For some, it represents the pinnacle of revelry: it’s where you go for birthdays, bachelor/bachelorette parties, or big celebratory weekends. The food and drink are plentiful and varied. You can’t walk more than a hundred feet without passing a slot machine. Opportunities for indulgence abound. The whole sentiment behind that well worn phrase which begins, “What happens in Vegas…” is an invective to cut loose and succumb to your most debaucherous impulses. It’s basically Bacchanalia 24/7.

Or it’s where you go for industry events.

At this point, I’ve been to Sin City more times than I care to count and I’ve yet to visit for the first reason. Whenever I’m asked if I’m travelling “For business or pleasure?” my answer has always been the former. This year, I came for Hexagon LIVE: one among the hundreds (thousands?) of attendees hosted at the Fontainebleau, the newest hotel on the strip. There were plenty of familiar faces and the usual trade show trappings, but there were also some big surprises and, of course, the food was amazing.

Here’s what I saw.

The world’s most sophisticated measuring tape

The Fontainebleau is a modern casino seeking to evoke the spirit of Old Las Vegas. Black and white photos of icons like Elvis and Sinatra line the walls. The opening night reception took place against a backdrop of live jazz, power bowls, and white-gloved hands holding glasses of champagne through a fake plastic hedge at the back of the room.

Packs of attendees with matching lanyards and matching looks of confusion streamed along labyrinthine marble corridors, trying to find their way to the reception. More than once over the course of the week, I heard someone in the elevator ask if we really had to traverse the casino to get between the hotel and the conference center.

I assume it was their first time in Vegas.

There’s a certain irony in the juxtaposition of a company that prides itself on precision hosting an event in a city so devoted to excess, but the spectacle of the venue ultimately accorded well with the vision presented in the opening keynote.

Ola Rollén, Hexagon’s former president and CEO and current chairman of its board of directors, took to the stage with his usual brand of laidback charisma, weaving a narrative that mixed the history of measurement – from the cubit and the furlong to the mile and the meter – with the history of his company, starting with its acquisition of Brown & Sharpe in 2000.

He presented the now customary metaphor of building a bridge between digital and physical worlds, buttressed by the idea that Hexagon (or more specifically, what it makes) is “the world’s most sophisticated measuring tape” with examples of coordinate measuring machines (CMMs) that boast sub-micron accuracy and satellite-based systems that can map geographic features from orbit to within a few centimeters.

All of this, while certainly technically impressive, was basically table stakes. But then Rollén made two announcements that I think most of us weren’t expecting.

Octave & AEON

The first was the introduction of a new spin-off built from some of the biggest pieces of Hexagon’s software business. Dubbed Octave, the new company will combine Hexagon’s Asset Lifecycle Intelligence and Safety Infrastructure and Geospatial divisions with ETQ and Bricsys.

The result will be a billion-dollar “start-up” (a sort of pre-fabricated unicorn) with approximately 7,200 employees.

At a time when consolidation is running rampant in virtually every sector, this divestment might seem unusual. Nevertheless, according to Octave’s new CEO, Mattias Stenberg, it’s the right move to make.

“Hexagon is an amazing company,” he said in a press conference. “But it’s also turned into quite a wide monster. So, I think what I and the board and several others have felt over the last couple of years is that it would be a benefit to focus like this. My message to customers is that we’ll have a bigger budget and more autonomy in deciding where to invest.”

The formation of Octave will have an impact in the near term but the second (and arguably more dramatic) announcement was made with an eye to the future. With a theatrical flare, Rollén introduced the world to AEON, Hexagon’s own entry into the rapidly expanding population of humanoid robots.

While it may seem an odd choice for Hexagon to get into the robotics game, the move fits with the general enthusiasm for AI that seems to be gripping the industrial tech world. Moreover, Hexagon’s particular expertise in advanced sensor technology – one of the prerequisites for humanoid robotics in particular – makes the company well-positioned to develop a robot of its own, at least according to Rollén.

“Hexagon’s legacy in precision measurement and sensor technologies has always been about enabling next-generation autonomy,” he said. “Hexagon is one of the best-placed companies in the world to lead and shape the field of humanoid robotics.”

It’s hard to say this early on whether AEON will end up going beyond the few pilot projects announced with Schaeffler and Pilatus, but I will note that Rollén’s attempt to shake AEON’s hand as he left the stage was entirely unacknowledged by the machine, suggesting that it’s still a far cry from being able to operate independently.

