Exploring the use of predictive analytics in reducing customer wait time in a college cafeteria

Predictive analytics, is a practice of extracting information from existing data sets in order to predict outcome and trends in the future. Techniques of predictive analytics such as data mining, statistics, modelling and machine learning are used extensively in the hospitality industry for predicting customer frequency, wait time, crop yields among others. For the purpose of this paper, we will be exploring the influence of forecasted data in reducing wait time at a college cafeteria, using modelling as the predictive analytics technique. Cafeterias provide meals and drinks to specific target groups of customers especially in schools, hospitals, organizations and companies at a charge. They are mainly different from the other food service providers in terms of their service styles, whereby there is no waiting staff but rather the customer serves from a buffets of meals and pays for meals by themselves. Due to this serving style, there is a challenge of preventing long queues and reducing wait time for customers to pick up food. An increase in wait time for customers could lead to customer dissatisfaction and this paper seeks to address this challenge. The objective of this study is to assess the impact of a predictive model in reducing the wait time at a cafeteria. To achieve this objective, a mathematical model is formulated to accurately predict the number of customers likely to visit the cafeteria at various times during the day. The impact of the predictions is then assessed in its ability to reduce wait time.
Thesis submitted to the Department of Computer Science, Ashesi University College, in partial fulfillment of Bachelor of Science degree in Computer Science, April 2016
predictive analytics, Ghana, Ashesi University College, cafeteria, customer satisfaction