Statistical Analysis and ML Modeling of Worker Efficiency

Collaboration between University of Connecticut and Eversource Energy

What is project about?….

The sponsor Eversource Energy desires the use of statistical and machine learning methods to make data-driven decisions with the goal of determining whether and to what degree any management policy decision impacts worker efficiency.

Need of motivation…

Worker behavior and crew efficiency are primary concerns for large organizations with employees working in skilled trades. These are work environments in which employees meet at a central location every morning, ride a truck to the job site, and return at the end of the shift. The job may require loading material on the work truck prior to departure. The operations of large organizations yields a wealth of data, which modern data science can offer quantitative methods to analyze the performance of. Successful analysis has implications for costs of operations, hiring and purchasing, and productivity forecasting.