Siemens and NVIDIA are advancing AI-driven manufacturing at a German electronics factory, deploying digital twins and AI agents to accelerate production preparation, optimise planning, and improve efficiency.

FROM SIMULATION TO AUTONOMOUS MANUFACTURING

Siemens and NVIDIA are advancing industrial manufacturing, unveiling new AI solutions at Siemens’ Electronics Factory in Erlangen, Germany, as part of their partnership to develop AI-driven, autonomous factories.

The applications combine artificial intelligence (AI) with digital twins to accelerate production ramp-up and enable more adaptive production planning. After the companies announced at CES in Las Vegas in January 2026 that they would build the world’s first fully AI-powered manufacturing sites, teams have been co-developing and testing applications in live production at Erlangen.

Serving as a living laboratory for industrial AI, the factory provides a blueprint for scaling these capabilities across industries. Siemens and NVIDIA aim to make AI the operating system of manufacturing, transforming how products are designed, manufactured, and delivered.

“The historic opportunity for Germany and Europe lies in bringing artificial intelligence out of the data centers and into the factories – and applying it on an industrial scale,” says Cedrik Neike, member of the Managing Board of Siemens AG and President Digital Industries.

“With an annual productivity increase of seven percent, Erlangen is one of the most advanced manufacturing plants in the world,” Neike adds, highlighting the facility’s role as a global meeting place for industrial innovation.

ACCELERATING PRODUCTION PREPARATION

Developed in less than ten months, the first new AI offerings combine NVIDIA’s AI, simulation, and accelerated computing technologies with Siemens’ industrial software, automation, and manufacturing expertise. Testing the applications in a live production environment has enabled both companies to address practical manufacturing requirements.

For new printed circuit board (PCB) assemblies, which are central to the drives and frequency converters manufactured at Erlangen, virtual production preparation can reduce engineering effort by up to 85 percent and cut time-to-market by up to 40 percent.

Previously, preparing quality inspections could take several weeks, as teams needed physical prototypes to train vision systems and manually configure inspection equipment. AI now generates photorealistic representations of PCB assemblies from their digital twins, letting engineers train vision systems and prepare inspections before physical prototypes are available.

This approach speeds production preparation while reducing reliance on physical prototypes during development.

MORE ADAPTIVE PRODUCTION PLANNING

AI is also changing how production is planned at Erlangen, where teams must balance capacity, material availability, delivery dates, and production-line requirements.

An AI agent now supports planners by automatically generating different production scenarios when new orders arrive. It evaluates each option against defined key performance indicators (KPIs), helping teams understand potential outcomes before deciding.

The system then validates the proposals using a digital twin of the entire production facility before implementation. This end-to-end, agent-based approach can enable cost savings of up to 20 percent while allowing manufacturing operations to respond more flexibly to new orders, material shortages, and changing customer requirements.

NVIDIA Cosmos world models generate realistic training data for quality inspection, while the NVIDIA Factory Operations Blueprint supports AI agents in creating production plans. NVIDIA Omniverse libraries allow teams to test those plans virtually using real factory data.

As Rev Lebaredian, Vice President, Physical AI Simulation at NVIDIA, explains: “Factories need to handle change as well as they handle repetition. AI and simulation make that possible.”

A BLUEPRINT FOR INDUSTRIAL AI

Established in 1971, the Siemens Electronics Factory in Erlangen employs approximately 1,000 people and manufactures power electronics and automation technology for industry and infrastructure, including machine tools, battery production, and process and discrete manufacturing.

More than 100 AI algorithms are already active across the facility’s production, logistics, quality inspection, and maintenance operations. After more than a decade of developing and deploying digital manufacturing technologies during live operations, the site is now set for further expansion through 2030, supported by a €500 million Siemens investment.

With further applications planned in the coming months, the collaboration with NVIDIA positions Erlangen as a blueprint for bringing AI into industrial environments at scale.

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Adani Enterprises and Jabil have signed a memorandum of understanding to establish a 50:50 joint venture manufacturing artificial intelligence (AI) data centre equipment in India, targeting growing global demand for advanced computing infrastructure, power systems and cooling technology.

Building India’s AI Hardware Manufacturing Capacity

Adani Enterprises and Jabil have announced plans to establish a joint venture focused on manufacturing and assembling AI infrastructure equipment in India.

The proposed 50:50 partnership would develop a multi-gigawatt manufacturing platform, producing hardware for hyperscale data centres, cloud providers and enterprise customers.

The companies signed a non-binding memorandum of understanding (MoU) on 13 October 2026, following their initial announcement in June of plans to explore a strategic manufacturing alliance.

The venture remains subject to definitive agreements, regulatory approvals and customary conditions.

Once established, it would combine Jabil’s engineering and manufacturing capabilities with Adani’s energy, infrastructure and data centre operations.

Manufacturing AI Servers, Racks and Cooling Systems

The proposed manufacturing operation would produce AI racks, servers, storage systems and networking equipment used in high-performance computing environments.

Its portfolio would also include power distribution units, transformers and liquid-cooling systems, reflecting the growing importance of power management and thermal efficiency in AI infrastructure.

The companies envisage multi-gigawatt AI rack manufacturing capacity, alongside installation and commissioning services.

Gautam Adani, Chairman of Adani Group, said: “The AI revolution will demand far more than just breakthroughs in algorithms. It will require an entire new industrial architecture where energy, hyperscale infrastructure, advanced computing and precision manufacturing converge to power the intelligence economy.”

The partnership would extend Adani’s involvement in AI infrastructure beyond energy generation and data centre development into the manufacture of critical computing equipment.

India Targets Growing Global Data Centre Demand

India’s data centre capacity is forecast to reach between five and eight gigawatts by 2030, supported by increasing AI adoption, cloud expansion and data localisation requirements.

The companies also point to more than USD$50 billion in planned hyperscaler investment across India’s data centre, cloud and AI ecosystems.

Globally, Adani and Jabil estimate the market opportunity for AI infrastructure at more than USD$3 trillion over the next seven years.

The proposed venture would establish an Indian manufacturing base capable of serving domestic customers while supporting international exports.

Mike Dastoor, CEO of Jabil, said: “The signing of this MoU is the next step forward in our shared efforts to establish a scaled AI infrastructure manufacturing platform in India, a market we believe will become increasingly important for both domestic and global AI infrastructure demand.”

“Integrating Jabil’s 60 years of engineering and manufacturing expertise with Adani’s significant infrastructure and energy capabilities, we can bring our rack-level data center solutions to more hyperscalers in India and around the world.”

Expanding Advanced Manufacturing and Infrastructure Investment

The partnership aligns with Adani Group’s commitment to invest USD$100 billion in developing five gigawatts of green-energy-powered, hyperscale AI-ready data centres by 2035.

For Jabil, which reported USD$36 billion in revenue during its 2026 financial year, the venture would strengthen its manufacturing presence in India’s expanding technology infrastructure market.

The company has also developed its data centre capabilities through acquisitions, including Hanley Energy Group, which specialises in power and energy management, and Mikros Technologies, a provider of liquid-cooling solutions.

India’s policy environment is expected to support further investment, including the Union Budget 2026 tax holiday for data centres extending to 2047.

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Mark Feathers, Product Marketing Manager at Epicor, examines what the latest ONS figures mean for British manufacturers and the challenges facing the sector.

The latest figures from the Office for National Statistics (ONS) paint a mixed picture for UK manufacturing. While manufacturing output rose by 1.2 per cent in August 2026, driven partly by growth in pharmaceutical production, the sector recorded just 0.1 per cent growth over the three months to August. With the wider production sector contracting by 0.2 per cent over the same period, questions remain about the strength of the UK’s industrial recovery.

