INDUSTRIES TODAY face a complex mix of pressures that make sustainable automation essential. Environmental responsibility is among the most visible. Industrial operations contribute significantly to global energy consumption and carbon emissions, and automation systems, particularly in manufacturing, assembly and logistics, are often energy-intensive. Material waste is another pressing concern. Scrap, defective products, and packaging inefficiencies increase costs and place additional strain on the environment. Sustainable automation addresses these challenges while delivering measurable economic benefits.

Rising energy costs and market volatility add urgency. Organisations that invest in energy-efficient equipment and process optimisation can achieve significant cost reductions. For example, a mid-sized automotive facility that replaced standard motors with high-efficiency variable-speed alternatives reduced electricity consumption by 20%, resulting in annual savings of more than £400,000. Process optimisation aimed at reducing material waste can have a substantial impact, particularly in industries where raw materials account for a large share of production costs.

Regulatory requirements further accelerate adoption. Across the UK and EU, carbon pricing mechanisms, mandatory energy reporting and emissions reduction targets establish clear expectations. Compliance with standards such as ISO 50001 provides a structured approach to improving energy performance, while transparent reporting strengthens stakeholder confidence. Organisations that address sustainability proactively can reduce regulatory risk, strengthen credibility and in some cases access financial incentives for early action.

Principles of sustainable automation

Sustainable automation rests on four key principles: energy efficiency, waste minimisation, lifecycle sustainability, and intelligent process management.

- Energy efficiency is foundational. Automated systems should operate only when required, and equipment such as motors, drives, conveyors and robotics must be correctly sized and configured to minimise power consumption. AI-driven scheduling and regenerative technologies can further reduce energy demand. In one industrial automation project, the introduction of regenerative robotic arms, combined with AI-based scheduling, reduced energy use by more than 20%, delivering annual savings of £350,000 and significantly lowering carbon emissions. When applied at scale, decisions like these have a substantial cumulative effect.

- Waste minimisation focuses on preventing scrap, rework, and inefficient use of materials. Robotics and precision automation improve accuracy, while sensors and analytics detect inefficiencies before they escalate. Closed-loop recycling systems, where scrap materials are reintegrated into production, further reduce waste. In electronics manufacturing, precision robotic assembly reduced defective product rates by 30%, cutting material waste and lowering disposal costs.

- Lifecycle sustainability requires organisations to consider environmental impact from system design through to end of life. Selecting recyclable materials, designing equipment for maintainability and planning responsible disposal ensures sustainability is addressed from the outset rather than added later. Predictive maintenance extends equipment lifespan and reduces unnecessary replacement, delivering both environmental and financial benefits.

- Intelligent process management ensures continuous improvement. IoT sensors and digital twins enable real-time monitoring of energy use, material consumption, and machine performance. AI-driven analytics identify inefficiencies and recommend operational adjustments before issues emerge. In food processing environments, AI-based production scheduling reduced energy consumption during peak periods while lowering spoilage, demonstrating how intelligent management can improve efficiency and sustainability simultaneously.

Enabling technologies

Numerous technologies enable sustainable automation, each contributing distinct capabilities. However, the greatest gains are achieved when these technologies operate as an integrated system rather than in isolation.

Robotics and smart manufacturing systems improve accuracy, reduce human error, and lower material waste. Energy-efficient drives, regenerative technologies, and intelligent scheduling reduce power consumption, while integrated control systems ensure different parts of the operation communicate effectively, limiting inefficiencies across production and logistics.

Artificial intelligence and machine learning play a critical role in predictive maintenance, process optimisation and demand forecasting. Predictive maintenance reduces unplanned downtime and extends equipment life, while process optimisation supports more energy-efficient production. Accurate demand forecasting aligns output with actual market requirements, helping to reduce excess inventory and material waste. In a large packaging facility, AI-based scheduling reduced peak-hour energy consumption by 18% and lowered spoilage by 12%.

