It seems like the manufacturing sector is forever in the midst of a structural shift. Competitive pressures, supply chain disruptions, and evolving customer expectations are constants, driving companies to continuously rethink how they produce goods and where they produce them. Digital transformation systems—a convergence of advanced analytics, IoT, AI, and cloud-based platforms—are at the center of this current shift.

For engineers and executives alike, these systems are more than IT upgrades. They are tools—sometimes very simple, sometimes quite complex—that reconfigure logistics, streamline supply chains, and make localized manufacturing practical and profitable.

Digital transformation and logistics optimization

Historically, logistics in manufacturing has been reactive to disruption—responding to bottlenecks, freight delays, or warehouse shortages as they arise. Digital transformation turns this reactive model into a predictive and adaptive one.

Real-time visibility provided by IoT sensors and connected devices track goods in transit, raw material consumption, and production progress. By tracking the data from these devices, engineers gain line-of-sight of their raw materials and products from factory floor to customer delivery. AI-driven routing and scheduling algorithms forecast delays and dynamically reroute shipments or adjust production schedules to maintain throughput.

And then there’s digital twins, which aren’t just for product design. By creating a digital twin of logistics networks, engineers can simulate different transportation strategies, warehouse configurations, or production-distribution trade-offs before making capital commitments.

The result is lower transportation costs, higher on-time delivery rates, and fewer emergency interventions to solve unexpected problems.

Digital transformation and supply chain resilience

In the last five years, supply chain fragility has become more than just a boardroom issue. Digital transformation systems can help bring resilience by unifying fragmented data and enabling proactive decision-making.

Instead of relying on the siloed ERP and supplier systems of the previous decade, companies can use integrated supplier data platforms to build digital ecosystems where quality, lead times, and pricing data are visible in one place. The advanced analytics produced by these digital ecosystems can help users flag single-source dependencies or regions exposed to external risks, such as natural disasters, geopolitical snafus or disease outbreaks.

At this stage, either humans or AI systems can find and recommend shifts to alternate suppliers, suggest redistribution of inventory, or gameplan adjustments to current stock levels. For manufacturers, this means fewer surprises on the production line and the bottom line—it’s a little bit of assurance that production won’t grind to a halt due to a single point of failure.

Digital transformation and enabling local manufacturing

Local manufacturing—sometimes called near-market manufacturing, regional manufacturing or nearshoring— simply means manufacturing your products closer to end customers. The strategy reduces transportation costs, shortens lead times, and lowers emissions. The downside is that it introduces complexity through operating multiple regional plants, relying on varied supplier networks, and adjusting to different regulatory environments. Digital transformation systems provide the infrastructure to make this a viable strategy for a larger cross section of businesses.

With a digital infrastructure, standardized production data models help engineers replicate validated processes across sites, ensuring consistency in quality while tailoring to local market needs. Linking and integrating cloud-based manufacturing execution systems (MES) and enterprise resource planning (ERP) software allow plant managers to coordinate production planning across geographies while everyone works from the same set of data. Real-time sales and consumption data flow directly into local production schedules, aligning output with regional market demand.

The outcome is that companies gain the agility to serve markets faster while maintaining engineering rigor and cost control.

Digital transformation and engineering leadership

Digital transformation systems don’t come cheap. Aside from the initial cost, they require significant time and staff resources during start up. However, the next-level strategic and tactical functionality enabled by digital transformation has also never been more accessible. Rapid advancements in technology, compute power and the availability of could storage make it attainable to anyone willing to invest the time and money. The capabilities mitigate the initial costs by reducing “firefighting” and manual reporting. They provide tools to model, test, and optimize logistics and supply chain variables virtually before committing to a plan. Indeed, for executives and manufacturing engineers, these systems turn supply chains into a strategic advantage, enabling informed investment in new sites, supplier diversification, and sustainable practices. Digital transformation is a lever for both resilience and growth, improving reliability today while positioning the enterprise to compete globally tomorrow.

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ABB Robotics has added AI capability to its Robotstudio Suite, an offline programming and simulation tool for robotic applications.

