What is TwinOps?
TwinOps is a portmanteau for Digital Twin Operations.
TwinOps is a software engineering practice focused on the lifecycle of taking digital twins from design to production, and then providing the infrastructure to maintain and monitor them once operationalized.
Since taking a digital twin from design to production requires multidisciplinary expertise, TwinOps is inherently collaborative and intended to empower a wide range of users from process modelers to data scientists and site engineers to IT.
What are Digital Twins?
Digital twins are digital representations of one or more physical processes or assets. This could include industrial equipment, such as pumps, motors, unit operations such as bioreactors and chromatography columns, or entire industrial/manufacturing plants.
Building digital twins is a key step in an industrial or manufacturing organization’s digital transformation roadmap since they unlock tremendous value in the design and operation of equipment and processes. For example, digital twins are used by engineers for a broad range of applications such as optimizing process controls, diagnosing or troubleshooting equipment failures, and increasing key performance indicators (KPIs) such as production yield or uptime.
Digital twins can vary significantly in their complexity and application, but typically have the following universal attributes that make them valuable to manufacturers:
- They are able to ingest data from their physical counterpart
- This can include multiple online or offline data sources (i.e. operations, maintenance, lab datasets)
- They are able to leverage this data to accurately simulate or model their underlying physical counterpart
- This can include real-time process data or simulated data designed to test alternative operating conditions (i.e. virtual experimentation)
To learn more about the steps involved in building digital twins, click here.
Why do we need TwinOps?
Productionizing digital twins in an industrial, regulated environment is challenging. It requires subject matter experts across multiple teams to work synchronously to meet stringent engineering, regulatory, and cybersecurity requirements.
From an engineering perspective, digital twins need to be explainable and grounded in the physical system’s physics, biology, and/or chemistry. From a regulatory perspective, diligent record keeping is required for auditability (i.e., tracing when models were built, what data was used for training, how model outputs were consumed, etc.). Lastly, from a cybersecurity perspective, IT departments often require strict controls on how digital twins may interface directly or indirectly with control systems and/or other mission-critical databases.
Shipping digital twins also require collaboration and hand-offs across multidisciplinary teams, given the many complex components of the digital twin lifecycle.
First and foremost, data needs to be ingested from a variety of disparate sources and then transformed into a unified, usable format. Transformations could include reshaping data, aligning measurements with various resolutions, and filtering sensor noise.
Once the data has been reconciled, process modelers will often incorporate engineering knowledge or scientific domain expertise to build robust, explainable models of the process they wish to characterize. This is an iterative process involving model training (i.e. parameter estimation) and validation.
When the user has successfully validated a digital twin model, they then need to deploy the model to a production environment for execution. This could involve deploying it to an API endpoint where a live stream of operational data or the latest batch of lab measurements could be fed to it. Typically, a model will be used for process optimization use cases (i.e. determining the optimal control setpoints for a given process or equipment). This involves executing the digital model in tandem with a multivariate optimization algorithm at scale. To learn more about optimization algorithms, click here.
Once in its deployed state, the digital twin model will require continuous monitoring to ensure its accuracy is sufficiently maintained. Once a drift in model quality is detected, the user may choose to take the model offline for retraining before redeploying it back to production.
TwinOps thus encompasses the continuous iterations of the Digital twin lifecycle.
What are the benefits of TwinOps?
The primary benefits of TwinOps include the ability to make continuous process improvements efficiently at scale, all the while minimizing business risk.
Continuous Process Improvements: With a live digital twin, engineers can continuously improve their production processes by identifying optimal control setpoints and maintenance schedules, and ultimately extending asset service life for mission-critical unit operations.
Improved Time to Value: TwinOps allows process modeling teams to achieve faster digital twin model development, validate and deliver higher quality digital twins models, and operationalize models to production sites faster and more reliably. This enables engineering organizations to extract value from digital twin initiatives significantly faster.
Scalability: TwinOps also enables vast scalability across an entire manufacturing organization from R&D to global commercial-scale production sites. This means thousands of models can be designed, templated, managed, and monitored for continuous delivery and deployment. Specifically, TwinOps enables SMEs across multiple geographies and production sites to easily reproduce Digital twins pipelines, thereby reducing conflict with IT, and accelerating digital transformation velocity.
Risk reduction: Manufacturing optimization initiatives often require regulatory/compliance scrutiny and validation, and TwinOps enables greater transparency and faster response to such requests and ensures greater compliance with an organization’s or industry’s cybersecurity and cGMP or other regulatory policies.
Who Should use TwinOps?
TwinOps is a cross-functional practice that involves a wide range of personas including but not limited to process modelers, process engineers, data scientists, and IT professionals. Each of these groups serves to benefit from TwinOps at scale, from engineers unlocking deeper process understanding, to data scientists tapping into simulated datasets, to providing IT the ability to view current process status in real-time across the organization and encouraging data centralizing and contextualization.
Many organizations are currently embarking on their digital transformation journey and the implementation of digital twins is a key step to unlocking the potential of Industry 4.0. Even for organizations early on in this journey, adopting a TwinOps paradigm will facilitate and accelerate the adoption of digital technologies across the organization.
If you're interested in learning more about TwinOps and how companies like Basetwo are helping manufacturers rapidly adopt and scale digital twin technologies, feel free to check out our use cases and white papers on our resource page.