Manufacturers are under pressure to deliver customized products faster while maintaining high quality and efficient operations. This white paper highlights why modern MES and Manufacturing Operations Management are becoming essential in discrete and complex production environments, and how a connected digital approach can improve productivity, visibility and coordination across the enterprise.

What You’ll Learn

• How discrete manufacturers can overcome common production, business and IT challenges by modernizing their operations.
• How integrating MES with PLM, ERP, automation and quality improves visibility, consistency and decision-making.
• Key capabilities that help enhance quality, reduce rework and maintain full traceability and compliance.
• The way digital workflows support faster engineering changes and more efficient shop floor execution.
• How material flow, operator guidance and work order management can be streamlined for higher throughput.

Your download is sponsored by Siemens.

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Manufacturers are managing greater volumes of data, growing product complexity and increasing pressure to deliver reliably. This white paper explains how closed-loop manufacturing connects digital information with real production activities, helping teams work with clearer visibility, better coordination and more predictable outcomes.

What You’ll Learn

• Why today’s manufacturing environment requires closer coordination between design, planning and production.
• How closed-loop manufacturing aligns as-designed, as-planned and as-built data for more consistent results.
• How the digital twin and digital thread support clearer insights and better decisions.
• The role of Manufacturing Operations Management (MOM) in linking PLM, automation and shop-floor systems.
• Why unified data and real-time feedback help identify issues earlier and improve overall process reliability.

Your download is sponsored by Siemens.

The post Improve Manufacturing Performance With Closed-loop Processes appeared first on Engineering.com.

• Operational applications such as manufacturing planning or control.
• Data analysis and visualizations, including dashboards, for problem analysis and forecasting.
• AI applications for insights into manufacturing troubleshooting, optimization, or innovation.

While every engineer’s implementation is shaped by their business objectives, data source technology, and governance requirements, most pipeline projects share a consistent sequence of steps. An extract, transform, load (ETL) application is another name for a data pipeline. A clear understanding of these steps enables engineers to deliver more predictable outcomes, better maintainability, and smoother scaling as data volumes and use cases expand.

1. Project organization

A data pipeline project, like all projects, will be more successful with a reasonable amount of initial planning. The likely steps include:

• Identifying an executive champion with a relevant data problem.
• Writing a project charter that describes the project, including resources, likely budget and approximate schedule.
• Seeking approval for the project charter from the executive champion and the steering committee.
• Assembling a project team that includes multiple disciplines.

2. Requirements definition

Successful data pipelines begin with a detailed project requirements definition that articulates:

• Business objectives that focus on expected analytics and insights.
• The list of data sources. A simple data pipeline will have fewer than 5 data sources.
• The list of data consumers. It often includes engineers, business analysts, and data scientists.
• The list of consuming applications, data visualizations, dashboards and reports.
• The expected service level. A frequent expectation is that a data pipeline will be back in operation after an outage within 1 day.
• The acceptable data quality standard.

“When building data pipelines, many organizations fail to include sufficient functionality for data pipeline monitoring,” says Nelson Petracek, formerly the Chief Technology & Product Officer at Board International, a leading business intelligence and performance management software vendor. “Monitoring ensures that the expected data actually arrives where it needs to be in the correct time frame, and with the right level of accuracy. Without monitoring, the organization cannot depend on their data. Lack of confidence eliminates most of the benefits associated with deploying a data pipeline.”

The cost and elapsed time of a data pipeline project are largely proportional to the number of data sources, the complexity of the data transformation and the number of artifacts to be produced.

3. Data source inventory

Teams determine what data the pipeline needs, where it originates, and how it will be used. The data storage technology of the data sources often varies significantly. Examples include databases, files, exports from SaaS applications, real-time records from Industrial Internet of Things (IIoT) devices, and data from external vendors.

When engineers implement data pipelines, they often start by bringing together manufacturing and financial data. Later, they may add real-time IIoT data from the production process, along with performance metrics such as quality, yield, and schedule.

This step sets the foundation for design decisions regarding data transformation logic, latency, and storage choices.

4. Data ingestion and connectivity design

With requirements and a data source inventory in place, attention turns to how the pipeline will bring data into the staging environment. The two connectivity strategies are:

• Batch pipelines run scheduled extracts, often overnight, using connectors supplied by software vendors or API calls.
• Streaming pipelines use message brokers or event capture services to ingest data in near real time.

