Hexagon AB’s potential spin-off of its Asset Lifecycle Intelligence and Safety, Infrastructure & Geospatial divisions, the company has announced the new business will operate with the business name “Octave.” The company says the new name communicates its intent to increase, accelerate, and optimise customer outcomes.

In addition to Hexagon’s existing Asset Lifecycle Intelligence and Safety, Infrastructure and Geospatial divisions, Octave will also include ETQ (currently operating under the Manufacturing Intelligence division) and Bricsys (currently operating under the Geosystems division).

This new business will be a pureplay software and SaaS company focused on helping customers make smarter, more data-driven decisions. Octave’s portfolio will help customers design, build, operate and protect assets more effectively, enabling clearer insights and better incident response.

“As we prepare for the potential separation from Hexagon AB, Octave will be a powerful identity to reflect the significant growth opportunity,” said Mattias Stenberg, current President of Hexagon’s Asset Lifecycle Intelligence and Safety, Infrastructure & Geospatial divisions and incoming Octave Chief Executive Officer. “As a separate, stand-alone company Octave will have the depth, scale, and expertise necessary to capitalise on software and services opportunities across the industrial and public sector spaces and deliver intelligence at scale.”

Collectively, Octave had approximately 7,200 employees as of the end of 2024, and revenues of approximately $1.654 billion (EUR 1,448 million).

If approved by stakeholders, it is the Hexagon Board’s current expectation that the separation and listing process will be completed in the first half of 2026.

“The good thing about Octave is that it’s fully integrated. It’s one company with one go-to-market strategy, one sales force. It’s not just a collection of companies. That’s the 1+1=5 effect,” said Stenberg at the press conference. “We’re already bringing together ALI and ETQ. We’re well on our way there. That will take maybe six months to a year. Bricsys is more of a standalone unit for now. It’s very much an online-sold product and operates a little differently, so that will take more time. It’s a bigger business.”

The separation, spin-off and listing remain subject to this ongoing process and final approval of the Hexagon Board and shareholders, as well as being subject to other conditions, consents and regulatory approvals.

Hexagon leadership cautioned that there can be no assurances that a separation, spin-off or listing will occur.

— With files from Ian Wright

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What is an Automated Building Management System (BMS)?

An Automated Building Management System (BMS) is a centralized system that manages and controls various building systems and operations in a manufacturing facility. These systems typically manage areas like heating, ventilation, and air conditioning (HVAC), lighting, energy consumption, security, water management, and access control. BMS integrates these functions into a cohesive platform, allowing real-time monitoring, control, and optimization.

By using sensors, automation, and data analytics, BMS helps facilities make informed decisions, improve efficiency, and monitor the overall health of building systems. In terms of sustainability, BMS acts as a vital tool for reducing resource consumption and minimizing a facility’s environmental footprint.

The backbone of sustainability

Energy usage is one of the most significant costs in manufacturing, and managing it effectively is crucial for achieving sustainability. A BMS enhances energy efficiency by automating the control of heating, cooling, and lighting systems to ensure that energy is used only when necessary.

Real-time energy monitoring and control

Through continuous monitoring of energy use, BMS systems can detect energy inefficiencies and automatically adjust operations to minimize waste. For example, lighting systems can be automatically dimmed or turned off in unoccupied areas, and HVAC systems can adjust their operations based on the real-time temperature and humidity levels within the facility. These energy-saving measures help ensure that energy consumption is minimized, especially during non-peak hours when the facility may not be fully operational.

Demand-response and peak load management

One of the key features of an advanced BMS is demand-response capabilities, which enable the system to respond dynamically to fluctuations in energy demand. By adjusting the use of HVAC, lighting, and other systems, the BMS can reduce energy consumption during peak hours when the grid is under strain. Manufacturers can use this feature to avoid high energy costs and reduce their environmental impact by using energy more efficiently during critical times.

