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Using a 3D Digital Twin for Manufacturing agility

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Part 4 of the 4 part series: Digital Twins through the full lifecycle Using a 3D Digital Twin for Manufacturing agility What is Manufacturing-as-a-Service? As asserted by 82% of manufacturers, flexibility is critical to growth. Manufacturing-as-a-Service (MaaS) is the ultimate in flexible manufacturing. Let’s drill down on that a little. Imagine a Manufacturing company that owns a global network of manufacturing locations, each with a highly flexible set of capabilities, able to reconfigure at will, and able to accept a wide variety of orders to manufacture goods in order to take advantage of fluctuations in global supply and demand for materials, pricing, currency, and other types of supply chain risk. Companies will need to achieve a high degree of flexibility, adaptability, and configurability to realize this visionary concept of Manufacturing-as-a-Service. What steps can manufacturers take to get there in measured increments?  82% of manufacturers believe flexibility is critical to growth. Creating a 3D Digital Twin as your visual and digital system of record for viewing, understanding, planning, and monitoring work in digital assets and processes can be a stepping stone. What is Manufacturing Agility? Flexibility, adaptability, and configurability are aspects of manufacturing agility. Specifically, agility in manufacturing operations will entail: The ability to make changes quickly, by taking in all relevant information The ability to stay in sync with these changes The ability to simulate (play forward) planned changes to your operation Low cost, effort and complexity to keep your models in sync with the real world As we’ll discuss now, these aspects are achievable by using a 3D Operational Digital Twin. As we’ll discuss now, these aspects are achievable by using a 3D Operational Digital Twin. What does 3D visualization in operations have to do with agility? Agility requires the ability to act quickly, respond quickly, and use data to inform those actions and responses so they are accurate, safe, and rapid. At its core, agility requires a holistic understanding of the manufacturing operation, as described in the Digital Twin Consortium’s recent blogpost by the same name. Below are some key examples in which visual input married with a detailed digital model of the asset or process can improve the speed, safety, and certainty in which we take informed action quickly: Remote monitoring is the most general situation that benefits from having “eyes on” our manufacturing processes, and large equipment centers, as skilled operations management consolidate in larger regional operations centers. Using visual scenario building software, aided by Augmented and Virtual Reality tools allows for investigating, planning, and rehearsal/training of processes and procedures without being there physically Incorporating a 3D visual model into robotic process automation can greatly accelerate planning for turnarounds, setups, and line reconfigurations Optimized inspection, repair and maintenance planning are functions that are more and morefrequently done with the aid of a 3D Digital Twin By carrying a mobile version of a 3D Digital By carrying a mobile version of a 3D Digital Twin during field work, operations can perform in situ parts ordering, service request ordering – i.e., rather than waiting until personnel return to their desk or operations room, they can make requests at the scene. Closing the loop on other observations in the field, personnel can use a 3D Operational Digital Twin to write-back to any authorized transaction-based system – when data observed in the field are visibly inaccurate, notes taken can be attached visually to the equipment, tag, or circuit affected. Moreover, data discrepancies can be immediately rectified between the physical asset and its records in the Asset Management. What each of these scenarios have in common is that: they benefit from the ability to carry out analysis and planning remotely, without physical presence (adds speed, precision/accuracy, and safety). where the operations change frequently (esp. manufacturing in small batches, made-to-order, or constantly adjusting conditions), this also depends on a robust MMOC capability and thus, when the actions are eventually carried out, they are more accurate and quick (because we avoid errors because of all the preplanning and verification), AND, because we provide access to all the information that was used in the planning at their fingertips, the notepad / write back while they are in the field observing it  (i.e., ability to generate a request for parts, is just one example). As a result, when we take the physical out of the equation, we reduce the time needed to be there in person, leading to the further benefits of improved safety, and lower carbon intensity (by lowering energy spent on transportation). Start with an Operational 3D Digital Twin Partner? The scenarios and examples described earlier can be enabled using a 3D Digital Twin, but only a True-to-life 3D Operational Digital Twin, as described in our previous post “True-to-Life 3D Operational Digital Twins”. That is because when teams put their trust and judgement into a model replica, it must be:   an accurate and true to life system of record connected to other operational systems of record in real time Available 24×7 for use The Take-Away As manufacturers strive for the vision of Manufacturing-as-a-Service, they must build in an agile mindset. Some say this will not happen until they achieve software-configured production lines, so they can incorporate Continuous Integration / Continuous Development (CI/CD) flexibility of software development. That road is fraught with dangers as discussed. But a more realistic, near-term goal would be to use tools like an immersive, operational 3D Digital Twin to minimize the time it takes to make changes and adjust their operations to new requirements, new product types, verify and rehearse (simulate) planned changes to reduce errors, speed up changes, and ultimately, make tangible progress toward that goal.

