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. 

As commercial vehicle operators face mounting pressure from regulations, rising costs and the shift to electric power, Webfleet, a subsidiary of Bridgestone, is focusing on turning ever-growing volumes of vehicle data into practical recommendations that improve performance and ease the daily burden on fleet managers.

Founded in Leipzig in 1999 as one of the world’s first web-based fleet management platforms, Webfleet has grown from a pioneering software-as-a-service (SaaS) start-up to Europe’s leading solution and a core part of Bridgestone’s global offering. Today it serves drivers across more than 180 countries with AI-powered insights, electric-vehicle tools and sustainability data that turn complex operations into clearer, more efficient decisions.

In an exclusive interaction with Tyre Trends, Marc Notenboom, Director of Product Management at Webfleet, shares how the company has watched the fleet-management sector evolve over more than a quarter of a century. Interestingly, it was just last year the company marked its 25th anniversary.

From its inception, Webfleet has seen the volume of available data explode, yet the real challenge remains converting that information into meaningful guidance.

“Fleets give you more data but not more insights, which continues to be a challenge. Because of new regulations, new sensors, increasing competition and customer demands, we end up generating more data. We also have access to more data, which is growing exponentially. And then comes the real question, how do you make sense of that data?” Notenboom observed.

Marc Notenboom, Director of Product Management, Webfleet

Webfleet obtains its information through black-box devices installed in vehicles, sensors or direct connections to original-equipment manufacturers. This includes telematics, GPS, video, driver behaviour and vehicle diagnostics, among others. The company then processes that stream to deliver fleet-management services.

The next step, he explained, is moving beyond retrospective analysis. “Insights tell you what happened and why it happened, perhaps, but not what you need to do to improve your performance. That is what we are doing now with the help of artificial intelligence (AI). For example, we analyse data, see if patterns are different or you have anomalies in your data to predict what is happening or what is going wrong. And then we also use AI to analyse our data to see how you are performing, for instance, against competition. And then give customers suggestions on how to improve on certain topics like fuel consumption or driver safety,” he said.

The industry, in Webfleet’s view, is therefore evolving from raw data collection towards insights, recommendations and concrete actions.

SHARED PRESSURES ACROSS DIVERSE FLEETS

Webfleet serves a wide spectrum of customers, from large transport and logistics operators to small fleets of one or two vehicles, service-and-maintenance businesses and last-mile delivery companies.

Each customer faces distinct operational issues, yet common themes emerge. “What they all have in common is that the amount of data and the pressure to enhance their performance is increasing a lot. And then there is the regulatory compliance pressures as well,” said Notenboom.

Performance, in this context, covers delivery times, cost per kilometre, driver wages, safety and fuel consumption – metrics that matter differently according to the sector, but all that demand continuous improvement.

One of Europe’s most persistent difficulties is the shortage of skilled drivers, which has led to greater reliance on migrant workers whose experience of road conditions may vary widely between countries.

Webfleet addresses part of this challenge through tools such as OptiDrive, which shows fleet managers how individual drivers perform on fuel use and safety. Language barriers are tackled with automatic translation features built into the driver’s work app or tablet.

“They can interact with their fleet manager in their own language, and the fleet manager can also interact back in their own language,” explained Notenboom. The same interface delivers active coaching: if a driver exceeds the speed limit, for example, an immediate warning appears. These software-based interventions help maintain standards without requiring constant direct supervision.

MEASURABLE GAINS AND EV TRANSITION

Notenboom agrees that customers understandably scrutinise the return on any additional technology cost. He reported that Webfleet users typically achieve efficiency improvements of up to 15 percent, though results depend heavily on the starting point. This ranges from fuel savings, faster turnaround, downtime reduction or improved driver behaviour, among others.

“If a company is already really good at saving fuel, then adding tools and driver coaching and technology might not change a lot. At the same time, even if it’s a small improvement, especially with the high fuel prices today, it could already translate to a lot of savings,” averred Notenboom.

The arrival of electric trucks introduces a fresh set of complexities. Research indicates that driver behaviour exerts an even greater influence on energy consumption in electric vehicles than in conventional combustion models. Smooth operation becomes critical to range, while decisions about when and where to charge add further layers of planning. Webfleet supports the transition by helping managers determine the right vehicle for each duty cycle.