The product was launched at the China International Industry Fair in Shanghai.

The new Robotstudio AI Assistant uses generative Al to make robot programming faster, easier, and more accessible. ABB Robotics president Marc Segura says this is the company’s latest effort to commercialize AI by enhancing robot versatility and encouraging their use beyond traditional manufacturing.

“The demand for AI in robotics is driven by the need for greater flexibility; faster commissioning cycles and a shortage of the specialist skills traditionally needed to program and operate robots. By adding this generative AI Assistant, we are expanding its benefits to reach less experienced users and help experts solve technical challenges faster,” Segura says.

Featuring a Large Language Model (LLM) that understands and interprets human language, Robotstudio AI Assistant draws from ABB’s library of manuals and documentation to deliver accurate responses to questions, enabling users to set up faster and quickly find answers to technical challenges.

Robotstudio is more than a straightforward LLM, however. Magnus Seger, Global Product Manager, Simulation Software, ABB Robotics, says it also has “agentic” qualities because it and autonomously selects from and uses multiple tools, depending on the user’s request.

The AI assistant scans and queries ABB manuals using a retrieval augmented generation (RAG) system. It also reads robot code from the user’s active RobotStudio project to ground answers in the user’s own code, making it more applicable to their specific enquiry and task.

Seger says that beyond the select product manuals and the user’s local project, no external data is accessed.

“The model itself is trained using selected manuals (RobotStudio, RobotWare, RAPID) that are made available via a RAG system. ABB’s documents include current, leading-edge information that best fits challenges relating to ABB Robots,” says Seger. “ABB’s document library is updated regularly, and new documents are created alongside each new robotics launch, which is why using this model gives users information that is up-to-the-minute and retrieves the latest technology documentation at runtime.”

Queries are handled through the Azure-hosted RAG system and data is transmitted to the Azure cloud using encryption. Customer-provided data is only used for the active request and never for training.

“The safety of our customers’ data is very important to us; our protocols for storing data are reviewed and updated regularly, in line with current best practice and the ABB library is not locally stored each time there is an interaction with RobotStudio,” Seger says.

It’s important to note this is truly an assistant. ABB’s AI assistant will not program a robot autonomously with no user interaction, and users can’t train the AI to do so.

“Both RobotStudio and RobotStudio AI Assistant currently require user involvement, and any code suggestions must be reviewed and approved by the user before being applied to a robot controller, cautions Seger. “However, RobotStudio AI Assistant is part of ABB Robotics Autonomous Versatile Robotics (AVR) portfolio, as it supports ABB robots and software to move closer to versatility and intelligence by enabling them to develop skills that include planning, adapting, and executing diverse tasks, independently, in real time, and without human intervention.”

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ABB will invest $110 million through the remainder of 2025 to expand its R&D and manufacturing of advanced electrification solutions.

The company says the cash will create nearly 200 new jobs and support expected future growth in key industries, including data centers and the power grid. Rapid expansion of data centers in the US is expected to keep annual electricity demand growth above 2% in both 2025 and 2026, more than double the average growth rate over the past decade, according to the IEA.

“This $110 million investment in the US is part of our long-term strategy to support future growth in our biggest global market,” said Morten Wierod, ABB’s Chief Executive Officer. “Demand is being driven by key trends, from the surging power needs of AI in data centers, to grid modernization and customers improving energy efficiency and uptime to reduce their costs.”

ABB will invest $15 million to create a new production line for Emax 3 in its Senatobia, Mississippi site. The Emax 3 air circuit breaker improves the energy security and resilience of power systems in large facilities with high power demands, including data centers, advanced manufacturing sites, and airports. The new line is expected to open in 2026.

A $30 million project will double the footprint of ABB’s Richmond, Virginia facility adding a new test center, warehouse and new assembly lines. The new facility, opening in Q4 2025, will create around 100 new production and engineering roles.

In Arecibo, Puerto Rico, an investment of more than $30 million will increase the size of the facility to accommodate three new production lines. Technologies produced in Arecibo include smart circuit breakers and switching devices, essential power components that help distribute electricity, protect equipment and monitor energy usage. The expansion will create 90 new jobs by the end of 2026.

