Analyzing and Improving Production Line Efficiency Using Simulation in the Auto Parts Industry

Paying attention to the production and productivity of industries can accelerate industrial growth and development while guiding it on a correct and sustainable path. Evaluating production efficiency and striving to improve it play a crucial role in the progress and advancement of industries. This study proposes an innovative approach to evaluate and improve the efficiency of a production line using simulation as the primary tool and also addresses the reengineering of production line processes. The main objectives of this research include identifying bottlenecks in the production process, analyzing production cycle times, evaluating buffer capacities within specified time intervals, and determining the optimal resource capacities required in the factory.

This research examines and models a fully automated production line and provides a systematic framework based on discrete-event simulation. The modeling process was conducted in two stages. In the first stage, rework and separation activities were excluded from consideration, while in the second stage, these details were incorporated into the model. In this phase, real data collected from a case study were applied to the model. To ensure the accuracy of the designed model, the logic of the modeled process was continuously reviewed, and the model's outputs were compared with actual system data. After identifying the factors contributing to reduced production line efficiency, four improvement scenarios were proposed and analyzed using the simulation model. Arena software was utilized to evaluate the scenarios and conduct sensitivity analyses.

The results reveal that incorporating details such as operator break times and downtimes into the simulation model—bringing it closer to reality—reduced the production line efficiency from 80% to 57%. Rework and separation activities also significantly impacted the efficiency. Four improvement scenarios were designed and evaluated within the optimized model. In the first scenario, changes in resource capacities related to the main processes were thoroughly examined, leading to a significant reduction in waiting times in process queues and overall process duration. In the second scenario, reducing the percentage of parts sent to rework and separation resulted in a considerable improvement in production efficiency. The third scenario focused on minimizing process time by determining optimal control variable values, while the fourth scenario aimed to maximize efficiency by optimizing resource capacities. In all scenarios, increasing resources at bottleneck activities, through logical and balanced combinations, significantly enhanced process efficiency. Sensitivity analysis confirmed the practical applicability of the improvement scenarios in real-world conditions.

The findings indicate that discrete-event simulation is an effective tool for managers, enabling them to make informed decisions about improving production efficiency without incurring irreversible costs. Additionally, the results align closely with prior studies that have utilized discrete-event simulation to optimize various organizational processes, further confirming the positive impact of this approach on improving process performance.