Industrial IoT provides the real-time data required for informed decision-making. Sensors monitor energy usage, machine performance and environmental conditions, generating actionable insights. By analysing this data, organisations can identify inefficiencies, prevent waste and continuously optimise production processes.

Renewable energy integration complements automation technologies by reducing reliance on grid electricity. Solar, wind and biomass systems can supplement or replace traditional energy sources, lowering carbon emissions. Smart energy management systems coordinate renewable energy supply with production schedules. This helps maintain operational continuity while maximising the use of low-carbon power.

The common thread across these technologies is integration. Robotics alone does not reduce energy consumption and renewable energy alone does not eliminate waste. Meaningful results come from connecting systems, processes and people so they operate as one.

Strategic recommendations

For leaders considering investment in sustainable automation, the path forward can be summarised into a few key actions.

- Start with insight, not technology. Organisations should begin by auditing energy use, waste and operational performance to build an accurate view of where improvement will deliver the greatest value. This clarity helps prevent misallocation of capital and ensures efforts are focused on areas with the strongest impact.

- Design holistically. Automation should be approached as a connected ecosystem rather than a collection of isolated assets. Software, hardware, energy, maintenance and people must work together. Integration should be a design principle, not an afterthought.

- Prioritise lifecycle value. Decision-making should extend beyond initial purchase cost to consider total cost of ownership. Maintenance requirements, energy consumption, operational lifespan, recyclability and carbon impact all influence long-term value. In many cases, the most sustainable option is also the most financially resilient.

- Invest in people. Sustainable automation depends on skills as much as technology. Developing capability in energy management, automation systems, data analytics and sustainability leadership strengthens execution. When teams understand the purpose behind the technology, adoption improves and outcomes become more consistent.

- Build strong partnerships. No organisation delivers sustainability in isolation. Collaboration with technology providers, integrators, energy specialists, academic institutions and supply chain partners enables access to broader expertise and shared learning, leading to more effective outcomes.

- Measure, review, improve. Sustainability performance is not static. Continuous measurement, review and adjustment are essential. Embedding sustainability metrics into day-to-day operations ensures progress is monitored consistently rather than treated as an annual reporting exercise.

- Embed cultural ownership. Sustainability should form part of organisational identity. Recognising success, sharing results and encouraging innovation at all levels fosters shared ownership. When sustainability is embraced collectively, it becomes a sustained capability rather than a top-down directive.

When organisations follow these principles, sustainability moves beyond cost containment. It becomes a strategic capability that supports long-term resilience, performance and value creation.

Dan Migliozzi is sales director for the UK, EU and North America at AGITO Global

agitoglobal.com

 

The new facility has been designed as a structured environment where the full portfolio of HaiPick Systems, including the company’s latest upgraded HaiPick Climb system, can be assessed under real operating conditions. By bringing together live HaiPick systems, engineering expertise, and project experience in one location, the centre enables more practical discussions on system design, scalability, and deployment across a range of industries including e-commerce, apparel, grocery, and industrial applications.

“The innovation centre provides a space for more informed conversations around automation,” comments David Burggraaf, marketing director EMEA at Hai Robotics. “Rather than focusing on specifications alone, it allows customers and partners to evaluate how systems perform in real operational scenarios and how they can be adapted to specific application needs.”

The facility has already hosted several technical sessions with industry partners prior to its official opening and will continue to serve as a hub for customer engagement, partner collaboration, and industry dialogue across the EMEA region. 

With increasing demand for flexible and scalable automation solutions in Europe, the innovation centre reflects Hai Robotics’ commitment to supporting customers throughout the design and deployment process, from early-stage evaluation to long-term system expansion.

www.hairobotics.com

Image: Agratas

Agratas' project will strengthen economy security and reduce Britain's reliance on imports by turbocharging domestic battery production and generating around £43 billion worth of economic growth over a 25-year period when the facility is in full operation.

