When the tyre industry speaks today about digitalisation, virtual validation and sustainability, it often does so in abstract terms – models, data sets, algorithms and computing power. Yet, at its core, tyre development remains an intrinsically human endeavour. Grip, stability, steering feel and ride comfort are ultimately experienced by people, not machines. Bridging that divide between digital precision and human perception has become one of the defining challenges of modern tyre R&D.

Few companies sit more squarely at that intersection than Ansible Motion. Known globally for its high-fidelity Driver-in-the-Loop (DIL) simulators, the company has, over the past decade and a half, quietly reshaped how vehicle manufacturers, motorsport teams and – most notably – tyre makers think about simulation-led development.

At the centre of this evolution is Salman Safdar, Executive Director at Ansible Motion, whose perspective is shaped not only by technological ambition but also by a deep understanding of how tyres influence the driving experience in ways that no other vehicle component can.

ORIGINS ROOTED IN FIRST PRINCIPLES

Although Ansible Motion is frequently associated with motorsport and advanced vehicle simulation, its origin story is less about racing glamour and more about questioning inherited assumptions. When the company was founded in 2009, the dominant simulator architectures used in motorsport had been adapted from aerospace applications – an approach that Safdar and his colleagues believed was fundamentally flawed.

“When we started the company in 2009, it was to provide an alternative to aerospace-derived simulator architectures that were beginning to make their way into motorsport applications. At the time, many high-level racing teams were investing in technologies that were, from a first principles perspective, better suited to simulating aircraft than ground vehicles,” Safdar explains.

Aircraft and cars, after all, interact with their environments in profoundly different ways. Aerodynamic forces act over long distances and gentle arcs, while tyres generate immediate, localised forces through a constantly changing contact patch. Subtle road surface irregularities, rapid directional changes and short-range visual cues define the driving experience on the ground.“We intentionally departed from the popular, but limited, hexapod – or Stewart platform – and invented a novel, six-degree-of-freedom motion system built in logical layers corresponding to primary ground vehicle axes. The intention was that it would be linear, agile and highly dynamic – and that it would be much better suited to simulating ground vehicles than anything else,” Safdar explains.

Tyres, he notes, were central to that architectural rethink from the very beginning. “Tyres are one of the fundamental reasons why ground vehicle simulators need to be architecturally different from aerospace simulators. Directional changes are immediate with tyres… subtle disturbances that result from pavement irregularities are ever-present… human sensory experiences regarding vehicle control and stability are fundamentally different,” he says.

In that sense, tyre performance was embedded in Ansible Motion’s DNA long before the tyre industry itself became a direct customer.

FROM VEHICLE OEMS TO TYRE MANUFACTURERS

For much of its early life, Ansible Motion’s simulators were deployed primarily by vehicle manufacturers and elite motorsport teams. The tyre industry, traditionally more conservative in its adoption of immersive simulation, took longer to engage directly. That has now changed decisively.

“Today, the tyre industry is a core strategic pillar in our simulation R&D and sales pipeline, alongside OEM vehicle development, advanced mobility research programmes and motorsport. Currently, Michelin, Continental, Nexen, and most recently, Kumho Tire are trusting Ansible Motion driving simulators to develop their next generation of tyres,” Safdar says.

This shift reflects broader pressures reshaping tyre R&D. Development cycles are shortening, sustainability targets are tightening and the cost of physical testing – both financial and environmental – is under intense scrutiny. At the same time, the rise of electric vehicles has introduced new performance trade-offs, forcing tyre engineers to balance rolling resistance, noise, durability and grip in unfamiliar combinations.

Against this backdrop, Driver-in-the-Loop simulation has emerged as a powerful complement to conventional modelling and laboratory testing.

WHY DRIVER-IN-THE-LOOP MATTERS

At its simplest, DIL simulation places a human driver inside a virtual vehicle, interacting in real time with simulated tyres, roads and vehicle systems. For Safdar, the value lies precisely in that human presence.

“The key aspect of Driver-in-the-Loop simulation is the human element. Unlike other simulation and lab testing approaches, DIL simulation invites – in fact, it requires – human participation,” he says.

Modern tyre development depends on a complex interplay between objective metrics and subjective perception. Measurements of braking distance, lateral force or rolling resistance must ultimately align with how a tyre feels to a driver – how it communicates grip, how it responds on centre, how it rides over imperfect surfaces.

DIL simulators allow these subjective attributes to be explored much earlier in the development cycle and more frequently than is possible with physical prototypes alone. Crucially, this happens in parallel with traditional simulation and modelling work, not in isolation.

