Meanwhile, China is taking the lead in terms of supply chain transparency, digital twin technology, automation, and AI. India, Mexico, and the US. are also modernising and implementing things faster than companies in the United Kingdom, for example. In fact, according to companies’ own assessments, the level of digitalisation in the UK’s industrial sector has even declined. 

Bodo Philipp, CEO of MHP Consulting UK said: “Our analysis makes one thing clear: China and the US are driving the transformation of their manufacturing sectors with a strong focus on software and data, while the United Kingdom and the German-speaking markets of Germany, Austria, and Switzerland have yet to build similar momentum. Only six and three percent of companies respectively are very familiar with Software-Defined Manufacturing, compared to 30 percent in China and India. Without the strategic integration of production control, data, and software, it will become increasingly difficult to remain competitive internationally.”

More than 1200 people from industrial companies were surveyed for the Industry 4.0 Barometer 2026. Specifically, they were asked to assess the status quo with regard to Industry 4.0 in their own companies in the DACH region (Germany, Austria, Switzerland), the United Kingdom, the US, China and, for the first time, India and Mexico. The study highlights successes, but also reveals gaps in the subjects surveyed, including supply chain transparency, digital twin technology, Artificial Intelligence (AI), and Software-Defined Manufacturing (SDM).

UK digitalisation declines

Internationally, the degree of digitalisation ascertained in industry continues to rise, with the overall barometer figure increasing from 48% (2022) to 66% today in all subject areas. However, two regions have fallen significantly behind: The United Kingdom declines to 62% (-2% compared to the previous year). The DACH region (Germany, Austria, Switzerland) remains stagnant at 57%. Meanwhile, China reaches 72% (+3%), the US 69% (+3%), India 68%, and Mexico 67%

“The degree of digitalisation in industry is increasing worldwide, with Europe also making progress,” said Dr Johann Kranz, Professor of digital services and sustainability at LMU Munich. “In a comparison between countries, however, the US and China are implementing digital production technologies faster and taking a more integrated and scalable approach than European companies. India and Mexico, which we were analysing for the first time, are also showing better results in some cases.”

Causes of the faltering transformation

If the digital transformation is being hampered, it is usually due to technical reasons: heterogeneous legacy systems, fragmented data landscapes, and limited interoperability make it difficult to adopt new technologies. By way of example, 40% of surveyed companies in the United Kingdom cite data silos as a barrier, while 47% point to their legacy IT systems. It is a similar picture at companies all over the world. Yet this year’s study reveals that these classic obstacles are overcome at different speeds, particularly in the areas of digital twin technology, Artificial Intelligence, and Software-Defined Manufacturing.

These differences are particularly striking in the case of digital twins. The barometer figure for their use in plants and machines has risen from 54 to 62 percent, while in logistics it has increased from 61 to 67% representing the largest jump from the original figure of 30% (2022). This shows that the digital twin is taking off faster than any of the other technologies addressed in the survey.

Across all application fields, China is clearly in a leading position when it comes to the digital twin. This is especially evident in the context of logistics, where 84 percent of Chinese companies say they partly or fully use this technology. This is followed by Mexico (74%), India (68%), the US (61%), and the United Kingdom (54%), with the German-speaking markets lagging behind on 42 percent.

AI hype gap

China and the US also take a leading role in the use of Artificial Intelligence in production environments. When partly or fully using AI, Chinese players are out in front on 71%, followed by India on 61%, and the US on 57%. Mexico (51%) and the United Kingdom (48%) are in the middle of the field, while the DACH region (Germany, Austria, Switzerland) is behind on 37%.

These findings reveal that many European companies are taking a rather cautious approach here. To date, they have only been using AI on a pilot basis, with a lack of deep integration into production processes. At the same time, the future impact of AI is rated highly. By way of example, 61% of companies in the United Kingdom expect it to have a “significant” or “groundbreaking” impact in the next five years. This gap highlights the fact that smart algorithms cannot be productive without solid foundations in terms of data infrastructures, sensor technology, and digital twins. Accordingly, AI remains a future promise in industrial practice but will not become an effective productivity factor (“AI hype gap”).

