Manufacturing Line Simulation & Bottleneck Analysis

Key Engineering Contributions

1. 01
   Stochastic Process Modeling: Configured dynamic processing times using triangular, normal, and uniform distributions to accurately simulate real-world manufacturing variability across three consecutive operations.
2. 02
   Resource Constraint Analysis: Modeled complex operational logic by simulating parts seizing a limited number of shared fixtures before routing through the manufacturing line, successfully capturing the cascading impact of resource scarcity.
3. 03
   System Performance Tracking: Monitored critical performance metrics over a 60-hour simulated production period, computing the Time-Averaged Work-In-Progress (WIP), maximum queue lengths, and individual workstation utilization rates.
4. 04
   Bottleneck Identification: Pinpointed Workstation 1 as the primary system bottleneck by analyzing its near-complete utilization rate (99.31%) and high average queue time (8.38 mins), providing data-driven justification for targeted resource allocation.

Visual Documentation

Figure 1

01.png

Arena model of the multi-stage manufacturing line

Figure 2

02.png

Plot of total Work-In-Progress (WIP) and by-workstation queue lengths used to dynamically identify production bottlenecks over a 60-hour simulated period