Unless it was a deliberate slight, in which case we should all be worried.

State of manufacturing

Along with new product announcements and customer use cases, Hexagon LIVE included data from not one but two major surveys from the manufacturing sector. A global survey focused on executive perspectives while a second targeted the US and included insights from entry-level employees as well as management.

The global survey, entitled Advanced Manufacturing Report and conducted by Forrester, includes responses from 1,000 manufacturing executives. The US survey, conducted by Hexagon’s Manufacturing Intelligence division, is not yet available to the general public, but engineering.com got a sneak preview of the results as part of our attendance at Hexagon LIVE. Given that exclusivity, let’s focus on the results of the latter report.

Recent discussions about the prospects for U.S. manufacturing tend to concentrate on two main challenges the sector is facing: tariffs and talent shortages. While the former is a (relatively) novel issue, the latter has been under discussion for decades, going at least as far back as 1998. That’s when the National Skill Standards Board (NSSB) and the National Association of Manufacturers (NAM) began expressing concerns about a skills gap, with NAM stating that nine of its ten member associations were unable to find enough skilled workers to meet their needs.

But here’s one challenge for manufacturing that you might not expect to top the list: outdated technology. Nevertheless, that was one of the major findings of the US report, with 72% of respondents stating that outdated technology is preventing them from attracting and retaining workers. If that’s right, it implies that the underlying cause of the skills gap in manufacturing might not be misperception of the sector – as is often speculated – but a lack of new technology.

Indeed, other results from the survey appear to support this conclusion, with 60% of respondents reporting that they’re doing enough to make the sector more appealing to new talent. However, there’s also a notable disconnect between executives and entry-level employees regarding the question of whether or not the perception of manufacturing is improving: 86% of executives say it is, while only 59% of entry-level employees agree.

What explains this apparent disagreement?

According to Stephen Graham, executive vice president and general manager of Nexus, Hexagon’s digital reality platform, the cause may be due to a discontinuity between generations.

“I’m not aware of us doing a survey back in 1998,” he said. “But I’ll bet that the perception [of manufacturing] has gotten a lot worse since then because now we have Gen Z coming in, and they’re used to using social media to collaborate on everything. Most manufacturing organizations don’t have technologies that are anything like that.”

Paul Rogers, Hexagon’s president and CEO for the Americas and Asia Pacific, echoed that sentiment in a press event discussing the survey.

“My kids are Gen Z,” he said, “and when you think of what they would identify with manufacturing, it’s dark places with sparks flying, dirty coveralls, things of that nature. But what they’re really expecting is a fully digital environment where everything is high-tech and automated. So, we have to change the perception for Gen Z but, more importantly, we have to change the perspective of the existing workforce and retrain them to think more digital.”

Rogers went on to contrast the user experience with industrial technology with that of consumer tech, where the latter tends to be much more sophisticated and, more importantly, intuitive. “I’ve talked to some major customers and they’re indicating that what they need is for someone to walk in off the street and be ready to go in a few hours,” he said.

Ultimately, this suggests that manufacturing’s perception problem and its technology deficits are interrelated. If that’s right, then both challenges will need to be addressed to deal with the skills gap. The adoption of new, more intuitive tech could help improve the perception of manufacturing – particularly for Gen Z – while more Gen Z members entering the manufacturing workforce could help accelerate that adoption in turn.

Hexagon LIVE 2025

There’s much more to cover from this year’s event, including some incredible stories involving additive manufacturing (stay tuned for those). But, as I headed home, I found myself looking ahead to wonder what next year’s Hexagon LIVE will look like.

Will those white-gloved hands holding champagne be replaced by AEON robots?

Will Octave and its components still be part of The Zone show floor or will the new company have its own event?

Perhaps, most of all, I wondered this: How do you top the announcements of a billion-dollar spin off and a humanoid robot in the same keynote?

I guess I’ll have to wait until next year to find out.

The post Las Vegas Fontainebleaus: A recap of Hexagon LIVE 2025 appeared first on Engineering.com.

Well-defined digital transformation project deliverables lead to project success. Vaguely defined or missing project deliverables create a risk of missed expectations, delays and cost overruns.

