IoT is the key to implement digital twin technology: Nabuath Ulla Khan, SAS India

A digital twin is basically a virtual representation that serves as the real-time digital counterpart of a physical object or process. In an interview with Dataquest, Nabuath Ulla Khan, practice head, IoT analytics, SAS India talks about the role played by IoT in the implementation of digital twin technology, and much more.

What are the solutions you are offering for a digital twin?

Automation, digitalisation, and the adoption of IoT are important requirements for companies that wish to drive lower costs, enhance and unearth new efficiencies that can lead to new business opportunities. SAS helps clients in identifying and quantifying critical quality drivers across the entire production process and allows them to explore and develop a model of the entire end-to-end process (digital twin). The model quantifies the impact parameters on key quality metrics and provides a better understanding of the process, resulting in optimised process parameters. This ensures the best quality in the first place and a lesser number of products with defects entering the market.

How can India build a resilient supply chain by digitisation?

The pandemic impact has driven home the need to address the weaknesses of several traditional supply chains. The imposition of lockdowns and halt in production across locations severely affected the global distribution of inputs and final products. While collaboration has become essential to diversify sourcing across various segments of different supply chains to minimise risks from disruption and enhance resilience, this alone is not an indicator that can help speed up the building of a resilient supply chain.

A few additional indicators (such as sustainability, agility, flexibility, redundancy, visibility, security, public-private partnership, supply chain network design, and much more) become very critical aspects. All of these need to be looked at together to find correlations, critical signals, and so on. As a result, digitisation becomes much more important than ever it was.

In order to create a fully optimised supply chain, analytics and digitisation is necessary. To drive value from supply chain digitisation, we believe three broad areas are critical: Strategising and planning, building a supporting ecosystem, and enablement.

How is digital twin crucial to the development of IoT technology?

Digital Twin is most commonly defined as a software representation of a physical asset, system or process designed to detect, prevent, predict and optimise through real-time analytics to deliver business value. Digital twins can be used to predict different outcomes based on input variable data. By using comparatively low-cost data processing and increasingly accurate sensor technology, we foresee huge potential, especially in the field of simulation. With additional data and analytics, digital twins can often optimise an IoT deployment for maximum efficiency, as well as help engineering designers, figure out where things should go or how they operate before they are physically deployed.

Contrary to the question, my view is that IoT is the key to the implementation of digital twin technology. The increasing affordability of sensors, widespread use of Wi-Fi, and the data-throughput capacity of the cloud combine to make the application of large-scale digital twin modeling affordable for a range of manufacturers operating in the industrial IoT (IIoT).

What organisational challenges do you see in India? How will digital twins play a role in identifying those issues?

When it comes to implementing IoT solutions, IT/OT convergence is critical to success. While internal IT and OT challenges are very much visible with the organisations in India, The starting block of having a simple visualisation layer of critical KPI’s and leveraging the streaming data to see the behavior change on a real-time basis roughly referred to as a digital representation or digital twin can help sort out a lot of issues and misnomers at hand and get the relational bias completely out of the system and have a streamlined process in place.

How will the implementation of AI and data analytics in the digital twin enable us to gain more insight?

The heart of the digital twin is analytics. It’s not just about, ‘Can you collect the data and visualise it like a Digital twin?’, but ‘Can you turn it from data to valuable transformative information?’ The main driver for that is analytics. This means you have to be able to collect and move the data in effective ways. Then you must understand what the data is telling you, but beyond that, you need to drive the action so that you can achieve that expected result on the back-end.