Software-Defined Manufacturing (SDM) is the new key skill

SDM separates production control from physical hardware and creates a central software layer that makes the manufacturing process flexible and scalable across different sites. CIOs play a key role here by becoming architects of the digital factory and taking responsibility for IT/OT integration, data literacy, and investment prioritization. Companies with a CIO state much more frequently that they are familiar with the SDM concept (+33.2%) and are more likely to integrate it into their overall strategy (+18.4%). There is also a greater propensity to invest (+13.8 %), while the proportion of the budget earmarked for maintenance expenses is lower (-26.2%).

When comparing familiarity with the still nascent SDM concept, India and China are blazing a trail, with 30% of respondents in each of these countries attesting to a “very high” level of familiarity. The proportion is significantly lower in the United Kingdom (6%) and in the German-speaking markets (3%). The US (14%) and Mexico (18%) are in the middle of the field.

Further upheavals are expected

The majority of respondents worldwide expect significant upheavals in the next ten years as a result of digitalisation and software-driven approaches, with 31% holding the firm belief that their industry will undergo fundamental change and another 51% considering it likely. Once again, there are considerable regional differences in this assessment. In India, for example, 44% of respondents are convinced that software-driven approaches are altering their industry, while the figure in the United Kingdom is only 17%.

Digitalisation requires a real willingness to invest: 71% of respondents in India state that their companies are willing to spend significant sums of money on new digital technologies. Mexico (65%) comes next, followed by the US (59%). The figure is critical for the United Kingdom, where just 36% are willing to invest. Only the DACH region (Germany, Austria, Switzerland) is scoring lower at 29%.

“European companies are focusing on efficiency and cost optimisation, which means that strategic potential for growth, flexibility, and innovation often remains untapped,” explained Prof Christina S. Reich from FOM University of Applied Sciences, who is also a manager at MHP. “Emerging markets such as India, China, and Mexico, meanwhile, are pursuing more differentiated strategic goals. India, for example, is specifically focusing on improving quality due to its historical competitive position and global pressure. The aim here is to meet international standards and open up new markets.”

Overall, the findings highlight the fact that Europe is facing a huge modernisation task. The main way for companies to maintain their competitive ability on the international stage lies in breaking down the technical barriers, standardising IT/OT structures, and consistently gearing production toward software-based, scalable architectures. Software-Defined Manufacturing is becoming a yardstick for future industrial viability – and a critical success factor in the context of Industry 4.0.

About the Industry 4.0 Barometer 2026

The Industry 4.0 Barometer has been published since 2018 by the management and IT consultancy MHP in cooperation with Prof Dr Johann Kranz from Ludwig Maximilians University (LMU) Munich. The 2026 edition analyses the statements of 1,206 people from industrial companies in the DACH region (200), the United Kingdom (202), the US (200), China (200) and, for the first time, India (200) and Mexico (204). The most strongly represented sectors are mechanical and plant engineering and information and communication technology, each with 13%, followed by the automotive industry (10%). The participants have roles across all hierarchical levels, most frequently working in IT (23%) and production (24%).

The questionnaire covers four areas – technology, IT integration, strategy, and goals – while equally examining barriers and drivers. There was also a focus on Software-Defined Manufacturing this time. Recommended courses of action round off the study for decision-makers, along with success stories from user companies and expert interviews.

The full study can be downloaded at:

www.mhp.com/newsroom

Tailored for McLaren, Rescale’s digital engineering platform, powered by NVIDIA, applies a ‘perfect-fit’ AI stack to bring rapid speed and scale throughout the design and engineering development process.

McLaren can now explore more design space, run complex test and simulations significantly faster, tune every component with greater precision and reduce manual, repetitive tasks through leveraging engineering agents. As well as dramatically accelerating product development, it also protects McLaren’s engineering heritage by creating an environment for teams to focus on high value design and engineering thinking. 

Nick Collins, chief executive officer of McLaren Automotive, explained: “This is a genuine strategic transformation for the business. By continuously compounding and optimising our data, our intelligence and our engineering philosophies at unimaginable speed, we can deliver product developments at pace, while protecting the DNA of our company.”