Building streaming pipelines requires more skill than batch pipelines.

The data ingestion design will consider authentication, in-transit encryption, and the avoidance of data duplication.

5. Source data management

The next step in building a data pipeline is to copy data from all the data sources to the staging environment, often a data lake. Preserving the original data supports auditability, replay, and recovery.

Metadata collection for file sizes, schema definitions, load times, and quality indicators typically occurs during the copy of source data to the data lake.

6. Data profiling and quality assessment

Before transforming the data in the pipeline, the team examines the data from each data source to understand its structure, patterns, completeness, and anomalies. Teams use automated profiling tools to generate statistics on:

• Null distribution. Nulls indicate data quality lapses.
• Mismatches of key values. Mismatches prevent joins required for data transformation.
• Unlikely values for non-key data columns. Unlikely values indicate data quality lapses.
• Frequency of outlier values. Outlier values suggest possible data quality lapses.
• Data type inconsistencies. These prevent comparisons in queries.

These data issues manifest themselves in misleading query results that undermine engineers’ confidence in the associated recommendations.

These assessments identify the required cleansing rules and provide an initial estimate of the effort needed to standardize and clean the data. Establishing data quality expectations early in the pipeline reduces future rework and provides transparency to end-users.

The cost and complexity of data profiling and quality assessment are largely proportional to the number of data columns of interest across all data sources.

7. Data cleansing and standardization design

With data quality insights in hand, the project proceeds to shape the pipeline data for integration and analytics. The design involves defining rules for:

• Recognizing and removing duplicates.
• Harmonizing data types.
• Standardizing data values for keys, reference codes and units of measure.
• Filling in missing values and correcting unlikely values where possible, and reporting them when it’s not.

The design also includes the report for the number of:

• Rows processed.
• Changes made.
• Changes attempted but not made.

This sequence of steps, and the ones in the follow-on article, describes a disciplined, repeatable approach to building robust, scalable pipelines aligned with business needs.

The post Building a simple edge-to-cloud data pipeline – Part 1 appeared first on Engineering.com.

However, as engineers and plant managers race to stand up new facilities and expand domestic capacity, they are colliding with a stubborn reality: the labor market is tight, and the workforce is changing rapidly. The Deloitte & The Manufacturing Institute 2024 Talent Study warns that the U.S. could face a need for 3.8 million new manufacturing employees by 2033—and without significant changes, 1.9 million of those jobs could go unfilled.

To bridge this gap, companies are tapping into a broader, more diverse labor pool. New data from the Bureau of Labor Statistics reveals that foreign-born workers now make up 19.2% of the U.S. labor force. Crucially, this concentration is significantly higher in the industrial sector, with foreign-born workers accounting for over 20% of production and material moving occupations and nearly 30% of construction and extraction roles.

This demographic shift creates a distinct systems engineering challenge on the shop floor. The industry is building the “Factory of the Future” with the most advanced robotics and IIoT sensors available, yet running them with a linguistic fragmentation that legacy communication tools simply cannot solve.

When a frantic safety alert in Spanish is met with a confused stare from an English-speaking supervisor, it becomes dangerous. And when a process improvement idea in Vietnamese never makes it to the plant manager, operational intelligence is lost. For U.S. manufacturing to truly scale, language barriers must no longer be treated as an HR issue, but as an operational constraint requiring a technological solution.

The hidden tax of the language gap

For the practicing engineer, efficiency is a formula. But variable human communication is often the error term in that equation. This operational drag is often invisible until it triggers a catastrophe, creating what is called the “hidden tax” of the modern diverse factory.

It compounds the already staggering cost of inefficiency. A 2024 report by Siemens found that unplanned downtime now costs Fortune Global 500 companies approximately 11% of their annual turnover, totaling nearly $1.5 trillion. In a linguistically fragmented workforce, this “downtime” isn’t always mechanical. Often, it is conversational.

Research from Relay indicates this tax is far higher than most executives realize, discovering that hidden labor costs due to language barriers likely cost the average industrial business $500,000 or more annually. This stems heavily from bilingual employees serving as unofficial translators, spending an average of 4 hours per week translating for colleagues instead of performing their primary roles. This alone costs businesses an average of $7,500 annually per bilingual worker in lost productivity.