The Path to Sustained Recovery

The latest Office for National Statistics (ONS) Index of Production figures are encouraging, with manufacturing output increasing by 1.2 percent in August. But I think we need to look beyond the headline to understand what’s really happening across the sector. In the three months to August, output grew by just 0.1 percent, so we’re still some way from seeing a sustained recovery.

For me, the bigger story is that UK manufacturing isn’t experiencing the same conditions across the board. Some sectors are finding opportunities to grow, while others continue to face significant challenges. The pressures around operating costs, margins, capacity, and unpredictable demand haven’t gone away. While a positive month is welcome, it doesn’t necessarily translate into greater confidence or stability for individual manufacturers.

Prepare for Continued Uncertainty

While the manufacturing sector remains resilient, recovery is still fragile, and businesses need to be prepared for continued uncertainty. They must focus on what they can control rather than waiting for economic conditions to improve. That starts with having a much clearer understanding of what’s happening across their business. Where are they making money? Where are costs increasing? Are they using their capacity effectively? And how quickly can they respond when customer demand changes?

These might sound like fairly basic questions, but getting accurate answers isn’t always straightforward, particularly when information sits across different systems or departments.

A Selective Approach to Investment

Manufacturers also need to take a more selective approach to investment and start with the business problem they’re trying to solve. Technology needs to deliver something tangible, whether that’s reducing waste, improving productivity, protecting margins, or helping people make better decisions.

Ultimately, manufacturers can’t remove uncertainty from the market, but they can put themselves in a much stronger position to deal with it.

That is where the focus needs to be right now.

The post Opinion: Why UK Manufacturing’s Recovery Remains Fragile Despite Rising Output appeared first on Manufacturing Outlook.

IN2 Engineering’s strategic acquisition of YES Engineering combines greater engineering capacity with an established international Tier 1 client base, underpinning an ambitious roadmap to grow revenues and expand further into Europe.

IN2 Engineering Group, the international mechanical, electrical, and plumbing (MEP), energy, and sustainability consultancy, has acquired London-based YES Engineering as it enters its next phase of growth.  

The transaction significantly expands the group’s international footprint, combining the depth of IN2’s engineering resources and multidisciplinary capability with YES Engineering’s established commercial-sector expertise and international client base. 

The move strengthens IN2’s presence and capacity in London, whilst giving the YES Engineering team and its clients access to the wider resources of the IN2 Group. Together, the businesses form an international team of more than 100 people, supported by specialist expertise across contract-for-difference (CFD) analysis, building physics, building analysis, planning consultancy, sustainability, and whole-life carbon.  

A key opportunity for the combined business is the growing demand for sophisticated engineering solutions across digital infrastructure and data centres, alongside the transition towards lower-carbon buildings and infrastructure.  

Bringing together YES Engineering’s established international client relationships with IN2’s specialist engineering expertise and resources creates greater capacity to support clients as their requirements become larger, more complex, and increasingly international. 

The acquisition forms part of a broader growth strategy for IN2. Founded and headquartered in Ireland, it has grown from a domestically focused engineering consultancy into an increasingly international business.  

Prior to the transaction, IN2 had already delivered significant data centre projects in the UK, including its most recent 30MW refurbishment for a facility in West London; 1Energy, a major district heating and decarbonisation project in Bradford; and a county-wide decarbonisation programme in Norfolk.  

YES Engineering has an established international track record, having completed projects across the UK and Europe, including Luxembourg, Vienna, and Zurich, for large, major commercial, and multinational organisations, as well as other work in 22 countries across three continents. 

“The acquisition is a core part of IN2’s international growth strategy, combining targeted acquisitions with organic expansion,” commented IN2 CEO, Barry Flynn. 

IN2 also brings proven project experience on hyperscale data centre projects in London, Amsterdam, and Frankfurt, alongside established relationships with major operators and contracting partners.  

The acquisition provides the enlarged business with the resource depth needed to pursue future developments in this sector, supported by specialised technical capabilities including in-house computational fluid dynamics (CFD) modelling, detailed mechanical and electrical design, and on-site delivery management.  

Commenting on the acquisition, Greg Parker, Director at YES Engineering, said: “Clients are seeking to deliver larger and more technically complex programmes across multiple markets.  

“Combining the expertise and experience of YES Engineering with IN2 makes perfect sense, as we can present stronger multidisciplinary teams that can support projects internationally and provide valuable continuity from early-stage engineering and design through to delivery.”

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New research commissioned by Beko reveals that women working in UK manufacturing are significantly more exposed to generative artificial intelligence (AI) than men, raising concerns about recruitment, pay and career progression as businesses accelerate technology adoption.

Women Face Greater Exposure to AI in Manufacturing

Women working in UK manufacturing are disproportionately exposed to generative artificial intelligence (AI), according to a new report commissioned by home appliance manufacturer Beko.

The study, The Default Setting: Women, AI and the Future of Work in UK Manufacturing, found that 41.1 per cent of women’s manufacturing employment is in occupations exposed to generative AI, compared with 23.6 per cent of men’s.

Although women account for 27.8 per cent of the manufacturing workforce, they represent 63.4 per cent of employees in the most AI-exposed occupations.

These roles are predominantly administrative, sales and customer service positions rather than production-line jobs.

Authored by Zoi Roupakia, Founder and Director of Noetic AI, the report combines a survey of 890 UK manufacturing professionals with employment and occupational AI exposure data.

AI Bias Raises Concerns Over Pay and Recruitment

The research found that women are more likely than men to believe AI bias could negatively affect employee wellbeing, pay and career opportunities.

Some 41.1 per cent of women identified employee wellbeing as a concern, compared with 28.7 per cent of men. Meanwhile, 33.3 per cent of women expressed concerns about pay and job opportunities, against 22.9 per cent of men.

Women were also less likely to trust AI-driven decisions, at 32.1 per cent compared with 42.9 per cent of men.

Despite these concerns, 58 per cent of respondents were unaware of measures their employers were taking to address AI bias, while 87 per cent of both women and men wanted greater transparency in AI systems.

Roupakia emphasised that the findings do not demonstrate that AI is replacing women’s jobs in manufacturing.

“Adoption still remains at an early stage, so the decisions that will determine whether AI widens or narrows opportunities for women are being made now, in how employers introduce the technology, redesign work and govern these systems,” she said.

Manufacturers Urged to Adopt Responsible AI Policies

The report recommends that manufacturers introduce stronger AI governance, employee training and monitoring processes to prevent existing inequalities being reproduced through automated decision-making.

It also highlights the importance of retaining human oversight of AI-supported recruitment, promotion and workplace decisions.

Teresa Arbuckle, Regional Managing Director of Beko UK & Ireland, said: “AI, used in partnership with human expertise and decision making, presents a significant opportunity to help manufacturing businesses become more efficient and productive. But this should not be at the cost of greater representation.”

Beko is using the findings to strengthen its responsible AI and people policies, including risk assessments, workforce training and greater involvement of women in technology-related decisions.

Balancing Manufacturing Productivity and Workforce Inclusion

The research found that better organisational preparedness was associated with greater trust in AI-driven decisions, although it did not eliminate concerns about pay and career opportunities.

This suggests that training alone may be insufficient to address the potential workforce implications of AI adoption.

The report calls for manufacturers, technology providers and policymakers to consider how AI systems affect different employee groups as the technology becomes more widely embedded in business operations.

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Continuing its seafaring journey at the helm of Malaysia’s maritime defence ambitions, Lumut Naval Shipyard is charting a course in sovereign capability. Group CEO, Ir. Azhar Jumaat, updates us on how the company has navigated the past year.

ANCHORED IN ENGINEERING EXCELLENCE

We last spoke to Lumut Naval Shipyard (LUNAS) in October 2025; since then, it has been a defining period for Malaysia’s shipbuilding and ship repair industry.

