A Preliminary Study of Food Court Analytics

Open Access
Author:
Arunachalampalawiappan, Arunachalam
Graduate Program:
Industrial Engineering
Degree:
Master of Science
Document Type:
Master Thesis
Date of Defense:
None
Committee Members:
  • Dr Soundar Kumara, Thesis Advisor
Keywords:
  • Analytics
  • Food Court
  • Restaurants
  • Data Mining
  • Food Court Analytics
Abstract:
Embedded software packages have revolutionized the field of data collection, assimilation and associated analytics through the ease of visualization tools such as customized report generation and plug-and-play dashboards. The impact of this though far-reached is largely left unexplored in the food industry where opportunities to optimize end-to-end data collection, processing and management could potentially have a multi-fold impact. Information is critical for making decisions, and communication of this information is harder as the attention span of people has been reduced due to the speed with which the world is moving today. Thus newer approaches like dashboards in data visualization are used to convey large amount of information in a constrained display. In this thesis, we will explore how the food court management can monitor the customer, employees and the restaurants through some components of a dashboard. We shall discuss an optimization model that will create an optimal schedule for employees so that they are used only during their most productive hours. Next we shall rank the restaurants based on sales, number of visitors and number of revisits to understand how each restaurant is doing compared to others. Two other analyses is on customer summary to generate insights about the customers and association rule mining to see the rules for items being bought together. This information inferred can be used to make recommendations like, how much the customers should refill the card for so that they can avoid entering the queue for multiple refills, the customers can be recommended new food options that they might be interested in based on their previous purchases, and management can use these association rules to make marketing strategies and generate coupons. Another application for these analytics is to schedule employee shifts based on traffic and make sure the employees are not over worked and they are at the peak of their performance to serve the customers to the best of their ability.