The tax manifests in three critical areas:

Safety Latency

In an emergency, seconds matter. OSHA estimates that language barriers contribute to 25% of workplace incidents. This aligns with Relay’s findings, where 64% of respondents believe language barriers negatively impact employee safety at their facility. If a warning has to be mentally translated before it’s shouted, the accident has likely already happened. On a noisy floor, where auditory cues are already compromised, adding a linguistic filter can be fatal.

The Stalled Digital Thread

The industry has invested millions in digital transformation, yet the “last mile” of that data is often human. You cannot fully digitize a workflow if the worker cannot read the screen or understand the voice command. In fact, 86% of manufacturing and warehousing professionals believe their workplace loses productivity due to language barriers, with 42% estimating those losses exceed 5% of total output.

The “Knowledge Trap”

Experienced workers who speak English as a second language often hold deep tribal knowledge about machine quirks and material handling. Without a seamless way to share that, the knowledge remains trapped. It retires when they do, or worse, it creates an artificial ceiling for talent. Relay’s data shows that 48% of respondents agree language barriers reduce promotion opportunities for affected workers, fueling dissatisfaction and turnover.

The universal translator is no longer science fiction

For decades, the standard “solution” was a bilingual shift lead or a game of telephone. Today, Artificial Intelligence has altered the physics of this problem.

As highlighted in Deloitte’s 2025 Manufacturing Industry Outlook, 80% of manufacturing executives plan to invest significantly in smart manufacturing initiatives. The most impactful of these investments will be those that empower the human worker. The sector is moving past the era of static, one-way radios into the age of voice-first operational intelligence.

New advancements in Large Language Models (LLMs) allow for near-instantaneous translation of voice communications on the shop floor. This is distinct from consumer-grade translation apps, which often fail to grasp industrial context. Modern industrial AI is being trained to understand that “the line is down” refers to a production stoppage, not a geometric shape.

Imagine a scenario: A line operator speaks a maintenance request in Spanish into their device regarding a specific hydraulic failure. The floor supervisor hears it in English. The understanding is instant. Instead of searching for a translator, the supervisor dispatches a repair technician immediately, preventing a minor stall from becoming a major downtime event

This creates a truly interchangeable workforce. It allows a plant manager to balance shifts based on skill set rather than language compatibility. It removes the structural barrier for talented workers who may be technically proficient but linguistically isolated, allowing them to rise to leadership roles.

Signal-to-noise: filtering for danger

Furthermore, this technology can also address the “signal-to-noise” ratio that plagues busy engineers. On a standard radio channel, a supervisor hears everything—every request for a pallet, every break check. Eventually, ear fatigue sets in, and critical information is missed.

Modern two way radio platforms can now utilize AI for active cross-channel monitoring, listening for context rather than just rigid keywords. It allows a supervisor to filter out the chatter of a thousand daily radio transmissions and only be alerted when specific, high-risk topics (like “leak,” “break,” “injury,” or “lockout”) are mentioned.

This moves communication from a passive stream of noise to an active safety monitoring system. It empowers every worker on the floor with a direct line to safety protocols without the friction of complex workflows. Instead of hesitating to find the correct channel or recalling a specific alert code, a worker can simply state the issue naturally, trusting the AI to detect the urgency and notify the right team immediately.

Reliability—the prerequisite for inclusion

While AI translation is the software solution, software does not exist in a vacuum. It relies entirely on a hardware reality that is often ignored. You cannot have an inclusive, AI-enabled workforce if the device they are holding is a brick.

The current “Buy, Break, Replace” cycle of legacy hardware disproportionately affects the frontline. When batteries die mid-shift or devices shatter on concrete floors, the worker is silenced. For a non-native speaker, the psychological barrier to communication is already high. If their digital connection fails, they may be unlikely to walk across the factory floor to struggle through a face-to-face conversation in a second language. They may simply guess, or remain silent.

To support a diverse workforce, engineers must demand an “outcome-based” approach to hardware. The mindset needs to shift from buying radios to optimizing for uptime. If facilities guarantee machine uptime, why accept downtime for the human workers operating them?

A diverse workforce requires communication devices that prioritize continuity, ensuring that the tool will honor the work regardless of who is holding it. This means durability standards that match the environment, battery life that outlasts the longest shift, and connectivity that penetrates the deepest parts of the facility. Inclusion is impossible without reliability.

Engineering the human-centric future

The resurgence of American manufacturing will not be defined solely by how many chips companies can produce or how much steel can be poured. It will be defined by how quickly a new generation of workers can be integrated into a cohesive, safe, and efficient unit.