“We are witnessing an important evolution from building platforms to building enduring national capability,” introduces Ir. Azhar Jumaat, Group CEO.

Indeed, the launch of the National Defence Industry Policy (NDIP) earlier this year provides a strong strategic foundation for the industry and reinforces Malaysia’s commitment to a more resilient, self-reliant defence industrial ecosystem.

It accelerates localisation, strengthens local supply chains, nurtures highly skilled talent, and creates more opportunities for Malaysian companies to participate in high-value defence programmes.

Equally encouraging is the transformation of international collaboration. Today, partnerships are no longer centred solely on procurement; they increasingly focus on technology transfer, capability development, knowledge exchange, and building sustainable industrial capacity.

For LUNAS, this shift is reflected in its strategic collaborations.

“During Defence Services Asia 2026, we signed seven memoranda of understanding (MoUs) with both international and Malaysian companies, namely HD Hyundai Heavy Industries, Hull Vane, Ocean Infinity, Labuan Shipyard and Engineering, G7 Marine Technology, MTC Engineering, and a tripartite with MHV Holdings and Sillinger S.A.S.

“Our collaborations represent more than commercial ventures. They reflect our commitment to building a stronger maritime ecosystem by combining global expertise with Malaysian capabilities,” Ir. Azhar expresses.

Through these alliances, LUNAS aims to accelerate knowledge sharing, strengthen local participation, and create opportunities for Malaysia to progressively enhance its sovereign capabilities whilst remaining connected to the global defence and maritime communities.

“I believe Malaysia is at an important inflection point as it drives a sustainable maritime industrial ecosystem that develops world-class engineering talent, propels innovation, creates high-value employment, and strengthens national security whilst contributing meaningfully to economic growth,” he emphasises.

BUILDING TALENT, STRENGTHENING CAPABILITY

Since we last spoke to LUNAS, the company has grown in both people and capabilities.

“The past year has been an important journey for us, not only through our programme’s progress, but also by strengthening our organisation and building the expertise needed to support Malaysia’s defence industry aspirations,” declares Ir. Azhar.

“Our people have been central to this. We have continued to build Malaysian talent through international knowledge transfer, technical collaboration, and hands-on experience gained from one of Malaysia’s most complex naval shipbuilding programmes.”

These developments have strengthened LUNAS’s engineering, systems integration, and shipbuilding capabilities, whilst building a team increasingly confident and capable of delivering complex defence solutions to global standards.

“We are not only building expertise but also nurturing the next generation of Malaysian maritime and defence professionals who are confident, capable, and ready to take on greater responsibilities. Our greatest investment will remain in our people, because they are the foundation of our long-term growth and success,” he continues.

Equally, LUNAS continues to work closely with its strategic partners, including Naval Group and Thales, to deepen technical capabilities whilst progressively increasing Malaysian participation across the programme.

Each step strengthens the company’s local shipbuilding capability, bringing it closer to greater self-reliance.

Diversification is also an important part of LUNAS’s long-term growth strategy. As such, entering the oil and gas sector and working on exploration and drilling vessels is an advantageous move for the company.

“Whilst defence remains our core business and strategic priority, we are also looking at opportunities across adjacent maritime sectors where our capabilities can create greater value for the industry and the nation,” Ir. Azhar explains.

“Our experience in complex shipbuilding, advanced maritime engineering, fabrication, systems integration, and project management provides a strong foundation to support opportunities beyond naval platforms, including offshore energy, specialised commercial vessels, and other maritime solutions.”

The capabilities developed through LUNAS’s Littoral Combat Ship (LCS) Programme have strengthened its technical expertise, operational discipline, and workforce. These competencies are highly transferable and position the company to contribute across a wider maritime ecosystem.

“Our objective is to build a more resilient and sustainable business portfolio by maximising our world-class facilities, growing our talent base, and creating new opportunities for Malaysian maritime to thrive,” acknowledges Ir. Azhar.

Ultimately, diversification enables LUNAS to strengthen its role as a trusted partner whilst continuing to support Malaysia’s ambition to develop a competitive and sustainable maritime industry.

FROM SHIPYARD TO SEAPOWER

Whilst there is a slight delay due to cancellation of missile from Norway, LUNAS is on track for the physical handover of its first frigate, LCS1, to the Royal Malaysian Navy (RMN) in April 2027, marking a historic milestone as the first locally built warship.

“The upcoming physical handover of the first LCS will be a significant milestone not only for us, but also for Malaysia’s defence industry and maritime capability,” Ir. Azhar proudly states.

However, this achievement does not stand in isolation; it is built on a strong foundation.

LUNAS previously demonstrated its shipbuilding capability by successfully constructing, integrating, and delivering the Kedah-class Next Generation Patrol Vessels (PV 3 to 6), all in its own shipyard.

That programme proved Malaysia’s core shipbuilding capability. However, that capability is being taken to a new level.

“The LCS Programme moves our shipyard from building patrol platforms to building complex, state-of-the-art surface combatants,” he emphatically tells us.

“We have come a long way to reach this point. This achievement reflects years of commitment, collaboration, and perseverance involving the RMN, the government, our strategic technology partners, trust and support from our stakeholders, and the many Malaysians who have contributed to the programme.”

The LCS Programme is one of Malaysia’s most complex defence schemes, requiring advanced engineering, complex systems integration, and close coordination across multiple stakeholders.

More importantly, this milestone demonstrates Malaysia’s growing capability to build, integrate, and sustain a modern naval combat platform by combining international expertise with locally developed skills and experience.

“Beyond the delivery of the ship itself, the greatest value of the LCS Programme lies in the capability it has built within our people and our industry,” Ir. Azhar outlines.

“Thousands of Malaysians, including engineers, naval architects, technicians, project managers, local suppliers, and industry partners, have gained valuable knowledge and experience throughout this journey. This is an important investment in Malaysia’s human capital and industrial capability.”

For LUNAS, it means building a stronger foundation for the future, strengthening Malaysia’s maritime industry, enhancing self-reliance, and developing capabilities that will support the nation for years to come.

A STRONG MARITIME FUTURE ON THE HORIZON

For 2027 and beyond, LUNAS’s priorities remain clear – to deliver on its commitments, continue strengthening capabilities, and position the company for long-term growth.

“Our immediate focus is to deliver the LCS Programme successfully. We remain committed to working closely with the RMN, the government, and our partners to ensure every stage meets the required standards of quality, safety, and operational readiness.

“Ultimately, this is not just about delivering ships but delivering the capability the RMN requires and the nation deserves,” Ir. Azhar resolutely says.

At the same time, LUNAS will continue strengthening as an organisation by improving processes, embracing digital solutions, enhancing productivity, and further developing engineering and shipbuilding capabilities.

“We want to build a more efficient and agile organisation that is ready to take on more complex maritime projects in the future,” reports Ir. Azhar Jumaat.

“Beyond defence, we will continue exploring opportunities across the wider maritime sector, including commercial and specialised vessels. We aim to build on the capabilities we have developed and create a sustainable maritime industry that contributes to Malaysia’s economic and industrial growth.”

Meanwhile, the company will demonstrate its capabilities at a premier event in the near future – the Langkawi International Maritime and Aerospace Exhibition (LIMA’27), taking place in April.

“LUNAS is looking forward to participating in LIMA’27, where we plan to showcase LCS1 as a representation of Malaysia’s growing maritime and defence capabilities. Bringing LCS1 to LIMA will be an important milestone for us,” Ir. Azhar excitedly concludes.

As LUNAS continues to invest in its workforce and deliver increasingly complex vessels, it builds on more than four decades of experience, helping Malaysia navigate an innovative, resilient, and self-reliant maritime future.

