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Dell Validated Design for Manufacturing Edge - Design Guide with 5 Independent Software Vendors
Architecture Introduction System Design Accelerated Time to Value
Overview
Service level management
Scaling up environments to manage growth
Tuning the environment for higher performance
Creating templates to rapidly deploy new environments
Leveraging analytics for smart manufacturing operations
Ensuring time zone consistency across devices
Accelerated time to value with Telit Cinterion
Accelerated time to value with Cognex
Accelerated time to value with XMPro
Accelerated time to value with Claroty
High Availability and Disaster Recovery Application Consolidation Cybersecurity Gateways Streaming Data Platform ISV Interoperability Sizing and Scaling Guidance Bill of Materials Additional Tasks Additional Information References

Accelerated time to value with XMPro

Home > Edge > Manufacturing Edge > Guides > Dell Validated Design for Manufacturing Edge - Design Guide with 5 Independent Software Vendors > Accelerated time to value with XMPro

Accelerated time to value with XMPro


  • Overview

    In the manufacturing industry, a digital twin is a virtual replica of a physical manufacturing system that allows engineers and operators to simulate, analyse, and optimize the performance of the system, before it is built and during its operation.

    Accelerated time to value for a digital twin in the manufacturing industry can refer to the speed at which the digital twin is able to help manufacturers realize the benefits of Industry 4.0 technologies such as IoT, AI, and automation.

    This can include:
    • Reducing design and development time for new products and processes by allowing engineers to test and optimize designs in a virtual environment before they are built
    • Improving the speed and efficiency of commissioning and validation of new production lines
    • Identifying and resolving issues in real-time, thus reducing downtime and increasing overall equipment effectiveness (OEE)
    • Improving the accuracy of production forecasting and optimization
    • Reducing costs by identifying areas for energy efficiency and reducing material waste
    • Improving safety by simulating and analyzing potential hazards before they occur

    Accelerated time to value for a digital twin can be realized by integrating the digital twin with data from existing manufacturing systems and implementing tools for analysis and simulation, making sure the data governance protocols are in place. Furthermore, the inclusion of a collaborative environment, design for flexibility and scalability, and continuous improvement enhances the value for the system.


    Types of digital twins

    Digital twins are use case driven as per the definition above. XMPro identified three patterns for IoT use cases and created three digital twin types based on these patterns:

    Status twin

    Typically used for basic condition monitoring applications such as dashboards and simple alerting systems. It indicates operating parameters and is generally created with visualization tools. XMPro provides dashboard and HMI views in support of these twins.

    Operational twin

    Provides more extensive information that is typically used in decision support by operators, reliability engineers, and other decision-makers. It is linked to a set of actions or workflows where users can interact with the twin and change operating parameters where control capability is allowed.

    Simulation twin

    Leverages different types of simulation or artificial intelligence capabilities to predict, forecast, or provide insight into future operational states. It can be used for predictive maintenance or to improve the recovery yield of a processing plant.


    How to use AI/ML in digital twins

    AI/ML can be embedded in digital twins to improve performance, efficiency, and reliability of physical assets while reducing costs. Real-time analytics are used to look for anomalous behavior, predict future states, and optimize production. Decision support is provided through predictions from the digital twin, and prescriptive analytics are used to create recommendations on the best actions to take based on real-time data.
    • Real-time analytics

      As a digital twin ingests data in real-time, it can apply AI and machine learning to look for anomalous behavior, predict future states, and optimize production. This advanced real-time analytics is the first step to getting the most value out of your digital twin.

    • Decision support

      This additional layer of intelligence is used to display predictions from a digital twin. It provides decision support for engineers when they need to make real-time decisions. By providing details and predictions about metrics like remaining useful life or stock levels, the responsible team is empowered to respond faster to critical business events.

    • Prescriptive analytics and recommendations

      The final way to leverage AI and machine learning in digital twins is to use them for prescriptive analytics and to create recommendations on the best action to take next based on their predictions. In this scenario, the digital twin does more than provide real-time status updates. It helps the responsible team take the actions that are most likely to produce the best results based on real-time data.

    Use a digital twin to predict stock levels at different nodes of the supply chain or to predict the remaining useful life of a machine. AI and machine learning provide the essential capabilities to help maximize the value received from digital twins.


    How can XMPro help

    XMPro is a company that provides a platform for digital twin creation and management, as well as other solutions for process automation, IoT, and Industry 4.0.

    The XMPro platform can help manufacturers accelerate the time to value for their digital twin by providing a range of tools and capabilities that support the creation, management, and optimization of the digital twin. Some of these capabilities include:
    • Data integration—The platform allows for the integration of data from a wide range of sources including IoT devices, sensors, and other systems, making it easy to create a comprehensive digital twin of the manufacturing system.
    • Process automation—The platform provides a range of tools for automating and streamlining business processes, which help to reduce design and development time, improve operational efficiency, and reduce costs.
    • Collaboration—The platform offers a collaborative environment to enable collaboration and alignment of the different stakeholders, including engineering, operations, and maintenance, to work together to improve the digital twin performance and speed up the value generation process.
    • Scalability and flexibility—The platform is designed to scale and adapt to the changing needs of the manufacturing system and integrate with various Industry 4.0 technologies such as AI, machine learning, and so on.

    Overall, the XMPro platform helps manufacturing organizations to create and manage digital twins, automate their processes, gain insights to optimize their systems, reduce costs and downtime, and enhance performance and production efficiency.

    Summary

    AI and machine learning are used to enhance the value of digital twins by providing real-time analytics, decision support, and prescriptive analytics and recommendations. This allows organizations to predict anomalous behavior, optimize production, predict future states, and make real-time decisions based on metrics such as remaining useful life or stock levels.