Introduction
Efficiency in the heavy industrial equipment world is not only a goal—its a moving target. That's particularly so in the textile machinery sector, where demand ebbs and flows with international market conditions, and the equipment coming together is anything but small. For one of India's top producers of textile machinery, keeping up with demand while fine-tuning manpower and achieving consistency on the shop floor had become an increasing challenge.
At the center of the problem was their Tapeline Machine Assembly Shop, a place where intricacy meets artistry. The company went to Production Modeling India (PMI) to impose order in the chaos—through setting work standards, streamlining manpower deployment, and optimizing cycle time without sacrificing the quality or integrity of their machines.
The Challenge
This was not a run-of-the-mill case of labor ineffectiveness. Tapeline machines are enormous, multi-machine systems—each product is literally a build of six machines. With long cycle times and complex workflows, the customer was experiencing:
- Unpredictable manpower allocation because production volumes were going up and down.
- Inconsistent processes between product versions.
- Longer cycle times, resulting in backlogs and delayed deliveries.
- Poor visibility into what "optimal" actually looked like on the shop floor.
The client’s goal was clear: create repeatable, scalable processes that could adapt to demand, not collapse under it.
43% Productivity Boost & 30% Manpower Optimization: PMI Streamlines Tapeline Assembly for India’s Leading Textile Machine Manufacturer
PMI’s Multi-Layered Strategy
To tackle the complexity head-on, PMI launched a structured, yet adaptive study built around three core phases:
PMI began with a full walkthrough of the existing facility. A cross-functional team (CFT) was formed, bringing together engineering, operations, and shop-floor personnel to ensure every perspective was captured.
For a period of several days and on all three shifts, intensive data collection was carried out by a group of six PMI engineers. Activities encompassed:
• Capturing base model assembly processes.
• Recording exclusive activities for model variations.
• Capturing tool usage, movement of materials, and idle time.
This raw information formed the basis for high-resolution work content estimation.
PMI utilized Standard Work Combination Tables (SWCT) to dissect each task into its walking, automatic, and manual components. This allowed:
• Accurate per-unit and per-station work content estimation.
• Static simulation to simulate manpower and process flow for up to 12 units/month.
• Detection of optimum manpower allocation and productivity bottlenecks.
A suggested shop floor layout was also created for optimized movement, tool proximity, and workstation flow.
Findings and Recommendations
The research showed definitive opportunities for change:
• Cycle time fell from 1.3 days/unit to 0.9 days/unit.
• Productivity improved by 43%.
• Manpower decreased by 30% without loss of output.
Other findings were:
• Operator deployment has to be in harmony with process rhythm, not random headcount.
• Tools and material stores should be placed at 15–20 steps from the workstation to avoid wasted time
• Jib cranes were suggested for heavy units such as the Water Bath to avoid physical fatigue and enhance safety
• Dedicated storage units for each assembly station were suggested to minimize backtracking and confusion
• The end result wasn't a layout, it was a plan for sustainable, scalable manufacturing.
Conclusion
This case study demonstrates that even in heavy engineering-dominated industries with long cycle times, smart work design is the game changer. With careful observation, accurate measurement, and well-supported simulation, PMI assisted the client in bringing order, efficiency, and speed to their assembly line. In an industry where size is all too frequently assumed to be synonymous with complexity, the answer wasn't to make the product simpler, but to make the process simpler.