Load shedding and optimization

BMS also helps to manage peak demand through load shedding—reducing non-essential energy consumption during periods of peak demand. This can significantly reduce the overall energy consumption of the facility while maintaining operational efficiency.

Manufacturing facilities, particularly those that rely heavily on energy-intensive processes, contribute significantly to carbon emissions. In addition to improving energy efficiency, BMS plays an integral role in helping manufacturers reduce their carbon footprint.

Renewable energy integration

Many manufacturing facilities are now incorporating renewable energy sources, such as solar panels or wind energy, into their operations to further reduce their carbon impact. A BMS can play a crucial role in managing the integration of these renewable energy sources. By prioritizing renewable energy usage when available and balancing it with grid electricity, the system ensures that the facility uses as much clean energy as possible, reducing reliance on non-renewable resources.

Water conservation and waste management

Water is another crucial resource that many manufacturing facilities rely on for processes such as cooling, cleaning, and production. As water conservation becomes increasingly important, a BMS helps manage water usage effectively. BMS systems track and monitor water usage in real-time, alerting facility managers to wasteful practices or water leaks. By identifying areas where water is used inefficiently, such as excess cooling or leaky connections, manufacturers can take steps to reduce consumption. Additionally, BMS can provide automated control over water-based systems, ensuring that they are used optimally throughout the day.

For facilities with on-site wastewater treatment systems, a BMS can assist in monitoring and controlling wastewater processes, ensuring they run efficiently. By optimizing water treatment, BMS systems can help ensure that water reuse is maximized, contributing to sustainability efforts and lowering operational costs.

Predictive maintenance and preventing waste and downtime

Manufacturing facilities rely heavily on machinery and equipment to keep operations running smoothly. However, poorly maintained equipment can lead to inefficiencies, increased energy consumption, and even premature failure. BMS plays a critical role in predictive maintenance, which helps avoid these issues and contributes to long-term sustainability.

Using real-time data analysis, BMS systems can identify potential issues with equipment before they become critical, enabling proactive maintenance. This minimizes unplanned downtime, reduces the need for emergency repairs, and extends the lifespan of equipment, reducing the need for replacement and cutting down on waste.

Predictive maintenance also ensures that equipment runs at peak efficiency, avoiding overuse of energy due to malfunctions or inefficiencies. This can significantly reduce energy waste and lower overall energy consumption, directly supporting sustainability efforts.

Compliance with green building standards

As sustainability regulations become more stringent, manufacturers are often required to meet specific green building certifications, such as LEED (Leadership in Energy and Environmental Design) or BREEAM (Building Research Establishment Environmental Assessment Method). A BMS helps facilities stay compliant with these standards by providing valuable data on energy consumption, water usage, and overall environmental impact.

Pathway to a sustainable future

In the quest for sustainability, manufacturers must leverage technology to optimize resource usage and minimize waste. Automated BMS are an essential tool in achieving these goals, offering real-time monitoring and control of energy, water, and equipment. By automating critical systems, BMS help manufacturers reduce their carbon footprint, lower energy costs, and improve operational efficiency.

Monitoring and managing resources efficiently through BMS gives manufacturers a competitive edge, not only by helping them meet regulatory standards but also by reducing operational costs and contributing to a more sustainable future. As we move toward a greener industrial landscape, BMS will play a pivotal role in driving sustainability efforts and ensuring that manufacturing operations are as efficient and environmentally responsible as possible.

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Robot path planning is a complex, with most workcells using multiple robots requiring tedious work to create interference zones and interlock signals that ensure there are no collisions during manufacturing.

Manually validating the mechanical design, planning robot paths, determining sequencing to hit optimal cycle time targets, and defining those interlocks can take a team well over 100,000 hours for a single project. This complexity often leads to failures in hitting cycle time targets, adding significant rework.

Resolver works by selecting and testing potential solutions tens to thousands of times faster than a human programmer. The goal is to quickly generate optimal, collision-free motion paths and interlock signals. This can accelerate workcell design from months to days.

