Digital Twins Are Underutilized for Human Safety and Productivity
Worker safety is critically important. Modern organizations are constantly on the lookout for the means to improve proactive safety and response times, and enhance data-driven strategies. This has created growing pressure to embrace new technologies. Organizations need a better understanding of operational risks. And, they need more effective ways to use this understanding to improve safety across the workforce. A key way to do this at scale while taking advantage of existing digital and IIoT investments is to consider the human element in digital twin efforts
Gartner defines a digital twin as “a digital representation of a real-world entity or system that mirrors a unique physical object, process, organization, person or other abstraction”. Analyzing the responses of the digital twin to various stimuli allows organizations to detect, predict, or even prevent issues from happening while simultaneously optimizing the performance of systems and devices through real-time analytics. Benefits include:
In industries where even a simple mistake can cause tremendous harm to workers, equipment, and profits, digital twins allow users to test equipment processes and scenarios before devices are built or deployed, or physical inspections and activities are undertaken, preventing mishaps. By taking real-world data as inputs and making predictions, digital twins show how physical objects or systems will be affected by those inputs. The technology has largely been used to date to enable a more efficient, accurate, and cost-effective way of deploying new technology, systems and processes, but it can and should be used to help improve the way we work and the overall safety of the workforce.
Digital twins have been around for decades. NASA famously used one to assess problems and simulate solutions for issues in space in the 1960s, notably rescuing the Apollo 13 mission. But, recent advancements in cloud and IoT technologies have propelled the technology into new applications with much greater capabilities.
Here are five ways digital twins can now contribute to the safety and productivity of the workforce:
Predict accidents before they happen
Managers can confidently take a behind-the-scenes look into real-time system and device data. Digital twins enable them to create next-generation predictive analytics models. These models can help predict when an accident might happen and under what circumstances – these parameters can be integrated into connected worker solutions, to proactively workers to prevent accidents. For example, in the construction sector, if the builder has digital twin of an in progress site taking live readings from both static censors at the site, and the workers moving throughout, and they want to bring in modular building components, they can they can use the digital twin to determine the best time, and process by which to do so. Answering questions like: When would the delivery and deployment be least disruptive to other site activities? What workers with the needed skill sets would be free at this time? What would be the best physical way to bring the components in? What new training or alerts need to be pushed out to which workers for this to go smoothly? And more. Digital twins can help identify poor or unsafe procedures in the specific context of the site, before the components are even ordered, allowing organizations to make amendments to improve the conditions for the workforce and help ensure greater success for the project overall.
Beyond the predictive analytics benefits, the technology also delivers significant improvements in equipment efficiency – which drives right to the bottom line. With equipment downtime costing billions of dollars globally, digital twins can deliver a comprehensive view of machines and equipment, allowing organizations to increase asset uptime, while cutting inspection and maintenance costs and reducing equipment failure by a large margin. This becomes more powerful when human worker data is integrated. For example, in the manufacturing sector, digital twins can understand what is happening on the production line in real-time as well as give a sense of what could happen in the future as specific conditions change. Say for instance, if a shutdown could be triggered by a worker entering an unauthorized zone too close in proximity to equipment that could cause lacerations or serious injury. Using a digital twin that integrates human movement into its model can trial and test different alerts at ranges of proximity to prevent injury as well as shutdowns and the resulting recalibration of equipment, processes, and systems. Making better decisions for worker safety can improve OEE.
Enhance inspection accuracy
Digital twins also help ensure the integrity of process systems. By eliminating the backlog of inspection reviews that have the potential to impact asset safety and production levels, the technology helps assess the current asset performance against industry best practices and improve the accuracy of inspections. Through frequent gap analysis, it allows for findings to be integrated into processes. For example, in the chemicals industry, digital twins can be used to train operators on atypical operations – especially during unexpected start-ups or shutdowns. Such training simulations can help avoid potentially dangerous situations that can lead to safety and environmental incidents. Using digital twins, organizations can proactively identify critical items and risks quickly, moving away from ad hoc approaches to issue management, towards a proactive risk analysis model – improving the performance and safety of systems and processes.
With digital twins you have better visualization of physical assets. Using this technology, organizations can easily and effectively integrate user feedback, and design products and processes according to their specific needs. From different parts and colors, to different specifications and capabilities – using digital twins, the organization can easily get an idea of how an end product or process will look and feel, even before it gets physically rolled out or implemented. Such visualization can help meet user needs flawlessly, while improving the productivity and efficiency of the workforce. For example, for wind turbine manufacturers, digital twins can allow users to see what’s happening at the site – without actually being present. They can constantly factor in sensor data across wind speed, weather data, power usage, and more to ensure alignment with customer specifications. Moreover, such analysis can also help identify common patterns, predict issues, as well as unearth impending failures that the human eye would otherwise miss – improving the overall performance and safety of equipment.
A digital twin can act like a virtual mirror to help increase a team’s productivity via improved training and communication. By virtually operating and maintaining systems and representing the efforts of workers on a project or task on the other side. This technology minimizes the likelihood of problems, while allowing for issues to be discussed in real-time. For example, in the petroleum industry where workers are working from different locations in a rig, they can be kept informed of all that’s happening through real-time communication and collaboration. Such communication not only saves time, but also takes productivity to the next level. Oil companies can also use digital twins to assess the impact of potentially hazardous operations in the virtual world and accordingly make changes to real-world processes and systems to deliver tangible operational efficiencies.
As the industrial workforce looks to overcome a unique set of growing challenges, continuing to expand use of and capabilities withing digital twins will help improve situational awareness, enhance emergency response, and ensure timely, accurate detection of risks and incidents. Although there’s a lot left to explore, this technology can already help improve worker safety, equipment efficiency, inspection accuracy, visualization of products, and communication – but first people need to be incorporated into the digital model by way of the data they create and use.