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Data Integration

How to sustain an operational 3D Digital Twin

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Part 3 of the 4 part series: Digital Twins through the full lifecycle How to Sustain an Operational 3D Digital Twin “The operational lifecycle of a 3D Digital Twin must incorporate ongoing reality capture in order to sustain its accuracy and fidelity” Digital Twin Consortium White Paper: Reality Capture: A Digital Twin Foundation While reality capture is a key requirement for sustaining a 3D Digital Twin system, it must be broadened to incorporate three important features (outlined below) to ensure this critical operational tool can live on and support the operation through its long, useful life.   Continuous Detection / Continuous Integration (CI/CD) management of change: Learning from best-in-breed software engineering techniques, sustainment requires a structured, human-in-the-loop, automated process that monitors and detects changes in the physical operation as they occur, including, but not limited to digital reality capture. Granular, modular version control and governance: Model version control and governance over historical, work-in-progress, and updated versions of the model, with rich, searchable visual metadata templates and catalogs. Always-on orchestration and synchronization: A way to synchronize and orchestrate access to models as they are being maintained while maintaining 24×7 live access to the source truth model of record. This is a key requirement for it to be “Operational”. Let’s dig into each of these. Continuous Detection / Continuous Integration management of change process  Over the past 15 years tremendous benefits have been realized from agile software engineering methods. Key among these developments have been continuous development / continuous integration (CI/CD), which has allowed for the unprecedented acceleration of bringing functions to market. 3D Digital Twin Systems are a collection of data and software that can leverage that same agile philosophy. A robust, CI/CD-enabled model management of change should:  Use best available techniques to detect and log changes: whether your organization has the latest and greatest in reality capture / change detection, or relies on simple scratchpad note taking, change detection should be visually and contextually integrated into the model. Schedule new visual content capture: whether new visual content is available, or must be manually gathered, the tasks should be integrated to follow scheduled and managed workflow. Apply AI and rule-based logic: Newly captured visual content must be taken through the process in a structured, way, with the ability for AI-based automation to convert input to model structures, and augment models. Human in the loop: AI-based methods must be backed up by human judgement, particularly in critical or sensitive areas. Probabilistic evaluation and reinforcement learning allow systems to alert when changes are simple and highly probably accurate or when human review is needed. Rules and thresholds supported by configurable policies are critical. Granular, modular version control and governance   3D Digital Twins are a rich, multi-dimensional reflection of physical reality. The information they encapsulate is not monolithic—that is—the model reflects many individual components, and systems of systems, each of which can be separately involved in changes and differing states of completion. For example, you may be planning a small construction or modification of the plant and implementing infrastructure changes incrementally. Or you may simply want to reflect the actions being planned in a turn-around or new setup separately from what is “live.” Critically, each individual component must be maintainable and traceable separately, without impacting the use of the model for daily operations. Each component must be versioned separately, with the visibility of who and how it was changed. That is granular, modular version control. Governance is the process of defining and structuring how model changes can be made, who has the right or privilege to make those changes, and how that authority is managed and enforced. The model owner and stakeholders must be able to define a customized process that suits its organizational security and other institutional needs. That customized process ought to be software defined – a configuration, rather than reprogramming or redesigning the model software. Configuration-based governance allows some organizations (and some parts of the model) to be managed with a “light touch” and others to be more heavily governed, as required by regulation, management policy, or other concerns, such as safety. Finally, the model, its versions, and its change history should be visible and auditable. Always-on orchestration and synchronization workflow Often overlooked in the sustaining process for a 3D Digital Twin is the ability to manage between the work in process changes that may be taking place as parts of the plant and its digital twin are modified. Orchestration and synchronization of the live model goes hand in hand with the design requirement for modularity and granularity. No Operations Leader wants the activity of updating the model to render it useless during this activity. Therefore, a structured orchestration and synchronization process, integrated with the change management workflow as defined, and the ability to do small, incremental changes at a fine grain make managing change robust, low effort, and non-disruptive. What operators should take away Managing change and incorporating it into the model for the useful life of the physical asset is critical capability of Digital Twins, particularly 3D Digital Twins. Making the sustainment easy, and not cost prohibitive, operators should work with a 3D Digital Twin that: Establish a 3D Digital Twin as your System of Record for visual information during the operational life of the asset/system. Work with a 3D Digital Twin partner who has a robust Model Management of Change that can be adapted to your unique operational, regulatory and management needs. Don’t underestimate the up-front effort to define. governance for your 3D Digital Twin, just as other systems of record require. Software tools that automate detection and integration tasks will minimize the cost and effort. Work with a 3D Digital Twin partner who have experience with the creation and ongoing, lifelong maintenance of the model over time.

