Process Design and Performance Analysis of Mixed Model Assembly Line Using Analytical and Discrete Event Simulation Method

The assembly line in any manufacturing industry serves the utmost importance in the entire manufacturing system as it represents the final production of the factory floor. The rate of production of industry is governed by the cycle time at the bottleneck station. Therefore, the cycle time analysis of the assembly line using standard work measurement techniques is of utmost importance for assessing the productivity of the shopfloor. In order to address the ever-increasing demands of capacity, the systematic methodology for work measurement, process design and two-sided mixed-model assembly line balancing (TSMMALB) has been proposed. Initially, the analytical model was presented to evaluate the performance parameters of the assembly line. The assembly line balancing problem was systematically analysed using industrial engineering techniques of time study, and the corresponding balancing of work elements was performed using the Ranked-Positional Weighs Method (RPWM). The number of workstations required to design an assembly line was kept fixed in accordance with the cycle time requirements. The problem was further extended to multi-objective genetic optimization (MOGA) of the assembly line with objectives of minimizing cycle time and workload variation and maximizing the throughput in terms of line efficiency. The entire cycle time measurement was performed by Predetermined Motion Time Systems (PMTS) as an established work measurement standard. The hypothesis test of cycle time against models was performed to analyse variations in the means and standard deviations of cycle times by Analysis of Variance (ANOVA) using MINITAB© statistical software. In the last part of the paper, discrete event simulation of the process was performed using AnyLogic© software. The simulation provided comprehensive results of standard productivity Key Performance Indicators (KPI), including mean flow times and capacity utilization, to evaluate the pace of the manufacturing system. In future, the correlation between the mathematical model and the discrete event model can be investigated for hybrid-flexible assembly systems.