These are the significant project deliverables essential to every digital transformation project plan:

1. Project charter
2. Data analytics and visualization strategy
3. Generative AI strategy
4. Data conversion strategy
5. Data profiling strategy
6. Data integration strategy
7. Data conversion testing strategy
8. Data quality strategy
9. Risk Assessment
10. Change management plan
11. Data conversion reports

These deliverables merit more attention for digital transformation projects than routine systems projects. We’ll examine the first five deliverables in this article and the remaining deliverables in a follow-on article.

Project charter

Digital transformation projects begin with engineers writing a project charter, which describes the project. The project charter deliverable contributes to digital transformation success by providing the following:

• A tangible document that the project team can use to socialize the project description among stakeholders to build alignment and commitment.
• Clarity on the project goal, usually a future state vision for the business, and related scope.
• A list of data sources that the project will transform and integrate.
• An initial estimate of the required project resources to achieve the goal.
• A summary business case.
• A conceptual project management plan.
• A list of risks and assumptions, with many focused on data issues.

The value of a comprehensive project charter lies in its ability to enable the project team and the stakeholder community to reach a consensus on the key elements of the digital transformation project. Managing digital transformation scope contains ideas engineers can use to write their project charter.

Data analytics and visualization strategy

The data analytics and visualization strategy deliverable is foundational to delivering the expected value from the digital transformation, as it outlines how the organization will measure the project results through data queries and visualization. It defines the following:

• Recommended tools for reporting, data analytics and visualization.
• Plan for the use of a data lakehouse or a data warehouse, if required.
• Planned views for datastores. Views significantly simplify query coding.
• The list of calculated values that will be persisted. These values ensure consistent reporting and speed query performance.
• Support infrastructure for engineers and other end-users who will be developing queries for data analytics and visualization.

Generative AI strategy

Many organizations now expect digital transformation projects to include some use of generative AI. Engineers can respond to this expectation by formulating an AI strategy deliverable for the project. It defines the following:

• Key goals for the use of AI such as improving customer experience or boosting employee productivity.
• Selection criteria for an AI solution. Read Making Smarter AI choices for more details about the selection criteria for an AI solution.
• Recommendation for a Software-as-a-Service (SaaS) or an On-premises AI solution.
• Risk assessment for an AI solution that includes skills and vendor risks. Read How engineers can mitigate AI risks in digital transformation for more details about common AI risks and mitigations.

Data conversion strategy

Not surprisingly, digital transformation projects focus considerable attention on data. These projects are crucially dependent on successfully converting data from multiple sources.

The data conversion strategy deliverable describes the following:

• The list of in-scope internal data sources is likely a combination of structured digital, unstructured digital, and paper.
• The list of in-scope external data sources is almost always structured digital data.
• The data conversion approach that will be used for each data source. The alternatives are automated, mostly automated with some manual augmentation or manual data entry.
• A preliminary indication of how much data transformation will be required for each data source. Data transformation, not to be confused with digital transformation, is a measure of the amount of data restructuring needed between the source and target datastore. The measure affects software development efforts.
• A preliminary indication of how much data quality improvement will be required for each data source.

The contents of the data conversion strategy deliverable provide the organization with a realistic sense of the effort and elapsed time that data conversion will require.

Data profiling strategy

The data profiling strategy outlines how the project will assess the actual level of data quality for specific data elements in each data source. It’s typically impossible and unnecessary to profile all the data elements. The minimum should be the keys and foreign keys.

The results of the data profiling strategy deliverable will inform the following:

• The data conversion approach that will be used for each data source.
• The estimated effort required to bring the data quality in each data source up to the expected data quality level.
• The specific subject matter experts (SMEs) that will be needed to participate in data quality improvement.

The data profiling strategy will help the organization achieve a consensus on the approach, ensuring sufficient data quality across the datastores.

Paying detailed attention to these digital transformation project deliverables will ensure the success of your project.

The post 5 project deliverables that drive digital transformation success appeared first on Engineering.com.

You’re likely feeling pressure from every direction right now. Customers increasingly expect mass customization, which means you’re being pushed to deliver more personalized products, faster. And if you’re in a fast-paced industry like consumer electronics, you’re also under pressure to keep up with shrinking development cycles, where companies are setting the standard with yearly product launches.

