Jeremias Neuhaus: “One of the biggest challenges in adopting data-driven solutions in the tyre industry is the availability of data and convincing customers not just of the solution itself but of the value of data transfer and real-time monitoring."

The tyre industry faces a significant challenge in data availability with fragmented supply chains, a lack of standardisation and minimal digital infrastructure limiting operational efficiency. Despite the growing importance of predictive maintenance, many manufacturers still rely on manual processes, while competitive concerns and regulatory restrictions further restrict data sharing. This absence of real-time insights hampers decision-making, making operations reactive rather than proactive. However, digitalisation and advanced data analytics are gradually reshaping the landscape. Solutions like HF Xplore aim to bridge this gap, offering real-time monitoring and predictive capabilities that can drive efficiency, sustainability and cost reduction.

Data availability is a challenge in the tyre industry due to fragmented supply chains, lack of standardisation and limited digital infrastructure. Manufacturers, distributors and recyclers operate in silos, making data consolidation difficult. Many businesses still rely on manual processes, while competitive concerns and regulatory restrictions further limit data sharing. Tracking end-of-life tyres (ELTs) is particularly challenging, impacting recycling efficiency.  

However, data availability is crucial for optimising operations, sustainability and innovation. Real-time data enables predictive maintenance, helping fleet operators reduce downtime and improve safety.

Accurate tracking of tyre usage and recycling supports circular economy initiatives and regulatory compliance. It also enhances research and development initiatives for advanced tyre materials such as electric vehicles and off-the-road (OTR) tyres, ensuring better performance and durability.

Improved data transparency can drive smarter decision-making, cost efficiency and sustainability in the industry. Digitalisation and data standardisation are key to overcoming these challenges.

However, HF Group’s Global Head of Digital Solutions, Jeremias Neuhaus, told Tyre Trends, “One of the biggest challenges in adopting data-driven solutions in the tyre industry is the availability of data and convincing customers not just of the solution itself but of the value of data transfer and real-time monitoring. Many decisions in the industry are still based on gut feeling rather than data-backed insights. Our focus is to bridge this gap by providing transparency into machine performance, enabling customers to make data-driven decisions instead of relying on intuition. By implementing real-time monitoring, we can significantly reduce downtime and help customers optimise processes. Even in the first step of implementation, simply visualising machine health and performance brings immediate value. Customers receive notifications for potential issues, allowing them to take preventive action before costly breakdowns occur.”

“The biggest opportunity in this space lies in the fact that data-driven insights can drastically improve operational efficiency. Once machines are connected and data is flowing, customers gain a much deeper understanding of the equipment, leading to better decision-making and optimised production cycles. Predictive maintenance and AI-driven analytics will further enhance operations by identifying potential failures before they occur. This is particularly crucial as manufacturers aim to reduce carbon emissions and energy consumption while increasing efficiency. Our approach stepwise towards AI-powered predictive solutions bring even greater efficiency and cost savings,” he added.

A critical concern in the tyre industry is data security and confidentiality, given how secretive manufacturers are about the respective production processes. “We address these concerns by focusing strictly on machine data rather than the proprietary tyre-making process. Our solutions do not need the actual process details to provide valuable insights. Additionally, the real, detailed data remains visible only to the customer, ensuring that they retain full control. In cases where AI-driven analytics are implemented, we collaborate closely with customers to develop models tailored to specific needs without compromising sensitive production data,” revealed Neuhaus.

The company launched the HF Xplore few years ago as a condition monitoring solution purely for curing presses. One of the significant developments following the merger of HF Mixing Group and HF Tire Tech Group has been the integration of previous initiatives into a joint project, creating a common condition monitoring solution. As a result, HF Xplore is now available for both curing presses and mixer lines. This expansion allows the company to offer real-time monitoring and predictive insights across two of the most critical processes in tyre manufacturing curing and mixing.

