Steps to Accessing the Value of the Digital Twin | Automation.com

Steps to Accessing the Value of the Digital Twin

Steps to Accessing the Value of the Digital Twin

By Eric Green, VP of User Experience, Advocacy, & Marketing, DELMIA, Dassault Systèmes

Across all industries, there is increasing discussion regarding the benefits of implementing a digital twin.  At its simplest, a digital twin is a virtual model of a process, product or service -- but not just a static model.  It includes design and engineering information regarding its geometry, materials, components, and behavior or performance.  A true virtual twin dynamically changes throughout its lifecycle by receiving data back from the field, reporting on its current status.  It learns and grows by capturing its past and current behavior from the physical state, providing clues on its future behavior. This dynamic pairing of the virtual and physical worlds allows for:

  • Monitoring of the operations and behavior of actual deployed products, assets, and processes
  • Predicting the modes of failure from the operations data acquired and the model behaviors
  • Prescribing the corrective actions and application to future knowledge, standards, and rules to improve the subsequent products/assets/processes deployment

 

Savings Potential That is Hard to Ignore

The intense interest in the digital twin is due to the potential for huge savings.  The goal of any company is to implement projects as quickly as possible, at lower cost and with minimal mistakes.  Yet, realities show that cost overruns and tardy deliveries are the norm.  An Ernst & Young reportindicates that 64% of oil and gas projects with $1B+ value have cost overruns and 73% report delays. In the construction industry, McKinseyresearch shows that large construction projects typically take 20% longer to finish  than scheduled and are up to 80% over budget. 

Benefits of the digital twin can be realized across a broad spectrum of industries. On the factory floor, digital twins can help manufacturers optimize equipment placement, workflow, maintenance and operational processes. By analyzing the data and monitoring systems, manufacturers can avert problems before they even occur, prevent downtime, develop new opportunities and better plan for the future by running ‘what if’ simulations.

A case at hand is Centerline Automation, a welding and assembly line manufacturer in Canada.  In 2008, a typical Centerline project incorporated 10 or less robots, and the company relied upon 3D design, 2D layouts and limited simulation to develop its processes. After partnering with Dassault Systèmes to implement a scalable, yet comprehensive digital twin strategy, Centerline has quadrupled its sales by transforming its capabilities, now designing, configuring, and assembling cells with up to 200 robots that require zero post-installation robot moves or tooling changes. Large job delivery time has been reduced from two years to less than 50 weeks, and robot teach time has been decreased by 75%. The financial gains to be made become obvious.

 

Basic Requirements

To realize the true value of digital twin requires a comprehensive approach to modelling, collecting, managing and manipulating digital data. However, the reality is that every company has multiple business departments handling multiple business processes using any variety of technologies, each doing their own thing in silos. The digital twin relies upon an assembled collection of data captured by numerous systems or tools that includes information from all stages -- planning, proposal, design, engineering, manufacturing, operation, etc.

The first step in a digital twin strategy is finding a way to collect, unite the data and it together in a dynamic model that evolves throughout the lifecycle, sharing the data in a way that provides the right view to each of the stakeholders involved. And, experience has shown that traditional IT applications and solutions are not able to address the comprehensive collaboration required by the digital twin.

Companies already incorporating solutions such a 3D CAD, digital simulation, Product Lifecycle Management, Manufacturing Process Management, Manufacturing Operations Management, augmented and virtual realities, and smart devices already have many of the tools in place to start a digital twin implementation.  However, to leverage the true value of a digital twin requires a business platform that can bring all of the information together to create a digital master – or a single source of the truth.

Dassault Systèmes’ 3DExperience is one such platform, enabling manufacturing business transformation by synchronizing all the data from manufacturing, supply chain planning, and operations in a unified digital environment. This platform approach allows the digital twin to constantly receive real-time information that automatically updates the model and provides the exact state at any time so that everyone involved is always working form the same data.  This creates a Value Network for the organization by unleashing the intellectual property of an enterprise; data created by anyone at any time is being collaboratively shared and enhanced along the entire lifecycle.

 

Start Small

Uniting all digital assets into one common platform can seem a little daunting. But, as in Centerline’s case, the whole elephant doesn’t have to be eaten at once. Companies can select a moderate project for piloting –and one that seems most likely to benefit from having a digital twin – and build from there. Those who have a new facility could start simply by optimizing the placement of equipment in a way that best uses space and facilitates workflow. But, it could also be about designing and fine-tuning a new process in an existing facility.  Either way, the first step is to determine KPIs or ROI objectives.

Here’s a very simplified overview of how this might progress. It should be noted that key to any digital twin is the 3D data fed into the platform for visualization and the connecting of all resource information to the platform for the required collaboration. 

  1. Create a 3D twin of the entire building.
  2. Simulate the transfer of the equipment into the building and optimize placement
  3. Simulate the manufacturing operations and validate the optimal scenario
  4. Balance the workload and simulate ramp-up within a multi-product context
  5. Plan and schedule parts production -- Load vs capacity balancing
  6. Connect MOM indicators to virtual twin

At this point, there should now be a fairly robust digital twin model of the facility and processes, all linked to the central platform, providing a real-time view of operations to all involved. There is now an ability to model, monitor, optimize and simulate multiple scenarios to optimize each step of the way. This information can then be used to improve the process each time. Each change in the physical is reflected in the digital and vice versa, to create a symbiotic, dynamic twin.

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