“This allows critical decisions to be made early enough to avoid delays and unexpected expenses in later stages of programmes. It also reduces costs and environmental impacts due to reduced prototyping,” Safdar notes.

Beyond efficiency gains, Safdar emphasises a less tangible but equally important benefit: collaboration. DIL simulators function as hubs where engineers, test drivers and decision-makers can converge around a shared experience.

“In a sense it enables tyre engineers to be engineers – so they can be more creative in a lower-risk environment,” he says.

THE KUMHO TIRE CASE STUDY

The partnership with Kumho Tire provides a clear illustration of how these principles translate into practice. Framed under the banner ‘Driving the Future with Digital Tyres’, the collaboration reflects a shared ambition to accelerate tyre development through digitalisation while embedding subjective assessment earlier in the design process.

“Both Kumho Tire and Ansible have a shared ambition to accelerate tyre development through digitalisation and to inject subjective assessments into earlier tyre design stages,” Safdar says.

Achieving that ambition requires more than just motion hardware. High-fidelity sensory cueing – perfect synchronisation between motion, visuals and steering feedback – is essential if drivers are to trust what they feel in the simulator. Equally important is process optimisation: a computational environment that integrates multiple modelling tools seamlessly and allows engineers to run tests efficiently and extract meaningful data.

Modern tyre development depends on a complex interplay between objective metrics and subjective perception. Measurements of braking distance, lateral force or rolling resistance must ultimately align with how a tyre feels to a driver – how it communicates grip, how it responds on centre, how it rides over imperfect surfaces.

Safdar believes Ansible Motion’s strength lies in precisely that integration capability. “We believe that Kumho Tire, in part, selected Ansible Motion due to our expertise in integrating advanced tyre models with other HIL, MIL, SIL software and hardware elements,” he explains, referencing hardware-, model- and software-in-the-loop methodologies. High-fidelity digital road surfaces, developed by Ansible Motion’s sister company rFpro, also play a key role.

There is also a market reality underpinning the partnership. “Within a highly competitive space, Ansible Motion supplies over 50 percent of engineering-grade DIL simulators to the marketplace. So perhaps there is some confidence in working with us,” Safdar notes.

FROM ASPIRATIONS TO MEASURABLE OUTCOMES

Digital transformation initiatives often falter at the point where aspiration meets execution. Safdar is candid about the need for clear targets and measurable outcomes if DIL simulation is to deliver real value.

“It’s important to have the aspirations in the first place. But it’s important to clearly identify targets and be able to measure achievements towards them,” he says.

He illustrates this using the concept of multi-attribute spider – or radar – charts, commonly used by tyre engineers to visualise trade-offs. For electric vehicle tyres, key attributes might include rolling resistance, durability, noise, wet and dry traction, load capacity and material sustainability. Improvements in one area often come at the expense of another.

“The end goal is to create a tyre that strikes an acceptable balance for a particular vehicle application,” Safdar explains.

The same logic applies to high-performance tyres, albeit with a different set of priorities: dry braking, wet handling, comfort, on-centre feel and tread wear, among others.

“Designing a tyre is a complex process. The utility of DIL simulation lies in its ability to keep real people involved with conceptual – digital – explorations of all the above trade-offs,” he says.

In practical terms, success can be measured in several ways. How much time was saved in reaching a design decision? How many prototype tyres were avoided? Did virtual prototyping improve alignment between objective data and subjective perception?

In some cases, entirely new metrics emerge, such as improved communication between tyre suppliers and vehicle OEMs during fitment programmes.

REPLICATING TYRE-ROAD INTERACTION

A recurring scepticism surrounding simulation is whether virtual environments can ever replicate the complexity of real-world tyre-road interaction with sufficient fidelity. Safdar’s response is clear: the fidelity depends less on the simulator itself and more on the quality of the models it integrates.

“DIL simulation – except for the human participant – is indeed a virtual environment. This means that human-experienced ‘tyres’ and ‘roadways’ and ‘vehicles’ are computer representations,” he says.

Ansible Motion does not develop tyre, road or vehicle models in-house. Instead, it provides an open, scalable co-simulation architecture – the Distributed Data Bus (DDB) – that connects industry-leading third-party models and customer-developed tools in real time.

“This gives our customers an engineering sandbox where they can use and combine different models that come from trusted third-party simulation providers as well as models that they might develop in-house,” Safdar explains.

The result is a test environment where subjective and objective assessments are conducted much as they would be on a proving ground – except that changes are made with keystrokes rather than tools, and hundreds of evaluations can be run without interrupting a driver’s mental state.

