The Indian automotive industry is going through a big change, blending advanced software technology with traditional manufacturing to meet the evolving demands of consumers. In this interview, Rohit Ramanand, Vice President of Engineering at New Relic India, discusses the growing importance of full-stack observability in this transformation.
Since vehicles become smarter with features like autonomous driving and real-time data sharing, having a seamless connection between cloud systems, devices, and in-car technologies is key.
As the industry faces increasing data loads and cybersecurity threats, Ramanand emphasises that observability is essential for improving production efficiency, enhancing customer experiences, and protecting against risks. This interview sheds light on how observability can help the automotive sector stay smart and strong in the future.
Why do you believe full-stack observability is crucial for the Indian automotive industry, and what impact can it have on operational efficiency?
The entire automotive ecosystem is a complex web of connected technologies. From manufacturing lines and supply chain logistics to dealership systems and aftermarket service platforms, automotive companies must have end-to-end visibility into a multi-layered IT environment. That’s what intelligent observability does. It provides a unified view across all aspects of the ecosystem, including cloud-based platforms powering vehicle connectivity, digital supply chain networks to anticipate parts shortages, advanced analytics engines to guide manufacturing robotics, and customer-facing applications for service diagnostics. Without intelligent observability, issues hidden in back-end apps could halt production lines, blindspots can create performance bottlenecks delaying infotainment updates, and undiscovered vulnerabilities could lead to data leaks or cyberattacks and impact customer trust and business growth.
Intelligent observability arms the automotive industry with resilience, ensuring production targets are met, supply chains operate seamlessly, and customer service is reliable. For example, leading car manufacturer Toyota uses intelligent observability to improve the quality of R&D and software projects across the entire company. Toyota uses observability to support the development of applications related to mobility and digital transformation that can improve the UX.
Can you elaborate on the primary risks of downtime in automotive manufacturing, and how it affects the overall production process?
Downtime in automotive manufacturing not only causes financial losses, it damages operations and erodes customer trust. Modern automotive production lines require a smooth interplay of machines, software, supply chains, and data-driven decision-making to function effectively. Unplanned downtime halts production, causing supply bottlenecks and eroding trust with dealers and customers who expect delivery to be on time.
New Relic’s State of Observability for Industrials, Materials, and Manufacturing report found that more than one in four businesses had critical outages that cost at least $500,000 per hour. Without full-stack observability, automotive brands could lose massive revenue, struggle with recovery, and face higher costs as operational issues bleed into production and supply chains.
In your experience, how does downtime in automotive manufacturing directly influence customer experience and brand reputation?
When downtime disrupts operations, whether due to equipment failure, supply chain issues, or other factors, it delays vehicle production and timely deliveries. For end users, this translates to longer wait times for vehicles, especially for customers eagerly awaiting on-time deliveries. For vehicle dealers, this adds to the risk, and they could end up choosing to purchase competitor vehicles, as lengthy lead times create poor customer experiences.
Frequent or prolonged downtime erodes trust and loyalty. If automakers don’t meet customer demand, it could lead to negative perceptions of brand reliability, quality, and commitment, resulting in lost revenue, negative reviews and a decline in customer confidence.
How can full-stack observability and real-time monitoring help mitigate the risk of downtime in automotive manufacturing plants?
Intelligent observability combines data from OT such as sensors, PLCs and robotic systems, along with IT systems managing inventory, workforce assignments, and production schedules. It holistically analyses data to detect early warnings of machine wear, predict supply chain bottlenecks, and refine quality control processes. Such a proactive and data-driven approach reduces unplanned downtime, enables proactive maintenance, and ensures production lines are efficient.
You mentioned that most business interruptions are caused by internal errors rather than external security incidents. Could you expand on this and explain how businesses can address internal IT risks?
External threats like cyberattacks certainly pose a great risk to businesses, but internal risks such as human error, misconfigurations, system failures and accidental data loss tend to occur more frequently, making it difficult to predict, especially in complex IT environments.
One of the most effective ways to address internal risks is with observability. It offers full visibility into the performance, health, and behaviour of systems across the entire IT infrastructure. Intelligent observability tools continuously monitor, gather, and analyse data from all systems, applications, and networks in real-time. Organisations can detect internal issues early before they escalate into larger disruptions.
Without intelligent observability, misconfigurations, performance bottlenecks and security vulnerabilities reside in blind spots, leading to potential business interruptions. Observability enables proactive issue detection and resolution, quicker incident response, better collaboration, continuous improvement, and system optimisation, which minimises internal risk.
What role does predictive analytics play in preventing downtime, and how does the New Relic Intelligent Observability Platform enable this for automotive manufacturers?
