Developing a Robotic-Integrated Leagility Adaptation Model Through Green Supply Chain Intelligence and Supply Chain Ambidexterity

This study develops a Robotic-Integrated Leagility Adaptation Model by combining Green Supply Chain Intelligence (GSCI) and Supply Chain Ambidexterity (SCA) to enhance sustainable performance in the manufacturing sector. The rapid evolution of robotics, cyber-physical systems, and AI-enabled decision technologies has transformed supply chain dynamics, necessitating an adaptive model that balances efficiency (lean) and responsiveness (agile). Using an integrated quantitative approach, this research examines how robotic automation strengthens leagility capabilities through real-time analytics, predictive intelligence, and environmentally oriented digital operations. The findings demonstrate that GSCI significantly enhances SCA, which in turn improves leagility adaptation and sustainable manufacturing performance. Robotic integration is found to play a catalytic role by enabling autonomous coordination, energy-efficient scheduling, and intelligent material handling as key enablers of green and responsive operations. This study contributes to the literature by proposing a technology-driven leagility model that links robotics, green supply chain intelligence, and ambidexterity within a unified smart manufacturing framework. Implications are provided for policymakers and industry leaders to accelerate sustainable transformation through robotics-enabled digital ecosystems.

@article{Huda2026,
  title     = {Developing a Robotic-Integrated Leagility Adaptation Model Through Green Supply Chain Intelligence and Supply Chain Ambidexterity},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Miftakul Huda and Mohammad Hatta Fahamsyah and Agung Nugroho and Arie Indra Gunawan and Pepen Komarudin and Andrean Bagus Saputra},
  doi       = {10.14569/IJACSA.2026.0170138},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170138}
}