A data driven approach for optimization of rest allowances

ABSTRACT : With all technological advancements in production technology and robotics, a considerable amount of manual tasks are yet performed by workers. In consequence, the occurrence of that musculoskeletal disorders (MSDs) is an expected common result of manual tasks. Fatigue is one of the common causes which leads to MSDs [1] hence one of the strategies for resolving this issue is to let workers have enough rest allowances to recover from the physiological consequences of exertion. This study aims to suggest well-timed rest allowances in the MAPNA factory (Iran, Tehran). For this purpose, we designed an experiment in a workstation to obtain input data (postures and forces) and used collected data to simulate the working condition for all workers with help of 3DSSPP software. We considered maximum voluntary contraction (MVC) of involved muscles. With all this, the critical muscle was determined for all workers based on specific tasks that they performed in their station. With the help of the Rohmert model, the rest time was calculated for the critical muscle of each worker. The results present worker and task-specific optimal work time and subsequent rest time to prevent MSDs. This approach was used because statistical analysis would highlight workers and tasks which are significantly effective on rest times (for both sources p-value was less than 0.05). This approach can be used for any workstation to suggest well-timed rest allowances.