Within the Rescale environment, the platform is trained exclusively on McLaren data and utilises NVIDIA AI infrastructure, AI physics models and agentic engineering libraries. This creates a unified platform approach that connects McLaren CAE, systems engineering and design into a unified AI data fabric which continuously learns and optimises while adhering to the integrity of McLaren’s quality standards and performance characteristics.

“Our foundational platform allows McLaren to leverage the latest agentic engineering technologies powered by NVIDIA AI infrastructure, providing a compounding source of competitive advantage for engineers in critical areas of product development, such as carbon materials, structural dynamics, durability, and ultimately the programmatic scaling of engineering excellence across every discipline, to deliver world-class products faster,” said Joris Poort, Founder and CEO of Rescale.

“The future of automotive engineering is being rewritten by agentic AI and advanced simulation, turning decades of design heritage into a live, generative engine that accelerates every stage of the vehicle lifecycle,” said Tim Costa, Vice President & General Manager, Computational Engineering at NVIDIA. “By integrating Rescale’s unified control layer with NVIDIA’s open models for agentic AI and accelerated physics, McLaren is compressing years of traditional simulation into hours of real-time design exploration.”

Transforming McLaren’s product development lifecycle

AI-accelerated workflows allow McLaren to operate and explore beyond the constraints of traditional physics and computational modelling methods. 

• Faster Virtual Simulation: AI-driven physics significantly cuts simulation time with every test feeding new data back into the system to continuously improve surrogate models and AI agents’ understanding of the physical world.
• Rapid Design Exploration: Engineers can evaluate thousands of design iterations in hours, covering multiple physics and engineering domains, fundamentally changing how rapidly an optimal performing design can be achieved.
• Real-Time Performance Prediction: Machine learning models enable instant predictions of manufacturing performance, for example in the production of high-performance carbon fibre structures and components.
• Agentic Engineering: Rescale helps McLaren automate complex repetitive engineering tasks, boosting expert productivity by 3x on infrastructure powered by NVIDIA.
• Knowledge-based Engineering: Rescale’s platform builds 'engineering knowledge graphs that capture insights from previous work, powering the agentic engineering workflows and accelerating product development decisions.

cars.mclaren.com

ACCORDING TO the European Asphalt Pavement Association (EAPA), the European road network consists of over 5.5 million kilometres of roads, and over 90 per cent of this is paved with asphalt. Asphalt is a mixture of aggregates and bitumen primarily used in road construction due to its durability and reliability. During production, these aggregates are dried, screened and mixed with bitumen.

There are currently around 4000 asphalt plants in Europe producing 300 million tonnes per year and at this scale reliability is non-negotiable. However, in an asphalt plant, the drive systems in the machinery required to carry out key processes are subjected to constant torque demands from heavy rotating drums and material conveyors.

At the same time, dust and abrasive particulates accelerate wear, placing strain on seals, bearings and lubrication systems. Standard drives often struggle in these conditions, but a carefully engineered approach can significantly extend equipment service life and improve reliability.

Reliable operation in challenging environments

High ambient and process temperatures mean that motors and gear units that can operate reliably under thermal stress are essential, as depending on the type of asphalt, the temperature can range from 100-190°C.

Using the right lubricant is key under these conditions, as for every 10-5 °C increase in temperature, the life of the lubricant is typically halved. Considering operating temperature and ensuring that appropriate high-temperature lubricants are used to protect critical bearing surfaces is one way that engineers can keep asphalt production machinery running under heat and load. Robust sealing solutions can also prevent heat-induced degradation of components and ensure smooth bitumen handling in the mixer and transfer lines.

Ensuring reliable operation at elevated temperatures also depends on choosing motors and gearboxes that are designed for continuous heavy loads and effective heat dissipation. Gearboxes like the Bonfiglioli 300 Series heavy-duty planetary gear unit can provide the high torque density and mechanical robustness needed for asphalt plant applications where load and temperature are combined.

