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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Apple has announced a new $100 billion commitment, adding to its U.S. investment that now totals $600 billion over the next four years. Today’s announcement includes the tech giant’s American Manufacturing Program (AMP), which promises more of Apple’s supply chain and advanced manufacturing will take place in the U.S.

The AMP will also incentivize global companies to manufacture even more critical components in the United States.

“This includes new and expanded work with 10 companies across America. They produce components that are used in Apple products sold all over the world, and we’re grateful to the President for his support,” said Apple’s CEO Tim Cook, in a press release.

As part of the program, Apple plans to directly hire 20,000 people in the U.S., mostly focused on R&D, silicon engineering, software development, and AI and machine learning.

Apple American Manufacturing Program

The first AMP partners announced as part of the program include Corning, Coherent, GlobalWafers America (GWA), Applied Materials, Texas Instruments (TI), Samsung, GlobalFoundries, Amkor, and Broadcom.

The American Manufacturing Program will help fund a major expansion of Apple’s long-standing partnership with Corning, bringing advanced smartphone glass production to a factory in Harrodsburg, Kentucky. The two companies will also open a new Apple-Corning Innovation Center in Kentucky.

Apple has also entered into a new multiyear agreement with Coherent, a longstanding partner that produces the VCSEL lasers that enable features including Face ID at Coherent’s Sherman, Texas, facility.

In July, Apple also committed to buying American-made rare earth magnets developed by MP Materials, the only fully integrated rare earth producer in the United States, significantly expanding their flagship Independence facility in Fort Worth, Texas. The two companies will also establish a cutting-edge rare earth recycling line in Mountain Pass, California.

End-to-end American silicon supply chain

Apple says its U.S. silicon supply chain is on track to produce more than 19 billion chips for Apple products in 2025. That includes TSMC in Arizona, which is producing tens of millions of chips for Apple using one of the most advanced process technologies in America.

 “We’re committed to supporting U.S. suppliers involved in every key stage of the chip-making process, from the earliest stages of research and development to final fabrication and packaging,” said Sabih Khan, Apple’s chief operating officer. “We want America to lead in this critical industry, and we’re expanding our efforts to grow a silicon manufacturing ecosystem that will benefit innovators across America.”

Apple is partnering with GlobalWafers America in Sherman, Texas, to produce advanced wafers for use in U.S.-based semiconductor fabs for the first time. American chip fabs like TSMC in Phoenix, Arizona, and Texas Instruments in Sherman, Texas, will use GWA’s 300mm wafers to produce chips for iPhone and iPad devices sold in the U.S. and around the world. GWA uses silicon from U.S. sources, including from Corning’s Hemlock Semiconductor.

Apple is also partnering directly with Applied Materials to boost the production of semiconductor manufacturing equipment in the U.S. The Applied site in Austin, Texas, is a pivotal hub for manufacturing cutting-edge chip equipment.

Another aspect of Apple’s AMP is a new commitment with Texas Instruments (TI), which will support additional tool installations at its facility in Lehi, Utah, and a new facility in Sherman, Texas. These facilities are home to TI’s most advanced process technologies and use American-made chip manufacturing equipment from Applied Materials’ factory in Austin, as well as advanced silicon wafers from GlobalWafers America. Apple is also working with Samsung at its fab in Austin, Texas, to launch an innovative new technology for making chips.

GlobalFoundries and Apple have also entered an agreement to bring more semiconductor manufacturing to the United States, focused on manufacturing cutting-edge wireless technologies and advanced power management, critical technologies that enable longer battery life and enhanced connectivity in Apple devices. The partnership will bring new capabilities, jobs, and technology to the GlobalFoundries semiconductor facility in Malta, New York.

Packaging is the final critical step in manufacturing silicon chips. Apple is investing in Amkor’s new advanced chip packaging and test facility in Arizona and will be its first and customer. This facility will package and test Apple silicon manufactured at the nearby TSMC fab, and create chips used in iPhone devices shipped around the world.

Apple is also working with Broadcom and GlobalFoundries to develop and manufacture additional cellular semiconductor components in the U.S.

New and expanded facilities

Earlier this year, construction began in Houston on the new factory supporting production of advanced Apple servers, and in July, the facility produced its first test unit. The 250,000-square-foot server manufacturing facility is slated to begin mass production in 2026. Previously manufactured outside the U.S., the servers from Houston will play a key role in powering Apple Intelligence and are the foundation of Private Cloud Compute, which combines powerful AI processing with advanced security architecture for AI cloud computing.

In Detroit, registration is now open for the new Apple Manufacturing Academy, which was announced in February and is set to open on August 19. The academy will offer consultations and courses to small and medium-sized business on how they can implement advanced manufacturing and AI into their manufacturing programs.

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After the wildfires burned thousands of acres, destroying homes, infrastructure, and natural habitats, this initiative will deploy the microfactory in Pacific Palisades, California, to build modular structures onsite, offering a glimpse into the future of affordable housing construction.

Watch the video on youtube.