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The importance of true to life 3D digital twins

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Part 1 of the 4 part series: Digital Twins through the full lifecycle The Importance of True-to-Life 3D Digital Twins “The Digital Twin is a living, learning model that allows you to deliver business value by constantly making sure that Twin is a replica of the asset so you can get insight into that asset and take action.” – Colin Parris, GE Digital In Colin Parris’s definition of Digital Twin, notice 2 very important phrases: Living, learning model: the model must be able to grow and evolve as its twin in the real physical world changes over its operational life. We must have a way to identify changes in the physical world, and then to convey and translate those changes to the digital version: a model management of change (MMOC) in common parlance. Replica of the asset: the model must correspond in all relevant ways to the physical asset or system it is modeling. In other words, the Digital Twin must maintain the true-life likeness for the full operational life of the asset or system. Why is this so Important Now? As highlighted in Verdantix’s, June 2022 research report on Applications of 3D Visualization, “3D Visualization Software Has Become Vital In the Age of Sustainability.” More and more remote management is taking place; the 3D digital twin takes the place of human vision and cognition. It replaces our physical presence with a way to understand what is going on without being there. Just like we use vision-correcting lenses, surgery, or other means to enhance our vision, if the digital twin replaces that temporarily, we want it to be as accurate, and precise as possible. That accuracy and precision plays out when making location-specific decisions; if a piece of equipment or its surroundings have moved or changed, but your model has not been updated with the new condition, location, orientation, or height, you waste time and energy, and potentially put personnel in danger. When planning intricate procedures that require understanding of access methods, or whereabouts to decide what order to do things, or to ensure junior associates are accurately guided through things, this accuracy is again what enables us to practice, verify, and train on a procedure to ensure safety, speed, and effectiveness. When the Digital Twin becomes out-of-sync When an organization sets out to plan and evaluate their operations remotely errors and inaccuracies in the model are detrimental. When that happens, it will cause the team to lose trust in the model, stop using it, and lose the benefits of it. The value of the time, money and effort declines over time. Worse, as it gets out of sync, discrepancies in the model have the potential to cause real safety issues if equipment arrangement is not as we thought, because we relied on the twin to inform our plan. Can’t I just use the Design Model output of CAD/CAM tool? While they both use the underlying framework of an accurate 3D model, there is a big difference between engineering CAD models and “as-built” operational models that reflect their different purposes. A CAD model is used as the blueprint for construction. Deviations from the original engineering plans during the initial build and the ”as-built” begin early, and often aren’t even made available to the operations manager, facilities manager, or owner of the assets, although they make an excellent starting point for an operational model. During the life of the operation (whether it be a building, manufacturing process, or other complex arrangement of assets) additional changes are made to the environment. In some cases, the differences are small, but they can rapidly accumulate to become significant. CAD models tend to be static. They are rarely updated unless the plant undertakes significant new construction, because the effort, and complexity of most CAD tools don’t make light work of this task. In contrast, Operational 3D Digital Twins are designed to allow for frequent changes, to enable the model to span many types of equipment from various OEMs, and to connect to many operational systems of record. In short, they are flexible, dynamic – an Active model, that lives alongside of the operation it is mirroring. What is an Operational 3D Digital Twin?And how is it different from a 3D Digital Twin from a CAD/CAM modeling tool? A true Operational Model contains: a robust Model Management of Change: able to incorporate changes to the environment without undue effort ability to connect and stitch together visual models from CAD/CAM design models, manually or automatically generated scan direct connection to operations systems of record that give it live access to real time data Operational systems and assets live a long time The operational life of the capital assets and the systems they participate in can be 30+ years. Maintaining the Digital Twin throughout that life is just as important as the maintenance of the assets themselves because it extends the useful life by continuously improving the ability to diagnose issues in the system of systems. Some operators will become reliant on the Digital Twin to fulfill regulatory requirements many operators to maintain the 2D models, P&IDs anyhow. For an operational 3D Digital Twin model to last and assist in sustaining the long life of its twin, it MUST be trusted as an accurate and up to date replica of the real-world physical environment. Otherwise, it loses its value as workers lose faith and ultimately stop using it. Because 3D Digital Twins mirror a complex operation, because they assist in extending the useful life, and the effectiveness of large capital investments, and because they require maintenance, a robust, simple to use, cost-effective Model Management of Change (MMOC) process is critical. The Model Management of Change that works If maintaining the Digital Twin is important to the effective operation and operational longevity of your system, it must not be difficult, complex, time-consuming, or expensive to do. Three important capabilities in the Digital Twin make this possible: Built-in change capture systems. The old days of manual scanning and model

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3D digital twins help improve worker safety