Safdar cites a recent example from Ansible Motion’s UK R&D centre, where a customer ran parallel DIL sessions on opposite sides of the globe. Within four hours, the teams gathered sufficient data to inform the next phase of tyre development. The equivalent physical testing, used as a correlation benchmark, had taken two weeks.

“Test drivers were scoring physical tyres against virtual tyres and seeking correlation within five percent – which they achieved,” he says.

THE DELTA S3 ECOSYSTEM

Central to many of these applications is Ansible Motion’s Delta S3 class of DIL simulators, including variants such as the Delta S3 Spin and S3 Thrust. Safdar is careful to describe them not merely as platforms but as complete ecosystems.

“They are turn-key DIL ecosystems that include all aspects of sensory cueing, including high-fidelity motion, visuals, steering feedback, haptics and audio,” he says.

Correlation with real-world data, he argues, is primarily a function of model quality rather than simulator mechanics. The simulator’s role is to deliver sensory cues accurately and collect driver inputs faithfully, while the DDB ensures synchronised execution across all models.

“If a simulator session and its supporting models are set up correctly… correlation is typically not an issue,” Safdar says. Deviations, when they occur, are often treated as valuable insights that help refine the models themselves.

WHERE SIMULATION DELIVERS THE GREATEST VALUE

From a tyre engineer’s perspective, the greatest benefits of simulation-based validation emerge early in the development cycle, when design freedom is at its highest.

“Simulation allows quick sanity checks on the numerous models and directs attention towards focused refinements of the selected few that show promise. This allows significant cost and time saving,” Safdar explains.

Further downstream, DIL simulation can eliminate entire rounds of prototype iterations, particularly in OEM fitment programmes. The return on investment is often easy for tyre manufacturers to quantify. Safdar points to Continental’s estimate that its simulator usage eliminates around 10,000 sets of test tyres per year, along with roughly 100,000 kilometres of physical driving.

MEETING THE EV CHALLENGE

Electric vehicles have intensified the demands placed on tyres. Higher torque loads, increased vehicle mass, stricter noise requirements and heightened sensitivity to rolling resistance all converge in ways that challenge traditional development approaches.

“Ansible Motion simulators can replicate a wide range of EV-specific scenarios, enabling engineers to tune vehicle performance by testing high torque behaviour, instantaneous load changes, lane changes, high-speed cornering and braking, while also modelling NVH and cabin noise more accurately,” Safdar says.

With lightweight vehicle structures limiting the use of sound-deadening materials, tyres play an increasingly prominent role in overall NVH performance. DIL simulators also allow safe exploration of energy efficiency, regenerative braking strategies and charge-deplete cycles.

Crucially, they enable engineers to explore rolling resistance optimisation in the context of competing trade-offs, such as reinforced constructions required to handle battery weight and torque.

DEFINING THE DIGITAL TYRE

Safdar defines a digital tyre as “a validated virtual representation of a real tyre which considers material properties, compound, tread design, tyre profile, contact patch information, aerodynamic and thermodynamic properties.”

Commercial viability depends on establishing strong correlation between digital and physical tyres, often through close collaboration with vehicle OEMs. When implemented effectively, virtual validation reduces reliance on early prototypes – saving time, cost and environmental impact.

“DIL simulation, in particular by incorporating the test driver’s subjective feedback at the early design phase, can inject insights that would otherwise not be discovered, thus avoiding costly late changes,” Safdar notes.

EXPANDING THE GLOBAL FOOTPRINT

Beyond established partnerships with Kumho, Continental and Michelin, Ansible Motion sees growing demand for digital R&D infrastructure across regions, particularly in Asia. OEM-driven virtual development programmes are increasingly mandating simulator use among suppliers.

Emerging markets and new entrants, especially in China’s rapidly expanding EV sector, represent a further growth opportunity. For these companies, simulation offers a way to compete with established brands on speed, cost and measurable ROI.

“Speed, reasonable cost and measurable ROI are key to success. And we’re happy that this falls within the core competencies of Ansible Motion’s products and solutions,” Safdar says.

LOOKING AHEAD

Over the next 5–10 years, Safdar expects tyre development to be shaped increasingly by digital twins and AI-generated models incorporating new compounds and manufacturing processes. Validation demands will rise, as will regulatory scrutiny, making simulation indispensable not only for development but also for homologation.

“Subjective driver evaluation remains a critical cornerstone of the driving experience and brand identity,” he says. Sustainability pressures will further accelerate the shift towards virtual validation.

