Predictive Analytics for Quick Service Restaurants

Nable IT Consultancy Artificial Intelligence (AI) based supply chain management models allow Quick Service Restaurants (QSR) to manage their product inventory at an optimum level i.e. to minimize both stock-out and over-stock events. The cost savings of active inventory management pays many times over the cost of model implementation. In addition, our flagship facial recognition product helps in differentiating client offerings and in turn increases the customer base and profitability. We have partnered with IIT Delhi to develop customized models to address unique requirements of various types of QSR clients deploying AI based predictive analysis tools.

A typical QSR has outlets at various locations across the service area. There are also kitchens or third-party suppliers in local hubs that supplies to several outlets replenishing stocks of semi cooked items and ingredients as per recipes several times a week. The supplies, inventory or items are categorized either as perishable (viz. bakery) or non-perishable (viz. cups, cutlery, boxes for take-out) items.

The outlets could be in different demographic areas – by income, by location type (office complex or a mall), by food preferences of the settled community (e.g. vegan). Moreover, demography of consumption could also vary by weather conditions (cold drinks and food is preferred in hot weather whereas hot foods and drinks dominate in cold weather), holidays, festival season, weekdays and weekends. Only a small selection of total menu items is kept at every store to optimize sales. Excess stock would create wastage due to expiry of unsold items and stock outs would result in possible loss of sales.

Our AI models can better predict optimum level of inventory by using hundreds of variables. With supervised and unsupervised training of models, utilizing past data and creating neural network patterns using deep learning algorithms, superior predicted replenishment cycles are computed. This provides increase in sales and reduction in stockout events that directly correlates to improvement in profitability.

Models can further be expanded with the following additional functionality:

1. Ideal location of a new outlet among many locations

2. Menu card for an outlet (each outlet could have a different menu)

3. Ideal location and number of kitchens

4. Replenishment cycle from kitchen to outlet as well as quantities supplied

5. Ideal pricing of recipes for maximizing profit and/or revenue (market share)

6. Introducing and recommending new recipes

7. Ideal stocking of nonperishable items to reduce excess storage cost

We are continuing to find new use cases with our engagement with customers and various prospects.

For a live demonstration and inquiries, please contact us