LUMUT NAVAL SHIPYARD PARTNER

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Volkswagen Group is using shared vehicle components, artificial intelligence (AI) and centralised design processes to reduce development costs across its electric vehicle portfolio, with its new Electric Urban Car Family delivering €600 million in savings while maintaining distinct identities across Volkswagen, CUPRA and Škoda.

Volkswagen Group Strengthens Electric Vehicle Development Synergies

Volkswagen Group has outlined how its multi-brand development strategy is delivering manufacturing and design efficiencies, with four new electric vehicles sharing more than 80 per cent of their components.

The Electric Urban Car Family comprises the Volkswagen ID. Polo and ID. Cross, CUPRA Raval and Škoda Epiq, all developed using a common technology platform.

The approach enables the automotive manufacturer to standardise components and development processes while maintaining different vehicle designs and brand identities.

According to Volkswagen Group, sharing vehicle elements that do not define individual brands has generated development savings of €600 million.

The strategy also supports the development and production of electric vehicles in Europe, with the aim of meeting different customer requirements while making the models accessible to broader markets.

Shared Components Deliver €600 Million in Savings

The Electric Urban Car Family demonstrates Volkswagen Group’s approach to balancing economies of scale with product differentiation.

Although the four models share more than 80 per cent of their components, their visible and tangible design elements are tailored to the individual brands.

This allows Volkswagen Group to benefit from common engineering and development activities without producing vehicles with identical appearances or customer experiences.

The company identifies the standardisation of non-brand-defining components as a key factor behind the €600 million in development savings.

For automotive manufacturing and supply chain operations, the strategy highlights the potential efficiencies associated with common vehicle platforms and shared component specifications.

Andreas Mindt, Head of Volkswagen Group Design, said: “Our task at Volkswagen Group Design is to create the conditions for strong and distinctive brands. We leverage the Group’s scale, expertise and synergies wherever it makes sense, while strengthening what makes each brand unique. The Electric Urban Car Family brings this approach to life.”

AI Reduces Vehicle Design Time and Costs by One Third

Alongside component standardisation, Volkswagen Group is introducing AI-powered tools to accelerate vehicle design and development.

Software developed in-house can transform initial design sketches into photorealistic vehicle images and video clips, allowing designers to evaluate concepts before producing physical models.

The technology also enables teams to visualise different colours and materials under varying lighting conditions and in different environments.

These capabilities support faster design reviews and coordination between teams, reducing the need for some time-consuming development activities.

Volkswagen Group estimates that its digital design applications can reduce design time and costs by up to one third.

The tools are developed jointly across the Group, allowing individual brands to benefit from shared digital capabilities and expertise.

Research and analysis activities are also managed centrally, enabling brand-specific design teams to concentrate on creative development.

Centralised Processes Support Multi-Brand Manufacturing

Volkswagen Group Design establishes common methods, processes and quality standards across the manufacturer’s design organisations.

The centralised structure is intended to improve consistency and efficiency while allowing individual brands to maintain control over their distinctive design characteristics.

The Electric Urban Car Family demonstrates how this approach works across three brands with different customer propositions.

Volkswagen’s ID. Polo and ID. Cross follow its Pure Positive design philosophy, focusing on stability, approachability and recognisable design details.

The CUPRA Raval adopts a more expressive design, with an athletic stance, sculptural surfaces and distinctive lighting.

Meanwhile, the Škoda Epiq follows the brand’s Modern Solid philosophy, emphasising functionality, simplicity and everyday usability.

Despite these differences, all four vehicles benefit from the same underlying technology platform and extensive component commonality.

Volkswagen Balances Standardisation and Brand Identity

The announcement highlights Volkswagen Group’s efforts to combine shared engineering and development resources with differentiated products.

Its approach separates common vehicle technologies from design elements that contribute directly to brand identity and customer experience.

By standardising the former, the manufacturer aims to reduce development expenditure and improve efficiency across its electric vehicle programmes.

The strategy also allows Volkswagen, CUPRA and Škoda to draw on shared research, digital tools and quality standards while developing vehicles for their respective markets.

With more than 80 per cent shared components, €600 million in reported development savings and AI tools reducing design time and costs by up to one third, Volkswagen Group is placing standardisation and digitalisation at the centre of its multi-brand electric vehicle development strategy.

The post Volkswagen Group Targets €600 Million Savings Through Shared EV Platform appeared first on Manufacturing Outlook.

Resilinc has introduced a Microsoft Copilot integration that allows procurement and supply chain teams to access real-time supplier risk, disruption and compliance intelligence using natural-language queries, helping businesses identify exposure across complex global supply networks.

Microsoft Copilot Gains Supply Chain Risk Intelligence

Supply chain risk management specialist Resilinc has launched a plugin for Microsoft Copilot, enabling enterprise users to access organisation-specific supplier and compliance intelligence directly within Microsoft applications.

The integration allows authorised users to ask questions about supply chain disruptions, supplier exposure, tariffs, sanctions, forced labour and regulatory compliance.

The technology connects Microsoft’s artificial intelligence (AI) assistant with Resilinc’s supply chain intelligence platform, providing information on suppliers, manufacturing sites, parts and materials.

The integration is designed to simplify access to risk information without requiring users to switch between separate systems or initiate additional analysis.

Gopkiran Rao, Chief Marketing Officer at Resilinc, said: “Supply chain teams need more than a general-purpose answer. They need intelligence connected to their suppliers, sites, parts and materials, along with a governed path from signal to action.

“Bringing Resilinc into Microsoft Copilot puts that context inside the tools customers already use. MCP provides the connection, while Resilinc provides the intelligence and operational context behind it.”

AI Connects Supplier Data with Procurement Decisions

The integration uses Model Context Protocol (MCP), a standardised connection framework that enables authorised systems to access approved information and capabilities.

Through MCP, Microsoft Copilot can retrieve intelligence from Resilinc’s supply chain graph, which maintains continuously updated and validated relationships between suppliers, sites, products, parts and materials across multiple supply chain tiers.

This information is combined with disruption monitoring and regulatory compliance data to help organisations understand how external developments could affect their operations.

For example, procurement professionals can investigate whether a supplier disruption affects particular components or manufacturing locations, or assess potential exposure to changing tariffs and sanctions.

The integration is intended to move beyond general information retrieval by connecting external risk signals with an organisation’s specific supply chain relationships.

Access remains subject to Resilinc’s existing security and governance controls, including authentication, tenant isolation, licensing and role-based permissions.

Users can access information within Microsoft Copilot according to their existing organisational authorisations.

Microsoft Collaboration Targets Manufacturing Resilience

The new plugin builds on an existing collaboration between Resilinc and Microsoft covering AI, manufacturing and supply chain resilience.

The companies previously demonstrated a Copilot-based supply chain experience at Hannover Messe.

Dayan Rodriguez, Corporate Vice President, Manufacturing and Mobility at Microsoft, said: “Manufacturers need a clear view of supply chain risk to make informed decisions.

“Integrating Resilinc’s risk and compliance intelligence with Microsoft Copilot can help teams identify exposure and act quickly, using the tools they already work in.”

The collaboration is also extending into industry-specific applications, including automotive supply chain risk management.

Procurement Compliance Extends Beyond Direct Suppliers

The Copilot integration also supports Resilinc’s broader strategy for managing supply chain compliance across multiple regulatory requirements.

The company recently introduced Compliance for EUDR, extending its existing risk intelligence capabilities to address the European Union Deforestation Regulation (EUDR).

The solution applies regulatory requirements to supplier, product, material and site information already held within Resilinc’s supply chain platform.

The same underlying data supports other compliance activities, including forced labour risk assessments.

By connecting these datasets, Resilinc aims to help organisations assess regulatory exposure across multiple tiers of their supply networks rather than relying exclusively on information from direct suppliers.

