Resolver intelligently simulates and tests robot path and sequencing options in the cloud, delivering optimized, collision-free motion plans and interlock signals in minutes instead of months. The platform empowers manufacturers to reduce engineering effort by up to 50%, minimize cycle times, and dramatically improve throughput, without the usual trial-and-error bottlenecks.

To explore the impact of this breakthrough and how it’s being received by the automation community, we sat down with Ville Lehtonen, VP of Product at Realtime Robotics, for a deeper look into Resolver’s capabilities, the problems it solves, and what lies ahead for cloud-powered robotic simulation.

Ville brings over 17 years of robotics experience across Life Sciences, Logistics, and Manufacturing. He contributed to automation innovation in Life Sciences at LabMinds and HighRes Biosolutions. In Logistics, he led product efforts at Pickle Robot, scaling AI-powered unloading systems into commercial deployment. He has recently focused on real-time motion planning and adaptive control for high-mix manufacturing environments. Ville holds graduate degrees from both Oxford University and Aalto University, with a background that spans business and computer science. Originally from Finland, he now resides in Boston, MA.

1. How does Resolver redefine the traditional robot path planning process, and what makes it a game-changer for manufacturing?

Resolver doesn’t just make path planning faster; it fundamentally shifts its position in the process. Traditionally, path planning happens late in the game, after most design decisions are locked in. It’s slow, manual, and often based on assumptions that don’t hold up under real-world constraints.

Resolver flips that model. It plans the entire cell at once – task allocation, sequencing, interlocks – all of it. Because it runs in parallel, you can test multiple robot positions and EOAT configurations with almost no overhead. And it doesn’t just tell you if something works, it shows you what works best. It’s not ‘can I reach the target?’ but ‘what setup hits my cycle time target with margin?

The ability to get real answers fast and early changes how people approach system design. Users no longer need to commit to every decision up front. Ten ideas can be explored in parallel, and teams can move forward with data, not guesswork. That’s a significant shift, especially for those used to spending weeks just to validate a single layout.

So yes, it’s a better path planner. But more than that, it turns path planning into a lever for speed, iteration, and design freedom. It reduces effort, but more critically, it has the potential to save tremendous lead time.

2. How does cloud-based simulation improve robotic workcell design, and how does Resolver leverage this to reduce engineering effort by 50%?

Resolver drives a dramatic shift in workflow. Early access users reported a 50% reduction in engineering effort. Still, more recent data suggests this can be as high as 90% for certain cells, particularly those with complex layouts or frequent design changes.

A big part of this comes from the core path planning itself. What used to take days or weeks of manual programming, often requiring expert tuning and collision avoidance tweaking, can now be generated automatically in hours. That alone is a massive saving of effort and time.

Resolver’s cloud-based architecture also adds another layer of impact:

• Lead time: Because everything runs parallel on powerful cloud servers, entire workcells – or complete lines – can be validated overnight. That’s impossible when you’re limited by human resources or desktop simulation.
• Workload: In iterative tasks like EOAT selection, fixture design, or robot placement, teams typically get stuck waiting on upstream decisions. Worse, engineers lose momentum when constantly pulled into firefighting for other teams. Resolver removes much of that friction; multiple configurations can be tested in parallel, and the system returns not just ‘viable’ paths, but optimized ones.

So while path planning is the obvious efficiency win, the bigger story is how cloud-native parallelism clears bottlenecks between teams. That’s where the full 50-90% reduction in engineering effort comes from.

3. Resolver claims to reduce months of manual robot programming to just days or even hours. Can you walk us through a real-world example?

The power of Resolver isn’t just in speeding up path planning – it’s in how it transforms the entire simulation and deployment process.

Traditionally, simulation engineers spend weeks answering foundational design questions: How many robots do we need? Can the weld gun reach all targets without collisions? Will the fixture and EOAT combination hold up? These aren’t just technical issues; they define cycle time, capital cost, and production feasibility. Resolving them manually is slow and expertise-heavy.

Resolver flips that process. It acts like a virtual simulation engineer, evaluating robot positioning, tooling choices, path feasibility, and interlocks, and does so all at once, across the entire cell. And it doesn’t just say ‘this works’; it highlights which setup delivers better cycle time, higher utilization, or fewer risks. That kind of insight improves decisions from day one.

After using Resolver across a few workcells, one customer said they realized they could feasibly load every cell of a large project into the system on day one – and then have optimized robot positions, EOAT choices, and full paths by day two. That hasn’t happened yet, but the recognition was clear: the bottleneck is no longer the software; it’s just whether the team chooses to operate that way.

Fixture design is still a separate workflow, but we’re seeing early interest from partners building generative AI tools that could co-design fixtures to match Resolver’s output. If that matures, fixture iteration could even be automated and validated overnight.

In short, customers are starting to rethink what the first week of a project could look like. That shift—fueled by Resolver—could compress months of work into just a few days—not through heroics but by changing the starting line.

4. What differentiates Resolver from existing robotic simulation and optimization tools, especially in handling multi-robot systems and collision avoidance?

Resolver fundamentally differs from traditional robot simulation and optimization tools because it doesn’t solve for one robot at a time. It plans the entire workcell holistically, including task allocation, sequencing, and interlocks, across all robots. That’s the structural shift. Once you’re solving globally, you simply unlock results that aren’t possible with single-robot, sequential planning approaches.

There are a few key differentiators:

• True multi-robot planning: Resolver can handle 10, 15, or 20 robots in a single cell.
• Automatic interlock handling: Customers often tell us that Resolver is the first tool they’ve used where the robots don’t crash. That’s not magic; it’s baked into how we solve the problem.
• Sequence and allocation optimization: We don’t just find valid paths; we decide which robot should do which task, and in what order, to hit cycle time targets.
• Faster path planning: When benchmarking against a major simulation tool, one customer saw Resolver produce 20% faster plans—even in relatively simple cells.
• Cloud-native architecture: Resolver runs in parallel on scalable cloud resources, which means a user can validate a full line overnight and come back with real answers.

