LOAD PLANNING AND CAPACITY ANALYSIS OF A TWO-WHEELER ROCKER ARM MANUFACTURING LINE USING A COMPUTATIONAL APPROACH
Ashwini V1., Paul Vizhian S2., Rathika M3., Gomathi R4., Pawan Kumar S. S.5
1Research Scholar, Department of Mechanical Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, Karnataka, India.
2Professor, Department of Mechanical Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, Karnataka, India.
3Assistant Professor, Department of Mechanical Engineering, Dr. Ambedkar Institute of Technology, Bangalore, Karnataka, India.
4Assistant Professor, Department of Civil Engineering, Sapthagiri Engineering College, NPS University, Bangalore, Karnataka, India.
5Research Scholar, Department of Mechanical Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore, Karnataka, India.
Abstract: 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.
Keywords: Load Planning; Capacity Analysis; Bottleneck Identification; Overtime Planning; Work-in-Process Inventory; Lean Manufacturing; Computational Decision Support.Learning Model
VOLUME 10 ISSUE 02 2026: 50 – 57