The site will not only support 4200 direct jobs but thousands more in the supply chain, as well as unlock 300 apprenticeships – backed by a specialised battery manufacturing training unit to meet the skills needs of Agratas' gigafactory and the wider battery sector.

During a vist to the company, business secretary Peter Kyle (pictured) said: “This government is backing the industries of the future by investing in auto firms, SMEs and battery manufacturers across the country, helping to boost economic growth and our resilience, secure jobs and put more money in people's pockets.

“In an unstable world, our Modern Industrial Strategy is providing investors the stability and confidence they need to plan not just for the next year, but for the next 10 years and beyond. That is what sets us apart from the rest, and will help ensure advanced manufacturing remains a thriving sector in the UK for decades to come.” 

Earl Wiggins, vice president of manufacturing operations, UK for Agratas said: “We welcome the UK Government’s investment as we build a battery manufacturing facility that will play a vital role in delivering net zero and strengthening the UK’s position as a global leader in battery manufacturing.

“This funding will support the development of our Somerset facility, enabling us to produce battery cells for our anchor customer, JLR (Jaguar Land Rover). Over the next year we will have over 2200 people working on the site, and that growth will continue over the coming years." 

The latest Quarterly Update reveals that since the launch of the Modern Industrial Strategy over £360 billion of private investment has been secured across its key sectors, supporting up to 120,000 jobs. Alongside this, the government is cutting electricity costs for energy-intensive manufacturers, reducing unnecessary planning delays and overhauling regulation that holds back our most ambitious businesses.

Government is injecting £47 million worth of support for key R&D battery projects through the Battery Innovation Programme, helping to create skilled jobs, a stronger supply chain and position the UK as a globally competitive destination for battery manufacturing.

Auto businesses will also benefit from a £190 million boost to ensure the automotive industry remains ahead of the competition on the global stage. Startups and well-established firms including Nissan and Jaguar Land Rover have been awarded £90 million in DRIVE35 funding to ramp up innovative prototype and cutting-edge projects, strengthening firms’ technological capabilities and improving the affordability of EVs for customers.

www.gov.uk/government/organisations/department-for-business-and-trade

In a similar vein, Total Productive Maintenance (TPM) has followed a very similar trajectory. Originating in Japan in the 1970s, this philosophy encouraged all operators to take a proactive role in machine care and introduced the concept of preventive maintenance. Now, the modern descendant of TPM, Overall Equipment Effectiveness (OEE), is a key shop-floor metric worldwide, producing data that can feed smart automation even further. This symbiotic relationship enables operators to create a reactive, self-improving production line.

The what and why of OEE

Stripped to the studs, OEE is a holistic way of answering what is, fundamentally, a pretty basic question: Is this asset operating at its full potential? 

Uptime and output can help answer that question in part, but they aren’t all-encompassing indicators of function. OEE provides a more holistic solution, built on the three dimensions of availability, performance and quality. 

• Availability: assesses whether equipment is running when it is supposed to. Downtime and stoppage, both planned and unplanned, are factored into this metric. 
• Performance: dives into the oftentimes hidden inefficiencies of the machine. A breakdown is easy to notice, but a drop in speed due to a misconfiguration can slip under the radar and accumulate costs over time. 
• Quality: captures how much of the output meets the standard. Defects and reworks both impact the quality score of a given piece of equipment, whether due to wear or input errors. 

Taken together, these metrics paint a clearer picture of equipment health than any single figure, but an OEE score is still not fully indicative of efficiency. For all its strengths as a single, universal percentage, the final figure can still obscure the complexity of the losses it measures. For example, micro-stoppages tend to slip through the cracks without triggering alarms, despite their cumulative impact on performance. 

In many facilities, OEE is still collected manually through end-of-shift batch reports, a process that entails several limitations. Manual logging introduces a margin for error, neglects finer details such as timestamps, and, most importantly, only identifies issues after they have already occurred.  Enhancing the value of this final OEE figure and addressing its limitations involves moving from retrospective analysis, to a live operational system. 