How does a Digital Twin help improve worker safety

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  How Does a Digital Twin Help Improve Worker Safety?   A digital twin can be a valuable tool in many industrial businesses for a number of reasons. Understanding how an asset will behave under different circumstances, monitoring its condition and quickly pinpointing any issues is important to worker safety. This works in several ways:   Reducing Time In Hazardous Conditions    Digital twins help improve worker safety by minimizing the time humans spend in hazardous environments.  This is accomplished through more efficient planning of inspection and maintenance activities. Let’s dive into some examples.     When there is a faulty valve or a valve is due for replacement, it would be quick and easy if there were only a few valves to look through.  But today’s complex asset will have hundreds, if not thousands of valves.  So finding that needle in the haystack is often the challenging part. With sophisticated 3D digital twin capabilities offered in solutions like V-Suite, time in the field is cut dramatically. An executive from a Top 5 global Oil & Gas company puts it this way: “An hour in V-Suite is like 8 hours in the field.”  Less time in hazardous contexts translates into safer worker conditions.     “An hour in V-Suite is like 8 hours in the field.”   By leveraging a digital twin to plan ahead from headquarters, one can identify a multitude of part replacements that are needed in a given area within the facility. Additionally, one can have a better understanding of what additional needs have to be considered – scaffolding for example – before getting on site.  Add it all up and you can see how digital twin technology reduces the back and forth between offices and the industrial assets that are being managed from them.  This not only helps to reduce time, energy and money on transportation, but it also means less time for the maintenance worker to be in potentially hazardous conditions.       Avoiding Hazards Through Preventive Maintenance   Because a digital twin is a digitized version of a physical asset and data is often exchanged by the systems in real time, monitoring an asset for preventative maintenance is greatly simplified and improved. This works because important areas of functionality are fit with IoT sensors, which feed data to a processing system that updates the digital twin of the physical asset. Component maintenance schedules are optimized because their exact condition is being constantly monitored and any repair or adjustments can be made before problems occur.     Preventing Dangerous Failures   The preventative maintenance discussed above circumvents potential catastrophic failures that, if they were to occur, might put workers in harm’s way. Modeling otherwise hidden elements makes it much faster to discover and correct what would otherwise be an unseen issue that could cause an accident or injury. The sensor structure also allows workers to view a single issue from multiple vantage points, creating faster and more precise solutions to ensure assets are in their safest, most optimal working order. Digital twins can also monitor environmental factors and trigger safety measures if outside hazards are impeding an asset’s ability to function safely. This allows businesses to avoid accidents and improve safety outcomes by preventing issues before they occur.     Minimizing Human-Machinery Interactions   Managing environmental hazards is just one way digital twins reduce human time in the field. Minimizing human-asset interactions in other ways as well is a tremendous advantage of digital twin technology. By identifying any unseen issues exactly at the source, the amount of time workers must spend interacting with potentially dangerous asset components is greatly minimized. In some cases, digital twins work alongside artificial intelligence (AI) and robots to repair assets which would have required hands-on troubleshooting otherwise.     Creating Safer Systems   Digital twins may be used in the research and development stages of an asset’s life cycle, resulting in safer machines, systems and physical structures. The spatial data rendered by a digital twin is also helpful in designing the systems that will operate within new machines and structures. This data-rich, robust design environment is ideal for fixing issues and creating the safest possible structures and systems before an asset ever hits the market.     Enhanced Training Opportunities   A digital twin enhances training opportunities for employees whose job sees them working on more hazardous components of the physical asset. While some industries already use simulations for worker training, a digital twin is far superior by virtue of being a completely replicated virtual reality environment. This greatly enhanced experience – and the ability to see elements that would have otherwise been hidden (or highly dangerous to access) increases a worker’s knowledge base in a safe and controlled environment. Employees are also better prepared and monitored when they do have to navigate dangerous mechanical components or structures.     Conclusion   Digital twins are becoming more common in the industrial and construction sectors and its impact on worker safety is already compelling. Applications of digital twin technology in the worker safety space will only grow, especially as digital twins are increasingly used in the R&D phase of system and structure creation. It is not unreasonable to project that digital twins may soon become the gold standard for employee training and safety programs in the coming years as more and more industries look to leverage this technology.        

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Digital Twin

What challenges does a digital twin solve?

