We offer deep tech knowledge across industries: Rohit Pande, IBM India

Rohit Pande, Country Head – AI Applications, IBM India/South Asia talks to Dataquest about the role played by the digital twin technology across the industry.

What solutions does IBM offer for a digital twin?

IBM has been involved with digital twins since the Apollo space program. IBM’s Real-Time Computer Complex (RTCC) was an IBM computing and data processing system at NASA’s Manned Spacecraft Center in Houston. It collected, processed, and sent to Mission Control information that directed every phase of an Apollo mission. The RTCC was so fast there was virtually no time between receiving and solving a computing problem.

IBM continues to do a lot of work with digital twin technologies, especially around our IBM Maximo solutions. And the applications keep growing across different industries. For instance, one of our global innovations has been bringing augmented reality (AR) into asset management.

The IBM Maximo lab services ‘turns on’ much visual and voice (natural language processing) feature for our clients’ workforce. This enables them to see their assets in a new dimension and get instant access to critical data. Those insights can be fed back to others using an AR helmet with voice and video in the visor. This makes ‘interacting’ the next evolution of work.

Our offerings are built upon IBM’s deep industry and technology knowledge across all industries. IBM supports our clients at all stages in the product lifecycle from inception to recycling or disposal.

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

A digital twin uses data from connected sensors that are part of the IoT setup to tell the story of an asset all the way through its life-cycle, from testing to using in the real world. With IoT data, we can measure specific indicators of asset health and performance, like temperature and humidity, for example.

By incorporating this data into the digital twin for, let’s say, an automotive OEM, the engineers will have a full view into how the vehicle is performing, through real-time feedback from the vehicle itself. Anyone looking at the digital twin will be able to see crucial information about how the physical thing is doing out there in the real world.

What this means is that a digital twin is a vital tool to help engineers and operators understand not only how products are performing, but how they will perform in the future. Analysis of the data from the connected sensors, combined with other sources of information, allows the organisations to make those predictions using solutions for digital twins like IBM Maximo Application Suite. With this information, they can learn more, faster. They can also break down old boundaries surrounding product innovation, complex lifecycles, and value creation.

How will the implementation of AI and data analytics in the digital twin help gain more insights?

Digital twins can help organisations stay ahead of digital disruption by understanding changing customer preferences, customisations, and experiences. This knowledge means businesses can deliver products more rapidly, with higher quality, from the components to the code. Yet the promise of digital twin can still go further.

The use of cognitive computing technologies like AI and analytics increases the abilities and scientific disciplines in the digital twin. Technologies and techniques such as natural language processing (NLP), machine learning, object/visual recognition, acoustic analytics, and signal processing are just a few of the features augmenting traditional engineering skills.

For example, using cognitive to improve testing a digital twin can determine which product tests should be run more frequently. It can also help decide which should be retired. Cognitive digital twins can take us beyond human intuition to design and refine future machines. No more ‘one-size-fits-all model. Instead, machines are individually customised. That’s because the cognitive digital twin is not just about what we are building, but for whom.