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Digital Twin Systems Engineering – A Competitive Advantage

Below are some of the learnings from the panel discussion on “Digital Twin Systems Engineering: A Competitive Advantage” from the 13th NASSCOM Design and Engineering Summit held in Oct 2021

 

The concept of digital twin – a virtual model of process, product or service and pairing with physical world has been prevalent for the past many years. However, with the advent of IoT, maturity of ecosystem including network and connectivity, software tools framework, it has been cost effective for businesses to opt for digital twins.

 

How can industries take advantage of digital twins and how do digital twins reshape the industry?

For the industry, there is tremendous advantage when customers and suppliers are connected along the same infrastructural platform. Many OEMs in Aerospace and Defence industry are moving along their digital transformation journeys, including digital twin systems to support logistics, build management, improve quality and product performance, research, and performance simulation etc. Digital twin systems are transforming the industry at a rapid pace.

In other industries like semiconductor manufacturing for companies like Robert Bosch for example, the built up for new manufacturing site would not have been possible without the digital twin system of the whole plant. It supports in having an oversight of the manufacturing footprint across many machines. Having a digital twin helps in expanding the facility during the operation without having to rely on manual drawings etc. It is a true enabler for such a complex operation.

 

How has use of simulation predictions in digital twin implementations for important product and operating decisions helped various organizations? What features and capabilities will be important for digital twin implementation?

  1. One of the crucial things in a project is to ascertain early on whether one wants to go into digital twins or not and decide what processes will be conducted in the conventional manner and which will have the digital twin. After this decision is made, one has to be sure about the effort needed to maintain the digital twin during the project. Sometimes once the first phase of project is finished, one must be careful to understand that the ground reality is fitting the digital picture and stay on course to take full advantage of the digital twin.
  1. The companies must also target core areas which are relevant for both the customers and the company. For companies like Collins Aerospace, one area of investment is prognostics health management. It is able to create a digital twin of the products in the aircraft. Digital twin, along with machine learning, helps in predictive maintenance as well. It also helps in providing feedback to the design teams to make sure to build robust products.

 

What are the pitfalls while adopting digital twin?

Pitfalls are –

  1. Companies get very engrossed in the infrastructure side of things to make it all connected. It is important to start small. One can start with one factory and from a critical pain point that is plaguing the company or the industry and then build on that.
  1. Domain competencies exist across different organizations, and one has to prepare the workforce for surprises if they follow a data centric. For the employees who have decades of expertise in a particular domain, it is important to prepare them to marry their expertise with the data driven models emerging from the digital twin systems to prepare for exigencies in the future.
  1. Investment in the past has been in physics-driven modelling. However, the organizations must combine the data-driven modelling of digital twin and physics-based models to get the maximum benefit from a digital twin system.

 

Digital twin requires an ecosystem of tools, software, frameworks and enabling tech like Cloud and connectivity. What is the assessment of the maturity of ecosystem and what are the learnings of dealing with the ecosystem of tools?

 

Digital twin

 

  1. A lot of traditional modelling software are mature in terms of modelling activities. However, the software that enables the interconnectedness between various systems are disparate as different departments have built their own individual tools and systems. The maturity to connect all the systems on a single platform is still low. This is especially a problem for large organizations having multiple PLM systems along with other systems running parallelly. There is a need for an over-arching system connecting all these sub-systems to allow seamless interaction and free flow of information from engineering, factories, field etc.  

 

  1. There is also a concern is towards the techno-commercial side of things. From a data science point of view, everyone would like to have seamless flow of information to harvest the full benefit of a digital twin. From a commercial point of view, the organizations want to maintain a competitive edge. There is a need to have specific tailoring of exchange of data to create the right balance between confidentiality and free exchange of information.

 

  1. Safeguard of intellectual property (IP) is another challenge. Recently, there have been some standards enabling interaction in an encrypted fashion and IP is protected. However, this continues to remain challenging while companies adopt a digital twin system.

 

  1. Cyber security across much broader infrastructure is needed.

 

  1. Contractual knowledge regarding contracts for digital twin enablement opportunities.

 

  1. Clear understanding of economics. The organizations must know the cost to build this entire infrastructure and maintain in the future and the return on the investment for doing the same. There needs to be clear deliverables for all of them.

 

What are the benefits derived from digital twin systems?

  1. From an engineering perspective, there is a change in the way engineers need to work moving forward. Historically, it has been around design-build-test and repeating the same format until the product is ready. However, now it is moving towards model-analyze-test sort of a system which is limiting the redo loops, thus shortening the time to market and reducing the costs of analysis, acceptance testing, certification at components and system level. Digital twin systems allow companies to optimize costs, designs, and provide robust offering to the industry. 

 

  1. From an operational standpoint, for a complex manufacturing plant, companies have been able to save on installation of complex equipment by upto 20% by having a digital twin system of the factory. This is highly welcome in the light of the semiconductor crisis as it shortens the time to ramp up the capacities in the factories.

 

  1. In addition, virtual testing of products has been possible as there is enough understanding of the product based on its digital twin. In automotive, in some companies, testing time has been reduced and therefore, time to market has been reduced too.

 

To read more about the Engineering R&D Industry of India, please follow the links to access the “Advantage India: Gateway to Global Engineering R&D and Innovation” report

NASSCOM Community – https://community.nasscom.in/communities/engineering-research-design/advantage-india-gateway-global-engineering-rd-and

NASSCOM Website – https://nasscom.in/knowledge-center/publications/advantage-india-gateway-global-engineering-rd-and-innovation

 

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