Predicting busy times in campus dining facilities.

Overview

Penn State Dining Facilities are concerned with lengthy wait times in the dining halls. They consider staffing to be a large factor in the issue. Through analysis of cashier revenue data, forecasting methods will be performed to predict the busy and down times in the dining halls. The analysis will then be used in developing a system to aid managers in selecting the number of employees to schedule the appropriate amount of people.

Objectives

-We plan on gathering the data and performing analysis to determine factors that affect staffing needs at each venue, as well as creating various regression models to help Dining Services better assign shifts depending on a variety of circumstances.

Approach

-Once we have received the necessary data, we plan on cleaning the data into a form suitable for analysis. We will then work to determine the impactful factors in the data and perform regression on those factors to help Dining Services better anticipate their staffing needs. Staffing assignments will be broken up by the standard shift times currently employed by Dining Services.

Outcomes

-A user interface system that implements a statistical model to predict the appropriate number of employees based on cash register revenue from 5 different food service venues. This system will also take into account different factors that could impact staffing needs and revenues.

-A final report on the project development process and results