“If we can help reduce environmental impacts and reliance on physical prototypes, we are happy to be a part of it,” Safdar concludes. “We would like to think that Ansible Motion is positioned as a key enabler of digital, data-driven tyre innovations.”

Artificial intelligence (AI) is steadily moving from experimentation to practical deployment in tyre manufacturing, where complex processes and variable raw materials often limit the effectiveness of fixed production standards. By analysing large volumes of plant data and responding to real-time process conditions, AI-driven optimisation systems are helping manufacturers improve efficiency, reduce waste and stabilise product quality. In an interaction with Tyre Trends, Vincent Barjaud of Braincube explains how such systems are transforming key production stages including mixing, extrusion and curing while complementing operator expertise.

The tyre industry indeed depends heavily on raw materials with significant variability, particularly those derived from natural sources and petrochemicals. These materials change over time and therefore are not always consistent in terms of quality and performance. Because industrial processes operate under constantly changing conditions, fixed production standards often create a hidden performance ceiling. Systems capable of adapting to real-time conditions allow plants to consistently reach the best achievable operating point.

Artificial Intelligence (AI) is helping manufacturers move beyond fixed production standards towards more adaptive approaches. Real-Time Process Optimisation (RTPO) processes historical and real-time plant data to continuously adjust operating setpoints based on live process conditions. By responding to variability in raw materials, equipment behaviour and operating environments, RTPO enables plants to consistently operate closer to their optimal performance point.

Speaking exclusive to Tyre Trends on the integration of AI, Technical Partner Manager at France-based Technology firm Braincube, Vicent Barjaud, said, “Our AI-driven solution provides real-time process optimisation by recommending the exact action operators should take, on which actuator and at what moment. Instead of suggesting a broad operating range, the system recommends the precise optimal value in real time. Because operating context evolve during production, this optimal value may change within hours. The system continuously adapts to these changes to maintain optimal performance.” The company devises solutions to address the entire tyre manufacturing process, but the software is particularly effective in compound mixing, extrusion and curing, where material transformation through machine actuation makes these stages highly process-oriented and suitable for optimisation.

The implementation typically takes six to twelve weeks from project kick-off to go-live. During this phase, plant data sources are connected and structured for AI analysis without requiring access to confidential compound formulations.

Since most industrial players maintain historical data through data historians, this data is injected into the system, enabling real-time optimisation and recommendations from day one, and in rare cases where no historical data exists, a few weeks are required to gather sufficient operational data.

The solution can be implemented in any plant equipped with PLC-based automation systems, while additional digital systems such as MES, ERP or LIMS improve recommendation accuracy, although valuable real-time operator guidance can still be delivered with only historian data and basic inputs.

ROOM FOR IMPROVEMENT

According to Barjaud, one of the biggest opportunities for improvement lies in the uniformity of the final tyre, particularly during quality control at the end of production. This is largely due to the curing stage.

“Plants often operate dozens of different curing moulds. Each mould functions as an individual asset, but many manufacturers treat them as if they were identical. In reality, each mould behaves slightly differently, which can affect tyre uniformity. Recognising and optimising these individual differences can significantly improve efficiency and product consistency,” he added.

It is considered beneficial to treat each curing mould individually because every mould has distinct characteristics including differences in lifetime, behaviour, wear patterns, maintenance history and the time since its last servicing.

When moulds are treated as identical, these variations are overlooked. By managing each mould separately rather than as part of a uniform group, process optimisation can be achieved more precisely, resulting in improved efficiency and performance.

“Strong optimisation results have also been observed in extrusion, where start-up phases of new process orders typically generate scrap as the first few metres of material are discarded before reaching a steady state. By adjusting process parameters more precisely, the time required to reach this steady state can be reduced, thereby lowering start-up waste,” noted Barjaud.

Braincube’s optimisation approach works similarly to navigation apps such as Waze or Google Maps, which continuously adjust routes based on real-time traffic conditions to reach a destination faster.

In the same way, Braincube dynamically updates manufacturing parameter recommendations as process conditions change. Similar to navigation applications such as Waze or Google Maps, the system continuously adjusts the optimal ‘route’ for the process as new conditions emerge.

The approach also applies to extrusion processes, where significant material waste often occurs during machine ramp-up. By helping operators set the correct parameters from the first seconds of operation, the company reduces the amount of material that must be scrapped at start-up.

