Overview
Digital Thread |
Applicable Industries
Applicable Functions
Market Size
The global Digital Thread market is valued at 86 million USD in 2017 and is expected to reach 1.798 billion USD by the end of 2023, growing at a CAGR of 65.91% between 2017 and 2023. Source: SDMR |
Business Viewpoint
What are the benefits of Industrial Digital Thread for manufacturers? - Improve product quality by avoiding mistakes in manual translations of engineering specifications along with the product value chain - Improve the velocity of new product introductions (NPI) and the communication of engineering changes along the product value chain - Increase the efficiency of digitally capturing and analyze data related to product manufacturing - Allow manufacturers to deliver new services to customers along with physical product leveraging the digital data now available on the product
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Stakeholder Viewpoint
Executives and Decision-makers: Executives prioritize the digital thread to drive innovation, improve operational efficiency, and gain a competitive edge in the market. They view the digital thread as a strategic initiative that enables data-driven decision-making, fosters agility and flexibility, and enhances customer satisfaction by delivering high-quality products and services. Product Managers and Engineers: Product managers and engineers are responsible for designing, developing, and optimizing products throughout their lifecycle. They view the digital thread as a valuable tool for streamlining product development processes, reducing time-to-market, and ensuring product quality and compliance with regulatory requirements. By leveraging the digital thread, product managers and engineers can collaborate effectively, iterate designs rapidly, and deliver innovative solutions that meet customer needs. |
Technology Viewpoint
Which technologies help Digital Thread work? - The connectivity technologies of the Internet of Things (IoT) are making it possible to collect data from factories with much less effort and cost than before. - Machine learning and artificial intelligence (AI) technologies are making it possible to process large amounts of data and gain insights to further predict outcomes and potential issues such as machine failures, component shortages, and quality issues. - Manufacturing processes are increasingly being integrated with other business applications to automate end-to-end business processes.
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Data Viewpoint
Unified Data Management: The digital thread integrates data from various sources, including design files, simulation data, manufacturing records, quality inspection reports, and service records, into a unified digital repository. By centralizing data management, businesses can ensure data consistency, accuracy, and accessibility across different departments and functional areas. Real-time Monitoring and Analytics: Data analytics and visualization tools enable businesses to monitor and analyze product-related data in real-time, allowing for proactive decision-making and problem-solving. By leveraging predictive analytics and machine learning algorithms, businesses can identify trends, patterns, and anomalies in the data, enabling predictive maintenance, quality optimization, and performance improvement. |
Deployment Challenges
Technology Integration and Infrastructure: Deployment includes the selection, configuration, and integration of digital thread technologies, such as product lifecycle management (PLM) systems, enterprise resource planning (ERP) systems, manufacturing execution systems (MES), and IoT platforms. These technologies are deployed to create a connected ecosystem that enables data sharing, interoperability, and collaboration across different systems and stakeholders. Process Standardization and Automation: Deployment involves standardizing and automating processes and workflows to ensure consistency, efficiency, and compliance throughout the product lifecycle. By defining standardized procedures, workflows, and best practices, businesses can streamline data exchange, automate routine tasks, and reduce errors and delays in product development, manufacturing, and service delivery. |