Saikat Chatterjee
Papers
1
Total Citations
3
H-Index
1
About
Saikat Chatterjee is a researcher whose work sits at the intersection of industrial engineering, manufacturing systems, and decision science. His recent contributions focus on the critical challenge of selecting collaborative robots (cobots) for assembly operations—a problem of growing importance as industries increasingly adopt human-robot collaboration. In his most-cited paper (2024, 3 citations), Chatterjee introduces an integrated decision-making framework that combines the LOPCOW method with the OPTBIAS approach, offering manufacturers a systematic, data-driven tool to evaluate and choose the most suitable cobot from a crowded market. This work addresses a practical bottleneck in modern production lines: how to match robot capabilities to specific assembly tasks while balancing cost, safety, and performance. Though early in its citation life, the paper signals Chatterjee’s ability to bridge theoretical multi-criteria decision-making with real-world industrial needs. His research is particularly valuable for students and practitioners in manufacturing engineering, as it provides a replicable methodology for technology selection—a skill increasingly essential in the age of smart factories and Industry 4.0.
Research Focus
Key Achievements
Top Papers
- 1