Optimization of Promotional Forecasting Platforms

Optimization of Promotional Forecasting Platforms

Welcome to a detailed exploration of the Optimization of Promotional Forecasting Platforms project. This initiative was undertaken at a retail compnay, where I led the transformation of the promotional forecasting platform by migrating it to a PySpark distributed computing framework and revising the logics and models deployed on AWS. The project resulted in a notable 2% reduction in forecasting error and a significant reduction in runtime by nearly 70%.

Project Overview

The core objective of this project was to optimize the promotional forecasting process by leveraging distributed computing and advanced data processing techniques. The existing forecasting platform which was using R runing on-perm was re-engineered using PySpark on AWS, which allowed for improved scalability, efficiency, and accuracy. This transformation played a crucial role in enhancing promotional strategies and operational efficiency across the company.

Technologies Used

  • PySpark: Utilized for distributed data processing, enabling faster computation and handling large datasets with ease.
  • AWS (Amazon Web Services): Provided the cloud infrastructure for deploying and managing the revised forecasting models and logics.

Achievements and Impact

  • 2% reduction in forecasting error, improving the accuracy of promotional demand predictions.
  • Nearly 70% reduction in runtime, significantly enhancing the efficiency of the forecasting process.

Challenges and Solutions

One of the primary challenges in this project was optimizing the existing forecasting models to operate efficiently in a distributed computing environment. By redesigning the logic and leveraging PySpark's distributed processing capabilities, we were able to overcome these challenges, resulting in a more scalable and faster forecasting platform.

Future Work and Aspirations

The success of this project has paved the way for further enhancements in promotional forecasting at the compnay. Future work will focus on integrating more advanced machine learning models and exploring additional optimizations to further reduce forecasting errors and enhance operational efficiency.

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.