Daisuke Watanabe

Tokyo University of Marine Science and Technology

Papers

Total Citations

H-Index

About

Daisuke Watanabe is an emerging researcher at the intersection of advanced manufacturing, robotics, and supply chain innovation. His work primarily focuses on leveraging digital twin technology and machine learning to transform supply chain management, with a particular emphasis on identifying key operational priorities through data-driven topic modeling. In his most-cited paper, "A Topic Modeling Approach to Determine Supply Chain Management Priorities Enabled by Digital Twin Technology" (2024, 16 citations), Watanabe employs machine learning to systematically map how digital twins can optimize logistics and production flows—a contribution that has quickly gained traction among scholars and practitioners seeking to integrate Industry 4.0 tools into real-world supply chains. Beyond this, Watanabe has explored human-robot collaboration, notably in "Force estimation via physical properties of air cushion for the control of a human-cooperative robot" (2015, 2 citations), where he developed a novel method for estimating force using air cushion physics to enhance safety and responsiveness in cooperative robotics. While his citation counts are still growing, Watanabe’s work demonstrates a forward-looking approach to bridging computational methods with tangible industrial applications, positioning him as a promising voice in the digital transformation of manufacturing and logistics.