At the same time, you’re navigating serious internal challenges. Volatility in the price of core components like steel and semiconductors is squeezing margins and making it harder to plan. And even when you can source materials, finding and keeping skilled labor is a constant battle. You’re likely balancing investments in automation, process optimization, and workforce upskilling—just to keep production moving.

All of this is unfolding against a backdrop of global instability, from supply chain disruptions to inflation and rising interest rates. Staying competitive now requires real control and visibility across your operations. But if you’re still relying on disconnected systems—ERP, spreadsheets, paper, or a patchwork of point solutions—it’s going to be nearly impossible to achieve the outcomes you’re being asked to deliver.

“With current geopolitical events, it’s becoming increasingly necessary for manufacturers to integrate their supply chain with their manufacturing so they can better react to market conditions and new regulations,” says Niranjan Kalele, Director of MES and APS Engineering at GoEngineer.

The challenges are even more difficult to manage when planning and execution operate in silos. Traditional planning systems assume infinite capacity and don’t take into account real-world constraints—such as a machine being down, or material shortages—often rendering the plan unachievable by the time it reaches the floor.

“Not having planning aligned with execution causes chaos in trying to meet production orders and schedules, especially when coordinating across multiple plants and geographies,” says Darren Kline, Director of Manufacturing Solutions for GoEngineer.

To stay flexible and cost-effective within this kind of environment, manufacturers need a unified solution. DELMIA offers an integrated approach that connects planning, scheduling and execution within a single system.

The problem with disconnected tools

For many manufacturers, planning still happens in the comfort zone of familiar tools such as Excel and ERP systems. However, this means building a custom scheduling and optimization tool from the ground up.

“You’re essentially recreating something that already exists on the market, and that tool is completely disconnected from what’s actually happening around your plant,” says Kline. “When things change, your model has to be manually updated, so you’re constantly behind in visibility.”

“Most ERP planning systems generate orders based on demand and push them to the shop floor regardless of whether it’s actually capable of manufacturing those quantities or meeting the deadlines,” adds Kalele. “You need a way to optimize without incurring extra costs like overtime or outsourcing, while still trying to meet demand. Creating that optimal schedule is often beyond human comprehension and requires specialized software.”

The DELMIA solution

DELMIA, part of the Dassault Systèmes portfolio, addresses these issues by reacting to real-time events on the shop floor. Two of its core scheduling products—Ortems and Quintiq—work at different levels to provide a more responsive view of manufacturing operations.

Ortems functions at the plant level to build production schedules based on constraints such as labor availability, material supply, and machine capacity. Teams can run what-if scenarios to come up with the best achievable schedule, which can then be pushed to ERP systems and pulled into MES for execution.

Quintiq operates at a broader, interplant level. “If you have an internal supply chain, where one facility produces products that become raw materials for the next, Quintiq helps optimize the entire production schedule across facilities so no plant is left waiting for materials,” says Kalele.

“It also handles multi-year sales and operations planning and logistics planning—managing and optimizing shipments across networks, whether by train, boat, or other methods,” adds Kline.

On the execution side, DELMIA Apriso serves as a Manufacturing Operations Management (MOM) platform. It touches all the operations on the plant floor, from receiving raw materials to shipping finished goods—with these activities residing on a single database through a unified data model. Users can configure business rules with Apriso’s built-in Process Builder tool.

Apriso extends beyond traditional MES functions by encompassing five components: production execution, quality management, warehouse and materials management, maintenance management, and time and labor management. “Customers can pick and choose which components to begin with,” says Kline. “It can start at a single-plant level and scale up to support global operations. Its Center of Excellence environment allows you to build a manufacturing operations template, deploy it across all locations, and tweak those templates for the unique aspects of each operation, while still managing everything centrally. That’s the best of both worlds.”

What distinguishes DELMIA from other platforms is its integration with Dassault Systèmes’ 3DEXPERIENCE environment. By sharing a common data model, DELMIA enables a natural flow of information and closed-loop operations across product engineering, manufacturing planning, and execution—allowing manufacturers to easily react to changes.

“If an engineering change is initiated based on a shop floor experience, that change can flow through the entire process—from engineering to manufacturing engineering and back to the shop floor—in a seamless way,” explains Kalele. “This digital continuity sets DELMIA apart in the marketplace.”