INTO MANUFACTURING

Braincube works with tyre plant engineering teams to define ideal performance targets such as acceptable tyre uniformity ranges. It analyses production data to identify the actuators and operating conditions that drive optimal results and provides real-time insights to operators so processes can be adjusted to keep tyres within the desired ‘super zone’ of uniformity.

In mixing, its system addresses inefficiencies during product changeovers. Since the first batch after a changeover starts under different conditions such as temperature, roll distance and machine state, it separates the recipe for the first batch from subsequent batches, ensuring consistent viscosity and composition while reducing the higher scrap rate typically seen in the first batch.

For curing, Braincube performs real-time optimisation by adjusting parameters such as steam injection, temperature and curing duration based on the specific mould and its operating conditions. It also helps extend mould lifetime by identifying moulds that can safely operate beyond the usual maintenance threshold of around 3,000 tyres, potentially extending their life by 20–50 percent.

Overall, waste reduction comes from replacing fixed production standards with dynamic optimisation, where the system continuously analyses real-time conditions and recommends adjustments to recipes and operating parameters, improving efficiency while lowering scrap and environmental impact.

“In one case with a top-five global tyre manufacturer that deployed Braincube across its factories, we observed waste reduction of around 70 percent during the extrusion start-up phase. This level of improvement can significantly reduce both material losses and production costs,” noted Barjaud.

MACHINE NEEDS MAN

Braincube approaches root-cause analysis by identifying the drivers of success rather than only analysing defects. Instead of focusing solely on scrap and deviations, the system studies past production data to determine the conditions under which the best tyres were produced.

By analysing the highest-performance production runs including machines, operators, raw materials and process conditions, it identifies the key factors behind superior performance and recommends settings that help replicate those results consistently.

Installing Braincube mainly involves resolving material traceability across the plant. During a six-to-twelve-week integration phase, the system connects to existing data sources and reconstructs where each product was at specific times in the factory.

Once this mapping is completed, Braincube can continuously process data and perform automated optimisation. Plants with strong traceability systems integrate more easily, while others may require certain assumptions during setup.

“Our solution’s recommendations typically achieve more than 90 percent accuracy, but the system is designed to assist operators rather than automatically enforce actions. Operators receive recommendations but remain fully in control of whether to apply them. If a recommendation is rejected, the system immediately recalculates a new suggestion based on the updated operating conditions,” explained Barjaud.

He added, “This human oversight is important because some real-world conditions may not be captured in the dataset. For example, a lower operating temperature may have produced good results in the past because a machine door was open, affecting process conditions. If that factor was not recorded by sensors, the system may initially recommend the same temperature again even though the door is now closed. In such cases, operators can reject the suggestion, ensuring that AI insights are balanced with practical judgment.”

Barjaud contended that operator expertise remains essential when using AI systems. While the system provides data-driven recommendations, experienced operators play a critical role in deciding whether to apply them.

Their deep understanding of the process ensures that AI insights are used appropriately, making the combination of human expertise and AI analysis key to achieving the best production results.

IMPLEMENTATION AND SAFETY

The company also partners with machine manufacturers through white-label agreements, allowing them to offer Braincube-powered optimisation services alongside their equipment. This enables customers to benefit not only from the machinery itself but also from continuous performance optimisation.

In the tyre industry, Braincube currently focuses on mixing, extrusion and curing and still sees major opportunities to expand optimisation in these processes. Even when analysing a specific stage such as curing or tyre uniformity, the system incorporates data from upstream operations like building and other production steps to understand the factors affecting final performance.

The emphasis on optimisation ultimately centres on the final KPI, since this reflects what customers pay for, which is finished tyre quality and uniformity. By integrating data from across the entire plant including upstream processes and raw materials, Braincube helps manufacturers consistently meet required product performance standards.

Also, many tyre makers have more than one manufacturing unit. Integrating Braincube’s solution across each one requires a simple collaborative excursive involving the French company’s team and a ‘Champion’.

“Most companies appoint a champion or a dedicated engineer responsible for replicating successes across plants. This person ensures that the best practices identified in one plant are standardised and implemented across other facilities,” explained Barjaud.

He added that companies usually deploy Braincube as a technical solution while also establishing a human organisational structure to drive replication and standardisation. The combination of technology and internal leadership ensures that improvements are scaled across multiple plants.

Besides, data security is a top priority for Braincube, especially because industrial manufacturing data is highly sensitive. The system complies with major cybersecurity standards such as ISO 27001 and SOC 2, and in its 18 years of operation, it has never experienced a data breach.

The company regularly conducts external penetration tests, maintains a dedicated cybersecurity team and operates under the supervision of a Chief Information Security Officer (CISO) responsible for vulnerability management and system protection.