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What challenges does a digital twin solve? Digital twins are becoming an increasingly important part of business operations and asset management. In many cases, the Digital Twin can be used as an aid in solving various problems. The most common use is for data analysis and visualization which helps with improving business decisions by providing insights on trends within your company’s operations or marketplace conditions that may affect future success rates of products you are developing; this also allows companies to track their investments more effectively than ever before. By creating a digital replica of a physical asset or system, companies can improve performance, predict failures, optimize operations and improve safety. How can a digital twin be used to optimize manufacturing processes A digital twin can be used to optimize manufacturing industry processes in several ways. First, it can help identify issues with the manufacturing process early on, before they become bigger problems. Second, it can help optimize the process to ensure that it is running as efficiently as possible. And finally, it can help predict future issues that may occur in the manufacturing process and take steps to avoid them.   Operational efficiency is one of the key benefits of using a digital twin. By monitoring the digital replica of a physical process, companies can identify areas where improvements can be made. This could include anything from reducing downtime to improving safety. Additionally, digital twins can be used to monitor energy usage and identify opportunities for cost savings. How can a digital twin be used to improve product quality A digital twin can be used to improve product quality in a few ways. First, it can be used to monitor the quality of the product as it is being manufactured. This can help identify and correct any issues before the product is shipped to customers. Additionally, a digital twin can be used to track customer feedback and complaints in order to help improve the product. This information can be used to make changes to the product before it goes to market, or even after it has been released. By tracking customer feedback, companies can ensure that they are always providing the best possible product. This information can also be used to improve the quality of future products.   Finally, a digital twin can be used to monitor the product after it has been sold. This can help identify any issues that may have gone undetected during manufacturing or shipping. By using a digital twin, companies can quickly and effectively improve the quality of their products. How can a digital twin be used to reduce downtime? One of the main benefits of using a digital twin is that it can help reduce downtime. By monitoring key performance indicators (KPIs), businesses can get an early warning when something is going wrong. This allows them to take corrective action before the problem becomes too serious. Additionally, by identifying trends and patterns, businesses can optimize their operations to avoid potential problems. As a result, digital twins can help businesses run more efficiently and avoid costly downtime. Improving safety by identifying hazards and risks through preventive maintenance Digital twins can improve safety by identifying hazards and risks through preventive maintenance. For example, if a company has a digital twin of a machine, they can use it to monitor the health of the machine and predict when it will need maintenance. This can help prevent failures that could lead to injuries or accidents.    Additionally, digital twins can be used to monitor environmental conditions and identify potential hazards. This information can help companies take steps to prevent accidents before they happen. Further, preventive maintenance with digital twins reduces time in the field, which, in turn, lowers the risk of accidents and injury. Conclusion Digital twins are an important tool for companies that want to improve their operations. Digital twins can be used in a number of ways to optimize manufacturing processes and improve product quality. They can help identify issues with products before they become bigger issues. Additionally, they can be used to optimize manufacturing processes and reduce downtime. Digital twins offer a unique way to solve challenges across a variety of industries. By leveraging data and advanced analytics, digital twins can help organizations achieve greater efficiencies, improve decision making, and optimize operations.

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Visionaize is a Digital Twin Consortium member

Visionaize is now a part of the Digital Twin Consortium

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  Visionaize is now a part of the Digital Twin Consortium     SAN JOSE, CA  — (July 6, 2022) — Visionaize, a global leader in the development and maintenance of 3D digital twins for highly complex industrial assets, is proud to announce that it has joined the Digital Twin Consortium (DTC), an organization that fuels collaboration with industry, academia and government experts with a dedication to the overall development of digital twins.   As our mission is focused on helping inspection, maintenance, and operations teams get more productivity out of their asset-intensive businesses, joining the DTC provides opportunities to both convey how immersive 3D visualization solutions can help companies extract maximum value from Digital Twins, but it also brings us closer to the best minds in the Digital Twin space to help guide our future work.   “Our membership in the Digital Twin Consortium will allow interested users to learn about the latest advancements in Digital Twin technologies including integration of AI, Virtual and Augmented Reality technologies to reduce downtimes and optimize inspection and maintenance activities” said Vikas Agrawal, CEO at Visionaize.   He added, “By joining the DTC, Visionaize confirms its goal to provide more value to customers by offering a true operational Digital Twin that helps both experts and non-experts be safe and more efficient in their daily jobs by giving context and delivering timely insights to the point of action. This is done by visualizing relevant real-time and historical plant/asset data from enterprise systems in the 3D model from anywhere at any time.”   “We are proud to welcome Visionaize as a member of Digital Twin Consortium,” said Dan Isaacs, CTO, Digital Twin Consortium. “Their deep  experience in combining immersive 3D visualization capabilities and contextual actionable insight will enable collaborative opportunities across our membership community as we enter an exciting new phase of digital twin adoption.”   “Digital twin partners and customers are among the most ardent users benefiting from the Visionaize solution. Working in concert with DTC members will provide tremendous benefit to these users and further digital twin innovation”, said David Reinhart, EVP of Visionaize and a pioneer in the the Digital Twin space.   To learn more about Visionaize, please visit visionaize.com.   About Visionaize:Visionaize transforms how our customers leverage and engage with data to drive operational efficiencies in complex industrial environments. Our mission is to improve safety, save time, and reduce downtime by delivering valuable insights at the point of action in a way that is quickly accessed and easily understood by operational workers. For more information, visit www.visionaize.com. 

