Praevisus Case Study


This case study focuses on enhancing the operational efficiency of small and medium-sized businesses (SMBs) in the retail sector by leveraging machine learning (ML) technologies. This initiative aimed to provide SMBs with advanced tools for accurate demand forecasting and efficient inventory management.

Key objectives of the project included improving demand forecasting accuracy, identifying seasonal demand patterns, fostering a data-driven culture among SMBs, and enabling data integration across diverse technological platforms. The developed ML solutions targeted these areas, with specific algorithms designed for precise demand forecasts and identifying seasonal trends, which could be integrated through APIs and adaptable data formats like CSV and Excel.

The technology stack for the project was built on Google Cloud Platform, using TensorFlow for ML model development. Challenges encountered during the project involved data normalization, API integration, and the technical complexities of implementing sophisticated ML algorithms.

The deployment utilized Docker for containerization and CI/CD pipelines for automation, facilitating a smooth integration with SMBs’ systems. The results were transformative, with SMBs achieving enhanced accuracy in demand forecasting, effective inventory and SKU management, and the adoption of dynamic pricing strategies. Overall, the project successfully demonstrated the significant benefits of ML in revolutionizing retail operations for SMBs, leading to better decision-making and improved operational efficiencies.

Submit the following form to access the full case study.