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Inez Berkhof-Hollander, Managing Director, EMEA at TreviPay, explains why operational complexity across dealer networks could become a barrier to commercial vehicle growth

How complexity becomes the barrier

A major facet of the USD$1.4 trillion global commercial vehicle sector is its heterogeneity. A leasing fleet might include BMW, Mercedes, Nissan, Toyota, and rising electric vehicle (EV) players such as BYD, while in the transport sector the mix can include everything from Scania and DAF to Volvo, MAN, and others.

The problem is that all these different models need repair and maintenance. For fleets operating across the UK or Europe, that means managing different vehicle technologies and parts suppliers across hundreds of workshops, while ensuring agreed parts prices are applied consistently.

With a typical Class 8 truck and trailer alone comprising 10,000–15,000 parts, keeping a single truck on the road is challenging enough. Managing the back office and payments for an entire commercial fleet of vans, trucks, lorries, buses, coaches, EVs, and more is considerably more demanding.

For commercial vehicle manufacturers, meeting those demands as their dealer networks expand creates operational complexity that puts pressure on the customer experience, increases costs, and slows growth. Is there a way out?

Scale can mean less profitability

The problem is that growth in the commercial vehicle industry often brings complexity: disconnected dealer processes, inconsistent invoicing, fragmented credit arrangements, and manual administration.

As volumes increase, these issues can constrain manufacturers’ ability to scale while maintaining a consistent customer experience. Undoubtedly, dealers provide valuable local expertise, customer relationships, operational support, and service capabilities manufacturers could never fully replicate through a centralised model.

However, dealer autonomy also introduces variation, with each location potentially developing its own processes for credit, invoicing, collections, and customer administration, even if pricing agreements were negotiated nationally.

For a business operating across dozens or even hundreds of dealer locations, those differences quickly become problematic. Administrative teams spend valuable time resolving exceptions instead of processing transactions efficiently.

This is not a reflection of poor dealer performance, but the natural consequence of operating across a network of independent businesses, each with its own systems, processes, and ways of working.

Complexity can grow faster than revenue

Another challenge is that operational complexity rarely increases linearly. Managing 20 dealer locations may be straightforward, but managing 200 locations across multiple countries, currencies, and tax regimes can see the administrative burden escalate dramatically.

Without consistent execution of policies and processes, growth can eventually create inefficiency. From consistent pricing and predictable invoicing to reliable payments and commercial terms, fleet operators increasingly judge manufacturers not only on vehicle performance, but on how easy they are to do business with.

Finally, visibility into fleet customer purchasing through independent dealers is often limited. Vehicle sales may be tracked centrally, but aftermarket purchasing activity frequently remains fragmented across dealer systems, making it difficult to understand purchasing behaviour and strengthen fleet relationships.

Automate and centralise

In an era where aftermarket services represent an increasingly important source of competitive differentiation, limited visibility becomes more than an operational inconvenience.

Dealers can still manage local customer relationships and service operations, but if the processes behind a transaction are automated and centralised, much of the friction immediately goes away.

When commercial policies are applied uniformly across dealer networks, fleet customers receive the same experience regardless of where work is carried out. As a result, ordering, pricing, invoicing, payment, and settlement become a connected commercial workflow rather than a series of separate administrative tasks.

Consolidated billing can further simplify the process. If pricing is validated upfront, customer information is captured during the transaction, and fleet operators receive integrated invoices, dealers spend less time managing collections and payment disputes.

Proof at the sharp end

We are working with a large global truck manufacturing group that, historically, lacked a complete view of fleet purchasing, including which fleets were buying which parts, at what prices, and where.

In the UK and 19 other European countries, it now consolidates parts transactions across dealers, enabling unified invoicing, shared fleet credit lines, and greater network visibility.

By automating invoicing, credit management, and payments, it can focus on sales and marketing while using original equipment manufacturer (OEM)-controlled pricing to drive loyalty, resulting in strong programme growth and a better purchasing experience for major transportation customers.

As growth increasingly depends on long-term fleet relationships, reducing operational complexity could become a valuable competitive advantage, making it easier for manufacturers to grow while giving fleet customers a better experience.

Inez Berkhof-Hollander is Managing Director, EMEA, at global business-to-business (B2B) Pay by Invoice provider TreviPay

The post Opinion: When Complexity Becomes the Real Barrier to Manufacturer Growth appeared first on Manufacturing Outlook.

Schneider Electric has agreed to acquire industrial software specialist PTC for approximately USD$22.6 billion, bringing together product design, engineering, operational and energy data as it targets the next generation of artificial intelligence (AI)-enabled manufacturing.   

Schneider Electric expands industrial software portfolio

Schneider Electric has signed a definitive agreement to acquire 100 per cent of PTC in an all-cash transaction valued at approximately USD$22.6 billion (€20.1 billion).

The deal values PTC at an enterprise value of approximately USD$23.7 billion (€21.1 billion), with shareholders set to receive USD$205 per share.

The acquisition is intended to create a larger industrial software and AI business connecting manufacturing data across the product and asset lifecycle, from design and engineering through to operation and maintenance.

Olivier Blum, Chief Executive Officer of Schneider Electric, said: “The acquisition of PTC represents an important step forward in our ambition to lead the new era of Energy and Industrial Intelligence. Together, we are creating the industry’s most complete Software & AI powerhouse and highest-quality portfolio bridging the physical and digital worlds.

“By connecting and contextualizing data across the lifecycle of products and assets, we will create a unique digital thread for the next generation of Industrial AI, helping customers to optimize their systems with greater intelligence from design and build to operate and maintain.”

PTC brings product design and engineering capabilities

PTC provides computer-aided design (CAD), product lifecycle management (PLM), application lifecycle management (ALM) and service lifecycle management (SLM) software.

The company serves more than 30,000 customers globally and has particular strengths in discrete and hybrid manufacturing. It generated €2.4 billion in revenue in calendar year 2025.

Schneider Electric said combining PTC’s product and engineering data with its existing process and energy data capabilities would establish a unified AI-ready data foundation spanning products, machines, processes and energy systems.

The combined portfolio is expected to have more than 15,000 software employees and serve more than 50,000 software customers. The transaction is also expected to expand Schneider Electric’s addressable market in industrial software by approximately three times.

Neil Barua, President and CEO of PTC, said: “PTC provides the software the world’s leading manufacturers and product companies rely on to design, build, and maintain great products and unlock more value from their product data in an increasingly AI-driven world.

“Joining Schneider Electric is an incredible opportunity to elevate the scope and impact of what we deliver for our customers globally. We gain substantial scale and resources to accelerate innovation, advance our Intelligent Product Lifecycle vision, and expand our business into more geographies and end markets to serve more customers.”

Connecting design with manufacturing operations

A central part of the proposed combination is the creation of what Schneider Electric describes as a unified digital thread.

PTC’s software provides information on how products are designed and built, while Schneider Electric brings operational and energy intelligence. The company said connecting these areas could help manufacturers design faster, improve production efficiency and continuously improve products and systems.

Schneider Electric’s industrial AI data foundation is also expected to be strengthened by its proposed acquisition of Cognite, although completion of that transaction remains subject to customary closing conditions and regulatory approvals.

The PTC acquisition is expected to generate €250 million in annual run-rate cost synergies by year three, alongside approximately €800 million in revenue synergies through areas including cross-selling, geographic expansion and AI-enabled development of digital thread solutions.

Deal expected to close in 2027

The boards of both companies have unanimously approved the transaction.

Schneider Electric expects the acquisition to close by the third quarter of 2027, subject to customary closing conditions, regulatory approvals and approval from PTC shareholders holding at least a majority of outstanding shares.

The company plans to fund the transaction through approximately €5 billion to €6 billion in new equity and approximately €16 billion to €17 billion in new debt.

