Case Study: Large Fast Casual Restaurant Chain saves $31M a year with Concurrency's AI demand planning

Large Fast Casual Restaurant Chain saves $31M a year with Concurrency, Inc.

A large fast casual restaurant chain faced over $50 million in annual wasted labor expenses due to inaccurate store manager forecasts, which caused erratic staff scheduling, long customer wait times, and numerous complaints. Concurrency, Inc. was engaged to address this critical demand planning challenge.

Concurrency, Inc. developed and implemented an AI demand planning system using Azure, Databricks, and Python. This fully automated solution accurately predicts hourly demand for each restaurant by utilizing weather and local event data. The results were significant, saving the chain $31 million annually across its 660 stores, which was 30% more accurate than previous forecasts. Additional benefits included saving store managers 10 hours bi-weekly on forecasting and reducing their stress.