For decades, the story of global manufacturing in India was told in the language of cost. Plants were built here because labour was affordable and scale was achievable, and the intelligence that guided what those plants made sat elsewhere, in design centres and R&D campuses thousands of miles away. That story is now being rewritten, and few industrial groups illustrate the rewrite as clearly as Freudenberg.
The German family-owned group, whose businesses span sealing technologies, vibration control, filtration solutions and specialty lubricants, serves industries as varied as automotive, aerospace, pharmaceuticals and food processing. In India, it is doing something that goes well beyond adding capacity. It is turning its operations into a connected, data-driven system in which machines talk to enterprise software, algorithms tune process parameters in real time, and engineers in India contribute directly to the group’s global product roadmaps.
G. Sivasailam, Director & CEO and Freudenberg Regional Representative, India, describes the transformation without hype. Digital initiatives at Freudenberg India are built on tangible operational gains, not technological spectacle, and are driven jointly by operations, engineering and technology teams rather than imposed by any single function. The result is a factory that is increasingly adaptive, a workforce whose value lies in judgement rather than repetition, and an India business that wants to be a benchmark for the rest of the world.
A hub that thinks, not just produces
The starting point for Sivasailam is a change in what India means to the group. “India is no longer just about cost-efficient production,” he says; it is about engineering capability, application development and the ability to serve global platforms as well as local customers. Each of Freudenberg’s business groups operating in the country is now deeply woven into global product development cycles, and the local R&D and application engineering teams feed directly into worldwide roadmaps.
The breadth of Indian industry plays a part in this. Automotive, general manufacturing, aerospace, pharmaceuticals and food processing all operate at scale in the country, which gives the group what Sivasailam calls a unique test bed for innovation. Ideas can be tried against a wide range of real-world demands, and what works can travel.
Policy is a tailwind. Freudenberg is aligning its expansion with the government’s Make in India and Production Linked Incentive frameworks, particularly in sectors where import substitution offers a long-term strategic opening. The ambition, as he frames it, is for India to contribute to the group’s global competitiveness rather than simply be a market it serves.
Building intelligence on a foundation of connectivity
Before any algorithm could add value, the plants needed to be able to speak. Freudenberg India spent several years on groundwork that rarely makes headlines: connecting machines through IoT, standardising how data is gathered, and creating the basic digital structure on which intelligence can be built. Machine condition, process parameters and production capacity now flow back into manufacturing execution systems and dashboard analytics, giving managers and supervisors the information to decide quickly.
The next step has been to bridge operational technology with enterprise systems, so that what happens on the shopfloor reaches planning, procurement and quality management without delay. The company is also building digital models of core production processes for testing and simulation, which allows changes to be tried virtually before they touch a live line.
The effect on daily decision-making is, in Sivasailam’s telling, a change of character. Where the morning meeting once meant discussing yesterday’s problems, live data from connected machines now allows teams to watch key indicators as they move and act before a deviation turns into a production issue.
Benchmarking has become faster too, with the best practices of the most productive production cells replicated in less successful ones, an advantage he calls often overlooked. Operational dashboards give a unified view of equipment effectiveness, scrap rates, causes of downtime and energy usage, cutting the time once spent gathering and reconciling data. And because everyone works from the same numbers, teams coordinate more readily when something goes wrong.
AI that has left the pilot stage
The conversation around industrial AI is often dominated by proofs of concept that never quite reach the plant floor. Sivasailam is emphatic that Freudenberg has moved past that stage. “These are not aspirational use cases,” he says. “They are operational, generating returns, and being scaled.”
Predictive maintenance is the clearest example. Machine learning models trained on time-series production data anticipate breakdowns before they happen, lifting equipment uptime and, in turn, overall equipment effectiveness. On selected product lines, computer vision now inspects for defects faster and more consistently than manual checks, bringing down defect escape rates and the cost of quality. Models also optimise process conditions such as temperature, pressure and curing time using live sensor data and historical performance, reducing variability and raising first-pass yield. In planning, AI draws on sales data, customer orders and market signals to sharpen forecasts, which trims excess inventory and improves on-time delivery.
Yet Sivasailam resists the idea that AI is merely a tool for efficiency. Across the group, it is reaching into material development, engineering and product design. At Freudenberg Technology Innovation, machine learning analyses material data and generates proposals that require fewer test series.
Generative design and simulation-based optimisation help engineers explore complex, customer-specific solutions and shorten development cycles. Digital twins of production lines let teams fine-tune layouts, material flows and human-robot interactions before anything is built. The group has signalled the scale of its intent by announcing that it expects to invest around €200 million in AI-supported research by 2028.
