Forecasting Engine for Retail Analytics

Forecasting Engine for Retail Analytics by Ali Zarreh

Welcome to an in-depth look at the Forecasting Engine for Retail Analytics project. This project was spearheaded at retail company, where I, Ali Zarreh, led the development of a robust forecasting engine utilizing Ray and Vertex AI on Google Cloud Platform (GCP). The project achieved remarkable results, including a 9% error reduction and a 15% reduction for promoted items, ultimately enhancing inventory efficiency by 5%.

Project Overview

The primary objective of this project was to develop a highly accurate and scalable forecasting engine capable of predicting demand for retail products, particularly focusing on promoted items. This engine was built using advanced technologies such as Ray for distributed computing and Vertex AI for managing machine learning workflows on GCP. The end goal was to minimize forecasting errors, optimize inventory management, and improve overall operational efficiency at the company.

Technologies Used

  • Ray: Used for distributed computing, enabling the engine to handle large datasets and complex computations efficiently.
  • Vertex AI: Managed machine learning workflows, simplifying model deployment and monitoring in a production environment.
  • BigQuery: Used for extensive preprocessing and feature engeering.
  • Google Cloud Platform (GCP): Provided the cloud infrastructure, ensuring scalability and reliability of the forecasting engine.

Achievements and Impact

  • 9% reduction in forecasting error, improving accuracy in demand predictions.
  • 15% reduction in forecasting error specifically for promoted items, significantly enhancing promotional strategy.
  • 5% increase in inventory efficiency, reducing waste and ensuring better stock availability.

Challenges and Solutions

During the development of the forecasting engine, one of the major challenges was managing the complexity of data across different product categories and promotional scenarios. By leveraging Ray for parallel processing and Vertex AI for streamlined machine learning workflows, we were able to address these challenges effectively, ensuring that the engine could deliver accurate predictions in a timely manner.

Future Work and Aspirations

Looking ahead, there are plans to further refine the forecasting engine, incorporating more advanced machine learning models and expanding its applicability to other areas of retail operations. The insights and methodologies developed in this project will also be shared with the broader data science community to contribute to ongoing advancements in retail analytics.

Explore More Projects

If you're interested in learning more about my work or discussing potential collaborations, feel free to explore more of my projects in the portfolio section or get in touch directly.