“If you do long-distance transport, electric trucks are perhaps not the best option. But if you do inner-city deliveries, then electric trucks become an interesting option. And trying to find out where the optimum is, when to use Internal Combustion Engine-vehicles, where to use electric vehicles, that is also a part where we can help the fleet manager to make that decision,” he said.

On the technology package itself, every Webfleet customer receives the software platform. Hardware can be selected according to need, or in some cases, dispensed with entirely through the OEM.connect service (access vehicle data directly from OEMs via APIs), without installing extra hardware that draws data directly from the vehicle manufacturer.

“A number of customers are preferring to use OEM.connect. So that is a solution where we don’t install hardware but take the data directly from the OEM offerings,” he explained. On the other hand, where original-equipment data fall short, Webfleet’s own devices remain available.

PRIVACY, BENCHMARKING AND THE ROAD AHEAD

Growing data volumes inevitably raise questions of privacy and regulatory compliance. Webfleet adheres strictly to General Data Protection Regulation (GDPR) – a strict European Union (EU) law that protects information privacy and data rights for individuals in the EU and the European Economic Area (EEA) – requirements.

Certain categories of information can be processed only with explicit customer permission, and for benchmarking purposes, the company anonymises data so that individual vehicles or fleets cannot be identified.

A recent example is Webfleet’s Commercial Trip Monitor, which aggregates anonymised information across the entire customer base to generate comparative insights.

Looking three to five years ahead, Notenboom identified three principal challenges for the commercial-vehicle industry: the practical difficulties of electrification, the continuing shortage of drivers (with autonomous vehicles potentially addressing certain use cases over a longer horizon) and relentless competitive pressure to deliver higher quality service at lower cost.

He regards these same pressures as sources of opportunity for those able to convert them into advantage.

Interestingly, he shared that Webfleet itself continues to evaluate new markets beyond its established presence in Europe, South Africa, South America and Australia. Bridgestone’s ownership of the US-focused Azuga platform complements the portfolio, while Asia-Pacific, including India, remains under active review even if no immediate plans have been confirmed.

As Notenboom puts it, the goal is not to simply collect more information but to translate that information into clearer decisions that keep vehicles productive, drivers safer and costs under control – regardless of whether the powertrain is diesel or electric.

Vaculug has released VMS Pro Max, the next generation of its fleet management application. The product, developed over a three-year period, pairs a redesigned interface with AI-enhanced software intended to make commercial fleet management faster and more intelligent. The app is now available for download through the Apple App Store and Google Play Store.

The upgraded platform offers clear, real-time access to tyre and vehicle data, accelerates background report uploads to reduce driver time and allows measuring tools to be connected for automatic tread depth capture. It also incorporates an embedded VAN Driver Guide and can instantly flag vehicles left uninspected across 30, 60 or 90 days. Existing users must sync all pending inspections and confirm cloud upload before updating to prevent data loss.

The Yokohama Rubber Co., Ltd. and dotData, Inc. have announced joint development an AI agent to help engineers form hypotheses, interpret findings and make decisions in manufacturing R&D. Before full deployment, the initiative will validate the agent's required capabilities and technical feasibility.

The solution combines technical documents such as design specifications and experimental reports with engineers' accumulated expertise and features extracted from measurement data via dotData's Signal Intelligence technology. It will provide insights tailored to each project's objectives, circumstances and constraints.

The agent is not meant to produce a single correct answer. Operating in a human-in-the-loop framework where engineers retain decision-making authority, it will present multiple possibilities, perspectives and risks to broaden thinking and support professional development.

Manufacturing R&D generates diverse data through experiments, simulations, production and evaluation. Developing hypotheses requires specialised knowledge and context. Since new technology R&D rarely has one predetermined answer, abductive reasoning plays a key role. AI can add value by using data and organisational knowledge to widen engineers' perspectives rather than replace judgment.

