Embracing predictive technologies with digital twins
The world of digital technology is continuously evolving, and businesses across various sectors need to adapt in order to remain competitive.
In this blog, we discuss the transformative power of digital twins and their ability to enable predictive technology adoption.
The Rise of Digital Twins
Digital twins are virtual representations of physical assets, processes, or systems that leverage near real-time and historical data and advanced and predictive analytics to improve performance, efficiency, and decision-making. Over the past decade, digital twins have rapidly gained traction in various industries, including mining, manufacturing, and transportation. The technology has moved beyond the hype and is now delivering tangible business value, from optimising asset performance and reducing operational costs to enabling predictive maintenance and dynamic debottlenecking to increase production. Critically, digital twins provide the scalable interface for the adoption of predictive technologies such as machine learning analytics and AI.
Key Components of Digital Twins
Digital twins contain many components and are designed for specific use cases. In order to move from human experience-led decision-making to data-driven decisions, most digital twins utilise a combination of OT and IT data, model outputs from ML and AI, and human-in-the-loop decision-making. Data and context enable information and predictions, in machines and humans, and are the keys to developing effective human/machine decisions.
Digital twins that support improved decision-making should be designed with 4 key principles in mind:
Context
Capture
Learning
Feedback & Control
The 4 key elements of an industrial digital twin
Context
A great user experience is essential for the success of a digital twin, as it allows users to access critical information, collaborate with others, and take informed action, without being overwhelmed by yet more systems and data. This includes providing a common operating picture, often a visual context such as spatial data, contextual metadata, and user-specific information and insights that enable users to make better decisions, faster.
Capture
Digital twins must capture changes in the physical environment, and they must also capture the changes in decisions and analytics from the virtual environment. The capturing of notifications, suggestions, recommendations and decisions (both accepted and more importantly, rejected recommendations), sets up the conditions for structured learning at scale.
Learning
Learning occurs in 2 loops - machine and analytical learning (of all sorts) and human learning and governance. Machine learning will improve machine learning and improve model accuracy, but this will only be used as and when expert rules are changed to allow predictions and prescriptions to be used. The rate of organisational learning is heavily influenced by the users’ experience and the extent to which they feel confident about model outputs and potential impacts.
Feedback and Control
Humans must and will be in the loop for many decisions, and even when decisions could be automated, a period of semi-autonomy may significantly benefit human users, and their feedback will benefit the machine learning loops. Similarly, control is an integral part of psychological safety and ensuring that risks are mitigated. Feedback and control enables all aspects of innovation to proceed at the appropriate speed in a given area, and encourages humans to use their experience to improve how we operate.
The Future of Digital Twins
The potential of digital twins goes far beyond the capabilities we see today. Some of the most promising advancements in this field include:
Standards-based APIs and models promote interoperability and collaboration, and ease of connection from new data sources and bespoke predictive models
Multi-user, collaborative experiences that foster teamwork and innovation across the global value chain
Human twins, that learn from the individual human and provide tailored learning and assistance and optimise reinforcement to improve performance
Integration with the emerging industrial metaverse, where digital twins will serve as the building blocks for immersive virtual environments.
Actionable Suggestions for Implementing Digital Twins
To successfully integrate digital twins into your business strategy, consider the following steps:
Adopt a product mindset: Digital twins comprise many systems, but they are not a systems engineering exercise. Digital twins change how people work, enabling innovation and experimentation. Adopting a product mindset focuses on the key elements of user adoption, measurement of value creation, and continuous learning to adapt and change, to increase user adoption and value creation.
Focus on outcomes: Ensure that your digital twin strategy is aligned with your organisation's strategic objectives and focuses on delivering measurable business outcomes. This includes improving asset performance, reducing operational costs, and enhancing decision-making capabilities.
Create and manage local bubbles: Adopting predictive products often requires changes to how the organisational system operates, and this creates challenges across functions and silos. In order to create the conditions for success, consider creating temporary governance and learning structures and support for cross-functional teams (business, IT and SME) to experiment and learn without needing to fight their own systems.
Measure your success: Establish key performance indicators (KPIs) to track the impact of your digital twin initiatives on your business and product objectives. Remember to include adoption and user metrics, and qualitative and quantitative measures of success. Where local bubbles need to be created, measure the impact on “BAU” processes and consider the scaling implications. Regularly review these metrics to evaluate your progress and adjust your strategy as needed.
Prioritise security and trustworthiness: As digital twins rely heavily on data, it's crucial to prioritise security, privacy, and trustworthiness in their design and implementation. Implement robust security measures, adhere to industry standards and regulations, and establish clear data handling and governance guidelines. Consider offensive security measures such as penetration testing (white hat hacking), red teaming or cyber war games to test the effectiveness of your governance policies.
Foster collaboration and knowledge sharing: Encourage collaboration between subject matter experts, data analysts, and other stakeholders to ensure that your digital twins are built on accurate, relevant, and up-to-date information. This will help you maximise the value of your digital twins and drive better business results.
Train your team: Empower your employees with the skills and knowledge necessary to work effectively with digital twins. This includes providing training on the technology, tools, and processes involved in creating, managing, and leveraging digital twins. As with all new technology and process rollouts, the quality of people support will heavily influence the chances of success.
Test and iterate: Continuously test, refine, and improve your digital twins to ensure their ongoing relevance and effectiveness. Monitor their performance, gather user feedback, and incorporate new insights and learnings to optimise their value and improve their ease of use over time.
Build a strong foundation to scale: Invest in the right technologies, systems, and tools to scale successful digital twin initiatives. This includes integration platforms, data management systems, analytics tools, and user experience solutions. The scale should come from success in the field, not from IT.
Collaborate with partners and industry consortiums: Engage with industry partners, suppliers, and consortiums, to stay informed about the latest developments and best practices in the digital twin space. This will help you stay ahead of the curve and leverage the full potential of this transformative technology.
Conclusion
Digital twins represent a significant opportunity for businesses across various industries to improve operational efficiency and drive innovation, which creates better outcomes. By implementing a well-planned digital twin strategy, focusing on context, capture, learnings and feedback & control, organisations can unlock the full potential of predictive technologies and stay ahead in the competitive landscape.
As a business leader, now is the time to embrace the future of digital twins and transform your business for success. Predictive technologies are changing the world, and digital twins provide the means to manage and drive that change inside your organisation.
If you are looking for help with the design and implementation of digital twins, reach out to the team to see how we can help.