LOAD PLANNING AND CAPACITY ANALYSIS OF A TWO-WHEELER ROCKER ARM MANUFACTURING LINE USING A COMPUTATIONAL APPROACH

Manufacturing lines operating under high demand and constrained resources require systematic load planning to achieve timely delivery and efficient utilization of available capacity. In multi-stage production systems, imbalance between upstream and downstream operations often leads to bottlenecks, excessive Work-in-Process (WIP) inventory, and dependence on overtime. This study presents a computational load planning and capacity analysis of a Two-Wheeler Rocker Arm (TWRA) manufacturing line using actual shop-floor data. Python-based computational programs were developed to evaluate daily and weekly production capacity, identify bottleneck stations, quantify demand gaps, and estimate buffer inventory under different operating scenarios, including normal working hours, demand-gap-based selective overtime, and extended overtime configurations. The results demonstrate that line throughput is governed primarily by downstream bottlenecks rather than total installed capacity and that targeted overtime at critical stations effectively bridges demand gaps. The proposed computational framework provides a transparent and repeatable decision-support tool for capacity planning and continuous improvement in multi-stage manufacturing systems.