The agent will explore measurement and experimental data patterns beyond factors engineers previously identified, combining quantitative insights with design documents and expert knowledge to reveal difficult-to-spot relationships. It will offer input from materials, design, analysis, production and evaluation perspectives, challenge existing hypotheses and raise questions about alternative interpretations and conditions. Engineers will then test hypotheses and feed results back into further dialogue, guiding Signal Intelligence processes so non-specialists can explore data features as needed.

Yokohama Rubber has advanced HAICoLab since 2020, using dotData as a foundational technology in tyre design and rubber mixing. This joint development expands those efforts, with engineers interpreting and evaluating the agent's suggestions using their own expertise rather than accepting them unquestioningly. The companies aim to transform R&D processes while strengthening engineers' creativity and judgment, establishing a new model for human-AI collaboration in manufacturing R&D.

Motoji Fujita, Head of Research & Advanced Development Division, The Yokohama Rubber Co., Ltd., said, “Since 2020, we have conducted research and development under the HAICoLab framework, drawing on the complementary strengths of people and AI to create a collaborative environment. We believe AI agents will give engineers access to perspectives beyond their own experience and enable them to evaluate hypotheses from multiple angles. Through this development initiative, we aim to advance our R&D capabilities while supporting the continued growth of our engineers.”

Ryohei Fujimaki, CEO, dotData, Inc., said, “Success in manufacturing R&D depends heavily on an organisation’s ability to connect its proprietary data and knowledge with AI. The quantitative context generated from data through automated Signal Intelligence provides a foundation for AI agents to engage engineers in practical, data-driven discussions. Together with Yokohama Rubber, which has many years of experience in advanced data utilisation, we intend to establish standards for using AI in highly specialised business domains.”

Bridgestone Aircraft Tire (Europe), a Bridgestone Corporation subsidiary, has announced the deployment of its ‘easytrack’ tyre management system at SR Technics Spain SAU, the tyre and wheel maintenance hub of global maintenance, repair & overhaul (MRO) provider SR Technics. The scalable digital platform is accessible on desktop and mobile devices.

Designed for airlines and MROs, ‘easytrack’ manages inbound and outbound tyre and wheel flows across single- or multi-site operations. It delivers real-time identification, tracking and documentation throughout each asset’s lifecycle. The project follows the solution’s first airline implementation in 2025, becoming its second customer rollout and first MRO application.

MRO providers handle large tyre and wheel volumes for multiple airlines, complicating operations. ‘easytrack’ simplifies asset management and improves traceability, accuracy, safety and productivity. At SR Technics Spain SAU, it digitalised paper-based processes, cutting processing time by 45 percent, reducing data-entry and identification errors and improving flow management across Palma de Mallorca and Madrid.

From left: David Sautois, ‘easytrack’ Product Manager, Bridgestone Aircraft Tire (Europe) S.A.; Antonio Colella, General Manager, SR Technics Spain SAU; Jean-Philippe Minet, Managing Director, Bridgestone Aircraft Tire (Europe) S.A.; Eugenie Baradzina, Commercial Director, SR Technics Spain SAU.

The results stem from a Bridgestone and SR Technics Spain SAU co-creation initiative built on long-standing partnership. ‘easytrack’ was adapted to support tyre and wheel combinations for the hub’s wheel and brake operations. Bridgestone’s expansion of airline and MRO solutions aligns with its E8 Commitment, emphasising efficiency and ecology.

Antonio Colella, General Manager, SR Technics Spain SAU, said, "The successful implementation of easytrack is a significant achievement for SR Technics Spain SAU and our teams in Mallorca and Madrid. From the very beginning, the collaboration with Bridgestone was built on trust, openness and a shared vision for innovation. Together, we have developed a solution that not only improves efficiency and traceability but also creates lasting value for our operations. We are proud to be the first MRO in the world to adopt this technology and look forward to continuing this journey with Bridgestone Corporation.”

Jean-Philippe Minet, Managing Director, Bridgestone Aircraft Tire Europe, said, “I’d like to thank SR Technics for their trust and another fruitful collaboration with Bridgestone. What the teams have jointly achieved is remarkable: co-creating through efficient teamwork and customising the Bridgestone easytrack application to digitise SR Technics tyre and wheel tracking operations. This is a brilliant example of how Bridgestone amplifies the value created at our partners.”