A $35 million investment will increase the capacity of ABB’s manufacturing facility in Pinetops, North Carolina. This will support expected demand for advanced low and medium voltage grid components from the utilities, and for data centers and industrial facilities. The upgraded facility will come online in 2026.

All of this comes on the heels of a4100 million investment in ABB’s Canadian facilities announced in August 2025. That investment in Montreal, Quebec will combine ABB’s existing Iberville and Saint-Jean-sur-Richelieu facilities at a new greenfield location. This will enable ABB to meet increasing demand in key growth industries, including utilities, renewables, transportation, and residential and infrastructure projects across Canada.

The new site is expected to open in mid-2027 and will be located in the South Shore region of Montreal, Quebec. The new building will integrate clean, energy-efficient electrical equipment and heating systems to reduce energy consumption and cut carbon emissions by over 95 percent, compared with the two existing facilities.

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The ANSI/A3 R15.06-2025 American National Standard for Industrial Robots and Robot Systems – Safety Requirements is now available and A3 says it marks the most significant advancement in industrial robot safety requirements in more than a decade.

“Publishing this safety standard is perhaps the most important thing A3 can do, as it directly impacts the safety of millions of people working in industrial environments around the world,” said Jeff Burnstein, president of A3, in a release.

This standard is available in protected PDF format and includes:
Part 1: Safety requirements for industrial robots
Part 2: Safety requirements for industrial robot applications and robot cells
Part 3: Will address safety requirements for users of industrial robot cells. It’s expected to be published later this year. Once available, it will be retroactively provided at no additional cost to anyone who purchases the full standard.

R15.06 is the U.S. national adoption of ISO 10218 Parts 1 and 2 and is a revision of ANSI/RIA R15.06-2012, which was launched by the Robotic Industries Association (RIA) before it became part of A3.

Key changes in ANSI/A3 R15.06-2025 include:

• Clarified functional safety requirements that improve usability and compliance for manufacturers and integrators
• Integrated guidance for collaborative robot applications, consolidating ISO/TS 15066
• New content on end-effectors and manual load/unload procedures, derived from ISO/TR 20218-1 and ISO/TR 20218-2
• Updated robot classifications, with corresponding safety functions and test methodologies
• Cybersecurity guidance included as part of safety planning and implementation
• Refined terminology, including the replacement of “safety-rated monitored stop” with “monitored standstill” for broader technical accuracy

“This standard delivers clearer guidance, smarter classifications, and a roadmap for safety in the era of intelligent automation,” said Carole Franklin, director of standards development, robotics at A3. “It empowers manufacturers and integrators to design and deploy safer systems more confidently while supporting innovation without compromising human well-being.”

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This is why the editors at Engineering.com designed our Digital Transformation Week event—to help engineers unpack all the choices in front of them, and to help them do it at the speed and scale required to compete.

Join us for this series of lunch hour webinars to gain insights and ideas from people who have seen some best-in-class digital transformations take shape.

Registrations are open and spots are filling up fast. Here’s what we have planned for the week:

September 22: Building the Digital Thread Across the Product Lifecycle

12:00 PM Eastern Daylight Time

This webinar is the opening session for our inaugural Digital Transformation Week. We will address the real challenges of implementing digital transformation at any scale, focusing on when, why and how to leverage manufacturing data. We will discuss freeing data from its silos and using your bill of materials as a single source of truth. Finally, we will help you understand how data can fill in the gaps between design and manufacturing to create true end-to-end digital mastery.

September 23: Demystifying Digital Transformation: Scalable strategies for Small & Mid-Sized Manufacturers

12:00 PM Eastern Daylight Time

Whether your organization is just beginning its digital journey or seeking to expand successful initiatives across multiple departments, understanding the unique challenges and opportunities faced by smaller enterprises is crucial. Tailored strategies, realistic resource planning, and clear objectives empower SMBs to move beyond theory and pilot phases, transforming digital ambitions into scalable reality. By examining proven frameworks and real-world case studies, this session will demystify the process and equip you with actionable insights designed for organizations of every size and level of digital maturity.

