Culture Impact on Digital Twin Strategies in Organizations? Is there any such thing?

Vijayakumar Kempuraj 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 the leading agile software engineering team for delivering vehicle health products and services for retails and fleet businesses using connected vehicle technology.

He is also pursuing his PhD in SRM IST Chennai.

Part-2

Q: What is the current market scenario of digital twin technology in India?

The digital twin market in India is witnessing a rapid growth. Boosted by the rapidly growing digitalization, government initiatives and expanding manufacturing sector, ample adoption of this technology is being witnessed in the country!

Key Market Dynamics:
NOT MATURE ENOUGH LIKE GLOBAL MARKETS It is still in its initial phase, yet INDIA has shown fast adoption across the board from manufacturing to infrastructure & smart Cities.

Government Initiatives: The focus on digital twin by the Indian government including initiatives like Make in India and development of smart cities will lead to country-wide adoption.

Manufacturing Taking The Lead: Standing tall as the backbone of India, the manufacturing sector in our country goes topsy turvy with its adoption for Digital Twin applications.

Transportation and Utilities Infrastructure : Increased Focus by the government on infrastructure development, i.e. transportation & utilities is a major driver for digital twins demand-line.

Key Challenges: However, data quality, cybersecurity and skills shortages remain to be significant challenges.

Market Size and Growth:
India’s digital Twin Market is Expected to grow at a whopping CAGR of 39.30% During the Forecast Period, Increasing from Around $1billion in 2024 to more than $12 Billion by 2032.

Key Players and Trends:
The market is taking form of a mix between Domestic and International players.

More and more emphasis on industry-specific solutions…and partnerships.

But as it is evolving its cloud-based digital twin platforms, Technology providers like Azure, Google, PTC, Siemens, GE etc may be interested in delivering integrations that do not require on-premises additional software.

Digital twins with AI and IoT integration: how companies innovate using them.

The Indian digital twin market is a formidable industry & offers robust growth avenues for the foreseeable future There are challenges – but the government backing, along with industry-led initiatives will help bring down these barriers and drive up commercial deployment of drones.

Q: Could you share challenges and outcomes of implementing a digital twin solution?

Challenges:
Requirement of High Quality Data: Due to vast and varied types of data from multiple points/sensors, process chains etc, it is very important that the data should be accurate (correct), complete (contain all essential features) also must arrive in real-time fashion. The challenges include data cleaning, standardization and integrating.

Model complexity: Creating accurate digital twins of complex automotive systems requires advanced modelling techniques and computational resources.

Real-time simulation: It is computationally expensive and needs high-performance computing infrastructure for supporting real time simulation capability for complex models.

Cybersecurity: Above all else, the most important aspect of an automotive OTA will be to secure sensitive vehicle and customer data against cyber threats.

Organizational Change: Adopting digital twin technology in existing workflows and overcoming resistance to change is hard.

Short of Talent: The lack or skill set to operate digital twin technology, simulation and data science can be the biggest bottleneck in implementing this concept.

Expense: The development and upkeep of digital twins could be expensive as they need a heavy investment in technology, infrastructure.

Outcomes:

Faster Product Development: Digital twins speeds up the time required to develop products and lowers costs while increasing product quality with virtual prototyping/ testing.

Improved manufacturing efficiency: Streamlining production processes, reducing downtime and increasing overall plant efficiency with the help of digital twin-based simulations.

Let it be Predictive Maintenance; ad receive highest fleet uptime, lower costs of maintenance and better customer service with predictive models for maintenance.

Supply Chain Optimization: Enhanced visibility in supply chain, inventory optimization and disruption management using digital twin based simulations.

Customer Experience Improvement: Personalized Customer Experiences, such as virtual vehicle configurators and remote diagnostics provided by the company.

New Business Models Data Monetization & value-added digital twin-derived services for new revenue streams.

Even as challenges remain, digital twin technology is being embraced by the automotive industry more and more for its ability to drive innovation, increase efficiency, and improve customer satisfaction.

Q: Is there any cultural play in how digital twin strategies are rolled out within an organization? How do you address cultural shifts during adoption?

Yes, culture is critical to the adoption of a digital twin strategy.
Organizational culture affects how new technologies are seen within the business and if employees will embrace these changes. These are but a few cultural aspects that could impede the success of a digital twin program, with resistance to change and data sharing issues among others playing an especially significant role in adoption.

Addressing Cultural Shifts
Solutions to defeat these problems are as follows:

Articulate the Vision: Communicate benefits to be generated by a Digital Twin and how it is aligned with organizations mission, vision.

Employee Engagement: Engage employees in the development process of digital twin, as this will give them a sense of ownership.

Create a Structured Change Management Program: Implement robust change management to guide employees through the process.

Create a data-culture – Create an organizational environment to promote sharing and collaboration of the best quality through user developed standards, policies, practices and procedures;

Reskill / Training: delivers comprehensive training modules to engage employees with the right skills for deploying digital twins.

Leadership Buy-in: One of the main reasons fintech digital twin is slow to adopt because it does not get enough support from Leadership.

Drive adoption through incentives: Identify a carrot that motivates the successful implementation of digital twins.

Acknowledge Successes: Identify some wins and use those successes to gain momentum, energy.

How the Car Industry Works by Major Culture

Risk Aversion: With the automotive sector often being risk averse, it was quite refreshing to see an innovative product like replacement glass roofing panels. However, proving that the digital twin is as reliable and secure in practice before it can be trusted.

Data Privacy (secure handling of sensitive customer and vehicle data): The proper treatment of ongoing processes with the available measures for data related to user, dealer or another physical person affiliated to Automotive industry ecosystem.

Collaboration: Creating a shared language between separate parts of the process (i.e. design, engineering, manufacturing and sales) is necessary to enable digital twin success.

Taking care to look at and rectify cultural characteristics will directly affect the success of digital twin applications, achieving this is not only key but should necessarily seek their full benefits.

Thank you.