The diversity of the workforce is not a temporary condition; it is the new permanent state of the American industrial base. As the “Silver Tsunami” of retiring boomers recedes, the void is being filled by a dynamic, multicultural coalition of workers.

Reshoring is an invitation to innovate, not just in production methods, but in how the workforce connects. By leveraging AI-enabled, voice-first technologies, the industry can dismantle language barriers and cut through the operational noise. This turns a diverse workforce from a logistical challenge into a safer, more efficient, and fully synchronized asset.

The engineer’s job is to solve problems. The language gap is a big one. Fortunately, for the first time in history, we don’t need a specialized gadget to overcome it. We can now seamlessly embed intelligence directly into the tools the team is already using on the shop floor.

The post Language gaps might threaten the U.S. manufacturing revival appeared first on Engineering.com.

This phase will deploy agentic AI to streamline operational communication, customer experience and employee engagement.

DHL Supply Chain has already successfully deployed HappyRobot’s AI agents across several regions and use cases, including appointment scheduling, driver follow-up calls, and high-priority warehouse coordination.

These agents autonomously handle phone and email interactions, enabling faster, more consistent, and scalable communication.

DHL Supply Chain says it has been identifying and validating operational use cases for generative and agentic AI technologies for the last 18 months.

“Building on our extensive operational experience with data analytics, robotic process automation, and self-learning software tools, we are now integrating AI agents to drive greater process efficiency for customers while making operational roles more engaging and rewarding for employees by automating repetitive and time-consuming tasks such as manual data entry, routine scheduling, and standardized communications,” said Sally Miller, CIO DHL Supply Chain.

Current deployments already in use across DHL Supply Chain target hundreds of thousands of emails and millions of voice minutes annually. AI agents are supporting key workflows such as appointment scheduling, transport status calls, and high-priority warehouse coordination.

Close collaboration between the product and engineering teams and DHL Supply Chain’s technology departments has been crucial to designing new agentic capabilities that reflect the nuances of DHL’s operations, according to Yamil Mateo, HappyRobot’s Head of Product.

“The DHL team understood very early the scale of enablement our platform brings to their organization. They were clear that they wanted a partner with state-of-the-art technology and infrastructure.”

To enable this rollout, the team created a unified AI worker orchestration layer across email, WhatsApp, and SMS, enabling omnichannel capabilities with built-in fault tolerance and recovery. Major reliability improvements in the infrastructure are underway to support the scale and criticality of the operational processes running on the HappyRobot deployment for DHL, said Danny Luo, a senior engineer on the team.

AI agents is the new operating model

These implementations have already shown measurable impact, significantly reducing manual effort, increasing responsiveness, and enabling teams to focus on more strategic tasks and exception handling.

“AI agents help us relieve our teams from repetitive, time consuming tasks and give them space to focus on meaningful, high-value work. That’s not just operational progress—it’s also a win for our people,” said Lindsay Bridges, EVP Human Resources at DHL Supply Chain. “

HappyRobot’s platform enables fully autonomous AI agents to interact via phone, email, and messaging, while integrating seamlessly with DHL’s internal systems. And DHL Group continues to expand its AI strategy across all divisions. Beyond current pilots, further use cases are also being tested.

“At HappyRobot, we envision AI workers coordinating global supply chain operations—not just moving data, but actively managing workflows,” said Pablo Palafox, CEO of HappyRobot. “Too often, people are stuck maintaining systems and inboxes, with little time to solve exceptions or improve processes. DHL recognized early on the potential of AI agents as a new operating layer—one that brings speed, visibility, and consistency to logistics.

The post DHL continues enterprise-wide AI rollout appeared first on Engineering.com.

Doing things the way they have always been done is no longer enough to manage rising operational costs, production process inefficiencies, and a tight labor market. Digitalization and automation are the game changers to navigate these challenges. Manufacturers can bolster their automation systems to guarantee adaptive and flexible production, helping their business stay profitable and competitive. Contrary to popular belief, modernization does not necessarily involve replacing every single piece of old equipment with shiny, new machines. 

Most manufacturers have many legacy plants which operate a diverse pool of machinery. And factories that have successfully incorporated modern automation processes had antiquated equipment which lacked the more advanced capabilities of more modern machines such as IoT connectivity and AI integration. So, where did they start?