September 24, 2025: Scaling AI in Engineering: A Practical Blueprint for Companies of Every Size

12:00 PM Eastern Daylight Time

You can’t talk about digital transformation without covering artificial intelligence. Across industries, engineering leaders are experimenting with AI pilots — but many remain uncertain about how to move from experiments to production-scale adoption. The challenge is not primarily about what algorithms or tools to select but about creating the right blueprint: where to start, how to integrate with existing workflows, and how to scale in a way that engineers trust and the business can see immediate value. We will explore how companies are combining foundation models, predictive physics AI, agentic workflow automation, and open infrastructure into a stepped roadmap that works whether you are a small team seeking efficiency gains or a global enterprise aiming to digitally transform at scale.

September 25: How to Manage Expectations for Digital Transformation

12:00 PM Eastern Daylight Time

The digital transformation trend is going strong and manufacturers of all sizes are exploring what could be potentially game-changing investments for their companies. With so much promise and so much hype, it’s hard to know what is truly possible. Special guest Brian Zakrajsek, Smart Manufacturing Leader at Deloitte Consulting LLP, will discuss what digital transformation really is and what it looks like on the ground floor of a manufacturer trying to find its way. He will chat about some common unrealistic expectations, what the realistic expectation might be for each, and how to get there.

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GE Aerospace and South Burlington, Vermont-based Beta Technologies Inc. have struck a strategic partnership to accelerate the development of a hybrid electric turbogenerator for advanced air mobility (AAM).

Applications include long-range Vertical Takeoff and Landing (VTOL) aircraft and future Beta aircraft and will combine Beta’s permanent magnet electric generators with GE Aerospace’s turbine, certification and safety expertise for large-scale manufacturing. This hybrid solution will leverage existing infrastructure and capabilities, such as GE Aerospace’s CT7 and T700 engines.

As part of the deal, GE Aerospace will make an equity investment of $300 million in Beta. GE Aerospace will have the right to designate a director to join Beta’s Board.

“Partnering with Beta will expand and accelerate hybrid electric technology development, meeting our customers’ needs for differentiated capabilities that provide more range, payload, and optimized engine and aircraft performance,” said GE Aerospace Chairman and CEO H. Lawrence Culp.

The deal is part of GE Aerospace’s pursuit of a suite of technologies for the future of flight, including integrated hybrid electric propulsion systems and advanced new engine architectures.

“We believe the industry is on the precipice of a real step change, and we’re humbled that GE Aerospace has the confidence in our team, technology, and iterative approach to innovation to partner with us. We look forward to partnering to co-develop products that will unlock the potential of hybrid electric flight, and to do it with the rigor, reliability, and safety that aviation demands,” said Kyle Clark, Beta Technologies’ Founder and CEO.

Beta’s “Alia” five-passenger VTOL and conventional electric aircraft charge in less than an hour, according to Beta’s website. They are engineered for all-weather performance and have been tested to operate reliably in a wide range of environmental conditions across the U.S. and Europe. ALIA’s electric propulsion and battery systems — which are developed in-house — offers reliable, high-tempo performance, as well as a quieter sound profile than conventional aircraft.

GE Aerospace and Beta also announced the two companies will collaborate to develop an additional offering for the AAM industry but offered no additional details.

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Last month Nvidia launched it’s powerful new AI and robotics developer kit Nvidia Jetson AGX Thor. The chipmaker says it delivers supercomputer-level AI performance in a compact, power-efficient module that enables robots and machines to run advanced “physical AI” tasks—like perception, decision-making, and control—in real time, directly on the device without relying on the cloud.

It’s powered by the full-stack Nvidia Jetson software platform, which supports any popular AI framework and generative AI model. It is also fully compatible with Nvidia’s software stack from cloud to edge, including Nvidia Isaac for robotics simulation and development, Nvidia Metropolis for vision AI and Holoscan for real-time sensor processing.