Regarding concerns about job replacement, Barjaud reported little resistance from engineers or operators. “Industrial environments have evolved through successive technological stages, from manual decisions to PLCs, closed-loop control, advanced process control and now AI. In this context, AI is generally viewed as the next step in improving efficiency, helping people make better decisions rather than replacing them,” he noted.

MARKET VIEW

Braincube operates globally with a full operational office in Europe but also has offices in United States and Brazil, which has supported the Latin American market for about 15 years.

From Europe, the company manages both European and Asian markets and works with several software distribution partners worldwide including in Thailand, India, Poland, Germany, Spain, Switzerland, UK and Italy, collaborating with firms such as Ematica to deliver and integrate its solutions.

In Asia, particularly in India and Southeast Asia, Braincube mainly relies on local partners rather than establishing its own offices. These partners, often industrial software distributors already working with automation systems, MES platforms and data historians such as AVEVA, handle integration and customer engagement.

The company is also engaging with new tyre manufacturers in Asia, typically through those partners who add Braincube’s AI-driven optimisation to their existing portfolios of PLC, SCADA and MES solutions.

Concluding the interaction, Barjaud pointed out that one of the biggest challenges for AI providers in the tyre industry is balancing multiple objectives such as throughput, energy consumption, material usage and product quality. n

Fornnax Technology Pvt. Ltd. stepped into the spotlight as a bronze sponsor at the India Rubber Expo (IRE) 2026, hosted at ITPO Pragati Maidan in New Delhi from 7–10 April. This exhibition, widely regarded as Asia's premier rubber industry gathering, connected worldwide manufacturers, recyclers and tech innovators. For Fornnax, it served as an ideal meeting point with tyre recyclers and waste management firms searching for answers to large volume preprocessing difficulties.

The company drew crowds with a live display of its main offering, the Primary Shredder. This powerfully built unit tears through end-of-life tyres, various metals, electronic scrap and cable waste without issue. Industry visitors got a close look at its blade system, rugged frame and real-world working rhythm, all fine-tuned to prepare consistent input material for intensive downstream recovery operations.

This showcase arrived at a turning point for tyre recycling. Major players such as GRP Ltd. and Fishfa Rubbers, already Fornnax customers, are pivoting towards profitable products like reclaimed rubber and recovered carbon black. Such high grade outputs demand pre shredding equipment that offers accuracy, steady flow and uptime. Fornnax has therefore pushed forward with design updates to blade angles, drive trains and overall machine layout to satisfy those tighter demands.

By showing up strongly at IRE 2026, Fornnax proved once again that it leads the industrial shredding field. With an expanding worldwide customer roster, nonstop investment in research and product development and a firm belief in circular economy principles, the company keeps redefining how waste turns into valuable resources across India and global markets.

Jignesh Kundaria, Director and CEO, Fornnax Technology, said, "At Fornnax, we engineer not just machines but the backbone of a sustainable recycling infrastructure. Our Primary Shredder is purpose-built to deliver the high-capacity, consistent particle-size output required for the downstream production of recovered carbon black and reclaimed rubber at commercial scale. As our clients evolve their processing lines, we evolve with them by continuously refining our shredding technology to meet tighter material specifications, higher throughput demands and stricter operational efficiencies. IRE 2026 was the perfect stage to reaffirm that Fornnax is not just a machine manufacturer but rather we are a long-term technology partner in the circular economy."

Artificial intelligence (AI) is beginning to reshape fleet management beyond conventional telematics that merely track vehicles. In India’s fragmented trucking ecosystem, where cost pressures, ageing fleets and operational inefficiencies remain persistent challenges, AI-led platforms are attempting to shift the industry from reactive monitoring to predictive decision-making. Mumbai-based Taabi Mobility Limited is among the companies advancing this shift, using large-scale data analytics to link driver behaviour, vehicle performance and operating conditions, offering fleets actionable insights aimed at reducing costs, improving safety and optimising asset utilisation.

Generally, most fleet management platforms track location, speed and unauthorised stops, making them mainly descriptive and not prescriptive. Mumbai-based Taabi Mobility Limited is changing the narrative leveraging the computing and predictive power of artificial intelligence (AI).

“Our AI solution adds value by correlating thousands of variables like driver behaviour, road conditions, load, ambient temperature, tyre age etc. and continuously learning in real time. It predicts outcomes. Moreover, traditional reports are static, while AI gets more accurate over time, adapting to different routes. Threshold alerts are not just fixed values. AI detects unusual rates of change and alerts proactively,” explained Chief Executive Officer Pali Tripathi.

