A Data Driven Approach for Optimization of Rest Allowances

Despite all technological improvement and automation in the production process, a majority of tasks are still performing by workers. Due to this challenge, the occurrence of musculoskeletal disorders (MSDs) is an expected common result of manual tasks. Fatigue is one of the common causes resulting MSDs. Hence, one of the strategies for resolving this issue is to schedule rest time to provide a recovery time for workforce from the physiological consequences of exertion. This study was aimed to suggest a preplanned rest allowance at MAPNA Company, Tehran, Iran. Therefore, we designed an experiment in a workstation to obtain input data (postures and forces). Then, the collected data was used to simulate the working condition for all workers using 3DSSPP software. We considered maximum voluntary contraction (MVC) of involved muscles. So, the critical muscle was determined for all workers based on specific tasks. The rest time for a critical muscle of each worker was calculated using the Rohmert model. Results showed that optimally work time schedule based on the task specification and subsequent rest time could reduce MSDs. This approach provides a comprehensive view of workers and their tasks (for both sources pvalue was less than 0.05). This approach can be used for any workstation to suggest preplanned rest allowances.