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V-Suite software powering The Industrial Metaverse

GE Digital & Visionaize partnership – combining APM with contextual 3D visualization

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Visionaize, the provider of world-class operational 3D digital twins solutions, announced a partnership with GE Digital. 3D Digital Twins’ high accuracy visualization capabilities are designed to help empower data-driven businesses and improve safety by saving time and reducing cost throughout asset intensive industries across all sectors where they’re used. The V-Suite Starter by Visionaize can be integrated into GE Digital’s APM (Asset Performance Management) Mechanical Integrity™ software to help streamline maintenance activities while increasing worker efficiency. With V-Suite Starter, industrial companies can view real time and historical asset data on the 3D model to deliver timely insights at their points of action. The “see it happen” visualizations will help them solve key challenges associated with downtime such as quicker resolution times for maintenance costs or workforce productivity improvements by providing a better understanding about what is happening in an equipment room before problems arise so they are prepared accordingly. The product allows you to see your investments more clearly through 360 degree views which helps reduce risk. “As companies in asset-centric industries — Oil &Gas, Power Generation, Petrochemical, and Mining — look to enhance their digital transformation journey, there is a growing acceptance to absorb Digital Twins into their operational decision-making workflows,” said David Reinhart, Executive Vice President for Visionaize. “To build out a more robust Digital Twin experience, there is a pressing need for rapid contextual awareness and visualization capabilities.” Visionaize helps mechanical engineers and industrial designers visualize 3D plant models with APM’s integrity attributes in order to make data-driven decisions. The integrated solution not only provides insights, but also enables users by color coding the model so they can focus on what matters most when it comes down to their job responsibilities of inspection, planning, maintenance, corrosion analysis and Risk Based Inspection. With GE Digital’s APM solution, workers can be safer and more productive with a better understanding of the environment around them. Accurate 3D models allow for faster resolution time as well as maintenance programs that are optimized to speed up planned downtime activities while accelerating unplanned events too. “Our partnership with Visionaize brings an enhanced scalable templated capability to APM that accelerates implementation and simplifies replication across sites and reduces implementation costs,” said Linda Rae, General Manager of GE Digital’s Power Generation and Oil & Gas business. “Incorporating this state-of-the-art visualization platform is valuable as asset-intensive companies mature in their operational use of Digital Twins.” Click on these links for more information about Visionaize’s Digital Twin Asset Performance Management Software and solutions for Manufacturing, Oil & Gas, or Power & Energy.

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Digital threads form the data foundation for which digital twins are built upon.

How are Digital Twins and digital threads different?

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The digital thread is the path that data follows as it is collected, analyzed, and used to inform decision-making. A digital twin is a dynamic virtual 3D model of a physical object or system that can be used to track its real-world counterpart’s performance and predict its future behavior. The digital thread is the foundation upon which digital twins are built. A digital thread can be thought of as a “single source of truth” for data about an object or system. This data can come from many sources, including IoT sensors, simulations, and manual input. The digital thread is also a permanent record of your product or system’s lifetime, from its creation to removal and provides traceability. A digital twin is a dynamic 3D model that uses data from the digital thread to represent the current state of a physical object or system, and to predict its future behavior. Digital twins can be used for many purposes, including monitoring workflows and diagnostics, performance optimization, and predictive maintenance. Digital twins are often used in manufacturing, oil & gas, and utility industries to improve quality control and optimize production lines. By having a 3D virtual model of a manufacturing process, engineers can make changes and test them before they are implemented. Can Digital Threads or Digital Twins be used on their own? Digital threads and digital twins are often used together, as they complement each other well. However, they can also be used separately. Digital threads can be used without digital twins, for example to track the progress of data through a process or system. This data can then be used to inform decision-making, but it will not be represented in a dynamic 3D model. Digital twins, on the other hand, require digital threads in order to function. This is because they rely on data being captured and tracked throughout the lifecycle of a product or system. Without this data, it would not be possible to create an accurate representation of the real-world counterpart. When multiple digital threads are collected and combined they can synergistically provide holistic performance of a physical object or system. How Digital Threads and Digital Twins Can Improve Manufacturing There are many advantages to implementing a digital thread and digital twin in manufacturing. Digital threads can be used to track the progress of data through a manufacturing process, from design to delivery. This data can be used to optimize the process, for example by reducing waste or increasing efficiency. Digital threads and digital twins can be used in manufacturing to improve quality control and optimize production lines. By having a virtual model of a manufacturing process, engineers can make changes and test them before they are implemented. This can lead to shorter development cycles, less waste, and higher-quality products. Digital twins can also be used to monitor and diagnose problems in manufacturing processes. This can help to identify and fix issues before they arise, thereby reducing the risk of defects. Conclusion Digital threads and digital twins are two terms that are often used interchangeably, but they actually refer to two different concepts. A digital thread is a single, continuous strand of data that flows through a manufacturing process from start to finish. This data can be used to track the progress of a product and optimize the manufacturing process. A digital twin is a digital copy of a physical product or process. This copy can be used to simulate different manufacturing scenarios and test new designs before they are implemented in the real world. Digital threads and digital twins are powerful tools that can be used to improve manufacturing. They can be used to track the progress of data through a process, optimize processes, and avoid costly mistakes. Implementing a digital thread and digital twin can help to reduce costs and improve productivity in manufacturing, utilities, power & energy and other industries that rely on heavy industrial assets.    

