Our client is a global Fortune 500 quick restaurant chain operating multiple brands in 150 countries.
The client depended on a simple forecasting process using recent historical data and a pre-set adjustment applied for corporate events. The forecast engine was a black box with limited visibility into demand drivers. The objective was to help in developing forecasting models at the menu item/sales/transactions x store x daypart (15 min) level for the upcoming 4-week period factoring in potential drivers of demand. Subsequently, the model was to be used to convert item forecasts into inventory orders and also for labor planning.
Average inventory holding cost across the test stores dropped by ~50% for daily counted items and by ~8% for weekly counted items considering orders based on the new algorithm
Developed a robust forecasting model with an accuracy that is 3-5% better than existing models. Improved labor forecasts for all role types led to an efficient roster that could be built three weeks in advance