The benefits of a connected system like DELMIA show up in measurable ways. On the planning side, manufacturers report improvements in on-time delivery, throughput, and lead times. Inventory levels are easier to manage, and planners save hours by running optimized scenarios that would otherwise take a full day in spreadsheets.

From the MOM standpoint, Apriso helps reduce scrap, rework, and work-in-progress by ensuring operators have up-to-date instructions and only see tasks they’re qualified to perform. The system also cuts paper use significantly and streamlines the process of onboarding new operations.

“With DELMIA, you gain instant visibility into plant floor operations,” says Kline. “Once you can measure what’s happening, the system enables you to build improvements into your operational processes—so the next time you execute, you’re doing so with updated best practices. It becomes a platform for continuous improvement.”

Watch GoEngineer’s on-demand webinar for an in-depth introduction to modern production planning, beyond Excel and ERP.

To learn more, visit goengineer.com.

The post How to align planning and execution in a time of disruption appeared first on Engineering.com.

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.

The post Data pipelines in manufacturing appeared first on Engineering.com.

What should engineers consider as they select AI solutions to realize AI’s business benefits, often as part of their digital transformation, and exercise some AI oversight?

Companies often digitally transform operations, production or logistics when implementing AI applications. Through advances in generative AI, machine learning, big data analytics, and automation, companies benefit from efficiency, safety, and environmental sustainability.

Most companies will be better off rolling out vendor AI solutions as they become available rather than designing and implementing an entirely new and exceedingly ambitious custom AI application.

Practical AI applications in most industries are not new systems. Instead, AI functionality adds value to existing applications through:

• More confident or accurate results.
• Reduced elapsed time to achieve the results.
• A significant reduction in staff effort to achieve the results.
• Increased scope or scale in factors such as the number of customers, products, inventory items or larger geographic coverage.
• Additional functions that couldn’t be achieved without AI.

More specific and detailed selection criteria are more likely to satisfactorily select an AI solution that will satisfy the scope of the planned AI project. More high-level, general and conceptual selection criteria will make differentiating the available AI software difficult. This situation will lead to selecting AI software based on appearances, such as marketing presentation quality, website appeal or impressions from the AI vendor demo, rather than capability.

The primary selection criteria engineers should consider when selecting AI solutions for their digital transformation projects are these.

Functional requirements

Functionality selection criteria consider the AI software’s fit against the planned digital transformation application’s requirements.

AI application control

When selecting an AI solution, deciding on a Software-as-a-Service (SaaS) or On-premises solution is the first consideration. These two alternatives vary significantly in the amount of control your company will have over the AI application.

A Software-as-a-Service (SaaS) solution offers these advantages:

• Pay-as-you-go on a monthly or annual basis.
• There is no computing infrastructure to operate.
• The AI vendor handles all software and data updates.

An On-premises solution offers these advantages:

• Total control over software, data, performance and application availability.
• Complete data privacy with no opportunity for the AI vendor to use your company’s data for model training or to sell it to others.
• Opportunity to build a smaller, focused, custom AI model for more accurate results, fewer hallucinations and faster performance.

Currently, most companies choose a SaaS AI solution because they don’t have the AI skills and computing infrastructure to build and operate an on-premises AI solution. However, this situation will likely change as companies build AI experience and want to tightly control their AI model and proprietary data for competitive advantage.

Problem fit

Engineers should consider whether the AI software can generate the desired output, typically text, images or audio, that aligns with their specific application requirements. Understanding the capabilities and limitations of proposed AI solutions in relation to the problem at hand is crucial for successful adoption.

Define specific problems or opportunities the AI solution will address as test cases. Perform the test cases for each proposed AI solution to differentiate them.

Accuracy

As engineers assess the accuracy of AI model outputs, they should consider related criteria such as the following:

• Quality of the generated outputs.
• Ability to generalize to different inputs or scenarios.
• Consistency of results.
• Frequency of hallucinations or inaccurate or misleading results.

Specify the desired level of accuracy for the AI solution’s output and test to establish whether or not the candidate AI software can achieve this level.

Non-functional requirements

Non-functional selection criteria consider the AI software’s quality attributes, such as security, usability, and scalability.

Performance

Engineers evaluate the performance of AI software to ensure that it meets their desired response time.