The company says Resolver is essentially infinitely scalable robotic simulation power that can used to reduce the time required for many tasks, including:

• Generating accurate proposals
• Designing optimal tools and fixtures
• Producing optimal robot programs
• Adjusting for as-built deviations during commissioning
• Assessing and minimizing the impact of product design changes

“It is widely understood that the future of the manufacturing industry lies in robotics and automation. However, that future is slow to materialize because of the outdated, time-consuming, and inefficient processes commonplace in the industry,” said Peter Howard, CEO of Realtime Robotics. “Few manufacturers have the time or resources needed to enact real change. We’ve engineered Resolver to help manufacturers improve their engineering, programming and production processes – and drive greater value from their current and future investments in robots.”

How it works

Realtime Robotics’ Resolver supports path planning with any number of robots, at any phase of the workflow, generating results in minutes. The solution requires minimal onboarding and currently allows users to work directly within Siemens Process Simulate. Support for other leading simulation platforms will be rolled out later in the year, enabling teams to work directly within their preferred simulation tool.

“Resolver has the computational power to generate better motion paths than human programmers in both simple and complex workcells,” added Howard. “This is because Resolver searches the possibilities open to robotic arms, while humans tend to stay within the possibilities of the human arm.”

Users upload the workcell information, configure their sequencing and conditions, and execute a run. In minutes, Resolver will generate motion paths—including interlocks. The longer Resolver runs, the more options it provides, shortening the cycle time until the desired outcome is reached. The paths and interlocks can then be easily imported back into the simulation software for validation and operation.

Beyond determining optimal motion plans and interlocks, Resolver can help with fixture design, reachability validation, target sequencing, and robot task allocation. It can also be used to design the paths and interlocks for an entire manufacturing line from the start.

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With this kind of potential, it’s not a surprise that it’s starting to get a lot of attention. In a recent blog post, Colin Masson, Director of Research for Industrial AI at ARC Advisory Group, calls MCP a “universal translator” that replaces the need for custom-built connections between AI models and industrial systems. Last month, Jim Zemlin, Executive Director at Linux Foundation said in a LinkedIn post  that MCP  is “emerging as a foundational communications layer for AI systems” and compared its potential impact to what HTTP did for the Internet.

Key features of model context protocol

MCP serves as a bridge between AI models and the environments they operate in, allowing models to access and interact with external data sources, APIs, and tools in a structured and secure manner. By standardizing the way AI systems communicate with external resources, MCP simplifies the integration process and enhances the capabilities of AI applications.​ Here are some of the reasons it is expected to improve AI functionality:

Modular and Message-Based Architecture: MCP follows a client-server model over a persistent stream, typically mediated by a host AI system. It uses JSON-RPC 2.0 for communication, supporting requests, responses, and notifications.​

Transport Protocols: Supports standard input/output (stdio), HTTP with Server-Sent Events (SSE), and optionally extended via WebSockets or custom transports.​

Data Format: Utilizes UTF-8 encoded JSON, with alternative binary encodings like MessagePack supported by custom implementations.​

Security and Authentication: Employs a host-mediated security model, process sandboxing, HTTPS for remote connections, and optional token-based authentication (e.g., OAuth, API keys).​

Developer SDKs: Provides SDKs in Python, TypeScript/JavaScript, Rust, Java, C#, and Swift, maintained under the Model Context Protocol GitHub organization.​

MCP has been applied across various domains. In software development it’s integrated into IDEs like Zed, platforms like Replit, and code intelligence tools such as Sourcegraph to provide coding assistants with real-time code context.​ Companies in many industries are using it to help internal assistants retrieve information from proprietary documents, CRM systems, and company knowledge bases.​ Applications like AI2SQL leverage MCP to connect models with SQL databases, enabling plain-language queries.​ In manufacturing, it supports agentic AI workflows involving multiple tools (e.g., document lookup and messaging APIs), enabling chain-of-thought reasoning over distributed resources.​