Making automation truly smart

OEE, paired with real-time data, becomes a driver of decision-making. This focus on intelligent, interconnected networks that exchange information and automatically adjust operations is central to the modern idea of the smart factory, much like how transport software solutions optimise real-time decision-making across logistics networks.

Within this ecosphere, OEE acts as a feedback loop between machines, systems, and operators. When combined with tools such as Manufacturing Execution Systems (MES) and SCADA platforms, the Overall Equipment Effectiveness data enables:

• Automated alerts: Immediate notifications when any OEE metrics fall below approved thresholds, along with predefined escalation protocols to ensure the discrepancy is addressed as soon as possible. 
• Dynamic adjustments: Operators or automated systems can rebalance and reroute workloads, or adjust speed, to compensate for production drops in real time. 
• Root cause visibility: Data highlights exactly where and when issues occur, dramatically cutting investigation time.

Closed-loop controls, such as those enabled by real-time OEE data, have been shown to improve the efficiency and responsiveness of production systems. Cyber-physical manufacturing, as envisioned in “Industry 4.0”, creates an environment in which automation continuously monitors and improves the production process.  

Reducing downtime and driving capacity

Perhaps the most immediate benefit of real-time OEE is the ability to optimise productivity from top to bottom. Small inefficiencies, such as start-up delays and micro-stoppages that are typically unseen by traditional OEE, are detected immediately. Managers no longer have to dedicate time and resources to frequent manual inspection, another hidden waste that adds up. 

Data draws attention to these losses by identifying performance patterns that manufacturers can address without additional investment. This is precisely why modern strategies emphasise leveraging real-time insights to reduce downtime and maximise OEE: it’s a cost-effective, calculated approach to eliminate inefficiencies.

A second, equally impactful benefit of real-time OEE data lies slightly beyond the present. Predictive and automotive maintenance allow operators to anticipate failures based on performance trends, trigger maintenance, and adjust capacity elsewhere to compensate for the stalled output, with minimal manual intervention. 

These “self-optimising systems” are becoming more accessible and effective thanks to advancements in AI and greater integration of smart systems in factories. 

From insight to sustainable impact

Making automation truly smart through OEE data is not without its challenges. For all of these systems to communicate, they must first be compatible, meaning any setting still using legacy systems or non-digitised machines faces a hefty upfront investment. Thankfully, this process can be gradual, giving manufacturers a chance to identify the right integration strategy and to encourage operator involvement, in line with the TPM philosophy. 

These barriers are well worth navigating, as industry trends move towards a shared operational language built upon data visibility. By embedding real-time OEE into everyday workflows, manufacturers are reducing inefficiencies, speeding up maintenance, and unlocking the full potential of their existing assets.

Image: MIT

To avoid such an avalanche of inefficiencies, researchers from MIT and the tech firm Symbotic developed a new method that automatically keeps a fleet of robots moving smoothly. Their method learns which robots should go first at each moment, based on how congestion is forming, and adapts to prioritise robots that are about to get stuck. In this way, the system can reroute robots in advance to avoid bottlenecks.

The hybrid system utilises deep reinforcement learning, a powerful artificial intelligence method for solving complex problems, to figure out which robots should be prioritised. Then, a fast and reliable planning algorithm feeds instructions to the robots, enabling them to respond rapidly in constantly changing conditions.

In simulations inspired by actual e-commerce warehouse layouts, this new approach achieved about a 25 percent gain in throughput over other methods. Importantly, the system can quickly adapt to new environments with different quantities of robots or varied warehouse layouts.

“There are a lot of decision-making problems in manufacturing and logistics where companies rely on algorithms designed by human experts. But we have shown that, with the power of deep reinforcement learning, we can achieve super-human performance. This is a very promising approach, because in these giant warehouses even a 2 or 3 percent increase in throughput can have a huge impact,” says Han Zheng, a graduate student in the Laboratory for Information and Decision Systems (LIDS) at MIT and lead author of a paper on this new approach.