Nvidia says it’s a big deal because it solves one of the most significant challenges in robotics: running multi-AI workflows to enable robots to have real-time, intelligent interactions with people and the physical world. Jetson Thor unlocks real-time inference, critical for highly performant physical AI applications spanning humanoid robotics, agriculture and surgical assistance.

Jetson AGX Thor delivers up to 2,070 FP4 TFLOPS of AI compute, includes 128 GB memory, and runs within a 40–130 W power envelope. Built on the Blackwell GPU architecture, the Jetson Thor incorporates 2,560 CUDA cores and 96 fifth-gen Tensor Cores, enabled with technologies like Multi-Instance GPU. The system includes a 14-core Arm Neoverse-V3AE CPU (1 MB L2 cache per core, 16 MB shared L3 cache), paired with 128 GB LPDDR5X memory offering ~273 GB/s bandwidth.

There’s a lot of hype around this particular piece of kit, but Jetson Thor isn’t the only game in town. Other players like Intel’s Habana Gaudi, Qualcomm RB5 platform, or AMD/Xilinx adaptive SoCs also target edge AI, robotics, and autonomous systems.

Here’s a comparison of what’s available currently and where it shines:

Edge AI robotics platform shootout

Nvidia Jetson AGX Thor

Specs & Strengths: Built on Nvidia Blackwell GPU, delivers up to 2,070 FP4 TFLOPS and includes 128 GB LPDDR5X memory—all within a 130 W envelope. That’s a 7.5 times AI compute leap and 3 times better efficiency compared to the previous Jetson Orin line. Equipped with 2,560 CUDA cores, 96 Tensor cores, and a 14-core Arm Neoverse CPU. Features 1 TB onboard NVMe, robust I/O including 100 GbE, and optimized for real-time robotics workloads with support for LLMs and generative physical AI.

Use Cases & Reception: Early pilots and evaluations are taking place at several companies, including Amazon Robotics, Boston Dynamics, Meta, Caterpillar, with pilots from John Deere and OpenAI.

Qualcomm Robotics RB5 Platform

Specs & Strengths: Powered by the QRB5165 SoC, combines Octa-core Kryo 585 CPU, Adreno 650 GPU, Hexagon Tensor Accelerator delivering 15 TOPS, along with multiple DSPs and an advanced Spectra 480 ISP capable of handling up to seven concurrent cameras and 8K video. Connectivity is a standout—integrated 5G, Wi-Fi 6, and Bluetooth 5.1 for remote, low-latency operations. Built for security with Secure Processing Unit, cryptographic support, secure boot, and FIPS certification.

Use Cases & Development Support: Ideal for robotics use cases like SLAM, autonomy, and AI inferencing in robotics and drones. Supports Linux, Ubuntu, and ROS 2.0 with rich SDKs for vision, AI, and robotics development.

(Read more about the Qualcom Robotics RB5 platform on Robot Report)

AMD Adaptive SoCs and FPGA Accelerators

Key Capabilities: AMD’s AI Engine ML (AIE-ML) architecture provides significantly higher TOPS per watt by optimizing for INT8 and bfloat16 workloads.

Innovation Highlight: Academic projects like EdgeLLM showcase CPU–FPGA architectures (using AMD/Xilinx VCU128) outperforming GPUs in LLM tasks—achieving 1.7 times higher throughput and 7.4 times better energy efficiency than NVIDIA’s A100.

Drawbacks: Powerful but requires specialized development and lacks an integrated robotics platform and ecosystem.

The Intel Habana Gaudi is more common in data centers for training and is less prevalent in embedded robotics due to form factor limitations.

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Digital transformation—the integration of digital technology into all areas of business—has revolutionized how manufacturing companies manage this task. By leveraging technologies such as IoT, ERP, PLM, RFID, blockchain, digital twins, and AI-driven analytics, manufacturers can gain comprehensive visibility into the lifecycle of every material and asset in their operations.

Lifecycle tracking definitions and objectives

For this discussion, the term material lifecycle includes all stages from procurement, receiving, inventory management, production usage, waste or recycling, and compliance documentation. Whereas the equipment lifecycle involves procurement, installation, usage, maintenance, inspection, upgrades, and decommissioning.