“Together, Cosmic and ABB Robotics are rewriting the rules of construction and disaster recovery,” said Marc Segura, President of ABB Robotics Division. “By integrating our robots and digital twin technologies into Cosmic’s AI-powered mobile microfactory, we’re enabling real-time, precision automation ideal for remote and disaster-affected sites.”

These microfactories integrate ABB’s IRB 6710 robots and RobotStudio digital twin software with Cosmic’s Robotic Workstation Cell and AI-driven Building Information Model (BIM) – an end-to-end platform that handles design, permitting, procurement, robotic fabrication and assembly.

Housed within an on-site microfactory, these systems fabricate custom structural wall panels with millimeter precision just-in-time for assembly at the construction site.

Cosmic uses ABB’s RobotStudio with its AI BIM allowing the entire build process to be simulated and optimized in a digital environment before deployment. Once on location, Cosmic’s AI and computer vision systems work with the robots, making real-time decisions, detecting issues, and ensuring consistent quality.

These homes are built with non-combustible materials, solar and battery backup systems, and water independence through greywater recycling and renewable water generation. Each home exceeds California’s wildfire and energy efficiency codes. By delivering a turnkey experience from permitting to final construction, Cosmic is redefining what’s possible in emergency recovery.

Cosmic says its mobile microfactory reduces construction time by up to 70% and lowers total building costs by approximately 30% compared to conventional methods. Homes can be delivered in just 12 weeks at $550–$700 per square foot, compared to Los Angeles’ typical $800–$1,000 range.

“Our mobile microfactory is fast enough for disaster recovery, efficient enough to drastically lower costs, and smart enough not to compromise on quality,” said Sasha Jokic, Founder and CEO of Cosmic Buildings. “By integrating robotic automation with AI reasoning and on-site deployment, Cosmic achieves construction speeds three times faster than traditional methods, completing projects in as little as three months.”

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New research is showing us how AI is being deployed in the manufacturing sector, and the results are not exactly what you would expect. Tata Consultancy Services (TCS), a global IT consulting firm based in Delhi, in collaboration with Boston-based MIT Sloan Management Review (MIT SMR), say their research shows the role of AI is across enterprise workflows from automatically handling the simple repetitive decisions to improving the entire decision making environment for company leadership.

“This shift is not just about improving processes—it is about empowering people to make better choices and building adaptive, future-ready manufacturing enterprises equipped to thrive in a changing world,” says Anupam Singhal, president of the manufacturing practice at TCS.

The study examines how global organizations are integrating predictive and generative AI to aid decision-making and gain a competitive edge and drew insights from experts and pioneers at manufacturers such as Cummins, Danaher, and Schneider Electric.

The report states AI is moving from a simple advisory role to more of a business architect, improving the quality of options available for decision-making rather than just optimizing processes.

This new paradigm is powered by intelligent choice architectures (ICAs). These are dynamic AI systems that combine generative and predictive AI capabilities to create, refine, and present optimal choices for human decision-makers. In manufacturing, ICAs equip leaders with better choices for driving measurable outputs and outcomes in performance, quality, and innovation.

“ICAs flip the script. They do not just learn from decisions — they learn how to improve the environment in which decisions are made. That’s not analytics, that’s architecture,” said Michael Schrage, MIT Sloan Research Fellow and a co-author of the report.

The research highlights how ICAs address key manufacturing challenges in each of these differing manufacturing environments.

Cummins is exploring how generative AI can simulate extreme scenarios in powertrain design, demonstrating how ICAs can improve resilience and reduce time to market by testing against exponentially more scenarios than can be even conceived by human engineers.

At Schneider Electric, generative and predictive AI models enhance the specificity and reliability of predictive maintenance interventions, reducing uncertainty about when and where to perform maintenance.

Lastly, Danaher is deploying ICAs to transform decision-making across its mergers and acquisitions, product strategy, and innovation roadmaps. This includes supply chain optimization, where advanced analytics can lead to substantial savings.

The study goes on to identify four key imperatives manufacturers should consider when looking to build and enable more intelligent decision environments with ICAs, including:

Identify, curate, and emphasize value-driving data

Perfect data is a myth. What matters is generating better choices with available data. Companies must prioritize the critical data that delivers the most business value, enabling “frugal data cultivation” and accelerating meaningful outcomes.

Design with economic clarity and business purpose

Every ICA initiative must have a clear business purpose and stated, desired outcome. Projects should deliver measurable results, not just chase technological speculation. This, as the authors put it, ensures the “juice is worth the squeeze.”

Orchestrate for intelligence

ICAs must coordinate humans, AI models, and automated workflows to maximize throughput and decision quality. This transforms siloed decisions into integrated intelligence. The report shows evidence of this using anecdotes of Danaher’s “massively better output” and Cummins’s transformation of its federal bid evaluation.

Establish a pervasive presence

ICAs must become part of the everyday flow of work. Cummins demonstrates how connecting design, production, and service functions through ICAs unlocks cross-functional insights and drives operational efficiency. ICAs that exist outside normal workflows fail to deliver sustained value.