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Image of Digital Twin for Asset Intensive Industries

What is a Digital Twin and how does it benefit industrial businesses?

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What is a digital twin? A digital twin is a 3D virtual replica of a physical object or system. In terms of industrial businesses this could be oil & gas refineries, manufacturing facilities, or utility power plants. A digital twin is a form of enterprise asset management software that allows for remote operations and predictive maintenance of these facilities. The term “digital twin” was first coined in 2002 by Dr. Siegfried Zielinski, a German scientist and professor at the Berlin University of the Arts. The purpose of a digital twin is to provide an accurate and real-time representation of a physical object or system. This can be extremely beneficial for industrial businesses, as it allows them to detect faults and potential problems with their products before they become an issue resulting in less downtime and greater productivity. It also allows companies to optimize their processes and improve product quality. How does a digital twin work? A digital twin works by using an initial 3D scan of the object or structure. IoT sensors are added to the asset to collect data about a physical object or system. This data is then used to create a virtual replica of the object or system. Sensors will then relay the physical object’s performance to the digital twin. Such data points can include energy output, weather conditions, or up and down time. The digital twin can then be used to monitor the object or system in real time, and it can also be used to run simulations to predict how the object or system will behave under different conditions. This helps businesses to optimize their processes and avoid potential problems. What are the benefits of a digital twin? There are many advantages of using a digital twin for industrial businesses. One of the main advantages is that it can help businesses understand and predict how the asset will behave under different conditions. This helps businesses to improve their decision-making and to reduce costs. Some additional benefits a digital twin can provide for an industrial business include: 1. Reducing downtime by identifying potential problems before they occur 2. Improving quality control by monitoring products throughout the manufacturing process 3. Reducing costs by optimizing processes and reducing waste. Remote operations also allow industrial businesses to reduce the amount of resources to monitor the asset. 4. Increasing customer satisfaction by providing a personalized experience 5. Improving safety by identifying hazards and risks through preventive maintenance 6. Reducing carbon emissions with fewer trips to the facility, since the fuller scope of what needs to be looked at can be better and more proactively coordinated through the use of a digital twin. What are some of the challenges of using a digital twin? There are several challenges that need to be considered when using a digital twin. One challenge is data privacy and security.  The digital twin contains data that can be sensitive, so it is important to consider how to protect this data. Another challenge is accuracy. The digital twin must be accurate in order to provide useful information. If the digital twin is not accurate, it can lead to incorrect decisions being made. Finally, scalability. The digital twin must be able to scale as the physical object or system changes. If the digital twin cannot scale, it will become outdated and unusable. How to create a digital twin for your industrial business There are a few things that you need to consider when creating a digital twin for your business. Firstly, you need to decide what data you want to include in your digital twin. This data will need to be accurate and updated in order to be useful. Secondly, you need to think about your current industrial business and how it is connected to IoT. Does it have all the correct sensors in order to be able to monitor data you are looking for? After that, you need to think about how you will keep your digital twin up-to-date. As the physical object or system changes, you will need to update your digital twin accordingly. This can be done manually or through automated means. Lastly, you need to consider how you will protect this data. Data privacy and security are important considerations when using a digital twin. If you take the time to consider these things, then creating a digital twin for your business can be a very valuable tool. The future of digital twin Digital twins are becoming more and more popular as they offer a multitude of benefits to businesses. As the technology continues to develop, it is likely that digital twins will become even more widespread and used in a variety of different industries. One potential future use of digital twins is in the medical industry. Digital twins could be used to create models of patients, which would then be used to test different treatments. This would allow for far more personalized and effective treatment plans. Digital twins could also be used in the construction industry. Construction projects are often very complex and involve a lot of coordination between different teams. By using digital twins, construction companies would be able to plan and coordinate their projects more effectively, which would lead to shorter project times and lower costs. Conclusion Digital twins are becoming more and more popular as they offer a multitude of benefits to businesses and have a lot of potential uses. As the technology continues to develop, it is likely that digital twins will become even more widespread and used in a variety of different industries. We can only imagine the ways in which digital twins will be used in the future.

