Keeping well-stocked shelves can be challenging for large grocery chains, which typically retail thousands of different items—not to mention having to deal with suppliers and manage retail locations spread across large geographic areas. To keep inventories up-to-date in such a fast-moving business requires careful planning and the right data processes.
For one major supermarket chain, achieving the proper requisitioning of supplies for next-day or future sales took each retail store manager upwards of five to six hours per day of inventory-taking and manual form-filling. The company needed a set of efficient data tools to not only stock stores with the right products at the right times, but also an automated system that could accurately predict future sales and replenishment needs based on location, time of year, and other crucial factors.
Rather than investing in a complex and expensive demand forecasting product, the client turned to Pythian’s data experts for a more tailored approach.
What we did
Created the infrastructure necessary to complete a semi-automated, data-consistent set of predictive model testing against the client’s real, historical data; this involved building a data replenishment pipeline running on Google Cloud (GC) AI Platform and orchestrated by Cloud Composer service along with Apache Airflow and custom Python scripts
Built a contingency pipeline running on AI Platform and Cloud Composer
Built a machine learning model in TensorFlow
Worked collaboratively with the client team to evaluate four predictive models against the company’s historical data
Implemented the chosen model to run daily predictions against the client’s sales data
Technologies used
Google Cloud
BigQuery
Pub/Sub
Cloud Functions and Cloud Composer
Apache Airflow
Jupyter Notebooks
TensorFlow 2.0
TensorBoard
Cloud Build and Container Registry
Key Outcomes
As a result of Pythian’s machine learning training and prediction pipelines, the client was able to optimize their supply replenishment process and increase staff productivity.