Helical bevel and parallel shaft gearmotors are also a good choice, offering compact designs for operations where space may be limited, along with efficient power transmission and improved heat management to maintain performance even in the harshest conditions.

Managing shock and load variability

Torque surges are another major challenge in asphalt mixing operations. Drum mixers and conveyors frequently experience sudden load changes during start-up or when material flow fluctuates. Without proper control, these surges can cause mechanical shock and accelerate wear on gearboxes and bearings.

To address this, plant managers can integrate intelligent drive control solutions such as variable speed drives (VSDs) and motor control systems. By precisely managing acceleration, speed and torque, VSDs reduce start-up stress by gradually increasing the voltage and frequency, providing a ‘soft start’ that can protect machinery from damaging shock loads.

Advanced control systems also allow operators to adapt to changing process conditions in real time. Adjusting motor speed to match material flow not only improves reliability but can also enhance energy efficiency and process consistency by limiting unnecessary energy usage.

Asphalt mixing plants will always push drive systems to their limits. However, with a carefully chosen combination of high-performance motors, durable gear units and smart drive control, the limits can be managed effectively. The right solutions can enable operations to withstand extreme temperatures, abrasive conditions and demanding torque requirements, delivering reliable performance and extended service life in one of industry’s toughest environments.

David Strain is technical director at Technidrive

www.technidrive.co.uk
 

MANUFACTURING IS undergoing structural redefinition driven by intelligent robotics. Automation is no longer limited to deterministic, high-volume repetition. Modern robotic systems integrate artificial intelligence, advanced sensing, and virtual validation to execute variable, high-precision, multi-robot workflows with minimal physical iteration.

This analysis examines the pivotal trends and technological innovations defining the future of manufacturing robotics. I explore how these transformative technologies impact production methodologies and outline strategic considerations for organisations navigating this industrial evolution.

The emergence of intelligent automation

Conventional automation has served manufacturing through predictable, high-volume operations. Today’s robotics generation distinguishes itself through cognitive intelligence. By incorporating artificial intelligence (AI) and machine learning (ML) capabilities, robots now perceive environmental conditions, execute autonomous decisions, and evolve through operational experience. This evolution from programmed automation to intelligent systems represents a fundamental pillar of contemporary smart manufacturing.

AI-enabled robots address task variability that previously exceeded automated system capabilities. Advanced vision systems enable robots to identify and categorise diverse components on conveyor systems. These systems conduct quality control inspections with accuracy and consistency surpassing human performance, detecting microscopic defects invisible to traditional inspection methods. This intelligence creates adaptable, resilient production lines that respond dynamically to shifting operational demands.

Collaborative Robots: Integrating Human Intelligence with Machine Precision

Manufacturing robotics has witnessed a paradigm shift with collaborative robots, or “cobots.” Unlike conventional industrial robots operating within safety enclosures, cobots integrate seamlessly with human operators. Advanced sensor arrays detect human presence and trigger automatic deceleration or stoppage protocols to prevent collisions.

This collaborative methodology harnesses complementary human and machine capabilities. Cobots assume strenuous, repetitive, and ergonomically challenging operations – lifting heavy components or executing precise assembly motions. This redistribution enables human workers to concentrate on higher-value activities requiring critical analysis, problem-solving, and complex dexterity. The outcome is enhanced operational efficiency and workplace safety, where human potential is amplified rather than replaced.

Key kechnologies accelerating robotic capabilities

Several breakthrough technologies are expanding advanced robotics capabilities and broadening manufacturing applications.

