Near Real-Time Demand Forecasting for a QSR Results in Drop in Inventory Holding Costs & Labor Optimization

Business Objective

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.

Challenges

Variety of data sources – data from historical sales, internal and external demand drivers
Understanding the client’s existing black-box model
Errors in backend migration resulted in inconsistent inventory data for one full year
Revolving menu with limited period offers that kept changing all the time
Arriving at a forecasting granularity of 15-minute interval for labor planning use case

Solution Methodology

Developed a robust forecasting model for all sales, transactions and menu items at store x daypart level for the next 4-week period using Amazon DeepState
Used the item level demand forecasts for:
- Inventory order forecasting by aggregating demand forecasts and factoring on-hand and previous order deliveries
- Labor optimization first required defining the various labor roles, their key drivers. Built forecast models for each driver type at appropriate granularity (example – service, order and delivery, required transaction-level forecast at 15-minute interval)
Incorporated labor demand drivers into the model, eliminating the need for store managers to adjust forecasts manually

Business Impact

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

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