How to Implement Digital Twins in A Digital Factory!

Table of Content:

Build

The build stage, as the name suggests, is when the digital twin is given its form. Development of DT Models which include simulations, behavioral, cause/effect, etc. takes place in this phase. The tech stack to build this digital twin consists of an IoT platform, and proficiencies across technologies such as cloud, AI/ML, AR/VR, statistical modeling. These technologies are then used to create the faceless twin.

These twins need comprehensive IIoT platforms to provide the functionality needed to flexibly build and securely scale mission-critical IIoT solutions. The platform capabilities influence the velocity of the development of the digital twins.

Then follows 3D visualization of the twin, scene creation, implementing energy optimization levers with benchmarks, and integration with real-time data sources from sensors and other sources into the digital twin.  These connections deliver real-time alerts & insights in conjunction with the data available in enterprise systems. Further, developers need to take care of the implementation of analytics and visualization tools to monitor, diagnose, and drive predictive maintenance capabilities.

Operate

Validating and testing the digital twin is an important part of the implementation process. Testing of the twin through simulations and other tests and confirming the accuracy and functionality of the digital twin take place in this stage. These ensure that the twin behaves as expected.

All rule changes, logic modifications, and validation made on feedback and changing requirements must be done before moving on to the next stage. The next stage of this process involves onboarding new assets, processes, workflows, etc., and implementing maintenance and support. Ensuring seamless escalation and issue management via L2, and L3 support as well as backend management support must be executed in this stage.

Deploy

During the final implementation phase of the digital twin, it is deployed in its manufacturing operational environment. This phase involves system validation for both the proof of concepts (POCs) and production applications. Taking a  platform-based deployment approach for this tt facilitates a seamless journey from representing an object in 3D, AR/VR to creating a digital replica in a virtual environment.

The deployment stage takes the digital twin maturity journey from a mere representation to reality. At this stage, virtual devices operate independently and eventually become digital twins with the autonomy to self-adjust.

The capacity to self-adjust eases many maintenance challenges and delivers insights that avoid downtime. Deployment, however, is never complete without proper knowledge transfer, training, and change management to help the workforce adapt to the digital twin in the digital factory.

To sum up

Digital twin adoption is only going to increase. Amongst other factors, an increasing focus on sustainability as it becomes linked with business outcomes, is driving digital twin adoption. Reports show that 85% of consumers prefer conducting business with organizations that focus on sustainability.

Creating digital twins, however, needs tech expertise along with domain expertise. Implementing digital twins is a complicated and often time-consuming task. It has thousands of endpoints that need to be seamlessly connected to create a cohesive ecosystem where both virtual and physical worlds converge.

Pratiti complements deep technology expertise along with domain expertise across manufacturing, healthcare, and energy. We have delivered a patented Digital Twin and IoT-enabled Performance Intelligence & Health Analytics solution for the Renewable Energy Sector.

Along with that, we have robust technology partnerships and alliances with leading industry players like AWS, GoogleCloud, Sitewide Shoonya ThingWorx, and Databricks. These partnerships allow us to deliver high-performance solutions like digital twins rapidly. Our implementation experience across the domain and tech stack ensures that our solutions provide enhanced speed, quality assurance, focused and efficient engagements, and end-user experiences.

Digital twins in a digital factory can be used to simulate different scenarios and test potential improvements, create design models for a future asset, and help manufacturers make more data-backed and informed decisions.  From simulating different machining strategies or creating a digital twin of a production line to identifying bottlenecks and optimizing the flow of materials and products; digital twin use cases across the digital factory are only expanding.

Organizations across different industries are now looking at new ways to leverage the data in their ecosystem and take advantage of digital twin technology. With advances in connectivity with edge computing and telecom like 5G, digital twin technology will become more accessible to even smaller players and fuel their competitiveness.

Connect with us to learn more about this technology and get started on your digital twin journey!