Digital Twin Implementation Strategies for Complex Manufacturing Ecosystems: A Multi-Level Integration Framework

Suchita B. Jadhav; Kirti A Patil; Kavita Tukaram Patil; Mokshda Nandkishor Kachave; Sharmila P Zope

doi:10.5935/jetia.v12i58.3071

Autores

Suchita B. Jadhav Department of Computer Engineering, SNJB's Late Sau K.B.Jain College of Engineering, India https://orcid.org/0000-0001-8196-1716 (não autenticado)
Kirti A Patil MET's Institute of Engineering, Department of Information Technology, Nashik, India https://orcid.org/0000-0003-1366-2380 (não autenticado)
Kavita Tukaram Patil SVKM Institute of Technology, Department of Computer Engineering, Dhule, India. https://orcid.org/0000-0002-2893-3270 (não autenticado)
Mokshda Nandkishor Kachave SVKM Institute of Technology, Department of Computer Engineering, Dhule, India. https://orcid.org/0009-0002-9792-4586 (não autenticado)
Sharmila P Zope Jawahar Education Society's Institute of Technology, Management and Research, Department of Computer Engineering, Nashik, India. https://orcid.org/0009-0000-0330-8199 (não autenticado)

DOI:

https://doi.org/10.5935/jetia.v12i58.3071

Resumo

Complex manufacturing ecosystems require sophisticated digital transformation strategies that can integrate multiple production systems, processes, and organizational levels while maintaining operational efficiency and data integrity. This paper presents a comprehensive multi-level integration framework for digital twin implementation in complex manufacturing environments, addressing challenges in standardization, interoperability, and scalability. We evaluate implementation strategies across four integration levels: component-level, machine-level, system-level, and enterprise-level digital twins, utilizing International Organization for Standardization (ISO) 23247 standards and edge computing architectures. Our analysis encompasses 15 industrial case studies spanning wire arc additive manufacturing, Computer Numerical Control (CNC) machining, flexible manufacturing cells, and multi-plant operations. The proposed framework demonstrates 34% reduction in implementation time, 28% improvement in data processing efficiency, and 42% enhancement in decision-making capabilities compared to traditional approaches. Results show that standardized digital twin architectures based on ISO 23247 enable seamless integration across manufacturing levels while maintaining scalability for complex ecosystems. The study establishes that edge computing-enhanced digital twins achieve 15-millisecond response times for real-time control applications and support zero-defect manufacturing initiatives through predictive analytics and closed-loop optimization. The framework provides practical guidelines for organizations implementing digital twin strategies in complex manufacturing environments.