Zheng is joined on the paper by Yining Ma, a LIDS postdoc; Brandon Araki and Jingkai Chen of Symbotic; and senior author Cathy Wu, the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of LIDS. The research appears today in the Journal of Artificial Intelligence Research.

Rerouting robots

Coordinating hundreds of robots in an e-commerce warehouse simultaneously is no easy task.

The problem is especially complicated because the warehouse is a dynamic environment, and robots continually receive new tasks after reaching their goals. They need to be rapidly redirected as they leave and enter the warehouse floor.

Companies often leverage algorithms written by human experts to determine where and when robots should move to maximise the number of packages they can handle.

But if there is congestion or a collision, a firm may have no choice but to shut down the entire warehouse for hours to manually sort the problem out.

“In this setting, we don’t have an exact prediction of the future. We only know what the future might hold, in terms of the packages that come in or the distribution of future orders. The planning system needs to be adaptive to these changes as the warehouse operations go on,” Zheng says.

The MIT researchers achieved this adaptability using machine learning. They began by designing a neural network model to take observations of the warehouse environment and decide how to prioritise the robots. They train this model using deep reinforcement learning, a trial-and-error method in which the model learns to control robots in simulations that mimic actual warehouses. The model is rewarded for making decisions that increase overall throughput while avoiding conflicts.

Over time, the neural network learns to coordinate many robots efficiently.

“By interacting with simulations inspired by real warehouse layouts, our system receives feedback that we use to make its decision-making more intelligent. The trained neural network can then adapt to warehouses with different layouts,” Zheng explains.

It is designed to capture the long-term constraints and obstacles in each robot’s path, while also considering dynamic interactions between robots as they move through the warehouse.

By predicting current and future robot interactions, the model plans to avoid congestion before it happens.

After the neural network decides which robots should receive priority, the system employs a tried-and-true planning algorithm to tell each robot how to move from one point to another. This efficient algorithm helps the robots react quickly in the changing warehouse environment.

This combination of methods is key.

“This hybrid approach builds on my group’s work on how to achieve the best of both worlds between machine learning and classical optimisation methods. Pure machine-learning methods still struggle to solve complex optimisation problems, and yet it is extremely time- and labor-intensive for human experts to design effective methods. But together, using expert-designed methods the right way can tremendously simplify the machine learning task,” says Wu.

Overcoming complexity

Once the researchers trained the neural network, they tested the system in simulated warehouses that were different than those it had seen during training. Since industrial simulations were too inefficient for this complex problem, the researchers designed their own environments to mimic what happens in actual warehouses.

On average, their hybrid learning-based approach achieved 25 percent greater throughput than traditional algorithms as well as a random search method, in terms of number of packages delivered per robot. Their approach could also generate feasible robot path plans that overcame congestion caused by traditional methods.

“Especially when the density of robots in the warehouse goes up, the complexity scales exponentially, and these traditional methods quickly start to break down. In these environments, our method is much more efficient,” Zheng says.

While their system is still far away from real-world deployment, these demonstrations highlight the feasibility and benefits of using a machine learning-guided approach in warehouse automation.

In the future, the researchers want to include task assignments in the problem formulation, since determining which robot will complete each task impacts congestion. They also plan to scale up their system to larger warehouses with thousands of robots.

The research was funded by Symbotic.

Smiths Interconnect brings a diverse portfolio of complementary products and advantaged capabilities in ruggedized custom connectors, contacts, RF components and optical transceivers for harsh environments. Smiths Interconnect also brings leading semiconductor test capabilities that complement Molex’s data communications and data center solutions to support growth in AI, along with a strong medical interconnect capability to support Medtech customers.

“We are excited to finalize the Smiths Interconnect acquisition, which expands our portfolio and engineering expertise worldwide,” said Joe Nelligan, CEO, Molex. “Along with their products and technology, we are excited about the Smiths Interconnect’s global team and the strong customer relationships that they have established.”

