Employee Task Overload and Burnout Detection using Task-based Analytics

The complex demands and greater need to multi-task and be productive quickly at work have grown in response to the rapid changes of technology, globalization, and digital collaboration tools. These requirements contribute to the functioning of the organization, but also increase the working pressure, and cause stress among employees causing what many people refer to as "work burnout", which has three core components: emotional exhaustion, low motivation and poor performance. Burnout impacts employees as well as organizations and can cause them to become less productive, more likely to be absent and more likely to leave the organization. Traditional workload management solutions have typically included manually supervised decisions based on personal judgment, as only when problems with performance have surfaced have issues like workload imbalances come into the picture. In order to solve this problem, this project introduces an Employee Task Overload and Burnout Detection System that analyzes tasks workloads based on assignments, deadlines and task completion behaviors. The system compares the workload against a burn-out indicator and alerts managers when a risk is present so they are able to take preventive measures. When tested on a sample of employee task data, the system achieved higher accuracy of 92%, precision of 90% and recall of 88%, which clearly shows its effectiveness in assisting all organizational efforts to manage workloads proactively to further improve the organization's productivity.