Achieving a more automated factory that is ready to face a volatile landscape starts with a plan. With a solid plan, manufacturers can more easily incorporate automation features into production lines. To guarantee harmony between old and new systems, manufacturers should look to the comprehensive Digital Twin. Utilizing the simulation capabilities of the comprehensive Digital Twin makes automation integration seamless and hassle free, enabling a wide range of resource-conserving testing capabilities and bringing agile, resilient operations onto the shop floor. 

Starting small, starting flexibly

Beginning a digital transformation journey leads to growth through improved efficiency and increased profitability. Becoming a digital enterprise has the added benefits of increasing productivity, improving operational efficiency, and reducing costs. It also gives organizations a chance to upskill employees while improving worker job quality and pay. However, while enhancing the versatility of the factory comes with plenty of advantages, the investment may seem intimidating at first glance.

Fortunately, this is where the comprehensive Digital Twin – the foundation of digital transformation—comes in. The comprehensive Digital Twin is a virtual representation of a product throughout its entire lifecycle, from design and production to performance. It ensures end-to-end data continuity between all stakeholders enabling cross-domain collaboration, traceability, and closed-loop feedback.

As well, the production Digital Twin is a key component of the comprehensive Digital Twin and provides a great starting point for brownfields to begin their digital transformation journey. Leveraging the Digital Twin’s simulation capabilities, they can see impressive gains from more modern forms of automation in the digital world to determine what technologies can be introduced without much interruption to their existing processes or before making any significant investments. With the power of the Digital Twin and AI, implementing adaptive automation is a worthwhile venture.

One of the key areas that companies can exploit for driving production efficiencies is with Robots.      Manufacturers that are intimidated by a full upheaval of their factories can instead look to collaborative robots, also known as cobots, or robots that aid human workers during production. Cobots work as a conduit between manual and automated operations and can be easily introduced into a brownfield environment. Using cobots, brownfields can:

• Improve the efficiency and profitability of their production processes through defect detection, data collection, and error reduction
• Alleviate workforce woes by supporting skilled workers and reducing the need for repetitive tasks
• Increase flexibility and adaptability and ultimately lower costs through scalable solutions and easy reprogramming

Cutting costs with virtual commissioning

The question becomes how best to introduce Robots and Cobots in an existing factory? It is crucial to test how and where certain processes will work before integrating Cobots, new machines, or processes onto a more traditional shop floor. The Digital Twin of the factory creates an accurate, real-time virtual representation of their factory that is both comprehensible and accessible for workers across teams and disciplines. Equipped with the near-accurate representations of potential modifications, engineers and designers can explore and then validate prototype systems and production lines to ensure they are both more productive and safer for human workers on the shop floor. 

Virtual commissioning and robot offline programming can significantly reduce the time and effort required to implement the latest automation processes into existing production environments. A Digital Twin empowers manufacturers to make informed decisions in the virtual world before investing in physical builds. A variety of trial runs of potential new systems can be done quickly without having to divert actual equipment from operations. On top of that, simulated verification can be completed sometimes within days, making it possible to have more optimized and flexible systems very quickly. 

Once the manufacturing line is up and running, the Digital Twin can be enriched with ongoing, real-time data from factory operations. Enabling factories to simulate, predict, and optimize performance in real-time helps manufacturers anticipate equipment failures before they happen. This minimizes, or even avoids, costly downtimes as maintenance can be completed before any critical errors occur. 

With the Digital Twin simulating production systems, manufacturers can focus on safely and effectively incorporating these machines into their more manual production lines. Virtual commissioning enables the factory to forecast human interactions with automated systems, improving ergonomic design and safety. By deploying these factors, brownfields have been able to introduce new, state-of-the-art equipment only where needed, quickly and at low cost.

Training robots quicker in virtual classrooms

When working in tandem with AI, the Digital Twin can also accelerate robot training and programming through the industrial metaverse, a virtual space that expands on the physical world which fosters efficiency, productivity, sustainability, and connectivity. The industrial metaverse is still new, but its training capabilities for robots are unparalleled. 

Taking full advantage of the industrial metaverse’s simulation abilities, manufacturers can create immersive settings that perfectly imitate physical factories and production lines. In this virtual classroom, robots can practice tasks, address common challenges, and develop problem-solving skills in just hours, rather than months or even years.