The desired outcomes from tracking material and equipment haven’t changed, only the way we track them. Reducing downtime and waste, improving traceability and compliance, optimizing resource use and enhancing forecasting and decision-making are all still the important goals. With the right mix of digital technologies, making correct decision to reach these goals will be a little easier.

Core technologies driving digital lifecycle tracking

Enterprise Resource Planning (ERP) Systems: ERP systems centralize and standardize data related to procurement, inventory, production, maintenance, and finance. They act as the backbone for lifecycle data management. An ERP suite will handle all sorts of tasks, including bill of materials (BOM) management; work order tracking; asset management; and integration with procurement and supply chain functions.

Product Lifecycle Management (PLM) Systems: PLM systems centralize and standardize data related to product design, development, engineering changes, and compliance. They act as the backbone for managing product information across its lifecycle. A PLM suite will handle all sorts of tasks, including CAD data management; version and change control; bill of materials (BOM) structuring; and integration with engineering, manufacturing, and quality processes.

Internet of Things (IoT): IoT sensors embedded in equipment or in the factory environment provide real-time telemetry data, such as temperature, vibration, pressure, and operating time. These sensors monitor equipment health and usage; ensure proper storage conditions for sensitive materials; and help automate maintenance schedules. Edge computing (the data processing near machines for faster decisions, reduced latency, and improved efficiency) enables pre-processing this data by the device/sensor to reduce latency and bandwidth costs.

RFID and Barcode Tracking: RFID tags and 1D/2D barcodes allow automated identification and tracking of materials and equipment across facilities. This tech can track real-time inventory updates; automate check-in/check-out systems; and audit trails for material handling. RFID is particularly beneficial for high-value or mobile assets, reducing human error and labor costs.

Digital twins: A digital twin is a virtual representation of a physical asset or process. It uses real-time data to simulate, monitor, and analyze the condition and behavior of the asset. This technology is currently being used for predictive maintenance; root cause analysis; and equipment lifecycle visualization. Digital twins integrate with IoT platforms, ERP, PLM and CAD systems, creating a multi-source feedback loop for continual improvement.

AI and Analytics Platforms: Machine learning models analyze lifecycle data to predict equipment failure, optimize material usage, and improve production planning. These aren’t new, per se, but are now being applied to all sorts of situations in manufacturing companies, such as anomaly detection in sensor data; forecasting inventory needs; and identifying underperforming assets, equipment or suppliers. AI powered analytics platforms often integrate with ERP or MES (Manufacturing Execution Systems) to generate actionable insights.

Lifecycle tracking workflows

Material lifecycle tracking begins at procurement, where ERP systems automatically generate purchase orders based on demand forecasts. Upon delivery, RFID tags or barcodes on materials are scanned and matched against purchase orders. Relevant data—such as supplier, batch number, and date—is logged into the system for traceability. In the storage and inventory phase, IoT sensors monitor warehouse conditions, triggering automated alerts if environmental parameters like temperature or humidity deviate from set thresholds. Materials are organized based on criteria such as shelf life, usage priority or regulatory guidelines.

During production, materials are scanned into batches, creating a digital link between raw materials and finished goods for full traceability. Waste generated is tracked and categorized (e.g., recyclable, hazardous) to support sustainability goals. After production, unused materials are either returned to inventory or flagged for disposal. All associated data is stored in the ERP system and, optionally, on blockchain networks for enhanced auditability and compliance.

Equipment lifecycle tracking follows a similar digital framework. Upon procurement, equipment records are entered into the ERP or an asset management system, and a digital twin is initialized using the equipment’s baseline configuration. During use, IoT sensors continuously collect operational data, which is analyzed using machine learning to detect early signs of wear, anomalies, or potential failures. This enables predictive maintenance strategies, with the ERP or CMMS (Computerized Maintenance Management System) automatically generating and assigning work orders. Maintenance history is logged and linked to each asset’s digital twin for a comprehensive performance record.

