|Sponsor||Siemens Industry, Inc.|
|Date||Monday, March 28, 2022|
|Time||3:30 PM - 4:15 PM|
This seminar defines the “digital twin” and “digital thread,” detailing how through advances in networking and automation, they significantly and measurably improve process efficiencies, quality, agility, and throughput.
Industry 4.0 is based on the following core principles:
• Pervasive networking of people, machines and “things” in physical and virtual
• Leveraging data that drives production efficiency and flexibility
• Improving key performance metrics (i.e. speed, quality) through virtual testing
• AI and Machine Learning-assisted planning, production, manufacturing and maintenance
Industry 4.0 thought leaders often point to “smart factories” as the harbingers of the fourth industrial revolution. These smart factories act less like settings for a series of sequential steps and more as networks communicating not only with disparate shop floor systems, but with external systems (e.g. supply chain) and the very products being produced.
The Siemens factory in Amburg Germany re-defines the “art of the possible” with the following post-digitalization performance:
• Process Optimization: 20% throughput increase though Machine Learning-based virtual testing
• Predictive Maintenance: 100% unplanned downtime reduction; 15% higher availability (Machine Learning)
• Speed: 1 product produced per second w/ 24-hour lead time, increased from 1 product every 12 seconds.
• Quality: <10 defects per million, reduced from 550 dpm
• Agility: 1,300 products per year, 250 virtual changeovers each day
• Sustainability: 50% CO2-Reduction since 2015
• Automation: Increased from 20% to 80%
• Transparency: Digital Twin performance dashboard
• Accountability: Worker compensation linked to daily KPI targets
Two concepts are at work - the Digital Twin and the Digital Thread. Digital Twin refers to a virtual representation of a physical asset (product or building) used to derive actionable insight on its performance. A digital twin is created from software technology that utilizes information from design specs, multi-physics simulation, field sensor info, machine learning and analytics. It is used to simulate, experiment, predict, and optimize the asset in the virtual world before investing resources into real-world production. A comprehensive digital twin will model both the current and desired future state of the asset and its processes. Digital Thread refers to the framework for end-to-end connection and integration of data throughout the entire lifecycle of the digital twin. Multiple micro-digital threads can exist within a digital twin for different processes within the system. Digital threads that are connected to consistently accurate data, intact throughout the entire life cycle (i.e. “closed-looped”) and networked to relevant external info (e.g. supply chain) will accelerate continuous improvement in a digital twin.
The value of Digital Twins (and their corresponding Digital Threads) lies in the mechanism to capture the information necessary to create an accurate replica of an asset’s current state. That data can then be used to simulate different use cases and experiment with adaptations that will make a future state of the asset more efficient. Artificial Intelligence and Machine Learning techniques can accelerate validation of different future state variations, reducing the time it takes to get to the “perfect” design for an asset. This is especially so for organizations with access to unlimited cloud computing power.
Digital Twins help link the real and digital worlds by creating a blueprint for the next iteration of the asset, which is expected with certainty to improve performance because it has been validated in the digital realm first. Performance enhancing adaptations are built into every new iteration of the
asset, creating a system of continuous improvement.
Digitalization provides measurable results
Those who embrace digitalization will have competitive advantage
The technology is available and proven
|Craig Henry||Siemens Industry|
Seminar sponsored by Siemens Industry, Inc..