The global tyre industry is in the midst of its greatest upheaval since the pneumatic tyre – driven by rapid digital transformation. Siemens, the German global technology company, is leading this revolution, quietly redefining how tyres are designed, manufactured and maintained worldwide.

In a sprawling industrial complex outside Nuremberg, Peter Haan, Head of Global VM Tire, Siemens, brings the enthusiasm of someone who has witnessed an industry’s complete metamorphosis to the oversight of Siemens’ global tyre operations. Recently, Haan outlined the company’s comprehensive strategy for modernising tyre production – addressing the price-sensitive manufacturers of Asia as well as the sustainability-focused plants of Europe.

“Digital transformation didn’t start yesterday, and it didn’t even start during the pandemic,” Haan explains, dispelling common misconceptions about the industry’s technological evolution. “We’ve been working on this for more than 10 years now. We had digital twins a decade ago, which might surprise people who think this is cutting-edge technology.”

The Industrial Metaverse Revolution

The foundation of Siemens’ approach lies in what Haan calls ‘digitalisation for design’ – the creation of what the company now terms the industrial metaverse. This comprehensive digital simulation integrates machines, programmes and entire production processes. It represents years of focused development and has achieved what Haan considers ‘very good status with our integrated approach’.

Haan’s descriptions of recent projects clarify these implications. “We are just building a new plant in Singapore and expanding an existing plant in Germany” he reports. Due to confidentiality agreements, he cannot display the complete digital representation on his computer. Instead of seeing only lines and geometric shapes that require imagination to translate into real machines, one can now observe photorealistic models: virtual people moving, machinery operating and materials progressing through the production process.

This industrial metaverse requires immense computing power, necessitating a close partnership between Siemens and Nvidia. While most consumers know Nvidia for its gaming graphics cards, the company also produces high-end simulation capabilities that enable Siemens and Siemens customers to run simulations in real time with an absolute realistic look and feel.

A second key pillar is digitalisation for operations, achieved through advanced planning and scheduling systems. Tyre manufacturing today exists in a dynamic environment, where customer demands can change daily, a stark contrast to the older, more predictable monthly or quarterly cycles.

“Today, a customer might want to capture one market segment. Tomorrow, they might pivot to electric vehicle tyres. Next week, they could have entirely different ideas based on market conditions,” Haan explains. “Traditional planning systems simply cannot handle this level of flexibility.”

The solution involves Manufacturing Operations Management (MOM) systems that provide immediate responsiveness to market changes. Siemens has successfully implemented this approach across multiple regions, including a completely new greenfield facility in Chennai, which was designed from the ground up using digital operations principles.

Modernising Legacy Infrastructure

The process of implementing digitalisation becomes more challenging when accounting for the hundreds of tyre manufacturing plants worldwide, many of which are equipped with machinery that has decades of operational history. Nevertheless, Siemens has crafted a methodical approach to address these ageing systems.

“This is actually easier to answer than most people expect, though the implementation requires careful planning,” Haan notes. The process begins with laser scanning systems that create high-precision three-dimensional digital representations of entire facilities, mapping every machine location, material flow and worker movement pattern.

This laser-generated data becomes the foundation for plant simulation software that models current operations. “The next crucial step is comparing our simulation results with actual reality to ensure accuracy,” Haan explains. “Initially, no improvement is achieved – we’re simply creating a digital mirror of existing operations.”

Once accurate digital representations exist, optimisation can begin in the virtual environment first. Companies can simulate workflow changes, test automated guided vehicle implementations and identify bottlenecks without disrupting actual production.

To add intelligence to existing machinery, Siemens utilises edge computing devices. “Our SIMATIC IPC127E, for instance, can connect to all existing automation systems, even equipment that’s 30 years old,” Haan says. “We can interface with legacy automations systems from Siemens and any supplier, thus adding intelligence to old machines”

The retrofit approach varies based on existing capabilities. Some situations require minimal hardware changes, while others demand comprehensive replacements of the automation system. “If you have a state-of-the-art automation system, you might need no new hardware at all – just download additional functionality,” Haan explains.

