American Journal of Advanced Multidisciplinary Research and Innovation

E-ISSN: XXXX-XXXX   •   Impact Factor: -

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 5 (September-October 2026) Submit your research before last 3 days of October to publish your research paper in the issue of September-October.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 5
September-October 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Digital Twins for Smart Manufacturing and Sustainable Industrial Development

Author(s) Glenn Hubbard
Country United States
Abstract Digital Twin (DT) technology has emerged as a transformative innovation in smart manufacturing and sustainable industrial development by enabling the creation of virtual representations of physical assets, production systems, industrial processes, and entire manufacturing ecosystems. Through the integration of Artificial Intelligence (AI), the Internet of Things (IoT), cloud computing, edge computing, big data analytics, cyber-physical systems, and advanced simulation models, Digital Twins facilitate real-time monitoring, predictive maintenance, process optimization, quality control, resource efficiency, and intelligent decision-making. As a cornerstone of Industry 4.0 and the emerging Industry 5.0 paradigm, Digital Twins improve operational efficiency, reduce production costs, enhance product quality, minimize environmental impacts, and strengthen organizational resilience.
This study investigates the role of Digital Twin technology in smart manufacturing and sustainable industrial development through a multidisciplinary perspective. Using a qualitative and analytical research methodology based on secondary data from manufacturing engineering, industrial engineering, computer science, information systems, sustainability studies, and operations management literature, the research examines Digital Twin architectures, enabling technologies, industrial applications, sustainability implications, implementation challenges, governance frameworks, and future research directions. Particular emphasis is placed on predictive maintenance, intelligent production systems, energy optimization, supply chain integration, circular economy, and Industry 5.0.
The findings indicate that Digital Twin technology substantially enhances manufacturing productivity, predictive maintenance, operational transparency, sustainability performance, and intelligent decision support while supporting digital transformation across industrial sectors. However, challenges including data interoperability, cybersecurity, infrastructure costs, workforce readiness, scalability, and governance remain significant barriers to widespread implementation. The study concludes that Digital Twins, integrated with responsible AI and sustainable industrial strategies, represent a foundational technology for the future of intelligent manufacturing and environmentally responsible industrial development.
Keywords Digital Twin, Smart Manufacturing, Industry 4.0, Industry 5.0, Artificial Intelligence, Industrial Internet of Things, Sustainable Development, Predictive Maintenance.
Field Engineering
Published In Volume 3, Issue 4, July-August 2021
Published On 2021-07-26
View / Download PDF File

Share this



E-ISSN XXXX-XXXX
CrossRef

CrossRef DOI prefix of AJAMRI is 10.00000/AJAMRI

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AJAMRI
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +1-872-302-3851 editor@ajamri.com