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Team 2119

Team Members

Faculty Advisor

Harsha Jain
Thomas Jakiela

John Chandy

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Statistical Analysis of Employee Data

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. A classic question in worker management is whether loading the trucks the night before a job, “pre-loading”, helps reduce time employees spend at the warehouse in the morning awaiting departure. This morning time, possibly involving truck loading as well as socializing, often constitutes a large portion of the work day. It is also desirable to know what other factors contribute to worker efficiency, such as weather, availability of manpower and equipment, etc. 

The operations of large organizations yields a wealth of data, which modern data science can offer quantitative methods to analyze the performance of. The sponsor Eversource Energy desires the use of statistical methods to make data-driven decisions with the goal of determining whether and to what degree management policy decisions impact worker efficiency. Successful analysis has implications for costs of operations, hiring and purchasing, and productivity forecasting.