Alluding to whether the AI platform only analyses data or also guides operators in real time, she explained that alerts differ by user. “Drivers get in-cabin voice alerts about tyre pressure, fatigue, collision risk etc. Fleet operators receive aggregated, actionable insights across many trucks via a live dashboard with critical exceptions highlighted,” Tripathi said.

She added that the effectiveness of AI relies on high-quality data. The control tower suggests actions like contact drivers, schedule maintenance or recommend coaching but does not fully automate vehicle control. Alert volume is configurable to prevent human fatigue.

She noted that the company’s solution also provides specific corrective actions. “A truck from Delhi to Jaipur showing left-tyre vibration and slow pressure drop triggers an alert for the driver to stop at the next halt. Fleet managers are also notified. The system identifies the issue, potential cause and suggested solution, not just the symptom,” explained Tripathi.

Tripathi contended that the fleet management sector in India is seeing multi-modal transport hubs, digitisation, improved road and waterway connectivity and better warehousing and last-mile efficiency. However, the industry is still not fully organised like in developed countries.

Taabi, she explained, is an operations intelligence platform designed to reduce total operational costs per truck by predicting issues rather than relying on fixed schedules. The system monitors vehicle behaviour, load, road conditions and tyre pressure to flag problems early.

“While fleets focus on fuel cost, tyre health directly impacts safety and performance. Fleet interest in tyre solutions is usually part of a holistic cost-reduction strategy rather than a standalone concern. A 10 percent improvement in tyre life can save crores of rupees for large fleets, making investments in platforms like Taabi worthwhile,” said Tripathi.

Companies in last-mile logistics and cement or steel transporters actively track these metrics through Taabi’s solution.

When asked about collaboration with tyre manufacturers and vehicle OEMs for data sharing, Tripathi indicated that such partnerships are still evolving and not yet fully formalised. She noted that major commercial vehicle OEMs along with tyre manufacturers already collect operational data independently for research and product development.

However, the company’s platform currently prioritises a customer-first approach, focusing on empowering fleet operators with actionable insights. Instead of directly supplying data to OEMs, the system enables fleets to use operational intelligence to hold manufacturers accountable for vehicle performance.

FROM GROUND UP

The company currently serves around 1,300 fleet operators across India. Growth is measured in assets deployed rather than just customers, as a single vehicle may use multiple solutions such as OBD devices, video telematics and fuel monitoring systems. Average deployments are about 272 assets per fleet with ranges from 50 to 4,000 assets.

The company has recorded 130–132 percent year-on-year growth, largely driven by expanding deployments within existing customers.

Nonetheless, Tripathi explained that the primary hurdle for the company was building trust in a completely new category of product. “Since fleets had operated for decades without such technology, convincing operators that the platform could deliver measurable value was difficult. We therefore positioned AI not as a replacement for human judgment but as a tool that enhances decision-making, highlighting hidden operational costs such as tyre wear, vehicle inefficiencies and the financial impact of driver behaviour,” she averred.

Another major challenge was the data ‘chicken-and-egg’ problem. AI systems require large datasets to function accurately, but fleet operators were hesitant to adopt the platform without proof of performance.

Although the company had access to global data, it began collecting India-specific road, load and operational data three to four years before launch to train its models. Early adopters and pilot customers were told transparently that the system would improve as more local data was gathered.

A further complexity involved customising the user interface and experience for different sectors. Construction fleets, buses, trucking companies and enterprise operators such as ambulance services all required different dashboards and operational insights. As a result, persona-based interface design became an important part of product development. When discussing adoption among smaller fleet operators, Tripathi noted that fleets with 5–20 trucks typically adopt the solution through larger enterprises or ecosystem partners.

To improve accessibility, the company offers subscription-based pricing similar to mobile phone plans, avoiding large upfront costs. The base plan provides simple alerts and WhatsApp-style notifications. More advanced features are included in Gold and Platinum plans, which deliver deeper analytics and operational insights.

IMPLEMENTATION

Addressing the challenge of deploying AI-based fleet monitoring on older commercial vehicles, Tripathi noted that a large share of India’s truck and bus fleet is 10–20 years old, meaning many vehicles lack factory-fitted OBD or tyre pressure monitoring systems (TPMS).

“To overcome this, we use a matchbox-sized device that plugs into aftermarket OBD ports typically available on trucks manufactured after 2000. The device captures key operational data such as engine performance, speed, RPM, load conditions and fuel consumption,” she noted.