As the largest acquisition in Molex’s history, the addition of Smiths Interconnect positions Molex to drive innovation across the high-reliability interconnect market—encompassing aerospace and defense, space exploration, industrial, medical and semiconductor test industries. The combination of Smiths Interconnect’s capabilities with Molex’s global footprint, engineering strength, culture and financial strength enables Molex to deliver increased value to customers around the world. 

“This acquisition reinforces Molex’s position as a leader across every sector where high reliability is critical,” said Michael Cole, SVP and president, Aerospace and Defense Solutions Division, Molex. “By unifying these high-reliability solutions under the Molex banner, we provide a borderless platform that enables engineers to leverage ruggedized, precision-engineered connectivity.”

The Smiths Interconnect acquisition expands Molex’s global footprint to over 90 plants across 22 countries, and its global headcount to over 55,000 employees. The integration creates strong design and technical alignment to enhance innovation and solution capabilities to provide more value to customers. 

This news builds upon the November 2024 acquisition of AirBorn by Molex, a foundational milestone in serving customers in the aerospace and defense market.

“The integration of Smiths Interconnect into the Molex engineering ecosystem is a significant win for the global design engineering community,” said Lew LaFornara, SVP, Global Interconnect Business Strategy, TTI. “As a prominent Smiths Interconnect distributor, we are excited to continue offering our customers access to a robust line of mission-critical solutions—spanning aerospace and defense, medical, industrial and semiconductor test—all backed by Molex’s world-class logistics and technical support."

www.molex.com

The POC focused on a real-world logistics picking workflow. During the project, the HMND 01 Alpha Wheeled robot received task instructions from the SAP AI agent, autonomously navigated to the designated pallet, retrieved a KLT box, and delivered it to a trolley. The robot then repeated the cycle as part of the order-fulfillment flow, showing its ability to integrate into existing warehouse operations.

The POC was powered by Humanoid’s proprietary KinetIQ AI stack, a four-layer AI framework for end-to-end orchestration of humanoid robot fleets across different environments. HMND 01 robots operated as a flexible fleet, directly tasked through SAP Business AI as autonomous agents, allowing the fleet to be instantly reallocated as production demands shifted. This orchestration layer ensured the robots were highly available, adaptable, and flexible.

A key milestone was achieved during the final integration phase. Humanoid’s robot API connected directly to SAP’s APIs through the SAP Joule agent layer. As a result, SAP Extended Warehouse Management was able to send tasks to the robot over the internet and manage its actions remotely, without relying on a local, custom-built control system. For Humanoid, this was the first time its robot had been controlled by an external enterprise system in a live production environment. With KinetIQ allowing robots to integrate into existing facility management structures, the robot became part of the company’s core IT system that manages orders, inventory, and tasks.

“Embodied Joule represents a fundamental shift in how robots understand and respond to business needs,” said Dr Lukasz Ostrowski, head of embodied AI and Robotics at SAP SE. “This proof of concept in the manufacturing industry allows us to demonstrate how humanoid robots can act as extensions of an organisation’s operations by providing business context awareness and integration with existing workflows.”

The project ran from January to February 2026 and followed a structured rollout plan: physical twin development, in-house testing, site preparation, and on-site deployment, including setup, training, optimization, and stakeholder demos. The robot delivered strong and successful results under real operational conditions. Moreover, the robot successfully handled three different tote types and operated with an 8 kg dual-arm payload limit.

“This proof of concept shows what matters: humanoid robots operating inside real production environments, connected to enterprise systems and measured against operational standards. That’s the bridge between experimentation and deployment. We’re proud of the progress we have achieved together with SAP and Martur Fompak, and are grateful to our partners for the trust to test our Alpha Wheeled humanoid where it really counts — on the factory floor,” said Artem Sokolov, founder and CEO of Humanoid.