If completed, the acquisition will extend Schneider Electric’s industrial software capabilities further into product design and engineering, connecting those functions with manufacturing operations, asset management and energy systems.

The post Schneider Electric to Acquire PTC in USD$22.6 Billion Industrial Software Deal appeared first on Manufacturing Outlook.

Caterpillar is investing approximately $1 billion in North Carolina to expand Cat Compact manufacturing capacity, strengthening its ability to meet growing demand from small contractors and businesses.

EXPANDING THE COMPACT FOOTPRINT

Caterpillar is strengthening its position in the growing compact equipment market with plans to invest approximately $1 billion in North Carolina.

The investment will support the company’s Cat Compact strategy, which aims to deliver a more streamlined and convenient experience for small contractors and growing businesses while helping customers gain faster access to the machines they need.

At the centre of the investment is a planned modern manufacturing facility in Sanford, North Carolina. The site is designed to increase production of compact track loaders and telehandlers, responding to growing customer demand for versatile equipment across a range of industries.

“As demand for compact equipment continues to increase, we’re investing to better serve this growing segment of customers,” says Rod Shurman, President of Caterpillar’s Construction Industries Group.

“This investment is another way we are solving our customers’ toughest challenges by expanding access to the products, solutions, and services they need to work more efficiently.”

POWERING JOBSITE PRODUCTIVITY

Compact track loaders and telehandlers are used across construction, landscaping, agriculture, and material handling applications.

Their versatility enables customers to move materials, manage jobsites, support workforce productivity, and complete critical work safely and efficiently. For Caterpillar, expanding manufacturing capacity therefore represents an opportunity to improve access to equipment that plays an important role across multiple sectors.

The planned Sanford facility will create a modern, technology-enabled manufacturing environment focused on improving safety, quality, and operational performance while supporting Caterpillar’s long-term growth.

The facility is expected to incorporate advanced manufacturing technologies, automation, digital production systems, and integrated logistics capabilities. Together, these capabilities are intended to create a more connected and efficient manufacturing operation.

INVESTING IN NORTH CAROLINA

Caterpillar selected North Carolina for the investment based on its longstanding presence in the state, the strength of the local workforce, and the opportunity to build on existing manufacturing expertise and operations in Sanford.

The company also highlighted the support of the State of North Carolina, Lee County, and the City of Sanford as it continues to invest in manufacturing and the customers it serves.

“We appreciate the partnership and support of the State of North Carolina, Lee County, and the City of Sanford as we continue investing in the future of manufacturing and the customers we serve,” says Shurman.

Beyond expanding production capacity, the project is expected to create opportunities for advanced manufacturing careers and support workforce development.

Employees will have opportunities to develop skills in areas including automation, robotics, digital manufacturing, and advanced production systems – reflecting the increasingly technology-enabled nature of modern manufacturing.

For Caterpillar, the investment combines expanded capacity with a broader commitment to US manufacturing, workforce development, and the continued evolution of its Cat Compact business.

The post Caterpillar Builds Capacity in North Carolina: “We’re Investing to Better Serve This Growing Segment” appeared first on Manufacturing Outlook.

RRD has established a unified digital platform across its global manufacturing network, bringing artificial intelligence (AI), automation and Industrial Internet of Things (IIoT) technology together to improve supply chain resilience while reducing energy consumption and material waste.

What is RRD’s SMART strategy?

RRD’s Systematic Manufacturing, Automation and Robotic Transformation (SMART) strategy connects manufacturing facilities and equipment through a shared data foundation, giving the company greater visibility across production and supporting real-time operational decisions.

The architecture links data across production lines and facilities, allowing manufacturing schedules to be adjusted according to incoming demand and material availability.

RRD is also investing in advanced manufacturing equipment, cloud technologies and workforce training as it develops the smart factory infrastructure.

Connecting production across three regions

At the centre of the SMART architecture is a standardised digital foundation connecting manufacturing operations in North America, Europe and Asia.

IIoT sensors and cloud platforms link factory floors with enterprise resource planning (ERP) systems, while standard data models synchronise production planning and inventory management between locations.

AI and machine learning algorithms analyse the resulting data to support predictive maintenance, demand forecasting and automated material resource planning.

Automation is also being introduced directly onto production lines. Collaborative robots are being used alongside technology including real-time automated in-line sheet scanning, which RRD says can reduce press setup times and paper spoilage while expanding production capacity without interrupting customer workflows.

RRD targets greater supply chain resilience

Connecting manufacturing locations also gives RRD the ability to balance production capacity across its network when individual regions face supply chain disruption or bottlenecks.

The company has invested in new sheetfed offset presses, digital presses, flexo label presses, multiple 57-inch die cutters, folder-gluers and finishing equipment.

Standardising equipment is intended to provide consistent quality controls and allow projects to move more quickly between domestic and international facilities.

For customers managing portfolios spanning print, trading cards, labels and folding cartons, work can be routed or divided between facilities according to available capacity, freight requirements and proximity to distribution sites.

“The transition to a connected manufacturing framework changes how we balance production capacity and manage resources globally,” said Al duPont, President at RRD.

“By establishing a single digital backbone, we give our clients more predictable turnaround times, consistent quality standards across regions and verifiable reductions in material waste.”

Smart manufacturing targets energy and material waste

The SMART strategy also links RRD’s manufacturing transformation with its customers’ environmental, social and governance (ESG) requirements.

Predictive scheduling and asset digital twins are being used to optimise production lines, reducing energy consumption and material waste. Automated in-line scanning is also designed to minimise paper spoilage during production.

The shared data infrastructure gives RRD a way to track resource use while coordinating production across different sites, supporting its focus on measurable resource conservation.

Automation has an additional role in workplace safety. RRD said automating high-risk mechanical positioning and routine administrative tasks can reduce exposure to these activities while moving employees towards technical support and quality control roles.

The company operates in 30 countries and provides marketing, packaging, print and business services, with customers including 92 per cent of the Fortune 100.

The post RRD Uses Smart Manufacturing to Cut Waste and Strengthen Supply Chains appeared first on Manufacturing Outlook.

Nine in 10 life sciences manufacturers now consider digital transformation necessary, as investment in artificial intelligence (AI), automation, cybersecurity and connected data becomes increasingly important to manufacturing quality, regulatory readiness and operational performance, according to new Rockwell Automation research.

Digital transformation becomes business-critical

Rockwell Automation’s The New Operating Model for Life Sciences Manufacturing: Moving from Compliance to Continuous Readiness report examines how life sciences manufacturers are building the digital infrastructure needed to support increasingly connected operations.

According to the company’s State of Smart Manufacturing research, 90 per cent of life sciences manufacturers believe digital transformation is necessary, while 58 per cent have already deployed smart manufacturing technologies at scale or across parts of their operations.

Investment is increasingly being linked to specific business outcomes. AI and machine learning are expected to deliver the greatest impact by 46 per cent of respondents, followed by process automation at 44 per cent and cybersecurity at 39 per cent.

Improving quality is the leading investment priority, cited by 43 per cent of respondents, ahead of digitising operations at 36 per cent and reducing risk at 35 per cent.

AI adoption outpaces manufacturing data use

The findings point to a gap between manufacturers’ ambitions for AI and their ability to make full use of operational data.

Among life sciences manufacturers already using AI, 64 per cent plan to expand its use within the next year and 49 per cent expect to increase investment over the next five years.

However, just 32 per cent say they effectively use more than half of the data they collect.

Quality control is expected to be the leading application for AI and machine learning over the next 12 months, identified by 50 per cent of respondents. Cybersecurity follows at 45 per cent, with process optimisation at 44 per cent.

Rockwell Automation said connecting data, systems and workflows across production, quality, engineering and business functions can provide greater operational visibility and create a foundation for AI, predictive analytics and continuous improvement.