AI software accesses a large language model (LLM) to formulate the required response to prompts. That process can take time. If the desired response time cannot be achieved, the actions to consider include:

• Rerun the test with similar but revised prompts to see if the response time changes materially.
• Rerun the test with another AI software offering.
• Build a smaller LLM that is more focused on the problem space.
• Confirm that there are no network bottlenecks between the workstation and the AI solution.

Scalability

An increasing number of end-users will likely access the AI application over time. Also, the number of prompts a given end-user will issue will increase over time.

To handle that growth, the AI application must scale well. Scalability criteria include:

• Performance remains the same even though the number of prompts increases.
• The quality of outputs does not deteriorate.
• The associated cost increase is acceptable.

Ethical considerations

To responsibly adopt AI solutions, engineers must consider ethical implications. They should consider factors such as:

• Data privacy protection.
• Fairness, as opposed to bias, in the output.
• The risk of potentially harmful or unethical uses.

Ethical considerations can be revealed by testing the candidate AI solutions.

Security and compliance

As engineers assess the various AI solutions, they should evaluate each potential AI solution’s security measures and compliance features.

Vendor criteria

Vendor selection criteria consider the capability and likely performance of the AI solution vendor. Vendor evaluations for AI solutions for digital transformation are risky because:

• Predicting the likelihood of AI vendors being absorbed by a merger or failing is difficult.
• Most vendors are so new that they have little track record with customers.

Vendor expertise

Engineers assess each AI vendor’s experience and capabilities in the relevant AI domain for their planned application.

Vendor support and maintenance

Evaluate each AI vendor’s support and maintenance services, most likely through customer references.

Customization and flexibility

Determine each AI vendor’s willingness and ability to adapt their solution to specific needs. If the evaluation suggests that customization will be required, that AI solution should be dropped from further consideration.

Costs

The cost selection criteria engineers consider when designing AI solutions include licensing, implementation, and operating costs.

License cost

Compare the license cost of the various on-premises AI solutions.

Implementation cost

Compare the implementation cost of the various AI solutions. The implementation cost includes:

• Computing infrastructure upgrades.
• People change management.
• Revisions to business workflows.

If the AI solution uses your company’s proprietary data, there will be significant data preparation costs.

Operating cost

Compare the operating and maintenance costs of the various on-premises AI solutions.

Compare the monthly usage cost of the various SaaS solutions.

Business case

The business case for the alternative AI digital transformation solutions will vary. The easiest choice is always the alternative with the strongest business case based on tangible benefits. However, there are always intangible benefits to consider, such as customer satisfaction or contribution to the strategic plan. The final AI solution recommendation should consider tangible and intangible benefits.

Allocating more effort to select an AI solution with more specific and detailed selection criteria will reduce risk and position the planned AI digital transformation project for success.

The post Making smarter AI choices appeared first on Engineering.com.

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.

The post Universal Robots launches UR Studio appeared first on Engineering.com.

The company says it’s new technology flags defects within seconds and requires no labeled data, vision expertise, or production downtime.

“Traditional inspection systems require you to shut down production during setup, but factories can’t afford that downtime,” said Arye Barnehama, CEO of Elementary. “VisionStream overcomes this challenge by learning while production runs.”

Citing Rockwell’s 2025 Smart Manufacturing Report, which indicates many Artificial intelligence and machine learning (AI/ML) pilots stall in the data management phase, elementary says its new product removes a key obstacle to scaling AI in manufacturing: the operational burden. First-generation AI turns factories into data managers, requiring weeks or months of data collection, review, and tuning, diverting resources and scarce engineering talent from other critical areas.

VisionStream eliminates this by learning through observation. In lab testing, it watched parts move down the line and reaches up to 99.9% accuracy within seconds. It requires no data preparation or line stoppage.

In one test, VisionStream took just 12 seconds to learn what “normal” spark plugs look like and then immediately flagged an electrode defect missed by human experts. The company

The technology was developed with five key capabilities:

• Live Learning – Captures and learns from real production data without staged defects or operator input
• Edge Processing – Runs locally for real-time results while syncing securely to the cloud
• High Accuracy – Detects up to 99.9% of defects, including subtle or unexpected flaws
• Operator Oversight – Incorporates human feedback to improve performance over time
• Universal Integration – Installs with new or existing cameras and connects to PLC, SCADA, MES, ERP, and BI systems

This rapid learning capability enables inspections that were previously impractical, such as complex or rare defects, high-mix production, short run production and urgent quality issues when you don’t have time to implement a brand new quality system.