• AI and machine learning integration: AI serves as the cognitive foundation for modern robotic systems. Machine learning algorithms enable robots to optimise movement patterns, predict maintenance requirements, and adapt to new tasks with minimal reprogramming. This continuous learning capability ensures robotic systems achieve greater efficiency and effectiveness over operational lifecycles, driving sustained process improvements.
• Advanced vision and sensing technologies: Sophisticated 2D and 3D vision systems provide robots with precise environmental interpretation capabilities. This technology proves essential for bin-picking operations, where robots must identify and grasp specific components from mixed-part containers. Force-torque sensors deliver tactile feedback, enabling robots to handle delicate components and perform intricate assembly tasks requiring precise pressure application.
• Industrial Internet of Things (IIoT) connectivity: Robots function as critical nodes within comprehensive Industrial Internet of Things (IIoT) networks. Connecting robots to sensor networks, machinery, and enterprise systems enables manufacturers to collect and analyse extensive real-time data streams. This connectivity provides comprehensive production process visibility, facilitating predictive maintenance, optimised resource allocation, and data-driven decision-making.
• Robotics advancing sustainable manufacturing: Advanced robotics plays an instrumental role in sustainable manufacturing initiatives. Process optimisation through robotics significantly reduces material waste and energy consumption. Robotic coating systems apply materials with superior precision compared to manual operations, minimising overspray and reducing volatile organic compound (VOC) usage. Automation enables efficient recycling and remanufacturing processes. Vision-equipped robots sort mixed waste streams with exceptional accuracy, recovering valuable materials that would otherwise reach landfills. This capability supports circular economy principles, promoting responsible resource utilisation.

Industries Pioneering Robotic Implementation

While robotics impacts all manufacturing sectors, specific industries lead adoption initiatives.

• Automotive: The automotive sector pioneered robotic automation and continues advancing implementation across welding, painting, and final assembly operations. Manufacturers deploy cobots to assist assembly line workers, enhancing both ergonomics and operational efficiency.
• Electronics: High-volume, precision-demanding electronics manufacturing aligns perfectly with robotic capabilities. Robots handle component assembly onto circuit boards, conduct quality inspections, and manage product packaging. Cleanroom requirements further validate robotic solutions.
• Pharmaceuticals: Pharmaceutical manufacturing demands precision and sterility. Robots execute vial filling, medication packaging, and laboratory testing operations. This implementation improves accuracy and consistency while reducing contamination risks.

Strategic robotics integration

Organisations considering advanced robotics adoption require comprehensive planning and strategic analysis. Success extends beyond equipment acquisition—it demands process re-evaluation and organisational preparation for operational transformation.

Identify optimal automation opportunities by focusing on repetitive, physically demanding, or error-prone tasks. Conduct thorough return-on-investment analyses considering productivity increases, quality improvements, and enhanced worker safety.

Prioritise workforce development initiatives. Robotics integration transforms employee roles across operations. Implement training and upskilling programs preparing teams for new responsibilities including robotic system operation, maintenance protocols, and production data analysis. Effective robotics strategies empower workforces rather than displacing personnel.

Select experienced technology partners capable of designing and implementing customised solutions. Collaborate with proven integrators who understand specific operational requirements. Phased implementation approaches, beginning with pilot projects, mitigate risks while building organisational momentum for broader adoption.

Manufacturing’s future is inextricably linked with robotic advancement. Organisations embracing these transformative technologies build resilient, efficient, and competitive operations, establishing foundations for the next industrial innovation era.

Buddharatn Ratawal is senior manager for strategic business development at DELMIA

www.3ds.com/products/delmia

The new robot from Prof. Angela Schoellig’s TUM Learning Systems and Robotics Lab looks like a broomstick on wheels with a camera mounted at the top. It is one of the first robots that not only integrates image understanding but also applies it to a clearly defined task.

To find a pair of glasses misplaced in the kitchen, for example, the robot has to look around and build a three-dimensional image of the room. The camera initially provides two-dimensional images, but these pixels also contain depth information. This creates a spatial map of the environment that is accurate to the centimeter and is constantly updated. A laptop also provides the robot with information about which objects are visible in the image and what significance they have for humans.

“We have taught the robot to understand its surroundings,” says Prof. Angela Schoellig. The head of the Robotics Lab at the TUM Chair of Safety, Performance and Reliability for Learning Systems aims to develop robots that can navigate any environment independently. Humanoid robots working in factories or robots in care settings in private homes require this newly developed basic understanding, which, as Schoellig explains, “is important for all robots that move in spaces that are constantly changing”.

The robot therefore understands that a table or window sill can be used to briefly set down a pair of glasses, whereas a stovetop or a sink are not suitable for this purpose.