MCP adoption and ecosystem

• OpenAI announced support for MCP across its Agents SDK and ChatGPT desktop applications on March 26, 2025.​
• Google DeepMind confirmed MCP support in the upcoming Gemini models and related infrastructure.​
• Dozens of MCP server implementations have been released, including community-maintained connectors for Slack, GitHub, PostgreSQL, Google Drive, and Stripe.​
• Platforms like Replit and Zed have integrated MCP into their environments, providing developers with enhanced AI capabilities.​

Comparing MCP to other systems

MCP differs from other AI integration frameworks in several ways:​

OpenAI Function Calling: While function calling lets LLMs invoke user-defined functions, MCP offers a broader, model-agnostic infrastructure for tool discovery, access control, and streaming interactions.​

OpenAI Plugins and “Work with Apps”: These rely on curated partner integrations, whereas MCP supports decentralized, user-defined tool servers.​

Google Bard Extensions: Limited to internal Google products, MCP allows arbitrary third-party integrations.​

LangChain / LlamaIndex: While these libraries orchestrate tool-use workflows, MCP provides the underlying communication protocol they can build upon.​

MCP represents a significant step forward in AI integration, offering a standardized and secure method for connecting AI systems with external tools and data sources. Its growing adoption across major AI platforms and developer tools underscores its potential to transform AI-driven workflows.​

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Total installations of industrial robots in the U.S. automotive sector increased by 10.7%, reaching 13,700 units in 2024, according to preliminary results reported by the International Federation of Robotics (IFR).

“The United States has one of the most automated car industries in the world. The ratio of robots to factory workers ranks fifth, tied with Japan and Germany and ahead of China,” says Takayuki Ito, President of the International Federation of Robotics. “This is a great achievement of modernization. However, in other key areas of manufacturing automation, the US lags behind its competitors.”

The majority of industrial robots deployed in the U.S. are imports from overseas, as there are few robot manufacturers producing there. Globally, 70% of robots are produced by four countries: Japan, China, Germany and South Korea.

Within this group, Chinese manufacturers are the most dynamic, with production for their huge domestic market more than tripling from 2019 to 2023. This puts them in second place after Japan and is driven by the country’s national robotics strategy. Its manufacturing industry installed about 280,000 units per year between 2021 and 2023, compared to a total of 34,300 installations in the United States in 2024.

In China, robotics and automation are penetrating all levels of production, resulting in a robot density of 470 robots per 10,000 employees in manufacturing—the third highest in the world, surpassing Germany and Japan in 2023.

China’s National Development and Reform Commission in March 2025 established a state-backed venture capital fund focused on robotics, AI and cutting-edge innovation. The long-term fund is expected to attract nearly 1 trillion yuan (US$138 billion) in capital from local governments and the private sector over the next 20 years. This initiative aims to continue China’s technology-driven manufacturing:

The U.S. ranks tenth among the world’s most automated manufacturing countries with a robot density of 295 robots per 10,000 employees. The country’s automation is heavily concentrated in the automotive sector with about 40% of all new industrial robot installations in 2024.

This is followed by the metal and machinery industry with 3,800 units, representing a market share of 11%. Installations in the US electrical and electronics industry has a market share of 9% with 2,900 units sold.

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The company says Maestro was designed to be fast, easy to use, connected and scalable. Its digital-first architecture delivers rapid measurement routines, an intuitive user experience and seamless data integration.

With modular software and hardware, it will scale with evolving production needs, making it ideal for aerospace, automotive, and high-precision manufacturing environments where there is a high demand for accuracy to deliver safety, compliance, and performance.

Hexagon developed an all new digital architecture, incorporating digital sensors, a single cable system, and a completely new controller with brand new firmware in an effort to address long standing issues with CMM technology.

“When we started developing this five years ago, we came up with a very crazy idea: let’s pretend nobody ever invented the CMM, and we invented one today, how would we build up a system from ground zero if we were making the first CMM in the world? So, this is how it started,” says Jörg Deller, General Manager Stationary Metrology devices at Hexagon. “And what came out [Maestro] was what we call the “all digital CMM.”