Cybersecurity moves into the manufacturing lifecycle

Cybersecurity is another significant concern, with 54 per cent of life sciences organisations reporting at least one cyberattack during the past year.

IT systems and enterprise networks were identified as the greatest source of exposure at 41 per cent, followed by remote access and connected equipment at 33 per cent and information technology/operational technology (IT/OT) integration points at 29 per cent.

The report positions cyber resilience alongside areas including evidence management, industrial data operations, defined AI use cases and AI governance as part of seven actions manufacturers can take to establish what Rockwell Automation describes as “continuous readiness”.

Matt Weaver, Vice President, Global Industry – Life Sciences at Rockwell Automation, said: “Manufacturers used to treat regulatory readiness as a project — something they ramped up for.

“Now, it has to be a daily discipline built into how the facility runs. The companies connecting their data, securing systems and validating AI deployments today are the ones that will be ready for what comes next from regulators or from the market.”

From compliance to continuous readiness

Rockwell Automation defines continuous readiness as maintaining real-time operational visibility, data integrity and validated control across connected systems, allowing manufacturers to adapt to regulatory changes while continuing their digital transformation.

The findings are based on responses from 104 managers and executives working for life sciences manufacturers, original equipment manufacturers (OEMs), system integrators and engineering procurement companies (EPCs) across 16 countries.

The research forms part of Rockwell Automation’s 11th annual State of Smart Manufacturing report, conducted in association with Sapio Research. The wider study surveyed 1,560 decision-makers across multiple industries.

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Arti Garg, CTO at AVEVA, explains why responsible industrial artificial intelligence must combine trusted data, human oversight, and measurable value to transform manufacturing safely.

Q&A WITH ARTI GARG, CTO, AVEVA

Firstly, how do you define responsible industrial artificial intelligence? 

Arti Garg, CTO (AG): Responsible industrial artificial intelligence (AI) empowers people by delivering measurable value whilst operating within the physical, safety, and regulatory constraints of industry.  

Unlike consumer AI, an error in a manufacturing environment can affect human safety, equipment health, production outcomes, or the environment. 

Responsibility therefore requires trusted data, clear guardrails, transparent recommendations, and human accountability for managing risk.  

The goal is not autonomy at any cost, but to increase intelligence and autonomy in ways that serve human purposes whilst protecting people, assets, and operations.  

How does industrial AI differ from the AI most businesses use today?

AG: Most enterprise AI operates primarily in the digital world – drafting content, summarising information, or supporting knowledge work.  

Industrial AI interacts with the physical world. It works with engineering information, sensor readings, asset histories, and real-time operational data, and its outputs can influence how physical assets are controlled. 

That demands a much higher level of reliability and context. Industrial companies are therefore moving beyond general-purpose models towards domain-specific AI grounded in engineering and operational knowledge.  

Whilst industrial applications can benefit from general-purpose foundation models, these must be used carefully, particularly where outputs could affect physical operations. 

The most reliable approach often combines multiple types of models, including traditional machine learning (ML) models that behave more predictably. In addition, industrial systems can use more deterministic approaches where appropriate, selecting the right intelligence for each task. 

Why are the risks of AI greater in industrial environments like manufacturing?

AG: Because the consequences move from the screen to the plant floor. If an AI generates a poor summary for use in the back office, somebody corrects it. If an industrial system misinterprets an asset condition or operating constraint, the consequences could include downtime, damaged equipment, or even a safety incident. 

Industrial operations also have hard constraints imposed by physics. AI is very good at interpreting large volumes of information, but critical engineering calculations still need to be grounded in first-principles models and established engineering knowledge. Industrial AI should augment those systems rather than attempt to replace them. 

Where do large language models add the most value in manufacturing, and where do they fall short?

AG: Large language models (LLMs) are extremely valuable as an interface between people and complex industrial information. An engineer should increasingly be able to ask a natural-language question such as, “What are the likely causes for the reduction in efficiency of this machine?” and quickly surface an answer they can act on. 

This can reduce hours of searching through manuals, maintenance records, and operational data whilst helping preserve institutional knowledge. 

LLMs, multimodal models, and world models also have the potential to fundamentally change how people interact with software, shifting the way industrial user experiences are designed. However, these types of models need to work in concert with engineering models. 

They should not be treated as an authoritative source for deterministic calculations or safety-critical decisions. The real power of industrial AI comes when the language interface is grounded in trusted industrial data, models, and domain expertise. 

What role will agentic AI play in future industrial and manufacturing operations?

AG: An agent might detect an abnormal condition, bring together the relevant operational context, recommend action, and coordinate the appropriate response – from alerting an operator to initiating a maintenance workflow. 

Over time, specialised agents could work across engineering, maintenance, and operations. As AI becomes more capable of autonomous action, human oversight becomes more important, not less. 

This could be through control system automation as well as robotic automation. The opportunity is to reduce the effort required to move from detection to diagnosis and action, whilst operators and engineers retain oversight and control. 

Why are explainability and auditability essential for industrial AI?

AG: Trust is fundamental. An engineer needs to know where an answer came from, what information was used, and whether that information is current. If AI produces a recommendation without that traceability, it becomes very difficult to use confidently in an operational environment. 

Transparency and traceability are key elements in responsible AI and corporate AI governance. This is often achieved by having a solid, comprehensive platform where data is stored end-to-end along the process for full visibility and auditability. 

Why must humans remain accountable for AI-driven decisions?

AG: Because AI does not carry responsibility for the consequences of a decision; people and organisations do. Humans must set the goals and constraints for AI, interpret its outputs, and manage the overall risk. 

Operators also understand context a model may miss, including unusual plant conditions, competing priorities, and safety considerations. 

Aviation provides a useful analogy. Modern aircraft use enormous amounts of automation, but pilots remain responsible for objectives, oversight, and intervention.  

Industrial AI is moving in a similar direction: AI can perform analytical work and scenario modelling at a scale humans cannot, whilst people define objectives, constraints, and acceptable outcomes. 

What should CTOs at manufacturers consider before moving AI from pilot to production?

AG: The question should shift from ‘does the technology work?’ to ‘can it deliver measurable value reliably at scale?’ – AI cannot remain a science experiment; it must be tied to business outcomes. 

Manufacturers need a clear definition of success, with specific and trackable outcomes, whether that is avoided downtime, lower maintenance costs, improved efficiency, or reduced energy use. They also need to assess data quality, cybersecurity, governance, latency, and system integration.  

Manufacturers should also be very clear about the level of agency they are granting the system. Moving from an AI that recommends an action to one that executes it is a significant step.  

Production deployments need clear operating boundaries, escalation paths, and human oversight rather than simply scaling a successful demonstration. 

How can businesses ensure AI is grounded in trusted operational data?

AG: Start with the data architecture and quality. Industrial information is often fragmented across engineering systems, historians, maintenance applications, and other repositories.  

Sensors can also fail or produce incomplete data through dropouts. If sources are disconnected, inconsistent, or unreliable, AI can amplify the problem rather than solve it. 

Businesses need a connected data backbone that preserves the relationships between assets, events, and decisions, supported by strong data governance. AI can also help identify multivariate outliers and support data-quality detection and cleansing. 

If a sensor reading cannot be reliably mapped to the correct asset, however, even a high-fidelity AI model can produce the wrong conclusion. Trusted industrial AI starts with accurate, contextualised data that is maintained as operations change. 

How do digital twins, industrial cognition, and autonomous operations work together?

AG: Think of them as layers of increasing intelligence. The digital twin provides a living representation of the industrial environment; industrial cognition adds context by connecting operational data, engineering knowledge, and the relationships between assets and processes. 

Agentic capabilities can then use that context to understand what is happening, recommend what should happen next and, within defined guardrails, help coordinate the response. These guardrails should include physics-based constraints, ensuring recommendations remain consistent with how equipment and processes can operate safely in the real world. 

