Real time employees overtime predictor model
Gang, Isaac K.
MetadataShow full item record
Stearns, S., Gang, I.K. (2021). Real time employees overtime predictor model. Proceedings of the 2021 IEMS Conference, 27, 9-15.
Employers often struggle with a cost-efficient way to schedule workers and handle overtime. Paying employees overtime is a very inefficient and expensive way to keep a department going. In this paper, we will analyze data from New York City public workers to explore what factors influence the amount of overtime workers have accumulated. We are particularly interested in the factors that predict an increase in overtime and how to possibly adapt them. To accurately answer these questions, we use RStudio to analyze the predictor variables that would impact overtime for each New York City borough. We further analyzed the differences in overtime per Borough using ANOVA. Follow-up tests were performed using pairwise comparisons; differences per borough were corrected for multiple comparisons using Scheffe's method. After thorough analysis, we perform multiple regressions on each Borough.
Published in SOAR: Shocker Open Access Repository by Wichita State University Libraries Technical Services, May 2022.
The IEMS'21 conference committee: Wichita State University, College of Engineering (Sponsor); Gamal Weheba (Conference Chair); Hesham Mahgoub (Program Chair); Dalia Mahgoub (Technical Director); Ed Sawan (Publications Editor)