Maestro’s redesigned mechanical structure, single-cable digital platform, and advanced sensors enable fast measurement with sub-micron tolerances that satisfy stringent industry standards. Synchronised axis movements, rapid calibration, and cloud-connected software significantly accelerate set-up, programming, execution, and reporting.

An intuitive user interface, combined with next-generation cloud-native metrology apps powered by Hexagon’s Nexus platform, enable both expert metrologists and less-specialised staff to generate repeatable, standard-compliant measurements without the need for coding.

It was designed with the digitalization trend in mind—an Industrial Internet of Things (IIoT) native measuring device that integrates into Hexagon’s Nexus ecosystem to share real-time data across design, production, and quality teams, driving data-driven decision-making and improving overall equipment effectiveness (OEE). Near-line or in-line integration with automation systems is seamless.

With a modular design and a robust roadmap for future upgrades, scalability is ensured. Manufacturers can easily update software, sensors, and additional capabilities over time, ensuring that their investment remains future-proof and continuously supports evolving production needs.

“Manufacturers told us they needed a next-generation system that tackles rising quality demands and skills shortages,” said Deller. “By rethinking our hardware and software from the ground up, rather than iterating on existing systems, we’ve had the freedom to create a high-accuracy inspection solution that is so intuitive that anyone from expert to new hires become significantly more productive.”

Pilot users report dramatic productivity gains and reduced inspection lead times, helping to avoid production bottlenecks and to keep pace with fast-changing customer requirements. Customers have tested various sensors, ranging from high-speed laser scanning to tactile probes, with consistently strong results in both R&D and production applications.

“It has a lot of use cases. We introduced it to some selected beta customers before its release and every customer found their own unique part of it,” says Deller. He mentioned some customer anecdotes, including one user who was amazed by the synchronized movement. They can do five axis moves to get most efficient measurement in place, lock the probe in, then measure fast and move directly out again. Another beta user mentioned the connectivity, specifically the integrated camera to see what’s on the machine, wherever they are in the world.

“So, the big thing about Maestro is there is not one big thing. It’s a system which delivers a lot of value for very different users,” says Deller.  “it’s having the right technologies with the right people in mind and taking the time really to understand what is happening.”

Hexagon’s software tools and services such as PC-DMIS and the Metrology Mentor, Metrology Asset Manager, and Metrology Reporting Nexus Apps were developed in tandem with Maestro to create an integrated system that significantly boosts productivity from part loading to analysis, compared to isolated component solutions. The end goal is to deliver ease of use and fast workflows, from programming, execution and usage, to reporting and collaboration with colleagues in design and manufacturing.

Maestro will be offered initially in multiple sizes and configurations, each engineered for automated multi-sensor workflows utilising tactile probes and laser scanning probes from a new “digital rack” that tracks occupancy status, sensor supply health and status that can be accessed on-device and throughout the desktop and cloud-native apps. 

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Production employees just take a picture of their component’s cut edge with a hand scanner. Then, the AI assesses the edge quality, evaluating it using objective criteria such as burr formation. With this information, the Cutting Assistant’s optimization algorithm suggests improved parameters for the cutting process. Then the machine cuts the sheet metal once more. If the part quality still does not meet expectations, the user has the option to repeat the process.

This solution is available for all TruLaser series laser cutting machines purchased as of May 2025, which feature a power output of 6 kW or higher.

“The Cutting Assistant is a great example of how AI-enabled tools can help overcome problems related to the skilled worker shortage and also saves time and money. When it comes to productivity, this application creates a competitive edge for fabricators,” says Grant Fergusson, Trumpf Inc. TruLaser 2D laser cutting product manager.

AI makes optimization suggestions

When laser cutting, materials that are not optimized for laser cutting often produce edges with wide variations in cut quality, forcing production employees to constantly change the technology parameters. This involves adjusting each individual parameter one by one— a process which demands a lot of time and employee experience. By integrating the Cutting Assistant into the machine software, optimized parameters can be transferred seamlessly into the software without programming.