Together, these capabilities create a path towards more autonomous operations – not a ‘lights-out’ plant, but operations that can increasingly sense, understand, and respond whilst keeping people appropriately in control. 

Can AI improve productivity, safety, and sustainability at the same time?

AG: Yes, in industrial environments these outcomes are often interconnected. Better visibility can identify equipment problems before failure, improving safety and uptime. Better optimisation, meanwhile, can increase throughput whilst reducing energy, water, or material consumption. 

For example, chocolate manufacturer Barry Callebaut created a digital backbone for its smart factory programme. Predictive models enabled lines to be adjusted in real time, identifying an opportunity to increase capacity by 10 percent whilst reducing energy use to meet sustainability goals. 

Predictive AI can identify underperforming assets, support root cause analysis, and improve maintenance, fuel utilisation, and carbon performance. It can also help green energy assets, such as wind and hydro, perform more efficiently, reducing costs and increasing adoption. Advanced autonomous control can provide further sustainability benefits. 

The important point is to optimise across outcomes rather than treating them separately. AI can evaluate far more variables and scenarios than a human team could manually, giving organisations the opportunity to find solutions that improve performance and resource  
efficiency simultaneously. 

Finally, what will define the next generation of industrial AI leaders?

AG: The leaders will not necessarily be the organisations deploying the most AI. They will be those that move beyond experimentation and turn AI into trusted, repeatable, and measurable operational value governed by responsible AI practices. 

That requires strong industrial data foundations, domain expertise, responsible governance, and a clear understanding of where humans need to remain in control. 

The industry is moving beyond experimentation. The next generation of industrial AI leaders will be distinguished by their ability to connect AI, digital twins, and industrial knowledge to make better decisions, and turn those decisions into safe, measurable action whilst keeping people accountable and in control.

The post An Interview with Arti Garg, CTO, AVEVA appeared first on Manufacturing Outlook.

Apple’s first foldable iPhone brings together two displays, a redesigned camera experience, upgraded performance, and a new approach to durability, battery life, and connectivity.

UNFOLDING A NEW CHAPTER FOR THE APPLE IPHONE

Apple has launched the iPhone Duo, marking the company’s first move into the foldable smartphone market.

Measuring 7.6 inches (in) across its inner display, the iPhone Duo becomes Apple’s thinnest phone when fully opened. Folding the device reveals a 5.4-in outer display, which retains 90 percent of the screen area offered by the iPhone 18 Pro in a more compact format.

Both displays use the same aspect ratio, allowing content to transition between them without significant changes in scale, whilst a special nano-texture finish is designed to reduce glare.

At the heart of the device is a precision-engineered hinge, which reinforces the display when fully opened whilst providing a secure closure when folded.

Apple has also incorporated a new hardware architecture intended to support the demands of a larger, more versatile product.

Powered by the A20 Pro chip, the iPhone Duo combines a custom-designed vapour chamber with a dual-battery system to manage heat and maximise available power.

The device also introduces new ways of interacting with the camera, whilst artificial intelligence (AI) capabilities such as Apple Intelligence and Siri AI turn the camera into an interactive visual search and action tool.

With two displays available concurrently, the iPhone Duo can also support the simultaneous use of two applications, providing a larger workspace for increased productivity.

Elsewhere, touch ID is incorporated into the side button, allowing authentication whether the device is open or closed, whilst Apple Watch users can unlock the iPhone Duo using their watch.

The device is constructed from grade-five titanium with a polished finish and is available in star white and night sky. the iPhone Duo will officially launch on 23rd October, with pre-orders beginning on 16th October.

CONSTRUCTED WITH DURABILITY IN MIND

A key consideration in the development of the iPhone Duo has been the durability of its foldable construction.

Thus, the hinge mechanism has been engineered to keep the inner screen supported across the device’s centre when it’s unfolded, with magnets incorporated into the construction to bring the two halves securely together when closed.

Ceramic Shield protects the rear of the device, whilst Ceramic Shield 2 is used across the outer display to reduce glare and provide three times greater scratch resistance than previous generations.

The inner display, meanwhile, incorporates several laminated layers designed to maintain a flat surface during use.

During folding, the individual layers can shift independently rather than placing excessive strain on the display – an approach intended to help preserve the panel through repeated use.

ENHANCED CAMERA CAPABILITIES

The folding form factor also provides new possibilities for the iPhone Duo’s camera system.

The device features a 48-megapixel (MP) Fusion Ultra-Wide camera, allowing users to capture broader perspectives whilst retaining a high level of detail.

Camera Control provides quick access to the camera, including the new Siri Camera mode, which allows Siri to interpret what the camera is viewing and respond to questions or requests based on that information.

A new FaceTime camera sits beneath the inner display, enabling video calls regardless of whether the device is fully opened or positioned hands-free.

The dual-display configuration also supports several new camera functions, such as Dual Capture which allows front and rear camera feeds to be recorded simultaneously during FaceTime and Duo FaceTime, enabling another person to participate in a call using the outer display and camera.

For video recording, the 48 MP Fusion Main camera can capture footage at 4K and 120 frames per second, with users able to alter playback speed after recording to retrospectively create slow-motion footage.

Ultimately, the combination of two displays and a folding form factor gives the iPhone Duo a different approach to photography and video, allowing the device itself to become part of the experience.

BUILT TO LAST

Apple has incorporated a high-energy battery into each side of the device, using the available internal space created by the foldable architecture to increase capacity.

Its silicon- and software-based energy management system coordinates the two power cells, balancing their output according to how the device is being used.

This provides up to 31 hours of video playback when using the inner display, rising to 44 hours when using the outer display. When both screens are used equally, the device offers up to 24 hours of usage per charge.

Fast charging can take the iPhone Duo to 50 percent capacity in around 20 minutes, whilst wireless charging through MagSafe or Qi2 can reach the same level in approximately half an hour.

UNPARALLELED CONNECTIVITY

The iPhone Duo adopts an eSIM-only configuration worldwide, removing the need for a physical SIM card and creating additional internal space for the device’s other components.

According to Apple, eSIM is now supported by more than 500 carriers globally, including EE, O2, Vodafone, Three, and Sky Mobile, providing compatibility across a wide range of networks.

iOS 27 also introduces the iPhone Handoff, allowing users with supported carriers to switch between two iPhone models whilst retaining the same phone number.

Connectivity is supported by Apple’s N1 wireless networking chip, which provides Wi-Fi 7, Bluetooth 6, and Thread capabilities.

The C2 cellular modem system, meanwhile, incorporates AI-powered improvements intended to enhance cellular performance and reliability.

Apple is also launching a range of accessories alongside the iPhone Duo, such as the foldable iPhone Duo Folio which incorporates an integrated kickstand that can hold the device at different angles for reading, viewing content, or FaceTime.

In addition, the iPhone Duo Case is available in sand and navy blue and uses a two-part construction designed to protect both sides of the device, whilst keeping the overall profile thin and lightweight.

A MORE SUSTAINABLE IPHONE

Environmental considerations have heavily informed Apple’s approach to developing the iPhone Duo, as the company works towards its ambition of achieving carbon neutrality across its entire footprint by 2030.

As such, the device contains 35 percent recycled content overall, including 100 percent recycled cobalt in its battery and 100 percent recycled titanium in the cover of its 3D-printed hinge.

Across its supply chain, the iPhone Duo is manufactured using 60 percent renewable energy, including power generated through wind and solar sources. The device also meets Apple’s requirements for energy efficiency and chemical safety.

Furthermore, its paper packaging is entirely fibre-based and can be recycled through household recycling systems, continuing Apple’s wider approach to reducing the environmental impact of its product packaging.

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