Digital Twins And Real-Time Optimisation

The concept of digital twins running parallel to actual production represents one of Siemens’ most sophisticated technological achievements. These systems utilise edge computing to operate what Haan calls ‘live twins’ that mirror physical machine behaviour in real-time.

Tyre curing provides a compelling example of this technology’s potential. “The temperature inside the bladder during the curing process is challenging to measure directly, especially with traditional rubber bladders,” Haan explains. “But with our digital twin technology, we have virtual sensors so sophisticated that you can specify any point in the bladder, and our system can compute and calculate the exact temperature at that location.”

This capability bridges the gap between simulation and the real world, providing measurement data that is unobtainable through physical sensors. The digital twin processes information such as product geometry, material compression due to steam or water and flow directions influenced by the physical layout. With this, precise optimisation of curing parameters becomes achievable.

“The simulation can then influence real curing behaviour, making the process more accurate and potentially reducing curing time,” Haan notes. For electric curing systems, this precision enables targeted heating adjustments, such as applying additional heat to tyre edges while maintaining optimal internal temperatures.

Regional Market Dynamics

The global tyre industry’s digital transformation unfolds differently across regions, requiring distinct strategies tailored to local market conditions and regulatory environments. These differences significantly impact how Siemens approaches each market.

In China and the broader Asia-Pacific region, price sensitivity dominates decision-making processes. “Customers are extremely price-sensitive, focusing primarily on capital expenditure (CAPEX). Operational expenditure (OPEX) considerations often aren’t in scope initially,” Haan explains. “We have to continually focus about lifecycle costs versus initial purchase prices.”

This dynamic creates challenges for Siemens’ solutions. When comparing automated guided vehicles, for example, Chinese manufacturers often prefer locally-produced systems based on proprietary electronics that appear cheaper initially. Siemens takes a different approach, building AGVs exclusively with industrial automation components – standard PLCs, drives, motors and HMIs.

“Initially, our solution costs more compared to a proprietary electronics-based AGV – we simply cannot compete on initial price with local suppliers,” Haan acknowledges. “However, when you consider lifecycle costs, our approach becomes significantly less expensive.”

The advantage becomes apparent during maintenance scenarios. When a motor fails in a Siemens system, customers can replace it with standard components they likely maintain in inventory for other machinery. Proprietary systems require specific spare parts from original manufacturers, assuming availability and reasonable delivery times.

European Cybersecurity And Workforce Challenges

Europe presents entirely different challenges, beginning with the Cyber Resilience Act (CRA) that will fundamentally reshape the automation landscape starting in 2026. This legislation mandates that all industrial automation products meet specific cybersecurity requirements, with significant implications for existing equipment.

“The European Commission has decided that industrial production and critical infrastructure must be secured against cyber-attacks,” Haan explains. “Given that Europe is effectively at war and cyber attacks are a daily occurrence, this is not just theoretical.”

Siemens is proactively addressing this transition by working with customers to review their equipment bills of materials and provide updated specifications for compliant replacements. This affects both new installations and retrofits, as any significant upgrade must meet new security requirements.

Europe also faces demographic challenges that influence automation requirements. “We have an ageing society with fewer people than countries like India or China, and we’re experiencing a shortage of experienced workers and technical experts,” Haan explains. “This means our products must be simple to use, and machines of our customers must be operated simply.”

European manufacturers also demand continuous operation capabilities. “24/7/365 operation – production cannot be interrupted by unexpected downtime. Predictive maintenance isn’t just nice to have; it’s urgently necessary,” Haan emphasises. “When we work with major German tyre manufacturers, predictive maintenance is included from the beginning. If anyone offered a mixing line without predictive maintenance, they wouldn’t even be considered.”

Artificial Intelligence In Manufacturing

The application of artificial intelligence (AI) in tyre manufacturing has moved from experimental to essential, particularly in areas traditionally requiring human intervention. Visual inspection represents the most obvious opportunity for AI implementation.

“Even in highly automated plants – and I’ve visited completely automated facilities that are quite impressive – you still typically see 20 people doing visual inspection of finished tyres,” Haan observes. “But here’s the fundamental problem: after inspecting 100 tyres, human consistency inevitably declines. We’re not machines – our attention wavers, we get tired, we make mistakes.”