While developing the Cutting Assistant, Trumpf experts cut thousands of parts and drew upon many years of expertise, using their extensive knowledge to train the software’s algorithm. This work on the Cutting Assistant did not stop on its release—data from applications in the field will also be incorporated into the solution to enable faster and more reliable results.

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The partners stated in a press release that the collaboration addresses urgent manufacturing challenges, including a shortage of skilled welders, increasing part variability, and demand for faster lead times. The solution is built for complex, high mix welding environments, enabling manufacturers to automate without the need for traditional programming.

The companies said this partnership expands their long-standing relationship, ensuring that AI-powered robotics and intelligent positioning technology work seamlessly together to improve accuracy and accelerate throughput in industrial automation.

“ALM is the perfect hardware partner for Path as we expand across North America,” said Andy Lonsberry, CEO and Co-Founder of Path Robotics. “ALM’s teams and products are best in class and known across the industries we serve.”

Industries including heavy equipment, trailer manufacturing, energy, aerospace, and agriculture face increasing pressure to deliver high-quality, customized products at scale.

These sectors face common hurdles: shortages of skilled welders, high part variability, and demand for faster lead times. Traditional automation solutions often fall short in these complex, variable environments.

The ALM-Path partnership offers a solution that addresses these pain points with intelligent automation designed for high-mix, multi-pass welding with extreme part variability. The combined system, based on Path Robotics AW3 and ALM Positioners, intelligently adapts to each part and weld path without reprogramming, making automation viable where it previously wasn’t.

“Path’s technology is changing the way manufacturers view automation,” said Pat Pollock, President and CEO of ALM Positioners, Inc. “Their AI-driven solutions allow manufacturers to take advantage of the quality, throughput, and consistency of robotic welding, without all the programming and application challenges associated with traditional robotic automation.”

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What is a data fabric?

A data fabric is a technology architecture that connects diverse data sources (sensors, machines, ERP, MES, quality systems, supply chain tools) and standardizes and integrates that data in real time or near-real time. It uses metadata, AI, and automation to understand and manage data dynamically to provide a single, accessible layer across cloud, edge, on-premises, and legacy systems.

Data fabrics existed before AI became prominent, but their importance and value have grown dramatically alongside the rise of AI.

How data fabrics apply to manufacturing

Decision making in manufacturing relies on diverse data sets delivered from numerous sources, from sensor data on the factory floor to historical maintenance records and real-time inventory levels. A data fabric provides seamless access to all this data, eliminating silos that would impede the full visualization of the company through data.

With a data fabric, manufacturers can stream and analyze data in real time, allowing AI systems to perform the key functions expected from them, such as predicting equipment failure, optimizing production schedules and monitoring quality to detect anomalies as they happen.

These days, data fabrics often include built-in AI to automate data discovery, classification, governance, and integration. This significantly reduces the time it takes to train and deploy AI models and results in a clearer picture of the story the data is telling.

Manufacturers often operate in hybrid environments using cloud, the edge and on-premises layers. A data fabric allows AI models to access and process data where it lives, enabling fast decisions on the factory floor and deeper learning in the cloud.

For regulated industries or operations with strict quality control, data fabrics enforce consistent governance, lineage, and access controls, ensuring that AI models are using trustworthy, auditable data.

The benefits of data fabrics for AI in manufacturing include:

• Accelerated AI deployment
• Improved accuracy of AI models due to richer, cleaner data
• Reduced downtime and waste
• Faster innovation through easier access to data
• Scalability across plants, systems, and geographies

A data fabric is foundational for using AI effectively in manufacturing. It connects and organizes the vast, fragmented data landscape of modern factories, providing the data foundation it needs to drive smarter decisions, automation, and innovation at scale.

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Among the least talked about integrations are DX systems combined with smart building infrastructure, such as lighting, heating, and cooling systems. These integrations enable real-time decision-making and intelligent energy management, which are crucial for achieving sustainability goals while keeping costs in check.

