Background of the Study
Digital twins, virtual replicas of physical assets, systems, or processes, have gained popularity in the manufacturing sector as a means to enhance operational efficiency and optimize production (Feng & Zhang, 2024). In smart manufacturing, digital twins allow for real-time monitoring, predictive maintenance, and data-driven decision-making, which can significantly improve production outcomes and reduce downtime.
Industrial firms in Zamfara State, like many others in developing regions, are exploring digital twin technology to improve their manufacturing processes. By creating digital representations of their production lines and equipment, these firms can analyze data to predict failures, optimize energy consumption, and reduce waste. However, the adoption of digital twins is still in the early stages, and many industrial firms face challenges related to technological infrastructure, workforce skills, and integration with existing systems. This study seeks to examine the role of digital twins in improving manufacturing processes in industrial firms in Zamfara State.
Statement of the Problem
Despite the potential benefits of digital twins, many industrial firms in Zamfara State have not fully embraced this technology. Challenges such as limited technical expertise, high initial costs, and integration with legacy systems hinder the widespread adoption of digital twin solutions. This study will explore how digital twin technology can be leveraged to improve manufacturing processes in industrial firms and identify the barriers to its adoption.
Objectives of the Study
Research Questions
Research Hypotheses
Scope and Limitations of the Study
This study will focus on industrial firms in Zamfara State and evaluate the role of digital twins in smart manufacturing. Limitations include limited access to technological data and a small sample of firms adopting digital twin technology.
Definitions of Terms
Digital Twins: Virtual models that replicate physical assets or processes, allowing for simulation, analysis, and optimization.
Smart Manufacturing: The use of advanced technologies, such as digital twins and IoT, to enhance production efficiency, quality, and sustainability.
Operational Efficiency: The ability of an organization to deliver products or services with the least amount of resources, time, and waste.
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