Unlike physical environments where engineers are limited by both time and tangible constraints, simulated environments provide settings with no restrictions, enabling robots to learn to tackle even unanticipated problems. Additionally, since the virtual classroom is digitally constructed, creating training scenarios is quicker and more cost effective than setting them up in the real world. 

Virtual classrooms are not only reducing risk and operational costs but also could potentially prepare robots to create tomorrow’s products. The Digital Twin and industrial metaverse aids deployment of automation systems with dramatically reduced setup times, meanwhile, the enhanced adaptability of AI-powered tools aids the versatility of product lines in response to changing market demands.

Bringing in AI to accelerate towards the future

Once the Digital Twin is well integrated into the factory, AI can supercharge the factory’s operations. Solutions like AI Expert Toolbox can address AI makers as well as AI users. AI makers have dedicated know-how in building AI models. AI Expert Toolbox supports bringing and operating these models on the shop floor in an industry-grade environment.

On the other side for AI users, there will be dedicated services like Citizen-AI, which combine specific AI applications and allow everyone on the shop floor to run AI-based solutions with the help of improved user interfaces, understandable monitoring, automated model retrain, and more. 

Using AI together with the Digital Twin, organizations have crafted techniques that improve robot versatility. Solutions like SIMATIC Robot Pick AI can transform standard industrial robots into complex, agile machines. Pick AI harnesses both AI and Digital Twin capabilities. Trained on synthetic data and through computer visions, Pick AI prepares robots to handle unpredictable tasks with over 98 percent accuracy.

Keeping it cheap and simple

The Digital Twin has helped manufacturers make massive strides in their journeys to accelerate modern automation and enhance resilient, adaptive operations. Using the Digital Twin, factories can gain new insights due to the combination of physics-based simulations with data analytics in a fully virtual environment. This makes it possible to realize innovations faster and more reliable, while also requiring significantly fewer real prototypes.​

Leveraging the simulation abilities of the Digital Twin and AI, adopting innovative automation routines becomes seamless, hassle-free, and accessible while adding significant improvements in efficiency and productivity. Jumpstarting your factory’s digital transformation journey and reaping the rewards of automation is only a click away. 

About the author:

Rahul Garg is Vice President for Industrial Machinery Vertical Software Strategy at Siemens Digital Industries Software.

 As a customer-centric leader, one of Rahul’s great joys is helping simplify complex problems for customers and enabling success by delivering powerful, effective solutions that support small and mid-sized businesses.

Throughout his career having worked at three start-ups and now a large enterprise, Rahul has worked closely with SMBs and in technology-led industries to overcome key challenges and drive revenue growth with strategic solutions, smarter services, and better business practices.

Connect with Rahul

Sponsored Content by Siemens Digital Industries Software

The post From brownfield to smart factory: how to retrofit the past for the future appeared first on Engineering.com.

Headquartered in Ashburn, Virginia, Hanley designs and manufactures energy management and critical power solutions serving the data center infrastructure market.

The deal is expected to close in the first quarter of 2026, subject to customary closing conditions and regulatory approvals.

In June 2025, Jabil announced plans to invest approximately $500 million over the next several years to expand its footprint in the Southeast United States to support cloud and AI data center infrastructure customers.

This deal adds Hanley Energy Group’s extensive expertise in power systems and energy optimization to Jabil’s existing power management solutions for data centers, including low and medium voltage switch gear, PDUs, and UPS systems.

Ed Bailey, Jabil SVP and Chief Technology Officer, Intelligent Infrastructure, said Hanley’s expertise in designing, building, and commissioning turnkey mission-critical power solutions from the grid all the way to the hyperscale data center.

“[This] complements Jabil’s growing capabilities in AI data center infrastructure. With Hanley’s deep technical know-how and comprehensive lifecycle services, including design, consulting, deployment, commissioning, and field support services, we will be even better positioned to deliver secure, reliable, and energy-efficient power solutions to our global customers,” said Bailey.

Matt Crowley, Jabil’s Executive Vice President, Global Business Units, Intelligent Infrastructure, said the deal gives Jabil the capability to not only design and manufacture these solutions, but also to deploy and service them in the data center.

In Jabil’s 2025 year-end report, CEO Mike Dastoor said the company expects revenue of $31.3 billion, core operating margins of 5.6%, and adjusted free cash flow of more than $1.3 billion. Dastoor said the company sees “significant opportunities” ahead in areas such as AI data center infrastructure, healthcare, and advanced warehouse and retail automation, and said Jabil will be “deploying capital in ways that strengthen our capabilities and enhance shareholder returns.”

