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

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

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

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

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

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

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

ROOM FOR IMPROVEMENT

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

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

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

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

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

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

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

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

INTO MANUFACTURING

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

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

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

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

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

MACHINE NEEDS MAN

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

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

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

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

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

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

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

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

IMPLEMENTATION AND SAFETY

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

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

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

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

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

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

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

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

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

MARKET VIEW

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

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

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

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

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

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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.

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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.

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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.”

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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.”

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