For older vehicles without such capabilities, additional hardware such as fuel tank sensors are installed to track consumption and detect issues like fuel theft or reverse draining. The system can also monitor gensets and auxiliary equipment, while video telematics can be added when required.

Tripathi explained that this approach can actually make the platform particularly valuable for older fleets, enabling both small and large operators to access AI-driven monitoring and predictive maintenance.

The platform also supports intelligent cameras inside the cabin and facing the road, enhancing driver behaviour monitoring and safety analytics. For tyre monitoring, fleets can use external TPMS units, although these are relatively expensive. As a cost-effective alternative, the system derives proxy performance indicators from OBD data and telematics to estimate tyre health and vehicle performance.

“In minimal deployment scenarios, even a driver’s smartphone can provide basic telematics functions such as GPS tracking, route adherence, geo-fencing and idle detection, enabling gradual adoption of digital fleet management tools,” noted Tripathi.

The platform follows strict data security and privacy standards. All operational data is end-to-end encrypted using AES-256 and stored on cloud infrastructure within India through Microsoft Azure. Fleet data remains private to each operator, meaning one fleet cannot access another’s information.

Internally, only aggregated data is used for model training without exposing raw fleet-level details. Any external data sharing is tightly controlled and compliant with India’s Digital Personal Data Protection framework.

MARKET DEMAND

The company views the retrofit segment as the largest opportunity in India, as most commercial vehicles are older and new truck sales represent only a small share of the total fleet. Its strategy is to democratise access to fleet intelligence by enabling AI-driven monitoring on existing vehicles rather than waiting for fleet modernisation.

“We also see growing relevance in commercial EV fleets, particularly in last-mile delivery networks. Our platform acts as an intelligence layer for mixed fleets transitioning from diesel to electric vehicles, helping operators evaluate return on investment, identify suitable routes for EV deployment and manage operational economics. Vehicle-agnostic solutions such as video telematics can be deployed across cars, vans and EV delivery vehicles,” Tripathi contended.

Rather than relying solely on hardware innovation in tyres or vehicles, the company focuses on AI-driven insights derived from sensor data. “Continuous monitoring allows our system to predict performance issues and recommend interventions. The platform functions as an operational intelligence layer, offering voice-based guidance for drivers, cost-optimisation insights for fleet owners and operational support for fleet managers,” averred Tripathi.

Devices installed in vehicles perform round-the-clock monitoring of engine, fuel, tyre and other operational parameters, delivering predictive alerts and actionable insights. By simplifying complex data into clear recommendations, the AI platform aims to improve fleet efficiency, reduce costs and enable smarter operational decisions.

Michelin North America, Inc. has TreadVision by Michelin Retread Technologies at the Technology & Maintenance Council (TMC) Annual Meeting. This new approach transforms the retreading process by integrating artificial intelligence (AI), robotics and advanced data analytics to boost both the quality and uniformity of retreaded tyres, ultimately enhancing fleet operational efficiency.

A central component of this system is TreadEye. This advanced technology precisely evaluates tread depth by collecting 1,200 measurement points per tyre. It delivers accurate data on tread wear and casing condition, enabling fleets to determine optimal removal points, safeguard casing integrity and minimise unnecessary vehicle downtime.

The TreadVision process further incorporates proprietary automated inspections. These systems utilise AI and predictive modelling to detect subtle imperfections and anomalies that might otherwise be missed. The application of Vision AI to automatically interpret Casing Integrity Analysis results, specifically shearography, introduces a heightened level of objective, real-time quality control. This ensures that only casings meeting strict standards proceed through the retreading line.

In addition to inspection, the technology suite automates the physical handling and flow of tyres, which streamlines plant operations and can accelerate turnaround times. By automatically managing build specifications, TreadVision standardises production parameters, reducing variability and ensuring a more consistent final product.

These advancements in quality assurance and the reduction of human error are designed to produce more reliable retreads, directly supporting fleet uptime. The system is further enhanced by integration with Michelin’s Fleet Business Insights platform, which transforms operational data into actionable intelligence. Fleets gain clearer visibility into performance trends, asset tracking and cost control, optimising tyre management from first use through multiple retread lifecycles.

Janet Foster-Whitley, Senior Director, Enterprise Dealer & North America Retreading, said, “Michelin has a long history of innovation in the mobility space. With TreadVision, we’re driving the industry forward once again. Retreading plays a vital role in helping fleets extend asset life and control operating costs, and we’re evolving the process to deliver greater consistency, improved quality and faster turnaround times.”