When many of you hear the word Digital Twin, you generally associate it with a digital representation of a machine or physical asset. However, this is just one subset or application of it. At Simio, we have a more robust and holistic view of the digital twin that focuses on its ability to capture and represent heterogeneous data across multiple, interconnected processes into modeled information that supports real-time analytics.
Capturing data sets across industrial or manufacturing systems –from the supply chain, machine performance, throughput, and other shop floor relationships –and applying semantic data structuring to gain insight is the holistic approach to utilizing digital twin technology. Representing heterogeneous data in a logical way empowers your enterprise with the information to navigate through operational challenges and optimize outcomes. This post will discuss how Simio Digital Twin empowers manufacturing enterprises with the tools to leverage semantic data structuring and data-driven insight.
Applying Neural Networks to Integrate Complex Logic and Constraints
Process manufacturing involves dealing with diverse processes and by-passing production constraints to produce the throughput required to meet customer expectations or demand. These diverse processes and constraints produce their own set of data and impact industrial processes in different ways. Hence, developing digital twin models of process manufacturing facilities and operations must include the constraints and complex logic that occur in real-time.
Simio integrates the use of Neural Networks to simplify the process of modeling complex logic and including constraints within Digital Twin Models of manufacturing and industrial systems. This means instead of going through the labor-intensive process of manually creating complex logic, you can rely on NN to create and automate the recreation of logic where necessary. Leveraging neural networks shortens modeling durations and improves the accuracy of digital twin models for decision-making.
Visualize Data-Driven Insights with Dashboard Reports
As stated earlier, the Digital Twin empowers industrial enterprises with the tools to evaluate the operational process to make decisions and optimize productivity. Presenting the insights for decision-making the Digital Twin provides to stakeholders on the top floor and technicians on the shop floor require some simplicity. Showcasing streaming data will only end up confusing decision-makers more hence the need for visualized results. Simio Results and Dashboard Reports feature provides data analysts and technicians with the tools to visualize business intelligence in a way everyone understands.
Leveraging Dashboard Reports, analysts can easily showcase the effects of diverse parameters such as inventory availability, downtime, or increased demand for the production line. At the shop floor level, scheduling reports will inform workers concerning their responsibilities and the success achieved by following optimized schedules.
Implement Real-Time Industry 4.0 Business Models
Industry 4.0 business models such as predictive maintenance, data-driven plant performance optimization, and risk-based scheduling utilize semantic data for their implementation. Digital twin models provide manufacturing enterprises with the tools to implement a real-time monitoring and management system that support these business models.
For example, the digital twin’s ability to integrate real-time data into developing optimized schedules enables it to discover faulty assets and quickly produce applicable risk-based schedules to avert downtime. These real-time monitoring capabilities and the option to analyze historical data sets are also the driving force behind predictive maintenance.
Conclusion
The journey to getting the best out of your data through the application of Digital Twin models comes with its own challenges. Simio Software eases these challenges through the provision of extensive supportive features to ease the modeling and analytical process. You can learn more about utilizing the Dashboard Reports from the Simio Webinar and the use of Neural Networks from this YouTube Video.