Digital Twin Technology for Smart Manufacturing
DOI:
https://doi.org/10.47941/jts.2143Keywords:
Digital Twin Technology, Smart Manufacturing, Operational Efficiency, Predictive Maintenance, Industry 4.0Abstract
Purpose: The general objective of this study was to explore digital twin technology for smart manufacturing.
Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.
Findings: The findings reveal that there exists a contextual and methodological gap relating to digital twin technology for smart manufacturing. Preliminary empirical review revealed that digital twin technology significantly transformed smart manufacturing by providing real-time monitoring, simulation, and optimization of manufacturing processes. This advancement enhanced operational efficiency, decision-making, and product quality, while reducing downtime and operational costs. However, challenges such as high implementation costs, integration complexity, and the need for skilled personnel were identified. Despite these issues, the benefits of digital twins, including improved resource management and proactive maintenance, were deemed to outweigh the difficulties, positioning digital twins as a crucial component of Industry 4.0 and smart manufacturing advancements.
Unique Contribution to Theory, Practice and Policy: The Systems Theory, Cyber- Physical Systems Theory and the Simulation Theory may be used to anchor future studies on digital twin technology for smart manufacturing. The study recommended several measures to maximize the benefits of digital twin technology. It suggested developing standardized protocols to ensure interoperability, investing in robust data analytics infrastructure to handle the extensive data generated, and addressing high implementation costs through phased approaches and partnerships. Additionally, it emphasized the importance of enhancing skills and training for managing digital twins, advancing cybersecurity measures to protect sensitive information, and promoting supportive policies and regulations to facilitate technology adoption and innovation in smart manufacturing.
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