The adoption of digital technologies is vital for organizations to improve profitability and maintain competitiveness.
While the pressure to digitalize can be overwhelming, Siemens and AWS have joined together to ease and accelerate the journey to a Smart Factory with easy-to-adopt, universally transparent technology. The collaboration is helping customers realize value from a smart factory through the closed-loop digital twin.
Overcoming these challenges and remaining competitive in an increasingly connected world is what a Smart Factory enables. A Smart Factory is one where manufacturers are using digital technologies to transform their business operations to improve profitability for the entire organization. The Smart Factory uses a never-ending stream of integrated data gathered from system-wide assets, analyzes that data, automatically optimizes factory operations, and empowers humans to make better decisions faster. It helps organizations learn, adapt, and meet market and customer demands.
Making a factory smart means taking advantage of the industrial Internet of Things (IIoT) – technologies that collect and centralize mass amounts of machine data gathered from industrial environments and create value from that data. Solutions built on IIoT platforms collect, aggregate, analyze, and provide insight to enable you to quickly act on the data to boost operational efficiency and production, improve quality and quicken time to market with real-time performance and production feedback.
Industry 4.0 combines cyber-physical systems, artificial intelligence, machine learning, and IIoT to make digitalization a reality via the Smart Factory. In a Smart Factory, technologies drive operational efficiency by helping factories reduce costly downtime and require fewer raw materials, personnel, and energy. Manufacturers can make more custom products at a lower cost. They can iterate product designs and updates faster to be more productive. Customer satisfaction improves as products are built with fewer defects. Smart Factories often include digital twins – the modeling of physical assets in a virtual world. A digital twin is a virtual representation of a physical product or process, used to understand and predict the physical counterpart’s performance characteristics. Digital twins are used throughout the product lifecycle to simulate, predict, and optimize the product and production system before investing in physical prototypes and assets.
Using the power of IIoT, it is now possible to take the digital twin concept further than it has previously been able to go. By incorporating multi-physics simulation, data analytics, and machine learning capabilities, digital twins are able to demonstrate the impact of design changes, usage scenarios, environmental conditions, and other endless variables – eliminating the need for physical prototypes, reducing development time, and improving quality of the finalized product or process.
Gain Real-Time Feedback from Digital Twins
With real-time feedback loops, manufacturers can use data to adjust production quickly, improve product design, and enhance virtual models. Implementing a Smart Factory enables manufacturers to accelerate value by improving quality and quicken time to market with performance and production feedback for overall profitability improvements. The most innovative companies will not only reduce cycle times but also increase yields and create new business opportunities. They can also dramatically enhance their bottom line by implementing technology to improve their product throughout its entire lifecycle with insights gained using closed-loop digital twins from design through production to performance and back again. The data collected with the MindSphere on AWS IIoT solution provides detailed insights into production operations, equipment usage, and events. By taking this information and connecting it to high fidelity digital twin models across domains, manufacturers
Even when manufacturers are collecting data from connected machines, they often face challenges created by disparate data sources, stranded data, and ownership issues. These challenges make it difficult to effectively analyze data and identify insights. The Siemens MindSphere Integrated Data Lake, built on AWS, allows managers to pull together and analyze unrelated data, facilitating effective collaboration by developing a single version of the truth. When you add machine learning to the mix, you enhance real-time and predictive analytics capabilities to improve quality further and reduce waste.
The same capabilities used to improve quality can also increase machinery uptime and, in turn, drive production. When you combine machine data across production lines and even multiple factories into federated, integrated data lakes, then leverage machine learning, you get real-time and predictive analytics on your production equipment. These analytics help inform manufacturers of potential issues in asset health and performance that can lead to downtime. Manufacturers can leverage these insights to implement more optimal maintenance practices.
Secure Global Infrastructure
Organizations leveraging on-premises IT resources may lack the scalability and security to support their Smart Factory implementation. Additionally, provisioning on-premises IT infrastructure to bring your Smart Factory to fruition adds significant cost, time, and complexity to
the project. When you choose to operate on AWS, you gain access to a secure global infrastructure that can support IIoT deployments at virtually any scale. Native services and a world-class team of security experts help you protect sensitive data. Further, the members of the AWS Partner Network (APN) have reached the highest security standards and provide extensive knowledge to guide your digital journey.
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For more information: www.plm.automation.siemens.com