MONITORING TYRE FORMULATIONS

Curing and mixing are fundamentally different processes in tyre making, which presents a challenge in making HF Xplore compatible with both. The solution is to split monitoring into two layers viz-a-viz a common monitoring framework and machine-specific components.

The common framework includes monitoring cycle times, alarms, key performance indicators (KPIs) and production progress – elements that apply to both curing presses and mixer lines. However, each machine type has unique components that require dedicated monitoring.

For curing presses, especially electric-curing, HF Xplore focuses on monitoring hydraulic power unit and the electric curing, which is a crucial aspect of efficiency and quality control. On the other hand, for mixers, the system focuses on critical mechanical components such as the RAM movement, feeding mechanisms and drop doors, which are key areas that directly impact mixing performance and consistency. The drop doors, for instance, play a crucial role   in ensuring a smooth transition of rubber to the downstream process, making their monitoring essential for operational reliability.

The user interface of HF Xplore is designed to maintain familiarity across different machines. The dashboard layout remains consistent, so users who are accustomed to using it for curing presses will find a similar experience when working with mixer lines. This consistency reduces the learning curve and makes the system more intuitive for users handling both curing and mixing equipment.

Behind the scenes, the company is investing heavily in data modelling to refine and improve predictive capabilities. The company is developing individualised, flexible data models tailored to each machine type.

These models analyse operational patterns, detect anomalies and provide real-time insights to minimise downtime. By combining machine-specific expertise with data-driven intelligence, HF Xplore continues to evolve into a powerful predictive maintenance and performance optimisation tool for the tyre manufacturing industry.

INTO DATA MODELS

HF Xplore captures machine data but doesn’t analyse it directly. Instead, the system applies background logic to determine whether values are within acceptable limits. Currently, its primary function is real-time status monitoring, giving users an overview of machine condition. However, future iterations will introduce predictive maintenance capabilities, allowing companies to anticipate and address potential failures before they happen.

“At this stage, HF Xplore detects and predicts issues but does not provide specific solutions. As the technology evolves, it will go beyond identifying potential failures to offering actionable recommendations. This shift will help businesses move from reactive maintenance to a more proactive approach, reducing downtime and improving operational efficiency. To refine predictive maintenance, the system is being trained with large datasets in collaboration with customers. Over time, this will enhance the system’s accuracy, enabling it to not only flag potential issues but also suggest corrective actions,” informed Neuhaus.

AI and machine learning will play a central role in the company’s future roadmap, following a structured three-step approach – visualise, analyse and predict.

The first step will provide real-time machine status and process transparency. In the analyse phase, the company’s solutions will move beyond monitoring to offer deeper insights. The system will evaluate performance trends, identify operational limits and provide status feedback. The final phase will be where AI and machine learning take centre stage by analysing vast amounts of historical and real-time data. AI models will identify patterns, forecast failures and recommend preventive actions.

Commenting on whether implementing HF Xplore for a curing press or a mixing system presents different challenges, he said, “While both require detailed monitoring, mixing systems are more complex due to the interconnected components including upstream and downstream processes. Unlike curing presses, which operate as standalone units, mixing lines require data collection across multiple machines for effective monitoring. However, HF Xplore benefits from deep integration with its own equipment, leveraging PLC data to ensure seamless functionality across different systems.”

But for this to be a reality, different data models are pivotal. “The data model is essential for structuring and standardising the information displayed on the dashboard,” informed Neuhaus.

RETROFITTING HF XPLORE

HF Xplore is compatible with both greenfield and brownfield machines, though older models with outdated PLCs may have limitations. “While we cannot retrofit machines that are using old automation solutions, HF Xplore can be integrated into machines from the past few years, especially for condition monitoring. With electric-curing, it also enables precise tracking of electric curing performance, enabling deeper insights,” informed Neuhaus.

“One key challenge is data governance and security. Traditionally, machine data remained within the plant, but HF Xplore connects operational technology with information technology, raising concerns about data ownership and security. To address this, we have implemented user-based access controls, IP-based security and data encryption,” informed the executive.