At the end of an asset’s useful life, the system flags it for decommissioning when performance drops beyond acceptable levels. Relevant disposal or recycling data is recorded for regulatory compliance, and the asset is removed from active digital systems.

Integration and interoperability across these systems are crucial. Manufacturers often use middleware or integration platforms—such as MuleSoft or Apache Kafka—to link ERP systems with MES (Manufacturing Execution Systems), IoT platforms, and other operational tools. Interfacing RFID/barcode systems with inventory software and connecting digital twins to PLM (Product Lifecycle Management) tools ensure a unified data ecosystem. APIs, data lakes, and standardized data formats like OPC UA, JSON, and XML facilitate seamless, consistent data exchange.

Security and data governance are foundational to digital lifecycle tracking. Because these systems manage sensitive operational and supply chain data, robust cybersecurity practices are essential. This includes role-based access control (RBAC), encryption of data at rest and in transit, regular vulnerability assessments, and compliance with international standards such as ISO 27001, NIST, and GDPR. Blockchain technology can further enhance data integrity by creating tamper-resistant audit trails, while cloud platforms (e.g., Azure, AWS, Google Cloud) offer scalable, secure infrastructure for data storage and processing.

The benefits of digital lifecycle tracking span operational, financial, and regulatory domains. Operationally, it reduces downtime through predictive maintenance, improves inventory accuracy, and increases throughput via automation. Financially, it lowers operational costs, reduces waste and overstock, and enhances asset utilization and return on investment. From a regulatory standpoint, it simplifies audits and compliance reporting through end-to-end traceability and standardized documentation practices.

Digital transformation systems have become indispensable tools for tracking the lifecycle of materials and equipment in manufacturing. By integrating ERP, IoT, AI, RFID, and other technologies, manufacturers can gain real-time visibility, improve operational efficiency, and ensure regulatory compliance. As these technologies mature, the next evolution lies in greater automation, AI decision-making, and more resilient supply chains, all driven by data-rich, digitally connected environments.

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The report says North American companies ordered 17,635 robots valued at $1.094 billion in the first six months of 2025. Automotive OEMS led with a 34% year-over-year increase in units ordered. Other top-performing segments included plastics and rubber (+9%) and life sciences/pharma/biomed (+8%).

In Q2, companies ordered 8,571 robots worth $513 million, marking a 9% increase in units compared to Q2 2024. Life sciences/pharma/biomed posted the strongest sector growth in the quarter (+22%), followed by semiconductors/electronics/photonics (+18%) and steady gains in plastics, automotive components, and general industry.

 “It’s not just about efficiency anymore. It’s about building resilience, improving flexibility, and staying competitive in a rapidly changing global market. If these patterns hold, the North American robotics market could outperform 2024 levels by mid-single digit growth rates by the end of the year,” said Alex Shikany, Executive Vice President at A3.

Cobots’ rising influence

Collaborative robots (cobots) accounted for a growing share of the market with 3,085 units ordered in the first half of 2025, valued at $114 million. In Q2, cobots made up 23.7% of all units and 14.7% of revenue. These systems work safely alongside humans and address automation needs in space- or labor-constrained environments. A3 began tracking cobots as a distinct category in Q1 2025 and will expand future reporting to include growth trends by sector.

Automotive versus non-automotive sectors

The non-automotive sector took the lead over automotive in Q2, accounting for 56% of total units ordered. This move reflects the expanding role of automation in industries such as life sciences, electronics, and other non-automotive manufacturing sectors.

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The answer increasingly lies in digital transformation systems — specifically, in the deployment of remote monitoring technologies that turn real-time data into actionable sustainability improvements. From energy and water efficiency to predictive maintenance and emissions tracking, these technologies are reshaping how manufacturers optimize resource use and reduce their environmental footprint.

Indeed, the path to sustainable manufacturing runs through data — and remote monitoring is the bridge.

What is remote monitoring?

Remote monitoring involves using Internet of Things (IoT) sensors, embedded systems, and cloud platforms to continuously collect and analyze data from equipment, utilities, and environmental systems across a facility. This data is centralized through manufacturing execution systems (MES), enterprise resource planning (ERP) software, or dedicated building management systems (BMS).