This deployment speed is made possible by its hybrid architecture. Foundational inspection models train in the cloud, while edge neural networks adapt to each production line. This approach combines broad defect knowledge with on-the-fly learning from live production data.

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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

The post Hexagon spin-off takes shape with ‘Octave’ moniker appeared first on Engineering.com.

If you work on the shop floor or oversee engineering operations, you know the drill: tighter tolerances, faster turnaround times, and growing part complexity. But inspection hasn’t kept pace. Manual tools, older 3D scanners, and workflows that rely heavily on skilled operators are hindering the industry’s progress.

Traditional inspection methods are no longer sufficient. They’re slow, inconsistent, and require too much expert labor. That’s why more teams are switching to automated optical scanning systems paired with collaborative robots, like Revopoint Trackit. These solutions accelerate inspections, provide more reliable data, and scale seamlessly as production demand increases.

Where Traditional Inspection Falls Short

Manual scanning means results can vary from one operator to another. Many systems still require adhesive markers, which add setup time and risk damaging parts. Programming inspection paths often demands a skilled technician, and throughput is limited by human availability and batch size. Furthermore, integrating these traditional tools into production workflows is frequently expensive and complex.

These challenges all add up to create real bottlenecks, delay root cause analysis, and increase the chance that defects slip through. For manufacturers running short runs or high-mix production, these inefficiencies quickly pile up.

What Automation Changes

Automated optical scanning addresses these issues by combining accurate 3D scanners with robotic arms and intelligent software. The inspection process becomes faster and more consistent, requiring far less setup and supervision.

For example, marker-free scanning eliminates the need for adhesive targets, cutting down preparation time and keeping parts clean. Drag-to-tech programming enables operators to physically guide the robot arm to create scan paths, eliminating the need for coding. Scanning paths can also be generated directly from CAD models, reducing programming time while ensuring critical features are covered.

Vision-guided systems automatically adjust to the part’s position, eliminating the need for rigid fixtures. Additionally, blue laser scanning enabled the capture of both broad surfaces and fine details in a single pass. These robot platforms typically deliver repeatability within ±0.03 mm, providing the precision required for tolerance checks and first-article inspection.

Why This Matters

From an operations standpoint, the benefits are clear. Faster inspection results in shorter lead times and improved throughput. With less dependence on highly skilled labor, manufacturers can better manage staffing challenges. Reliable and repeatable data support traceability, compliance, and continuous process improvements. Because these systems are modular, they can often be integrated into existing production lines without costly overhauls. The payoff comes from time saved and a reduction in scrap rework.

QC Automation for the Real World

The push for more efficient, reliable quality control has been a key driver in the development of these automated scanning solutions. William Zhou, CEO of Revopoint, shared insights into the philosophy behind creating one of such systems – Revo. “Our aim was to break down the traditional barriers of complexity and expense that kept automated, high-precision QC out of reach for many manufacturers. We focused on building a system where the entire inspection process, from positioning to scanning and data analysis, could be streamlined, repeatable, and require minimal manual intervention.“

This vision highlights the creation of accessible quality control tools such as the Revopoint Trackit and Vbot 9, designed for modern manufacturing. It combines a compact, high-accuracy optical tracking 3D scanner with a collaborative robot, supporting programming methods like drag-to-teach, CAD-driven paths, and real-time vision guidance.

To explore how these solutions can be applied to your industry and improve your inspection processes, please visit https://revo.ink/Engineering-Trackit

Sponsored by Revopoint Global Inc

The post Reliable Quality Control Without Massive Overheads appeared first on Engineering.com.

20 years ago, many mechanical engineering experts predicted that hydraulics were dead. Rapid advancements in electric linear and rotary actuators promised an oil-less future, with cleaner, quieter and more energy efficient equipment, with lower overall cost of ownership and operating costs.

Despite considerable advancements in electric actuator technology, hydraulics haven’t gone anywhere, and don’t appear to be even close to obsolescence. How did the fluid power industry stand its ground, and even grow, in this high-tech age?

Fluid Power World editor-in-chief Mary Gannon monitors global trends in the industry closely, and she explains why hydraulics are more relevant than ever in conversation with engineering.com’s Jim Anderton.

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