For tyre plants with a mix of HF and non-HF machines, HF Xplore offers a custom dashboard creator with low-code functionality, allowing users to integrate and visualise data from different machines in just a few hours. A flexible data model further ensures standardised visualisation, even when machine types vary. While full integration with non-HF machines may require additional work, HF Xplore provides a comprehensive plant-wide monitoring solution for optimising performance.

“HF Xplore can potentially integrate with machines from other companies, but it depends on data accessibility and PLC compatibility”, contended Neuhaus, who highlighted the flexibility and modularity of their solution. 

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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.

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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.”  

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MICHELIN Connected Fleet, the data-focused fleet management arm of Michelin, has introduced Smart Predictive Tire, a new monitoring solution specifically engineered for the trailers of Class 7 and 8 fleets. This technology is designed to shift trailer tyre management from a reactive to a proactive model by delivering real-time data on pressure and temperature, alongside predictive maintenance alerts. The goal is to empower fleet operators to address tyre health issues before they escalate, thereby minimising unplanned downtime, controlling costs and extending tyre life while enhancing overall vehicle safety.

At the heart of this innovation is Michelin’s proprietary Smart Leak algorithm, which is capable of identifying subtle, early indicators of tyre degradation. By flagging these warning signs promptly, fleet managers can intervene early, avoiding more severe and costly problems. The solution not only helps in preventing roadside emergencies but also supports broader operational efficiency. Maintaining correct tyre pressure through this system can lead to a reduction in fuel consumption and slower tyre wear, contributing to a more sustainable and economical fleet operation.

The effectiveness of Smart Predictive Tire has been evaluated through international pilot programmes in Europe, where participating fleets experienced notable improvements. Data from these trials showed a significant drop (up to 80 percent) in tyre-related roadside events, an increase in the usable lifespan of tyres (up to 9 percent) in cases where chronic under-inflation was previously an issue and measurable fuel savings (up to 4 percent) when optimal tyre pressures were consistently maintained. While these outcomes are promising, Michelin notes that individual results will depend on various factors unique to each fleet, including its size, operational routes and maintenance routines.

Integrated into the company’s Trailer Premium offer, the Smart Predictive Tire solution provides flexible deployment to meet diverse fleet needs, marking a step forward in connected vehicle technology.

Damon Newquist, Vice President – Sales, MICHELIN Connected Fleet, said, “Emergency roadside service continues to be a major pain point for fleets of all sizes, especially with trailers. When there is a tyre-related event, the root cause is overwhelmingly attributed to improper inflation. Michelin’s proprietary Smart Predictive Tire solution uniquely empowers fleet operators with the tools and alerts to address these issues before they become critical. These tools are designed to help extend tyre life, reduce costs and help keep drivers off the side of the road.”

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Triangle Tyre Co., Ltd. has been recognised as an ‘Excellence Level’ facility in the 2026 Shandong Smart Factory Cultivation Library, an accolade announced by the Shandong Provincial Department of Industry and Information Technology. This acknowledgment highlights the company’s significant progress and systematic achievements in intelligent manufacturing.

This provincial initiative is a key strategy to promote new industrialisation and merge the digital economy with the real sector. Enterprises were evaluated and ranked into three tiers – Pioneer, Excellence and Advanced – based on their comprehensive capabilities in digital design, smart production, lean management and sustainable operations. Over 30 businesses from the tyre sector and its related industries, including manufacturing, steel cord, rubber additives and machinery, were selected. Among these, 1 achieved the Pioneer level, 15 attained Excellence and 15 reached the Advanced level.

For years, Triangle Tyre has steadfastly advanced its intelligent manufacturing strategy, focusing on complete process digitalisation and smart system integration. Looking forward, the company remains committed to principles of innovation and green development. It plans to further integrate digital technologies with manufacturing processes, aiming to establish a modern production base that is not only smarter and more efficient but also safer and more environmentally sustainable.

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