“The language model captures the relationships between the objects and we convert this information into the robot’s language,” explains Prof. Schoellig.

Two-digit numbers appear on the three-dimensional map of the environment, constantly recalculating the likelihood that the object being searched for is located there.

According to the research results, the robot then searches the probable locations almost 30 per cent more efficiently than if it searched randomly throughout the room. Artificial intelligence is used in two ways: on the one hand in image recognition and on the other hand through the use of a language model.

Another special capability of the robot: it remembers previous images and is able to compare them with new images of its surroundings. If a new object suddenly appears in the kitchen, it recognizes the change with a high degree of certainty (95 per cent) and marks these areas as “highly probable” search locations.

In the next step, the TUM scientist and board member at the Munich Institute of Robotics and Machine Intelligence (TUM MIRMI) also wants to search for objects that are in a drawer or behind a door. To do this, however, the robot will not merely have to draw on knowledge from the internet but will also have to interact with its surroundings. Robotic arms and hands must open a cupboard and determine whether it opens upwards or sideways and how best to grasp the handle. This will enable the robot to search even in closed spaces such as cupboards or drawers.

www.mirmi.tum.de/en/mirmi/home/

Chemco International, an employee-owned manufacturer of specialist protective and marine coatings, has secured a £670k trade loan to be invested in additional machinery to increase capacity and automate its processes.

Through the upgrades, the business, which currently employs 35 people, aims to triple its turnover by 2036 and create new jobs locally through this expansion.
Established in 1990, the Coatbridge based business designs and manufactures advanced protective coating used across marine and industrial sectors, including energy, offshore, petrochemical, and the cruise industry.

Colin Wade, managing director at Chemco International, said: “This funding is actively supporting us in our growth journey. As an employee-owned business, it’s vital that we invest in technology that supports our people and strengthens our capabilities. In this instance, securing part-funding to safeguard the long-term interest of our employee owners.

“Lombard and Royal Bank of Scotland have been key to seeing our vision realised, working closely with us to provide a solution that supports both our immediate needs and our long-term ambitions.”

Scott McGhie, senior relationship manager at Lombard, said: “Chemco is a strong example of a growing, purpose-led manufacturing business playing a vital role in supporting global industries, through innovative and environmentally friendly coating technologies.

“By providing this trade loan alongside Royal Bank of Scotland, we are helping the business invest in automation, increase capacity and deliver sustainable growth, while continuing to support jobs and communities in Scotland.”

Lombard and Royal Bank of Scotland’s investment builds on the business’ focus of supporting growth among organisations which aid in supporting a wide range of industries.

www.rbs.co.uk

Known for supplying collaborative and mobile robots, RARUK Automation has a longstanding partnership with Teradyne Robotics’ company Mobile Industrial Robots and has achieved MiR Gold Partner status due to sales achievements in the UK. Nord Modules offers AMR top modules designed specifically for the Mobile Industrial Robots range, providing additional functionality such as lifting mechanisms, roller conveyors, carts and other accessories.

Nord Modules products are designed to optimise space utilisation and offer modular flexibility for companies looking to deploy AMRs. With Nord Modules, the transportation of totes or pallets between conveyors, racks and other pick-up/ drop-off zones is seamlessly accurate. The full range consists of Quick Movers, Lifts and Conveyors, which come with the option of adding integrated blue light and barcode scanners, as well as other accessories. 

The new collaboration between RARUK Automation and Nord Modules reflects a shared focus on making automation simple, scalable and flexible for real-world manufacturing applications. When investing in an AMR solution, customers will gain faster deployment and smooth integration between mobile robots and existing factory infrastructure by choosing Nord Modules’ proven, standardised top module equipment. 

"Our partnership with Nord Modules strengthens our commitment to delivering simple, scalable and fully integrated AMR solutions to UK manufacturers. By combining Nord Modules’ proven top modules with our expertise in Mobile Industrial Robots, we will be able to bring immediate, tangible value to our AMR customers,"  said Ross Lacy, sales director, RARUK Automation.

