How to build achievable Digital Twin Strategies in Automotive Industry with Vijayakumar Kempuraj? He got over 14+ years of experience in the domain of Automotive-software solutions and services, Digital products and services for creating digital twin of end-to-end process.

He is also pursuing his PhD in SRM IST Chennai.

He is also leading agile software engineering team for delivering vehicle health products and services for retails and fleet businesses using connected vehicle technology.

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Q: Tell me about yourself and your journey in the field of Digital Twin within the automotive industry.

My professional trajectory has always been deeply rooted in the automotive sector, specifically in what I define Digital Twin technology.

I have helped to develop end-to-end digital twin solutions for over 14 years using my experience in automotive software and all levels of convolution from connected car to connected factory, combined with a deep understanding of the challenges and opportunities associated with digitalization in both engineering and non-engineering domains.

The journey started with Ford Motor Private Limited. My responsibility as a Digital Twin Leader was rate limiting progress on digital transformation endeavours.

This involves experience in creating and executing strategic technology roadmaps, fostering collaborative environments with technology partners, and driving innovation through incubation and future technology development.

Started with product development function with a strong commitment to continuous improvement, leveraging tools like digital twins and digital validation to optimize product design, process planning, and overall operational efficiency.

Next move to lead an initiative on creating digital factory enabling operational efficiency with Application Enablement Platforms, AI & Data analytics and VR/AR. And then to drive the development and launch of AI-based business process automation, specifically Digital assistants that boosted productivity for enterprise operations.

Recognizing the increasing importance of data-driven decision-making, I transitioned into roles focused on enterprise connectivity and digital product development.

In these capacities, I have led product management space leveraging vehicle health data, diagnostics, and connected vehicle platforms to enhance features, improve service lines, and create a more seamless and integrated customer experience. A journey on Product creation to industrialization to product experience with customers.

Then rotated to create enterprise process twin, architecting enterprise value stream and leveraging generative AI, large language mode, Low code no code solutions and process mining off the ground.

These initiatives have empowered citizen developers within the organization and resulted in significant process improvements, from fragmented processes to digitally threaded processes. I played key role in implementing robust strategies around data security and governance to protect sensitive information and ensure adherence to industry standards.

Since then, I am complementing my knowledge by studying a Phd in Computer Science that involves the theme of using neuromorphic computing and brain-computer interfaces for digital twins. This research ambition demonstrates my commitment to staying abreast of this revolutionary tech.

Outside of my work at Ford, I am an engaged member in the digital twin community. I am a sought-after keynote speaker and panelist at industry conferences, sharing my knowledge and insights.

I have published and spoken about areas related to Industry 4.0, digital twins & advanced computing technologies, To know more on my contributions please follow this space.

My obsession is driven by a conviction that digital twins will change the auto industry more radically and dramatically than anything since assembly lines. I am a lifelong learner and I love to learn new things, work with technology and people, finding ways of how these awesome capabilities can be leveraged.

Q: Why is digital twin technology important today, especially in the automotive industry? What does the future hold for this technology?

At a high-level, digital twin technology is an exact or approximated version of a physical object or system. It is revolutionizing the way vehicles are being designed, manufactured and services in Automotive space. Here’s why it’s crucial:
Faster product development time: Digital twins permit rapid prototyping and testing of new vehicle model configurations, reducing the cost and manufacturing lead-time.

Improved manufacturing efficiency: Digital twin predictably models the production process which helps fine-tune configurations, locate bottlenecks and boost overall productivity.

Enhanced Vehicle Performance and Durability: This capability will enable feeding real-time data from in-use vehicles to their digital twin, predicting vehicle service requirements as well as performance optimization based on safe operation parameters.

Better customer experience: By generating a digital twin for each vehicle, you can offer personalized experiences like predictive maintenance alerts and remote diagnostics.

Sustainability: Digital Twins can assist in optimizing vehicle design for energy efficiency, waste reduction and reducing environmental impact.

The Future has Already Begun Now, and The Way Ahead For Digital Twin Technology for Automotive
There is a great potential for digital twins in the future of automotive:
Driverless cars: To develop and field-test autonomous vehicles, digital twins will be used to simulate a plethora of driving conditions.

Connected vehicles: The digital twin will allow enhanced connectivity features, from over-the-air updates to predictive maintenance.

Manufacturing: Digital twins will power emerging Industry 4.0 technologies, like robotics and additive manufacturing Insights.

Custom mobility: Digital twins will make it possible to provide personalized vehicle experiences that reflect individual preferences and driving traits.

Digital twins will be combined with virtual and augmented reality for design, training, and sales/AR-based experiences.

What this really means is that the automotive industry will be revolutionized by digital twin technology, driving for more efficient, sustainable and customer centric products and services.

If you are interested in specific areas about the role of digital twins in automotive industry (for instance, how carmakers use DTs for autonomous vehicles or supply chain optimization), please let me know.

Q: How do you approach building achievable digital twin strategies? What are the key considerations during implementation?

This takes a structured approach and brings together a range of factors to create the right digital twin strategy for your business. This is a quick summary of the process :

1. Establish: Clear Objectives and Value Proposition
   Define business objectives: Define what you want to accomplish with the digital twin. It could to increase productivity, improve the quality of products or ensure a good experience for customers
   Define the benefit: Establish how having an accurate digital twin will deliver real value; monetary savings, revenue gains or operational efficiencies.

2. Select Appropriate Use Cases
Focus on high-impact, Return-on-investment areas
Know your data, legally speaking ‘ Be mindful of the availability and quality of datasets that can be accessed.
If data not there, you need a another bigger scope of initiative to generate data.

3. Data strategies and the infrastructure
Collection: Develop robust data collection methods and maintain the accuracy of your data.
Data Integration: Establishing a plan for extracting data from each of its sources and bring it all together on one platform.
Data Security and Privacy: Implement Strong security measures to protect confidential data.

4. Model Development

Level of Detail: Pick an appropriate level of detail (more complexity requires more computational resources to train and estimate)

Use Simulation Tools: Choose appropriate simulation tools to produce faithful virtual representations.

Ongoing validation: Continuously refresh and improve the digital twin with data from real-world conditions.

5. Technology Selection
Review platforms i.e., one that suits your business needs and scalability, also the budget you carry.
Integration Capabilities : makes your integration work seamlessly with existing systems and data sources.

6. Implementation and Deployment
Pilot Projects – Small scale projects to validate the idea and gather feedback.
Scalability: Make digital twin scalable to support growth in future.
Service transition: To implement change management, where organizational changes are addressed as well as employee training needs.

Critical Concerns While Execution
Data Integrity: It is important to ensure the accuracy and dependability of data in order for any digital twin strategy to be viable.
Cyber security, assuring that trust is not broken and disruption did:/
Integration Challenges: The integration of data from different sources may be hard and time-consuming.
Skill Gap: Building a team proficient in digital twins can be difficult.
ROI (Return on Investment): The need for ROI is obvious- it must be mapped and measured in order to demonstrate the return of investment.
Continuous Improvement: Digital twins will be “always changing” systems, and require continuous updates & refinements.
Taking these steps and taking things into consideration will put your organization on the fast track of capitalizing upon digital twin strategies enabling them to innovate as well drive their business operations.

Thank you for the information.