Based in St. Petersburg, Florida, Jabil operates 30 sites across the United States, combining automation, robotics, and process optimization.

The post Jabil acquires Hanley in $725M data center play appeared first on Engineering.com.

All manufacturing companies must manage an ever-growing mountain of priceless inspection data. Yet measurement results, process iterations, and approval reports are scattered across hard drives and USB sticks. We live in a digital world that advances daily, but obtaining, accessing, sharing, and tracking digital files often feels like digging through an overstuffed file cabinet—hoping to locate what you want without actually knowing if it is what you need.

Now, imagine a streamlined digital data management system where all your 3D measurement files exist in a central secure, searchable hub. Doubts and time-wasting disagreements on whether metrology tasks or reports were already done or not all vanish. This is impossible to accomplish with metrology data scattered across the enterprise on equipment hard drives or USB sticks in various locations.

Add intuitive indexing, search, and filtering tools that seamlessly retrieve data based on part number, serial number, or production line and you can be sure you’re working effectively. Gigabytes of 3D data you’ve already captured are turned into accurate, timely, and secure documentation and reports. All your invaluable inspection data stays organized, up to date, and readily available to engineering and QA teams, indeed to anyone who needs it.

The Manual Past

Surprisingly, many companies still store their 3D measurement data on the hard drives of the computers connected to their devices. This practice creates data silos, consequently amplifying the risk of errors. These measurement files can also quickly achieve gigabyte status. When manually handled, they must first be copied and zipped with retrieval instructions. Each team member, whether working on-site or remotely, must then follow those instructions and copy the file onto their own computer.

This not only creates duplicates, it strips downstream comments and actions from the context set by the inspection data. Catching a mistake or suggesting an improvement means sending it through a different communication channel. And when discussion is key to collaboration and product improvement, disconnecting it from the 3D measurement data compromises its value, delays decision-making, and weakens its overall impact.

The Digital Future, Now

Now envision a manufacturing organization working with multiple suppliers. Digital 3D data management across the enterprise means engineers and QA leads access formerly scattered data instantly. Identifying, defining, and sharing design changes boosts engineering efficiency and helps catch costly design issues early, before they escalate in production phases.

Updating, saving, and later retrieving such valuable 3D measurement data when needed also ensures efficient documentation and preserves data integrity over time. Instead of starting from scratch, teams can build on the latest model iterations as the starting point for future efforts. The ability to track and manage 3D data throughout its entire lifecycle empowers companies to make better decisions fast. Valuable insights can be extracted from the data, leading to enhanced product design, optimized processes, and ultimately, higher-quality outputs.

Advanced 3D data management brings modern digital communication features— including hyperlinks, tagging, and notifications—directly into the process. It also offers a discussion thread within every inspection project to facilitate information exchange between internal and external collaborators, no matter the physical distance.

From design engineering to the shop floor, inspection practices continuously improves, data silos evaporate, and a next-generation manufacturing company emerges.

You measure to know and grow. See what PolyWorks|DataLoop next-generation 3D data management can do for your organization. Schedule a demo today.

Sponsored Content by InnovMetric

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• Advanced materials
• Computer hardware and software
• Connectivity
• Embedded computing
• Fluid power
• Industrial automation
• Mechanical
• Motion control
• Power electronics
• Switches and sensors
• Test and measurement

These categories span four of our company’s engineering brands — Design World, Fluid Power World, EE World, and Engineering.com — and were selected by our independent, expert judging panel. For each category, judges could award Gold, Silver, or Bronze, but note that not all categories necessarily have all three medalists, depending on the scoring.

See the the winners of the 2025 LEAP Awards here!

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Manufacturing is the backbone of many global economies, but supply chain disruptions, market volatility, and increased global unrest have challenged the industry. In product development, the biggest challenges product developers face today in getting products to market lie in manufacturing. A recent Tech Clarity eBook, The State of Product Development: 7 Trends Shaping Product Innovation, indicates that the top two product development challenges globally are supply chain disruptions/market volatility (74%) and design-to-manufacturing handoffs (60%).

In response to these challenges, the manufacturing sector is undergoing a profound transformation as Industry 4.0 technologies reshape production processes and business models. Automation, artificial intelligence (AI), and advanced connectivity are redefining skill requirements, compelling companies to rethink how they attract, train and retain workers. For industry leaders and decision-makers, workforce resilience is more critical than ever.

