Effects of stochastic and heterogeneous worker learning on the performance of a two-workstation production system

Production systems in industries are undergoing transformative changes, with the rise of Industry 4.0 technologies amplifying the complexity of manual and semi-automated workstations, necessitating advanced training and adaptability from human workers. Human workers, due to their unique blend of cognitive and motor skills, thus flexibility, are indispensable and will continue to play a pivotal role. Because of their unique experiences and attributes, they inherently exhibit variability in their processing times and learning rates, which complicates frequent production ramp-ups. Recognizing the lack of comprehensive models that simultaneously account for stochastic processing times and heterogeneous learning during production ramp-ups, this study aims to bridge this gap. We developed an analytical model of a two-worker production system with an intermediate buffer by focusing on worker learning curves, stochastic processing times, and learning heterogeneity. Through an illustrative case, we derived insights into the performance of such systems, specifically in terms of measures including the mean throughput time of a batch, mean waiting time of a part in the buffer, mean idle time of workers, workin-progress distribution, and buffer usage during the production run. We found that deterministic learning models can significantly underestimate the throughput times, and even consistent average learning rates can lead to variable throughput times based on the learning patterns. Our findings emphasize the need for production managers to consider these factors for realistic and effective production planning, underscoring the novelty of our approach in addressing these intricate dynamics to improve not only system performance, but also worker well-being.

Automated summary

Production systems are undergoing transformative changes, necessitating adaptability from human workers. This study developed an analytical model to account for stochastic processing times and learning heterogeneity, revealing insights into system performance.