The team is currently preparing for the next on-site phase of the project, where the integration will be further validated in live production conditions. After completion, partners also plan to jointly assess the POC results, evaluate potential pathways to deploy Humanoid’s robots at Martur Fompak facilities, and explore more complex use cases and workflow scenarios within the production environment.

thehumanoid.ai

To improve electrical insulation between high-power components, the CHO-THERM HV Series incorporates robust dielectric layers. Alongside impressive thermal performance, these properties are designed to meet the stringent engineering demands of modern automotive and industrial systems. The materials exhibit dielectric constant as low as -0.05 at 1 MHz (ASTM D150 test method) and dissipation factor down to 2.8 at 1 MHz (Chomerics CHO-TM-TP13).

“CHO-THERM HV marks an important addition to our technology portfolio, meeting the thermal management needs of systems where enhanced electrical insulation is paramount,” says Keith McDonald, International Director of Sales, Parker Chomerics. “The materials offer superior performance across a broad spread of applications that include automotive electronics and on-vehicle power systems, lighting assemblies, high-voltage sensors and actuators, and electric vehicle charging stations.” 

CHO-THERM HV is also suited to alternative energy power conversion/inversion systems, heavy-duty industrial equipment and industrial motor controllers. Parker Chomerics engineered the materials to provide effective thermal transfer from heat-generating components such as CPUs, GPUs and high-power busbars to heat-dissipating components like cold plates, heatsinks, cooling pipes and vapour chambers. 

Additional benefits extend to minimal outgassing, V-0 flammability compliance under the globally recognised UL94 standard and ultra-low compression force characteristics that simplify installation. With Shore 00 hardness ratings of 30–35, CHO-THERM® HV solutions conform readily to uneven or textured mating surfaces, eliminating air gaps and improving thermal contact. 

Customers can now specify the pads in standard sheet formats for high-volume processing or select custom die-cut configurations tailored to complex geometries. Four RoHS-compliant material variants are available, with specialists from Parker Chomerics ready to assist with the optimal selection. PSA (pressure-sensitive adhesive) bonding options are available where added adhesion strength is necessary.

ph.parker.com/gb/en/series/thermally-conductive-pads

The event brings together over 1000 research and industry experts from across the continent to share ideas, create new connections and celebrate the best of robotics, one of the most important technologies that will shape the future of society for generations to come.

Since it first began in 2010, ERF has been hosted in many great cities and industrial powerhouse regions across Europe. Hosting ERF in Birmingham for the first time will, say the organisers, offer an opportunity to anchor conversations about the future of robotics within a region where innovation is not abstract but applied daily: in factories, laboratories, and innovation centres deploying next generation artificial intelligence, automation and robotics at scale.

ERF in Birmingham will be organised and hosted by The Manufacturing Technology Centre (MTC), the West Midlands-based research and technology organisation, and the University of Birmingham, in partnership with euRobotics, the European robotics association.

Birmingham is a fitting setting for an event of this kind. The city draws on its region’s industrial heritage with roots that reach back to the very birth of the industrial revolution, a proud history of manufacturing and precision engineering, a long-established and internationally acclaimed automotive sector, a fine tradition of research and innovation, with universities offering globally-recognised academic excellence, and a large, skilled, diverse population.

Karol Janik, ERF2027 general chair, and robotics and sutomation technology manager at the MTC said: “For more than two centuries, the West Midlands has stood as one of Europe’s most enduring centres of industrial ingenuity. From the steam engine to today’s breakthroughs in robotics, AI, advanced manufacturing, clean energy or healthcare, this region has repeatedly shaped the technological direction of the continent. Birmingham and the wider West Midlands remain one of its most innovative industrial heartlands: a dense ecosystem of global manufacturers, world leading research institutes, dynamic automation and robotics companies, and a new generation of ambitious entrepreneurs.