Siemens is collaborating with companies to develop AI-powered inspection systems that integrate high-quality optical equipment with sophisticated pattern recognition algorithms. “The AI must determine whether there’s a fault, what type of fault it is – is it a bubble, is it incorrect wire placement, is it a surface imperfection?” Haan explains.

When discussing accuracy expectations with plant managers, Haan maintains realistic perspectives. “When asked whether the machine recognises 100 percent of failures, I’m honest – no, not 100 percent. But I can say that it recognises defects more accurately and consistently than human beings.”

AI applications extend beyond inspection into production processes themselves. In curing operations, Siemens utilises AI through digital twin technology that operates in parallel with physical equipment. “We measure all incoming variables – electric power consumption, steam pressure, external temperature, internal conditions – and feed this information to our digital life twin running on edge computing devices,” Haan explains.

Using computational fluid dynamics simulations, the system accurately understands how heat behaves throughout the curing process. Real-time comparison between simulation predictions and actual conditions enables continuous optimisation. “For electric curing systems, we can even create different temperature zones – applying more heat to tyre edges while maintaining optimal internal temperatures.,” says Haan.

Sustainability Beyond Green Materials

Sustainability in tyre manufacturing encompasses far more than renewable raw materials, extending through entire product lifecycles from manufacturing to end-of-life processing. Siemens has developed comprehensive approaches to address these challenges.

The company’s ‘SiGREEN’ system calculates complete product-related carbon footprints using standardised communication protocols that include all supplier contributions. “Most companies, when asked about the carbon footprint of a specific tyre, can’t provide accurate data,” Haan notes. “Approximately three-quarters of a tyre’s carbon footprint doesn’t come from the manufacturing plant. It comes from purchased materials and the energy used to produce them.”

This complexity requires sophisticated tracking capabilities. “These complex calculations change dynamically as supply chains shift towards geographically closer sourcing locations,” Haan explains. “Our system links to the bill of materials for each product, tracking exactly what compounds are used in tyre treads versus sidewalls and maintaining complete supply chain traceability.”

This transparency is becoming crucial for business relationships. “Previously, negotiations between tyre manufacturers and automotive companies focused primarily on price. Now we have a new critical component: carbon footprint,” Haan says. Automotive manufacturers face government-mandated carbon limits with significant penalties for non-compliance, making tyre carbon footprints a competitive differentiator.

Tyre recycling represents another sustainability frontier where Siemens provides technological solutions. The company collaborates with several organisations that are advancing pyrolysis technology for tyre breakdown, including joint ventures involving major tyre manufacturers that utilise our completely web-based process control system SIMATIC PCS neo.

“Pyrolysis plants are sophisticated chemical operations requiring precise parameter control,” Haan explains. “You cannot simply shut down a pyrolysis plant during lunch breaks like some other manufacturing processes. These systems require continuous operation with carefully managed temperature, pressure and material feed rates.”

Siemens also supports ultra-high-pressure water jet technology for tyre breakdown, which uses high-pressure water streams to separate tyre components for direct reuse. “This technology requires precise PLC control to manage water pressure, flow rates and separation processes,” Haan notes.

Innovative Equipment Design

Siemens has identified fundamental inefficiencies in traditional tyre manufacturing equipment and developed innovative solutions to address them. Curing presses exemplify this approach effectively.

Standard curing presses typically feature large HMI screens for operator interaction, but actual utilisation analysis reveals these expensive displays are used less than five percent of operating time. “These screens are costly to build and maintain, especially in curing environments with high temperatures and corrosive gases that damage electronic displays. Yet they sit unused most of the time,” Haan explains.

Siemens’ solution eliminates local HMI screens entirely, replacing them with mobile devices, such as tablets, connected to centralised SCADA systems running WinCC software. Haan says, “All screens for all curing presses across a plant are hosted on centralised servers. When an operator needs to interact with a specific curing press, they log into that machine through their mobile device,” says Haan.

This approach provides identical functionality while dramatically reducing costs and improving reliability. “If a plant has 10 operators, providing 10 tablets costs far less than installing individual screens at each curing press. The mobile devices also have much higher utilisation rates and can be used anywhere in the facility,” adds Haan. Electrical curing technology represents another significant innovation thanks to Siemens’ modular ‘e-Starter’ systems, which control heating circuits in electric curing presses. The modular design accommodates various press configurations while providing automatic protection against electrical faults commonly found in high-temperature environments.

