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Digital Product Development Systems team anchors the digital transformation efforts at Tata Motors

The senior management has always supported and encouraged adoption of digital technologies (DT) in the organization to enable digitization of processes and thereby created significant efficiency in the system. A core team (the Digital Product Development Systems team) has been working towards implementing the digital product development roadmap to help create a platform for DT over several years

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Please share some of the achievements of the company’s Digital Transformation (DT) efforts.
The digital revolution of Tata Motors started in the early 90’s with the introduction of 3D Computer Aided Design (CAD) in its product development process, with a vision that the era of physical drawing boards based engineering is nearing its end. Thereafter we ceased to use physical drawing boards for new programs and this process was completely replaced by 3D digital mastering of product data.

This enabled us to take the product development processes to a new level. Further, development of new vehicle programs has become increasingly global, where many internal teams and external partners must coordinate their efforts within the boundaries of quality, cost & time (QCT). In this dynamic scenario, digital product development solutions are enabling us to meet accelerated product development schedules by providing tools and capabilities that address agility, quality and standardization.

Top of the mind achievements from the journey are:

  • 3D mastering of parts, aggregates and tooling with concept-to-cradle paradigm. All downstream external and internal consumers refer to this master data as a single source of truth.
  • Implementation of a unified PLM (Product Lifecycle Management) system for both Passenger Vehicle (PV) and Commercial Vehicle (CV) businesses across various locations of Tata Motors Ltd. (TML) operations
  • Today, PLM is used 100% for all new vehicle development programs with state-of-the-art change management process
  • Development and implementation of Knowledge Based Engineering (KBE) applications across various product design and safety domains. Using in-house developed knowledge based engineering framework, KNexT, the knowledge of engineering processes is captured in the form of rules and reused. These applications perform complex operations with automation, thereby eliminating the manual errors and providing quicker results
  • Deployment of Productivity enhancement tools across various product design and validation domains. These have been instrumental for designers to increase their productivity significantly through identified levers such as quality checks for design, drawing automation and process automation
  • Use of High Performance Computing (HPC) solution through CAE simulation using General Purpose Graphics Processing Unit (GPU) computation technology resulting in significant improvement for non-linear implicit simulations
  • Smart review tools were deployed delivering insights into engineering changes of vehicle data at each design release milestones, enabling project teams to review changes visually. In this, integrated approach of 3D Visualization, issue management and change comparison was helpful in accurate design reviews, identifying critical areas to focus and facilitate accurate decision making
  • Multiple Business processes have been digitized by converting either paper based or email based processes into standardized online applications using pFirst in-house developed platform.

Bringing in digital technologies when the current infrastructure is running on legacy is a daunting task. Please explain your scenario?

It is usually difficult for a legacy system to maintain, support, improve or integrate with the new systems due to its architecture and underlying technology. Eventually, they become a significant barrier to digital transformation. The underlying infrastructure becomes more expensive to maintain as it ages. Legacy systems often require a specific technical environment, including hardware. Thus, the infrastructure maintenance spending remains high, as compared to modern solutions. Legacy data represents another significant infrastructure issue. Transferring legacy data manually to a new database is a time- and cost-intensive task.

We at TML too experienced similar scenarios at multiple points in time wherein, they were handled with advanced planning for infrastructure enhancements and replacement in phases, deploying automation tools for database migration and conducting end-user training with strong in-house capabilities of the Digital Product Development Systems team ensuring a smooth transition for the end users.

As the compute power increases and the size of computer reduces, we shall see it being deployed onboard the vehicle with the edge computing paradigm, potentially supporting Artificial Intelligence (AI) and Machine Learning (ML) for real time decisions.

Backing from the top management is crucial for DT. In your organization, what kind of support was provided by the senior management and what steps did you take for change management?

The senior management has always supported and encouraged adoption of digital technologies (DT) in the organization to enable digitization of processes and thereby created significant efficiency in the system. A core team (the Digital Product Development Systems team) has been working towards implementing the digital product development roadmap to help create a platform for DT over several years.

With this support, company continues its endeavor in the R&D space to develop vehicles with reduced costs, substantially upgraded engineering quality and reduced time to market.

Today, we are at the cusp of the fourth industrial revolution – a turning point for the automotive industry. With many kinds of digital transformation, the expectations are significant for Industry 4.0. Initial work with emerging technologies like General Purpose Graphical Processing Unit (GPGPU), Machine Learning (ML) and Artificial Intelligence (AI), which today is a reality as compared to a decade ago, has resulted in a crunched product validation time. Our organization has been committed to work on these advanced toolsets in product development, validation and operations. AI will also augment the use cases related to prognostics and predictive maintenance as we deploy the IoT platform for the range of connected vehicles.

What are the skillset challenges faced / overcome and accommodations, which you made for realizing DT?

We have had a robust competence mapping & skill gap analysis framework at an organizational level. With digital and online skills assessment, the company identifies the learning priorities for each individual roles, with intervention through customized exercises and courses. By enhancing the skills of its employees on future technologies, our company has been ensuring a smooth organization-wide transition towards adoption of newer technologies and development of new products.

Challenges faced for new skill set requirements are:

      • Sometimes employees are anxious & resistive of using newer technology & methods. They don’t feel comfortable, and want to keep using their current skills with available tools for as long as possible. This is a typical “resistance to change” mindset. To counter this, motivation, training, and coaching are being used to get them aligned.

      • With multiple legacy systems across locations, their modernization requires prioritization and hence requires a dedicated effort and time window for each system, individually. As opposed to this, simultaneous modernization may lead to catastrophic impact that may go out of containment zone.

How do you define ‘establishing a culture for DT’ and what steps have you taken in your company?

We are trying to create a culture of digital transformation that is agile, progressive and makes our business more efficient, productive, creative and innovative.

Some of the steps taken in the direction are:

    • Forecasting of “in trend” & future looking digital tools & deploying a unified strategy suiting TML.

    • Enabling people understand the collaborative potential of new digital technologies through knowledge sharing sessions.

    • Acknowledging the anxiety that change can cause and proactively supporting people through various mentoring and training sessions

    • Helping people move to a flexible, forward-thinking culture of continuous improvement and innovation by encouraging them to participate in various innovative challenges

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