Introduction
Operating a multi-model assembly line is akin to writing a symphony—accuracy, timing, and coordination are essential. A major manufacturer, experiencing long lead times, inefficient manning, and repeated order postponement, wanted to rationalize its operations. The challenge was amplified by the complexity of handling multiple product models on the same line, making line balancing a critical concern. To address this, the company collaborated with Production Modeling India (PMI) to conduct a Work Content Estimation Study using the MODAPTS technique.
Client’s Challenge
• Low manpower utilization, averaging only 65%.
• Inefficient sub-assembly feeding onto the main line.
• High rejection rates and limited visibility of the plant’s actual capacity.
• Absence of standardized operations and measurable time data.
Work Content Estimation Increases Manpower Utilization to 85% on Mixed Assembly Line - A Case Study
PMI’s Approach
PMI began with extensive video documentation of every task performed on the assembly and engine lines. Using MODAPTS, each motion was broken down and assigned a time value to build accurate work content profiles.
The team:
- Applied MODAPTS indexes on all recorded activities
- Conducted multi-model line balancing to align processes with the desired TACT
- Identified non-value-added (NVA) activities
- Developed a standardized operations summary sheet to ensure consistency across shifts and models
Results & Impact
• Manpower utilization increased to an average of 85%.
• Productivity improved by 25% on the main assembly line.
• The engine line saw a 30% boost in output.
• The company gained insights into its true plant capacity, enabling better order planning and resource allocation.
• Sub-assembly feeding became more synchronized, reducing rework and delays.
Conclusion
PMI’s time-motion-driven approach helped transform an overburdened, underperforming system into a streamlined, synchronized assembly line. With productivity up and order delays down, the client is now better equipped to handle multi-model complexity with confidence.