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How AI-Driven Predictive Maintenance is Saving Big for Asset Intensive Enterprises

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The Importance of Digital Twins in Asset-Intensive Industries: How digital twins can redefine Asset Lifecycle Most manufacturers have adopted Internet of Things (IoT) to begin creating new revenue streams and boost efficiency. In asset intensive organizations or industries, asset management is moving from simple maintenance to a focus on operational efficiency that acts as an important business competency. Asset Performance Management (APM) is at the core of this digitization. For example, frequent production downtime in manufacturing costs companies hundreds of thousands of US Dollars per hour, while downtime of an Oil & Gas refinery can cost up to tens of millions of US Dollars per day.  As the physical assets within your business become more digitally mature with a quickly accelerating amount of Industrial Internet of Things (IIoT) data, there is a need to harness the dataset generated to cover the overall asset lifecycle. With modelling, sensor data, visualization, and analytical capabilities expanding all the time, it’s now possible to merge these technologies to make a digital representation of any physical asset. Digital twin models like Visionaize V-Suite® software can help organize data and align it into interoperable formats in order to optimize asset performance and reliability. This is done by replicating the behavior of the physical system or asset so that any change in the physical facility is instantly updated within its digital twin. Digital twins, as the name suggests, are realistic digital representations of physical assets. They help to realize value by enabling improved insights that support better decision making, leading to better outcomes within the physical world. Who needs a Digital Twin and Why? Every factory or plant floor and the management team look for digital twins that can quickly give the staff the insights that help to make important decisions at the right time. Global and Regional Heads – Using digital twins can give informative visualizations of large-scale assets, track and compare factory and fleets, and identify high-performing and under-performing plants. They can then conclude what makes them succeed or fail; it provides an overview of the plants and ensures that it is in control and that assets are safe and reliable. Factory Managers – A unified view of asset health to make sure factory production is predictable, safe, and efficient. Get accurate information for audits and course-corrections and give a comprehensive, consistent view of plant data for the team to collaborate and solve problems. Engineers – Quick identification of potential operational problems and considered solutions. Know that the information is trustworthy to research, troubleshoot, and make fast and informed decisions. Operations – Review as-operated and historical data to know what field changes and engineering decisions were made and why. Digital twins help operators see the large picture and optimize production. Maintenance and Reliability – Monitor and manage equipment health and simply identify trends and bad actors. Get a clear understanding of what engineering changes were made. Asset performance management and therefore the digital twin can help operations and maintenance teams achieve operational excellence by: Creating added value by applying digitally-visual events and processes Solving all existing and new operations and maintenance issues more quickly, proactively and efficiently Leveraging real-time performance metrics and analytics to reinforce plant reliability and safety Digital Twins of assets and infrastructure While the concept of a ‘digital twin’ has been around since 2000, it’s only because of the Industrial Internet of Things (IoT), engineering and operational technology that it’s become cost-effective to implement. Furthermore, digital twins are so imperative to business today and were recently named one of among Gartner’s Top 10 Strategic Technology Trends. Digital twin refers to a digital replica of physical assets, processes, and systems which will be used for various purposes. It has to be ensured that for the digital twin to be identical, the physical and digital planning, design, construction, operations and decommissioning, should be same. Various asset-intensive industries and organizations use the technology of digital twin for asset life cycle management and operations. By transforming unstructured information into a sensible digital asset, plant operators are empowered to visualize, build, and manage structures, systems and facilities of all complexities, ensuring safe and efficient operation throughout the entire life cycle. Using digital twin APM 4.0 puts organizations in a position to shape the maintenance strategies of the future. Role of ML & AI in APM The flexibility and low cost of sensor technology along with mobile inspection workflows result in data growth, and therefore, the Industrial Internet of Things provides greater accessibility to data. This influx and availability of data requires technology and computing power to translate the information and give insight into future outcomes. Machine learning and Artificial Intelligence are advanced techniques that provide even more analytical power. This capability surfaces otherwise unforeseen patterns, oncoming failures and their causes, process anomalies, and inefficient operations. Moreover, ML / AI models provide accurate prediction (>95%) that can help detect failures of machines, components and systems, and provide sufficient lead time (from 2 to 24 hours) to the maintenance engineers to prevent the malfunctioning and minimize the potential damages. Finally, AI models are often used to raise the bar from predictive to prescriptive maintenance by combining the ML model with maintenance experience and heuristics to not only identify what will fail, and when, but to suggest a recommended course of action. For process industries ML / AI meta models are often constructed and trained to find out what are the foremost critical variables to the method and therefore the final product so as to optimize resources used, machine utilization, increase profits and minimize expenses. Some benefits of Asset Performance Management, at a glance: With APM, asset-intensive and asset-centric organizations can address various critical issues to understand business outcomes, such as: Optimization of operational & maintenance costs (operational excellence) Mitigation and prevention of worker safety risks and to better control environmental, health and safety (EH&S) incidents Regulatory compliance and assessment of associated costs A greater Return on Asset (ROA) by powering asset performance, maximizing revenue and through the use of predictive analytics Increased output and productivity by helping

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