The role of digital transformation in smart building technology

Digital transformation encompasses a broad suite of technologies, including Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and cloud computing. When applied to building infrastructure systems—specifically lighting, heating, ventilation, and air conditioning (HVAC)—these technologies enable facilities to move from reactive to proactive and even autonomous operational models.

For example, IoT sensors embedded in lighting and HVAC systems collect real-time data on occupancy, ambient light, temperature, and air quality. DX platforms aggregate and analyze this data to optimize environmental conditions and energy usage dynamically. The result is a highly responsive facility that adjusts its energy usage based on actual need, not static schedules.

Smart lighting—beyond energy efficiency

Smart lighting systems are often the entry point for many manufacturers beginning their sustainability journey. However, these systems do more than just save energy through LED technology and motion sensors. When integrated with DX platforms, smart lighting systems can adjust brightness and color temperature based on the day’s task requirements. These systems integrate with occupancy and workflow data to provide optimal lighting only where and when needed. Lastly, the data collected by these systems will offer insights into space utilization, contributing to more efficient layout planning and capacity management.

When tied into a broader digital transformation ecosystem, lighting data can also add context to safety and productivity metrics, enabling data-driven improvements to workplace conditions.

Intelligent climate control

Smart HVAC systems aren’t new and have become a vital tool for many manufacturers. In environments where temperature and humidity control are critical—such as in pharmaceuticals, electronics, or food manufacturing—DX integration provides an edge.

AI-driven HVAC systems use predictive algorithms and the production schedule to deal with changes in external weather conditions, internal heat generation, equipment usage and product mix. These systems can:

• Adjust airflow and temperature in zones based on real-time occupancy and process demands
• Schedule maintenance based on predictive analytics, reducing downtime and energy waste
• Learn from historical data to optimize performance across seasons and production patterns

Data integration and visualization

One of the key advantages of digital transformation is its ability to unify data streams from disparate systems into a single platform. Manufacturing engineers can visualize lighting, HVAC, production, and utility data on centralized dashboards. This helps identify energy-intensive areas and inefficiencies while correlating environmental conditions with production metrics, creating and tracking more accurate sustainability KPIs in real time. With advanced analytics, facilities can simulate different energy scenarios, forecast future consumption based on orders, and model ROI on proposed sustainability investments.

Interoperability and open standards

For maximum impact, DX systems should be built on open standards that support interoperability among devices and platforms. Manufacturing facilities often use equipment and systems from multiple vendors. Ensuring that lighting, HVAC, and digital solutions can “talk” to each other minimizes integration challenges and futureproofs the infrastructure. Middleware and APIs are increasingly being used to bridge communication gaps, layering advanced controls and analytics without replacing existing systems.

Sustainability and compliance

Sustainability in manufacturing has become a competitive imperative, with many governments and industry bodies enforcing stricter energy efficiency and emissions standards. Digital transformation systems provide the transparency and traceability needed to demonstrate compliance with regulations such as ISO 50001 (energy management) and ASHRAE standards.

AI, edge computing, and autonomy

The next frontier in DX-enabled smart environments is edge computing and autonomous control. Instead of sending all data to the cloud, edge devices can process information locally, enabling faster decision-making and reducing latency.

For example, a local controller might detect a sudden drop in occupancy and immediately dim lights and reduce HVAC output in that zone—without waiting for a central system to tell it what to do. This distributed intelligence model enhances responsiveness and resilience, especially important in large or multi-site manufacturing operations.

There are many reasons to invest in smart building infrastructure, but ultimately companies opt to install smart lighting, heating, and cooling technologies to reduce costs. Integrating these systems into a digital transformation regime can transform passive infrastructure into intelligent assets that contribute directly to efficiency, comfort, compliance, and cost reduction. For manufacturing engineers, understanding and leveraging these integrations is critical for meeting today’s sustainability standards but also for building resilient, future-ready operations.

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