“Add to this a city with a rich and varied cultural offer spanning classic and contemporary, clubs to concert halls, gigs to galleries and from theatres to Think Tank, Birmingham’s famously interactive museum of science, and the considerable attractions of the UK’s ‘second city’ are obvious."

As the organising team prepares for this landmark event, it invites organisations, innovators, and industry leaders to partner as sponsors and exhibitors. This is a unique opportunity to showcase technologies, products, and services to a diverse and influential audience at the forefront of robotics, AI and automation in Europe and beyond. Interested parties are encouraged to contact the organising team to learn more about sponsorship and exhibition packages and secure their place at ERF 2027.

erf2027.eu/about/
 

Named the Technology Roadmaps initiative, it leverages expertise from Skunk Works to support the sovereign development and delivery of cutting-edge defence technologies to UK customers.

The company said the initiative introduces a grassroots model, focused on collaboration with small and medium UK enterprises (SMEs) while building on existing relationships with UK government organisations, academic institutions and industry partners in areas identified in the UK’s Strategic Defence Review.

Identified technology priorities include artificial intelligence, autonomy, cyber, material technologies and space manufacturing, and initial projects will focus on creating immediate capability from existing technologies. The initiative will share best practices, contribute to ecosystem learning and deliver purposeful innovation for the UK’s defence needs.

The announcement follows the successful delivery last year of Skunk Works first international project, the TIQUILA uncrewed aerial system programme for the British Army. Other recent successful Skunk Works UK activities include  “Project DEIMOS” live-fly demonstrations that showcased interoperability between the Royal Air Force’s F-35 fleet and NATO allies, and the collaboration with BAE Systems’ FalconWorks to develop a range of uncrewed autonomous air systems for global allies, announced at DSEI last September.

Tier 1 membership of the MTC will also see Lockheed Martin collaborate with that UK organisation on core research, capabilities and investments, and play a leading role in managing projects that address key challenges faced by defence industry supply chains.

“Lockheed Martin recognises that for the UK Government, defence is an engine for growth, and cooperation of this type delivers for the UK, the US and their allies - moving technology faster, driving supply chain resilience and creating shared economic growth for all parties,” said Paul Livingston, CBE, chief executive for UK and NATO at Lockheed Martin.

“The Technology Roadmap initiative with UK industry and partners such as MTC will help us build on the long legacy of transatlantic excellence to deliver mutual security benefits today and into the future,” said OJ Sanchez, vice president and general manager, Lockheed Martin Skunk Works.

Sir Rick Thompson, KCB, CBE, managing director of defence at the Manufacturing Technology Centre, said: “Lockheed Martin joining the MTC as a Tier 1 member sends a clear signal about the strength of the UK’s defence industrial base and our innovation ecosystem as it continues to grow in response to an increasingly uncertain world. By working with partners like Lockheed Martin, we can help companies across the supply chain adopt the technologies they need to compete, scale and seize the opportunities created by the government’s Defence Industrial Strategy as a driver of growth.”

Specific projects conducted in collaboration with the MTC will focus on delivering scalable, adaptable and agile manufacturing with reduced costs and lead times for customers. The efforts will concentrate on additive manufacturing, automation, augmentation and digital integration, addressing common challenges such as accelerating the delivery of new technologies to the front line.

The membership is also intended to accelerate SME technology readiness levels, helping emerging concepts progress more rapidly toward deployable solutions. The effort aligns with Lockheed Martin’s SMEUnite initiative which has grown to include over 180 British companies.

Headquartered in London, Lockheed Martin UK, the UK arm of Lockheed Martin Corporation, contributes around £2 billion to the national economy each year and sustaining around 26,000 British jobs – 2000 directly and 24,000 more across 800 supply chain partners, more than 500 of which are SME businesses.

As a trusted British-American strategic defence partner for over 85 years, it combines innovation, ingenuity and exploration to boost resilience, grow industrial capacity, sustain high-value jobs, and deliver meaningful economic impact on both sides of the Atlantic.

Lockheedmartin.com

www.the-mtc.org