“This isn’t AI as co-pilot. This is AI and humans working together as architects to redesign how people perceive, weigh, and act on choices,” said David Kiron, Editorial Director at MIT Sloan Management Review.

Read the full report free of charge on TCS Insights page.

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If you’re a manufacturer only just opening your eyes to the world of industrial data, this article will walk you through what a data pipeline is, how it works, and why it matters.

What is a data pipeline?

A data pipeline is a series of steps or processes used to move data from one place (the source) to another (the destination), often with some form of processing or transformation along the way. Think of it as an automated conveyor belt for information, designed to reliably carry data from machines, sensors, or systems to dashboards, databases, or analytics platforms.

In a manufacturing setting, a data pipeline might start at a temperature sensor on a CNC machine, pass through an edge device or gateway, and end up in a cloud-based dashboard where a plant manager can monitor operations in real time.

To understand how a data pipeline works, it helps to break it down into its basic parts. Data starts at the source. In manufacturing, common sources include sensors, machines, control systems and human inputs. Each source generates raw data such as numbers, states, or measurements that provide insight into how the equipment or process is performing.

Once data is generated, it needs to be collected and moved. This is often done using industrial protocols. OPC UA is a common standard for industrial automation systems. MQTT is a messaging protocol often used for sending data from edge devices. Modbus or Ethernet/IP are industrial stalwarts used to communicate with legacy equipment.

At this stage, edge devices may act as the bridge between your OT (Operational Technology) equipment and your IT infrastructure. Most of the time, this raw data needs to be cleaned, formatted, or enriched before it’s useful. This processing could involve filtering out noise or irrelevant data, averaging values over time, tagging data with machine IDs, timestamps, or batch numbers and detecting anomalies or generating alerts. All of this can occur at the edge, in a local server, or in the cloud, depending on the application.

After processing, data is stored or delivered to its final destination. This could be dashboards for real-time monitoring or databases or data lakes for long-term analysis. Manufacturing Execution Systems (MES) or ERP platforms are a prime destination for almost all manufacturing data and machine learning models will use the data for predictive analytics. The key is that data ends up somewhere it can be digested and acted upon, whether by people or machines.

Why data pipelines matter in manufacturing

Data and connectivity on the shop floor has been a reality for many years, but most of that time cost and complexity of the technology meant it was adopted by only the largest manufacturers. But advances in chip technology, AI and cloud connectivity mean even small manufacturers can implement these powerful technologies. As competition, complexity, and customer expectations grow, so does the need for smaller manufacturers to invest in connected, data-driven operations.

There are key benefits of implementing data pipelines that help manufacturers see quick return on investment, such as real-time visibility of what’s happening the shop floor instead of after a shift ends; noticing warning signs of failure before breakdowns occur; catching defects early using data from sensors and vision systems; monitoring usage patterns and energy requirements, and; tracking every part and process for compliance and recall readiness.

Indeed, even a basic data pipeline can replace clipboard checklists and Excel spreadsheets with automated, actionable insights. You don’t need to employ an army of data scientists to implement much of the current technology—most of it is designed with manufacturers’ deployment needs in mind, and low-code options are growing rapidly.

Start small, think big

If you’re new to data pipelines, the key is to start small. Pick one machine, one sensor, or one metric that matters. Build a basic pipeline that helps you see something you couldn’t before—then grow from there.

As factories become smarter and more connected, manufacturing engineers who understand how to harness data will be at the forefront of process innovation, quality improvement, and operational efficiency. Next time you look at a machine, don’t just see it as a tool, see it as a source of insight waiting to be unlocked.

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UR Studio was showcased at the UR booth at the Automatica trade fair in Munich. The company says it enables integrators to build 1:1 online simulations of their work cell and simulate every key aspect of its setup. Users can test robot movements, simulate reach, speed and workflow, and calculate cycle time.

“Whether you are new to automation or an experienced customer optimizing a complex cell, you want assurance and certainty before making the final decision on your solution,” says Tero Tolonen, Universal Robots’ Chief Product Officer. “With UR Studio, we now provide an intuitive, easy-accessible tool to simulate and visualize the end-user setup and thoroughly test the cell and its capabilities – mapping out details for maximum efficiency and performance.”

UR Studio interacts with UR’s robot portfolio and various components, such as pallets, machines, workpieces and end effectors—including standard grippers often used with UR cobots. Items can be configured to the user’s preferences with the option of importing elements to mimic the workspace. This ensures the final solution fits within the real-world environment and allows for potential issues to be identified early.

Surprisingly, UR Studio is free of charge and runs directly on desktop browsers requiring no installation—simply log into the UR Studio website to get started. Its intuitive interface makes navigation of the simulated environment effortless. It’s preloaded with templates for the most common applications such as machine tending, screwdriving, palletizing and pick-and-place. UR says new application templates will be added continuously.

UR Studio will initially be available in English, but will soon be released in German, Spanish, Chinese (simplified) and Japanese.

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