Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

I have worked in systems installation and quality assurance for more than 18 years, concentrating on the automotive manufacturing sector. My background includes working with leading organizations like Tenneco, Fitwel Gasket, and Exide Industries, where I led QMS implementation, internal and supplier audits, and process improvement initiatives aligned with standards like IATF 16949, ISO 9001, 14001, and 45001.

I’m a certified Lead Auditor for ISO and VDA standards, with hands-on expertise in conducting audits, gap assessments, and training on core tools, customer-specific requirements, and lean practices. Currently, I work as a freelance auditor and consultant, helping companies strengthen their systems, ensure compliance, and drive continuous improvement.

Making proactive, system-driven cultures that promote long-term performance and customer happiness out of reactive quality techniques is my strong suit.

 

Q2. How is the adoption of AI-powered visual inspection and predictive analytics reshaping quality assurance practices in automotive manufacturing? What impact does this have on operational efficiency, defect rates, and cost reduction?

AI-powered visual inspection and predictive analytics are bringing big changes to quality assurance in the automotive industry. Earlier, inspections were mostly manual, which could miss small defects or require a significant amount of time. Now, with AI and cameras, even tiny issues can be detected quickly and accurately—without human error. This aids in identifying issues early on, before they affect the client.

Predictive analytics looks for trends in machine and process data. It can alert us to potential problems so we can take corrective action before a flaw arises. This lowers customer complaints, rework, and breakdowns.

When combined, these technologies reduce unscheduled downtime, minimize scrap and rework, and speed up inspections, all of which improve operational efficiency. As a result, businesses reduce expenses, enhance the quality of their products, and more reliably satisfy client demands.

 

Q3. Which segments in the automotive value chain are most ready for AI-enabled quality transformation, and what are the expected ROI benchmarks for early adopters?

In the automotive value chain, the segments most ready for AI-based quality transformation are:

• Manufacturing
• Assembly lines
• Supplier quality control

These areas deal with large volumes and fast-moving parts, so using AI for visual inspection, defect detection, and process monitoring can give quick and visible results.

For example, AI can be used to check welds, paint quality, or part dimensions on the production line—faster and more accurately than manual checks.

In supplier quality, AI can help monitor incoming parts for defects, reducing the risk of faulty materials entering the production line.

Early adopters of AI in these areas often see a strong Return On Investment (ROI). In many cases, companies have reported 10–20% reduction in defect rates, a significant drop in rework and warranty claims, and cost savings within 12 to 18 months of implementation.

Overall, AI helps improve speed, accuracy, and consistency in quality, resulting in better customer satisfaction and lower production costs.

 

Q4. Are there specific AI platforms or tech partners currently dominating this space for Tier-1 or OEM manufacturers?

Yes, several AI technology partners are playing a major role in transforming quality processes for Tier-1 suppliers and OEMs in the automotive industry.

Global tech leaders like Siemens, IBM, Microsoft, and NVIDIA provide AI platforms that support data analytics, predictive maintenance, and process optimization. These tools help manufacturers improve quality, reduce downtime, and make better use of production data.

Specialized companies such as Cognex, Landing AI, and Instrumental focus on AI-powered visual inspection. Particularly in the assembly and electronics sectors, their products are widely used for real-time surface defect, alignment, and quality variation detection.

Keyence is also having a significant influence in this field. For quick and precise component and assembly inspection, their AI-enabled vision systems and smart sensors are frequently used. Keyence systems are easy to integrate, support AI-based learning, and are well-suited for high-speed production lines. Many Tier-1s and OEMs rely on their products for detecting tiny defects and improving inspection consistency.

In summary, companies that combine automation with AI, such as Keyence, Cognex, and Siemens, are leading this transformation. Early adopters benefit through improved inspection speed, fewer defects, and better decision-making on the shop floor.

 

Q5. With the rollout of IATF 16949:2024, how are automotive suppliers responding to the increased emphasis on cybersecurity, risk management, and the traceability of EV components?

With the new IATF 16949 update, automotive suppliers are starting to take cybersecurity, risk management, and EV component traceability more seriously.

Cybersecurity

For cybersecurity, suppliers are now working to protect their systems and data from cyber threats. This includes securing their networks, training employees, and making sure sensitive customer and organizational data is safe.

Risk Management

In terms of risk management, companies are now expected to look at risks in a more detailed way, not just in quality, but also in supply chain, production processes, and even IT systems. They are creating stronger risk plans and putting controls in place to avoid problems before they happen.

EV Component Traceability

Regarding the traceability of EV components, vendors are setting up mechanisms to monitor parts used in EVs from manufacture to final use. This makes it easier to maintain safety, adhere to regulatory standards, and promptly identify any problems in the event that something goes wrong in the field.

In order to satisfy these new standards and maintain compliance with the upgraded standard, suppliers are generally modernizing their procedures, educating their staff, and utilizing more digital technologies.

 

Q6. What strategies or frameworks are being used to improve traceability of EV components across complex supply chains?

To improve the traceability of EV components, many suppliers and OEMs are using a mix of digital tools and process frameworks.

Barcode or RFID Tracking Systems

Using barcode or RFID tracking devices, which assist in tracking each component through each step—from raw materials to final assembly—is one popular tactic. This offers an unambiguous record of each component's provenance and subsequent application.

Digital Manufacturing Platforms or ERP Systems

Another key approach is the use of digital manufacturing platforms or ERP systems, which connect different suppliers and track data in real-time. This helps manage information across a complex supply chain, making it easier to respond quickly in the event of a defect or recall.

APQP and PPAP Frameworks

Some companies are also following APQP (Advanced Product Quality Planning) and PPAP (Production Part Approval Process) frameworks more strictly for EV parts. These ensure quality is built into the product from the design stage and all changes are documented properly.

In my view, combining strong process discipline with smart digital systems is the best way forward. With EVs, traceability is not just about quality—it’s also about safety, compliance, and customer trust. The companies that invest in this now will be better prepared for future regulations and recalls.

 

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

A critical question I would ask senior management is:

What differentiates your quality systems and innovation approach from competitors? How do you maintain a competitive edge in a market that is rapidly shifting towards electrification and smart manufacturing?

This question targets the company’s unique strengths in quality and innovation, which are vital in today’s automotive landscape. With the growing focus on electric vehicles and Industry 4.0 technologies, companies must demonstrate how their quality management systems are evolving to address new challenges, such as the complexity of EV components, cybersecurity, and digital traceability.

I would look for evidence that the company invests in cutting-edge technologies such as AI-driven inspection, predictive analytics, and integrated supply chain transparency. Additionally, I’d want to understand how they foster a culture of continuous improvement and agility to respond quickly to market changes.

A strong, differentiated quality approach not only reduces risks and defects but also accelerates time-to-market and builds stronger customer trust—key factors that can sustain a company’s growth and profitability in a competitive, evolving industry.