“In steel moulds at high temperatures, insulation can fail, creating dangerous grounding or short circuit conditions. Our system recognises these automatically and sends immediate alerts to operators,” Haan explains. The flexibility allows manufacturers to configure systems precisely for their needs, whether they require eight heating segments or 20 or more.

Advanced Fleet Management

Material handling and logistics automation have evolved significantly. Siemens’ offerings include SIMOVE AGV technology along with comprehensive fleet management solutions, featuring free navigation capabilities that offer greater operational flexibility compared to traditional guided vehicle systems.

The company’s unique approach to AGV fleet management centres on the SIMOVE platform and fleet manager software. “We use only industrial automation components in our AGVs – standard PLCs, drives, motors and HMIs that customers already understand and maintain,” Haan explains.

The fleet management system supports VDA 5050, a standardised communication protocol enabling AGVs from different manufacturers to communicate with each other and central management systems. “Think of it like Profinet for industrial automation – a common communication standard,” Haan says.

Siemens can integrate proprietary protocols from various suppliers, with the fleet manager currently supporting over 20 different AGV communication protocols. This capability gives customers the flexibility to operate mixed AGV fleets while maintaining centralised control.

“The system can control both real-world operations and simulations. If you have a digital simulation of your plant, our fleet manager can demonstrate how AGVs will operate before physical implementation,” Haan notes.

Predictive Maintenance Evolution

Siemens’ Senseye predictive maintenance system distinguishes itself through two key advantages over competitor offerings. First, the system utilises data from standard automation components that generate extensive operational information automatically.

“All drives, all PLCs generate extensive operational data automatically. You can get current consumption, torque output, operating temperatures and many other parameters directly from standard components,” Haan explains. “Just by analysing this standard information, we can predict when machines are at risk.”

Pattern recognition enables early identification of developing problems. Unexpected changes in current consumption patterns might indicate bearing wear or other mechanical issues weeks before actual failure occurs.

The second advantage involves Senseye’s internet-based architecture, which, with customer permission, compares similar machines across Siemens’ global installed base by connecting to standard databases. “This means customers benefit not just from learning about their own machines but from patterns identified across all connected machines worldwide,” Haan says.

This global learning capability creates powerful network effects. When Siemens identifies a failure pattern in one facility, that knowledge immediately becomes available to prevent similar failures in other locations using comparable equipment.

Industry Consolidation And Future Outlook

The global tyre industry is experiencing dramatic structural changes, particularly evident in China, where consolidation has accelerated significantly. “Five years ago, there were approximately 500 tyre manufacturers in China. Now we’re down to fewer than 300,” Haan reports.

This consolidation reflects both market forces and deliberate government policies that promote industry efficiency and environmental performance. “China’s manufacturing policy demands higher technology adoption, better environmental performance and reduced energy consumption,” says Haan. Export challenges compound these pressures, making it difficult for smaller manufacturers to achieve the scale necessary for survival. Government support actively encourages consolidation towards larger, more technologically advanced companies capable of global competition.

Siemens is positioning itself for these changes through what Haan calls ‘glocalisation’ – the company’s new plant that is planned in Singapore reflects this approach. “We’ll see increasingly region-specific trends that require local adaptation while maintaining global technological standards,” adds Haan. The future belongs to companies embracing comprehensive digital transformation rather than piecemeal automation upgrades. “Companies must understand lifecycle costs rather than focusing solely on initial purchase prices. They need to integrate sustainability metrics into operations from the beginning, not as an afterthought,” Haan emphasises.

Most importantly, successful manufacturers will be those capable of rapid adaptation to changing market demands through flexible, digitally-enabled production systems. “The technology exists today to achieve this flexibility – the question is which companies will have the vision and commitment to implement it comprehensively,” says Haan. As Haan concludes: “The tyre industry’s digital transformation is no longer a future possibility – it’s happening now. Companies that delay this transition risk being left behind in an increasingly competitive and regulated global marketplace.”

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.”  

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.”

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.