Tata AutoComp’s Arvind Goel on how digital engineering, AI and software are reshaping auto suppliers

The automotive component industry is moving beyond a manufacturing-led model as digital engineering, AI and software increasingly become central to product development, quality and supply-chain management. For Tier-1 suppliers, the shift is creating pressure to build capabilities spanning simulation, electronics, software and systems engineering, while helping automakers develop and validate products faster. This transition will push Tier-1 suppliers towards becoming integrated technology and engineering partners, what he describes as “Tier 0.5’, believes Arvind Goel, Vice Chairman, Tata AutoComp Systems. 

In an exclusive interview, Goel discusses how digital engineering can reduce reliance on physical prototypes, how AI can make manufacturing and supply chains more predictive, and why engineering and technology capabilities will increasingly determine competitiveness. Edited excerpts

Technologies such as digital twins, virtual validation and so on are becoming central to automotive R&D. Can digital engineering significantly reduce physical prototyping?

Digital engineering is increasingly helping companies reduce dependence on physical prototyping, particularly in the early stages of product development. Simulation, CAE, virtual validation and data-driven design allow engineers to identify performance gaps, optimise designs and test multiple scenarios before physical validation.
However, physical prototypes are unlikely to disappear entirely. Automotive products operate in complex and safety-critical environments, making physical validation essential for final verification. The direction is not eliminating physical prototypes, but making physical validation more targeted, efficient and faster.

How will software-defined, simulation-driven development change the role of Tier-1 suppliers?

The role is evolving from component manufacturers to integrated technology and engineering partners, or “Tier 0.5”. As vehicles become software-defined, connected and electrified, suppliers will need capabilities across hardware, electronics, software, systems engineering and advanced manufacturing.
Differentiation will come from the ability to co-develop solutions with OEMs, integrate multiple technologies and accelerate product development through simulation-led engineering. Data will become a critical asset, enabling predictive quality, digital supply chains and AI-driven decision-making. As ADAS, connected vehicles and advanced safety systems expand, Tier-1 suppliers will also play a larger role in integrated hardware-software solutions.

Is the definition of a Tier-1 supplier fundamentally changing?

Yes. Manufacturing scale, cost competitiveness and quality will remain fundamental, but they will increasingly be complemented by engineering capability, software, electronics, simulation, data and intellectual property.
The Tier-1 supplier of the future is likely to be less defined by the individual component it manufactures and more by the complexity of the solution it can engineer and integrate. Suppliers will need capabilities across the product lifecycle—from design and simulation through manufacturing, validation and lifecycle support.

Which technologies will have the biggest impact on automotive manufacturing?

Rather than one technology emerging as the biggest disruptor, the next phase will be shaped by the convergence of automation, advanced manufacturing, digital engineering and data analytics.
Robotic assembly can improve consistency and productivity, additive manufacturing can enable greater design flexibility, while advanced moulding can support lightweighting and complex geometries. The larger shift will come from integrating these technologies with digital manufacturing systems and AI.
The broader direction is towards digital factories, where manufacturing systems, shop-floor assets and enterprise platforms operate as an integrated ecosystem.

How can enterprise AI make automotive supply chains more resilient?

AI can bring together data from suppliers, inventory, logistics, production schedules, market conditions and external variables to identify patterns that conventional planning systems may miss.
Predictive models can flag early indicators of component shortages, anticipate logistics disruptions and assess raw-material price movements. This allows companies to move from reactive intervention to scenario-based planning, enabling procurement, manufacturing and logistics teams to evaluate alternatives before a disruption affects production.

How will AI change quality assurance?

The next phase of quality management will be driven by the convergence of Total Quality Management, automation, data and AI. Computer vision can enable continuous inspection at production speed, moving beyond periodic or sampling-based inspection to real-time identification of deviations.
Predictive analytics can connect process parameters and machine conditions with quality outcomes, allowing manufacturers to identify conditions that could create defects and intervene earlier. The direction is towards quality being built into the process and monitored continuously rather than inspected only at the end.

How can AI improve EV battery management and thermal safety?

AI can enhance battery intelligence by analysing cell-level data to improve state-of-charge and state-of-health estimation, identify abnormal behaviour and optimise charging and discharging.
It can also support cell characterisation by identifying patterns across temperature, voltage, current and ageing. Combining sensor data with predictive models can help identify abnormal thermal behaviour earlier and enable proactive intervention.
As battery energy density increases, integrating thermal management, BMS, electrical systems and predictive analytics will become increasingly important for safety, performance and battery life.

Can generative AI compress CAE and virtual-validation timelines?

Machine-learning models can work alongside physics-based simulations to create surrogate models, identify design patterns and prioritise configurations requiring detailed simulation. Generative approaches can explore multiple design possibilities against performance, weight, cost and manufacturability constraints.
The objective is not to replace physics-based engineering or physical validation, but to create a simulation-first workflow where AI helps engineers explore a larger design space faster while conventional CAE and physical testing provide final validation.

Can AI turn existing factory CCTV networks into real-time safety systems?

AI-enabled video analytics can identify risks such as unauthorised access, unsafe movement, missing protective equipment or breaches of machine boundaries, and trigger alerts for human intervention.
This shifts CCTV from passive surveillance to intelligent intervention without necessarily replacing existing camera infrastructure. But AI should complement established safety systems and human accountability, with reliable models, clear escalation protocols, data governance and continuous monitoring of model performance.