Predictive analytics plays a central role in minimising downtime. It leverages historical data, machine learning, and statistical models to forewarn potential issues. By analysing patterns in system behaviour, predictive analysis tools identify signs of system performance degradation, ensuring proactive steps are taken to address them before they impact the bottom line.
Predicting potential issues minimises unplanned downtime, which is particularly valuable in industries like automotive manufacturing, where minor disruptions can spiral into full-blown operational hurdles and supply bottlenecks. Automotive manufacturers rely on predictive analysis to ensure reliable maintenance and optimise production processes and supply chains, with the New Relic platform playing a key role in that.
New Relic integrates data from across the entire IT infrastructure. This data is foundational for predictive analysis, enabling manufacturers to collect a holistic view of their environment—from sensor data on factory floors to performance metrics from backend systems. Real-time monitoring continuously tracks system performance, proactively detecting potential issues.
New Relic’s platform can predict when and where problems are likely to arise, forewarning teams to take necessary actions to prevent disruptions. For example, New Relic’s intelligent observability platform identifies patterns in production line downtime or equipment performance, helping manufacturers take a proactive approach to maintenance or process optimisation. In the event of downtime, the platform quickly analyses the root cause, allowing teams to pinpoint exactly where failures occurred, and speeding up resolution.
How can automotive companies leverage telemetry data to enhance performance and reduce operational disruptions in their production lines?
Telemetry data from devices such as manufacturing robots, conveyor belts, sensors and quality control systems in the production line play a critical role in optimising performance and minimising operational disruptions. By collecting and analysing telemetry data from various parts of the production line, automotive manufacturers can gain granular insights into their operations, improve efficiency, and proactively minimise downtime. For example, sensors embedded in production line machinery track factors such as temperature, vibration, pressure, and motor performance.
Intelligent observability tools collect this data in real-time, helping manufacturers identify signs of equipment malfunction like overheating, which could indicate the potential for failure. It helps identify when machines need maintenance; reducing disruptions.
Telemetry data also offers insights into how efficient manufacturing processes are. Intelligent observability tools offer the much-needed analysis of telemetry data, helping automotive companies identify bottlenecks, underperforming processes, or areas with excessive waste. For example, observability tools can identify stages in the assembly line that slow production, helping manufacturers fix the problem by adjusting workflows or reallocating resources.
It empowers automotive manufacturers to reduce disruptions by providing granular insights into every aspect of their production processes. Real-time monitoring, predictive maintenance, quality control, and operational analytics enable manufacturers to optimise workflows, prevent downtime, and drive efficiency.
As data volumes increase, what strategies can automotive manufacturers implement to manage and extract value from that data while avoiding costly IT incidents?
The rapid increase in data volumes due to the rise of new technologies, such as electric vehicles and IoT devices, requires manufacturers to adopt a multi-faceted strategy that prioritises data integration, advanced analytics, and robust observability adoption. Automotive manufacturers should consolidate data from multiple sources—such as sensors on production lines, connected vehicle systems, and IT applications—in a single, unified platform. This ensures all stakeholders have consistent access to accurate, real-time information.
Additionally, real-time analytics enables manufacturers to gain immediate insights into production performance, supply chain efficiency, or potential bottlenecks. This information can address anomalies on the assembly line, where deviations in robotic performance can be addressed before they impact production targets.
Lastly, using an intelligent observability platform enables manufacturers to monitor their entire ecosystem in real-time, providing full visibility into operations. By continuously analysing telemetry data and applying contextual insights, these tools ensure early detection of performance degradation, misconfigurations, and vulnerabilities. This proactive approach not only prevents costly IT incidents but also optimises data for better decision-making.
With environmental risks and cybersecurity being top concerns for the automotive industry, how do end-to-end monitoring and contextual analysis help mitigate these risks, especially in an increasingly digital manufacturing environment?
End-to-end observability and contextual analysis play a pivotal role in addressing the dual challenges of environmental risks and cybersecurity in today’s highly digitised automotive industry. By enabling comprehensive oversight and proactive insights into both physical and digital ecosystems, manufacturers are empowered to detect, address, and prevent risks before they escalate.
Observability enables the collection and analysis of data from across the production lifecycle, including energy consumption levels, emissions, and material waste. By contextualising this data with operational benchmarks and regulatory requirements, manufacturers gain actionable insights into where they can reduce environmental impact, such as inefficiencies in energy usage or emissions from factory equipment; enabling immediate corrective actions to ensure compliance and sustainability.
Additionally, predictive capabilities supported by contextual analysis can help manufacturers anticipate and mitigate risks, which can minimise environmental impacts and support sustainability efforts.