“We are very excited to partner with RARUK Automation, who are not only the sole MiR distributor in the UK but also hold MiR Gold Partner status. The UK is a high-priority market for Nord Modules, and RARUK Automation’s deep expertise in MiR and automation makes them an ideal partner for bringing our solutions to this market. With our top modules known for their superior quality and durability, and RARUK Automation’s strong local presence and technical expertise, we are confident this partnership will create real value for UK customers,” commented Frank Ib, CSO, Nord Modules.

www.rarukautomation.com

While 87% of manufacturing leaders and technical specialists report that ROI from their AIOps initiatives has met or exceeded expectations, only 37% say they are fully prepared to operationalise AI at scale. With 62% of AI projects still in pilot or development stages, and 90% of respondents agreeing that improving data quality is critical to AI success, the findings highlight a sector eager to leverage AI to streamline operations, reduce costs, and navigate increasingly complex global supply chains, yet still working to close the gap between ambition and enterprise-wide AI execution at scale.

As organisations in the manufacturing sector aim to advance their AI journey, there are several significant barriers hindering wide-scale adoption. While more than half (57%) of manufacturing organisations express confidence in their AI projects, and the vast majority agree that improving data quality is critical to success, persistent data quality challenges remain a central obstacle. Almost half (47%) lack confidence in the accuracy and completeness of their organisation’s data to be able to deliver the right outcomes, and only 34% rate their data as excellent for relevance and suitability. These gaps highlight a clear disconnect between leadership optimism and the technical realities of implementation.

“The manufacturing industry is investing heavily in AI to transform IT operations, and our survey results show that nearly nine in ten companies in this sector (87%) are already meeting or exceeding ROI expectations from their AIOps investments,” said Richard Tworek, Chief Technology Officer, at Riverbed. “However, many still face major challenges, including gaps in readiness and preparedness, as well as data quality issues which are hindering progress. As a data-driven company, we’re helping our manufacturing customers close these gaps with safe, secure and accurate AI built on high-quality real data; delivering practical AI-powered solutions that enable organisations to scale AI across the enterprise.”

Amid changing processes and varying priorities, manufacturers have pursued an array of IT tools to support shifting goals. The research found that, on average, organisations in this industry currently use 13 observability tools from nine different vendors. In response, 95% of manufacturers are consolidating tools to cut down on sprawl in an effort to reduce costs, streamline operations, and optimise efficiencies across IT operations. Vendors will be well-served to continue exploring their tools’ capabilities, with 91% of manufacturing organisations considering new tools as they look to consolidate. The top capabilities and drivers manufacturing leaders are actively considering when consolidating tools include enhancing tool integration and interoperability (48%), reducing vendor management overhead (47%), and improving IT productivity (46%).

With AI and remote work set to transform manufacturing organisations worldwide, the survey found enthusiasm for unified communication tools and their integration into operations.

• The research revealed that 42% of employees use UC tools throughout their work week and 66% of manufacturing respondents say that these tools are essential to operating effectively on a weekly basis.
• Despite growing adoption, these tools still have significant room for improvement. Less than half (45%) are satisfied with UC tools’ performance, and 42% of manufacturers report experiencing issues with video calls, messaging platforms, and more.
• The top three challenges organisations face with UC tools include limited visibility (51%), dropped calls (42%), and integration challenges with other enterprise systems (38%).

Manufacturing leaders surveyed also report their views on OpenTelemetry (OTel) and its place within their organisation. The research found that 44% have fully implemented OTel, with a further 42% adopting it, and overall, 97% agree that cross-domain OpenTelemetry correlation is critical to their observability strategy. The vast majority (93%) say that OTel is a foundation for future initiatives such as AI-driven automation and 37% cite that OTel is already a mandate in their organisation, indicating a substantial interest in this technology.

With data already identified as a key factor to critical success in the implementation of AI initiatives, 91% of manufacturing respondents cited the movement and sharing of data as important to their organisation’s overall AI strategy, with 31% stating it’s critical and foundational to how they design and executive AI. To further support AI initiatives, 75% of manufacturing respondents plan to establish an AI data repository strategy by 2028.

