyse real-time data and suggest optimal locations for the devices.

Solution

We developed PostNet, a geo-service that uses machine learning to analyse multiple factors: population density, transport accessibility, proximity to shops and other key points. The service helps to find the best locations for postamats and quickly adapt to changes in the city infrastructure.

What's done

1. Accurate data analysis

• Data on population, traffic, educational institutions and other facilities are taken into account.

• The machine learning model is updated in real time, ensuring highly accurate predictions.

2. User-friendly interface

• Intuitive map with heat zones for data visualisation.

• Ability to filter by various parameters: address, name, workload.

3. Technology base

• Frontend: React, Ant Design, Tailwind CSS for quick and easy interaction.

• Backend: PostgreSQL, PostGIS for spatial data processing, Django for business logic.

• Docker-based microservice architecture for flexibility and scalability.

Results

• A year of development has created a powerful tool for analysing and optimising the network of postamats.

• The service is already being used to find the best locations, which increases delivery efficiency and customer satisfaction.

• The system is easily adaptable to new cities and countries, which opens up opportunities for scaling.

Bottom line

‘My Postamat’ has got a modern solution that not only simplifies the search for optimal locations for postamats, but also helps to stay one step ahead in a rapidly changing urban environment. This is an example of how data and technology can solve complex business problems.