Here are five key trends driving workforce resilience and preparing manufacturing teams for the future.

1. Upskilling and reskilling programs

As Industry 4.0 advances, technical skills have become essential across all levels of manufacturing. Projections show that demand for technical skills could increase by 50% in the next decade, while manual labor demand may decline by 30%. To address this gap, manufacturers are prioritizing upskilling initiatives.

Integration of STEM education: Programs starting as early as high school can familiarize students with robotics, data analytics and machine operations, preparing them for advanced technical roles.

Apprenticeships and on-the-job training: These provide workers with hands-on experience, ensuring they are proficient in evolving technologies.

Stackable certifications: Short-term, targeted courses allow workers to build skills incrementally, aligning their abilities with new demands.

Companies investing in reskilling are not only bridging the gap in technical expertise but also boosting morale and retention, ensuring that employees feel valued and future-ready.

2. Attracting and diversifying talent

Labor shortages pose a significant challenge, with 45% of manufacturers admitting they’ve declined business opportunities due to insufficient workforce capacity. Companies must rethink how they attract talent, focusing on underrepresented groups and non-traditional labor pools.

Diversification initiatives: Actively targeting women and underrepresented minorities can tap into a larger talent pool. For instance, Nebraska’s goal to increase the share of women in manufacturing highlights a shift toward inclusion.

Awareness campaigns: Efforts such as “Creators Wanted” educate potential workers on the opportunities in advanced manufacturing, showcasing modern, technology-driven environments that challenge outdated perceptions of factory jobs.

Community-focused outreach: Programs that bring manufacturing career opportunities directly to schools and communities can spark interest among young talent early.

Broadening recruitment approaches can help manufacturers build a more dynamic workforce while simultaneously addressing critical shortages.

3. Enhancing workforce infrastructure

Talent acquisition efforts are incomplete without adequate infrastructure to enable workforce participation. Affordable housing, accessible childcare and transportation support are essential in attracting and retaining workers, particularly in rural manufacturing hubs.

Housing initiatives: States such as South Dakota have allocated funds to expand workforce housing, facilitating easier relocation for employees.

Childcare support: Tax incentives for employers building childcare facilities or providing subsidies for employees help remove common barriers for working families.

Community investments: Infrastructure improvements in manufacturing-rich areas create a more sustainable talent ecosystem.

By addressing these foundational needs, manufacturers create environments where workers can thrive, supporting long-term labor market participation.

4. Leveraging technology for workforce engagement

Technology is revolutionizing not only products but also the way teams operate. Automation, data dashboards and augmented reality (AR) tools are reshaping daily workflows, creating safer and more engaging environments for workers.

Predictive maintenance: Real-time monitoring systems detect issues before they occur, enhancing equipment reliability and reducing worker downtime.

AR for training and repair: Technicians equipped with AR devices can access step-by-step repair guides overlayed on actual machinery, quickly building proficiency and confidence in their tasks.

Workplace optimization: Technologies that track and manage inventory or streamline operations allow workers to focus on high-value tasks, fostering job satisfaction.

These advancements reduce errors, boost efficiency and create an environment that aligns with the aspirations of a tech-savvy workforce.

5. Sustainability as a workforce driver

Sustainability is no longer just a corporate responsibility — it is an asset in attracting and retaining talent. Employees increasingly value organizations that prioritize environmental stewardship and green initiatives.

Green factories: Facilities using renewable energy and smart resource management demonstrate leadership in sustainability.

Waste reduction: Technologies like 3D printing minimize material waste, aligning with global sustainability goals.

Corporate commitment: Communicating sustainability milestones and emphasizing eco-responsible practices bolster employee pride and attract like-minded talent.

Sustainable practices not only position companies as industry leaders but also foster a sense of purpose among workers, cultivating loyalty and collaboration.

The bottom line

Building a resilient workforce in the Age of Industry 4.0 requires a multi-faceted approach. Upskilling employees, diversifying talent pools, investing in infrastructure, leveraging technology and prioritizing sustainability are essential strategies for manufacturers aiming to remain competitive.

By aligning workforce development with technological advancements and community needs, industry leaders can create an adaptable, forward-thinking labor force ready to thrive in this era of transformation. Now is the time to empower workers, and in doing so, secure the future of manufacturing.

To learn more, visit Dassault Systèmes.

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