The larger prize, in his view, lies in combining AI with what Freudenberg already does best: understanding materials, engineering products and manufacturing them at quality. That combination allows the company to grasp material behaviour earlier, refine designs sooner and answer increasingly demanding customer requirements faster.
The factory as a system of systems
Behind these individual use cases sits a deeper change in what a factory is. For most of industrial history it was a physical asset, a collection of machines, materials and people organised to produce output. Sivasailam sees it becoming a system of systems in which the physical and digital are inseparable.
In that kind of environment, configuration, workflow and even quality standards can be adjusted digitally, sometimes remotely and sometimes in real time. Parameters that once demanded manual intervention are tuned by algorithms responding to sensor data. Product changeovers that took hours can be executed faster through digital orchestration of equipment and work instructions. The operator’s role shifts accordingly, from manual execution towards oversight, exception management and continuous improvement, supported by tablets, human-machine interfaces and augmented-reality work instructions. What the factory prizes in its people is judgement, problem-solving and domain expertise.
The factory of the future, as Sivasailam describes it, is adaptive, able to respond to demand shifts, quality signals and process variation without extensive manual intervention, and it learns, with data from every cycle improving the next. It even changes how capital is judged: returns on digital infrastructure, he notes, are often as significant as returns on physical equipment.
The price of connection
Every new connection widens the surface that an adversary can attack. Manufacturing technology was once largely air-gapped from corporate networks, and therefore insulated from most cyber threats. As machines are connected, sensors deployed, manufacturing execution systems joined to enterprise platforms and remote monitoring enabled, that insulation is gone. The consequences of a breach are no longer confined to data. Someone with access to connected production systems can shut down lines, manipulate process parameters or steal proprietary manufacturing knowledge.
Sivasailam treats cybersecurity and OT security as business-critical, on par with physical plant safety. Freudenberg segments IT and OT networks with appropriate controls, runs regular vulnerability assessments on connected systems, manages access privileges rigorously and builds security into new digital deployments from the design stage. It also invests in training, because the human element, from phishing to credential compromise to unintentional misconfiguration, remains among the most common routes to an incident. Operations teams are taught to recognise security risks as readily as production anomalies. With standards such as IEC 62443 growing more stringent, he considers that tightening entirely justified.
The next five years
Looking ahead, Sivasailam points to a handful of forces he expects to be truly disruptive. Generative AI and large language models, applied to engineering and operations, will go beyond optimising processes to help design products, select materials, predict failure modes and solve customer problems that current tools cannot, with enormous gains in engineering productivity.
Digital twins will become an operational reality, allowing virtual models of plants, processes and products to speed development, sharpen maintenance scheduling and permit safe experimentation. Advanced robotics and collaborative automation, paired with AI-based perception, will take on tasks that once required human hands only because of their variability and complexity.
Sustainability technology, including energy management AI, circular economy solutions and advanced materials, will matter as carbon standards tighten and raw material prices climb, rewarding companies that optimise their energy and materials footprints. And edge computing combined with 5G will push real-time processing down to the machine itself, enabling low-latency decisions without dependence on centralised cloud infrastructure.
A manufacturing revolution, and a place in it
Sivasailam’s long-term vision is deliberately ambitious. He wants Freudenberg India to be regarded not simply as one of the world’s finest manufacturers operating in the country, but as an active architect of the next generation of Indian industry. The moment, he argues, is unlike any in decades: a vast domestic market, policies favourable to industrial development, a technologically savvy young workforce and global supply chains that are diversifying all arrive together.
He sets out the goals plainly. Freudenberg’s Indian plants should become global benchmarks, places that the group’s facilities around the world look to for lessons rather than merely meet emerging-market standards. The India business should take its rightful place among the group’s product development centres. And the company should become the employer of choice for the country’s most promising engineers and technologists, while contributing to the wider ecosystem through partnerships with academic institutions, industry bodies and startups working on manufacturing technology.
The story India chooses to tell
There is a quiet confidence in this account. It does not rest on a single breakthrough technology or a headline-grabbing announcement, but on years of patient groundwork: machines connected, data standardised, models proven on live production lines, security built in from the start, and teams from operations, engineering and technology working as one.
What emerges is a picture of an Indian factory that no longer waits for instructions from elsewhere. It senses, learns and adapts, and the knowledge it generates travels outward into the group’s global products. If Sivasailam’s vision holds, the next chapter of Indian manufacturing will not be measured by how much the country produces for the world, but by how much the world learns from the way India produces. In his words, the aim is to prove that world-class industrial manufacturing is “not just possible but thriving here.”