Instead of relying on manual checks, logbooks, or periodic audits, engineers and facility managers get real-time visibility into performance metrics — allowing them to make faster, more informed decisions that directly impact sustainability.

Energy efficiency through real-time monitoring

Energy use is one of the biggest drivers of cost and carbon emissions in manufacturing. Remote monitoring enables a granular view of energy consumption across assets and zones, revealing exactly where, when, and how energy is being used or wasted.

Smart meters and sub-meters connected to a centralized dashboard can identify all sorts of conditions on the shop floor and beyond, including:

• Idle equipment that’s consuming power during off-hours
• HVAC systems operating outside of optimal temperature ranges
• Lighting systems left on in unoccupied zones
• Peak load times where demand charges can be minimized

By linking this data with control systems, manufacturers can automate load balancing, schedule equipment operations, and even initiate demand-response actions in coordination with utility providers. This reduces both energy costs and greenhouse gas emissions.

Water conservation and waste reduction

Water plays a crucial role in many manufacturing processes — from cooling and cleaning to production itself. However, leaks, inefficiencies, and overuse are common and costly. Remote monitoring helps tackle this by using flow sensors, pressure gauges, and smart valves to track water use in real time. Cooling systems can be optimized to reduce unnecessary water cycling and smart alerts can be triggered by unexpected consumption spikes, pointing to leaks or process failures. Usage trends can be analyzed to adjust cleaning cycles or reuse treated wastewater.

In plants with on-site wastewater treatment, remote monitoring can ensure compliance with discharge limits and optimize treatment operations, minimizing environmental impact while reducing chemical and energy usage.

Predictive maintenance and asset efficiency

We’ve covered this a lot in this series—for a reason. One of the most effective ways to reduce waste and energy consumption is to keep machinery operating at peak efficiency. With remote condition monitoring, engineers can track vibration, temperature, current draw, and operational hours of key equipment in real time.

Environmental monitoring and emissions tracking

Modern manufacturing operations are under pressure to reduce air emissions, particulate output, and volatile organic compounds (VOCs). Remote monitoring plays a vital role in tracking these metrics through ambient sensors, gas analyzers, and stack monitors connected to cloud systems.

These systems provide continuous emissions reporting for regulatory compliance and early warnings when emissions approach critical thresholds. They also maintain historical data used for environmental, social, and governance reporting.

This not only keeps operations within legal bounds but also supports a proactive approach to pollution prevention, enabling facilities to fine-tune combustion systems or ventilation processes based on real-time feedback.

As more facilities adopt on-site renewable energy—be it solar, wind, or combined heat and power (CHP)—managing the variability and integration of these sources becomes essential. Remote monitoring allows for dynamic balancing of solar generation output versus real-time load, battery storage availability and grid draw during peak versus off-peak hours.

This maximizes the use of clean energy, reduces fossil fuel dependency, and lowers emissions associated with energy use. In some cases, surplus energy can be fed back into the grid or redirected to storage systems, enhancing sustainability while reducing operating costs.

Digital twins and process optimization

Beyond monitoring individual systems, the technology and processes involved in digitization and digitalization (which combine to form the basis of digital transformation) enable the creation and application of digital twins of production lines or facilities. By integrating real-time monitoring data into these simulations, engineers can model energy and resource usage under different production scenarios and test any process changes before implementing them physically. This can identify optimal settings for production that also reduce scrap, cycle time, or energy used per unit produced

This capability is powerful for continuous improvement and sustainability planning, allowing facilities to adapt quickly to new customers or product mixes.

The engineering advantage

For manufacturing engineers, the integration of remote monitoring technologies into digital transformation strategies isn’t just a sustainability move — it’s a smarter way to run a business. These systems deliver granular, real-time insights that enable better decisions, faster response, and long-term efficiency.

As sustainability becomes more tightly linked to profitability, risk management, and brand reputation, engineers who understand and embrace these technologies will be best positioned to lead their organizations into a more resource-efficient and environmentally responsible future.

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