Respondents also confirmed their top three considerations when enabling their organisation to move and scale data effectively were:

• Network performance and ability (96%)
• Cost of data movement and storage (94%)
• AI model proximity to data, and interoperability between environments (both 93%).

Additionally, as manufacturing organisations strive to stay competitive, ensuring superior network efficiency and robust data security is a top priority, as 79% report that network performance and security are essential to their AI strategy.

Find out more about how organisations are overcoming gaps and navigating AI adoption and implementation by viewing the full report at:

www.riverbed.com/aiops-survey25/
 

The plan also focuses on how AMUK can help turn the UK’s R&D strength into economic growth and sovereign capability whilst establishing the UK as a world leader in the development, adoption and application of 3D printing and additive manufacturing technology.

Additive manufacturing has enormous potential to transform the way parts and products are designed and produced. This is the core reason why AMUK supports its members and the wider UK industry across the entire AM value chain, whilst also educating potential users about the opportunities this technology offers. The additive manufacturing industry delivers many benefits, such as reduced material waste, greater efficiency and lower production costs for small-batch manufacturing. This reduces the environmental impact of manufacturing, making parts and products more sustainable.

Additive manufacturing has already had a significant impact on high-value manufacturing sectors, including aerospace, space, automotive (including motorsport), energy generation equipment, defence, rail, marine, consumer goods (sport, leisure, jewellery), general industrial products, health, pharmaceuticals and medical equipment.

Joshua Dugdale, head of AMUK, said: “Our plan highlights challenges that we must address in order to accelerate the adoption of additive manufacturing technologies. Together with our members, we have identified supply chain, skills and standards as the top three challenges, which we will tackle during this year, as these are crucial areas impacting the AM industry.”

The global AM market grew to $21.9 billion in 2024, but despite this global growth, the value of the UK’s own market contracted, with its global share falling by approximately 4%. Last year's figures are not available yet, but they are expected to be about the same, with AMUK members reporting tough trading conditions. However, the market is showing some signs of recovery in the early part of 2026, which is why AMUK is determined to provide its members with an opportunity to achieve their portion of the UK’s potential of capturing a 7% market share, which could be valued at nearly $5 billion by 2030.

The AMUK Annual Action Plan provides a valuable roadmap for the development and growth of additive manufacturing in the UK. With a focus on collaboration, innovation and investment, the UK could become a leader in this exciting and rapidly evolving field.

To download the report, please visit:

additivemanufacturinguk.org.uk/amuk-strategy/

Developing a rotary interface with graphical display typically requires significant engineering effort across multiple disciplines, including encoder sensing, firmware architecture, GUI rendering, mechanical integration, and environmental sealing. These challenges can add months of development time and introduce substantial project risk. DRAGONEYE removes this barrier.

Built on the STM32U5 microcontroller and optimised for circular touch displays and rotary interactions, the platform provides developers with a ready-to-use environment for rapidly creating functional MVPs while maintaining a direct path to production through Anders’ engineering and customisation services.

At the heart of the platform is a newly developed IP65-rated brushless rotary encoder, engineered in-house by Anders specifically for sealed industrial HMI applications, featuring sealed high-resolution IP65 construction for harsh environments, a high-resolution 2.1” IPS-TFT display, contactless sensing for long-life, wear-free operation, integrated capacitive touch capability, haptic feedback support for tactile interaction, and the flexibility to be customised for different display sizes.

Together, the STM32U5 development platform and Anders’ rugged rotary encoder deliver a ready-to-run solution for rapid development of robust, production-grade rotary HMIs.

DRAGONEYE leverages the full STM32 development ecosystem, allowing engineers to use familiar tools, libraries, and software frameworks while accelerating GUI and application development.

The platform is designed not only for rapid prototyping but also as the foundation of a fully deployable production system, with Anders providing comprehensive customisation and optimisation services including application-specific PCB redesign and system integration, mechanical and enclosure adaptation, rotary interface material and finish customisation, firmware optimisation and tuning, as well as production scaling and full lifecycle support.

This enables customers to transition seamlessly from evaluation to a production-ready product manufactured by Anders.

www.andersdx.com