Resolver comparisonPut simply, Resolver isn’t a path planner with extra features—it’s a completely different model for planning, optimizing, and validating robotic cells.

5. How can small and medium manufacturers leverage Resolver to stay competitive, especially when they lack the deep engineering resources of larger enterprises?

Resolver is especially valuable for small and mid-sized manufacturers because it removes the need for one of the most brittle roles in automation: path planning. The problem isn’t just that path planning is specialized; it’s that it’s single-purpose. You might need ten hours of it one week, then nothing for a month. Compare that to CAD skills, broadly applicable across design, engineering, and manufacturing tasks.

This mismatch creates real overhead. Hiring a full-time path planner doesn’t make sense unless you’re running at a serious scale. But relying on an integrator whenever you need a change slows you down and eats into margins. And if you do hire someone, how do you know for sure that they’re good? What happens when they quit?

Resolver changes the equation. It gives organizations access to high-end planning capabilities without having to build a fragile in-house function or tie themselves to long-term contracts. More importantly, it lets you stay flexible. You don’t have to treat automation as a static thing just to make the economics work. You can adapt on the fly, without breaking the bank.

That’s how smaller manufacturers stay agile and competitive.

6. What’s next for Resolver—are there any upcoming integrations, AI enhancements, or features that will further empower robotic automation in Industry 4.0 environments?

The only integration we can publicly confirm right now is with Visual Components, which we’re launching at Automatica 2025. Several other integration projects are underway, but since they involve close collaboration with partners, we’ll coordinate announcements together with them. Longer term, you can expect integrations with more sophisticated 3D simulation platforms and even CAD tools to support early-phase design.

As for the product roadmap, we’re focused on three major pushes: some very AI-driven, others more standards- and rules-based. These include:

• Design-enabling tools: Think automatic robot positioning, EOAT selection, robot choice, and fixture design support. These are heavy on AI and aim to reduce the up-front work required to scope and build an automated cell.
• OEM standard adherence: For applications where robot motion needs to follow strict OEM guidelines, we’re building support to generate paths that align with those standards – no AI needed, just precision and correctness.
• Line-level tools: This includes features like line balancing, robot and EOAT allocation across multiple stations, and reachability validation at production scale. These will be deeply AI-enabled and cloud-powered.

The long-term vision is simple: customers bring the process they want to execute, and Resolver handles everything else. With a solid digital twin in place, this kind of full-stack automation isn’t theoretical – it’s within reach.

The post A conversation with Realtime Robotics’ Ville Lehtonen on the launch of Resolver and collision-free robot motion planning appeared first on RoboticsBiz.

In the medical world, where time and personalization can directly impact patient outcomes, rapid prototyping isn’t just a manufacturing trend—it’s a clinical revolution. This article explores how these technologies work, the techniques involved, and their powerful applications in healthcare today.

1. What is Rapid Prototyping?

At its core, rapid prototyping refers to a group of manufacturing techniques that quickly create three-dimensional (3D) physical models based on digital designs. Unlike traditional subtractive methods—where a block of material is carved down to a desired shape—rapid prototyping employs additive methods. These involve constructing an object layer by layer from scratch using powders, plastics, or metals.

This shift from subtractive to additive manufacturing allows for faster iterations, complex geometries, and greater flexibility in design, all of which are particularly valuable in the healthcare sector where customized and functional solutions are often needed urgently.

2. The Digital Workflow: From CAD to Physical Model

The process begins with the creation of a 3D digital design using computer-aided design (CAD) software like SolidWorks. These designs can include precise dimensions and details, mimicking the geometry of complex parts—such as joints, implants, or surgical instruments.

Once a CAD model is ready, it is converted into a Standard Tessellation Language (STL) file—a format that breaks the surface of the model into a series of triangles. The STL file is then “sliced” into thin cross-sectional layers. These slices guide the 3D printer or prototyping machine in building the object one layer at a time, essentially transforming a virtual design into a real-world item.

3. A Game-Changer in Clinical Settings

One of the most groundbreaking applications of rapid prototyping in medicine is its ability to convert medical imaging—like CT or MRI scans—into accurate 3D models of a patient’s anatomy. These models allow surgeons to:

• Rehearse complex procedures before actual surgery
• Customize implants and prosthetics to the patient’s exact specifications
• Improve communication with patients and surgical teams through tangible visual aids

Imagine a neurosurgeon preparing for a delicate cranial surgery using a 3D-printed model of the patient’s skull, generated from scan data. This model becomes an invaluable tool—not just for planning, but for designing tailored implants that fit perfectly on the first attempt.

4. Techniques in Additive Manufacturing

There are several additive manufacturing technologies used in the medical field, each suited to different materials and purposes. Two of the most prominent techniques covered are:

a. Selective Laser Sintering (SLS)

SLS is widely used for fabricating metal parts and is ideal for producing medical components from materials like steel, titanium alloys, and composites. Here’s how it works:

• A thin layer of powdered material is spread over a build platform.
• A high-powered laser selectively fuses the powder based on the cross-sectional design.
• The platform then lowers slightly, and another layer of powder is added.
• The process repeats, building the object layer by layer.

Key advantages of SLS include:

• Ability to produce intricate and mechanically robust components
• No need for support structures due to self-sustaining powder beds
• High productivity due to batch processing of multiple parts at once

This technique is particularly useful in orthopedics and implant design, where strength, durability, and biocompatibility are paramount.

b. Binder Jetting (Inkjet 3D Printing)

Binder jetting works similarly to an inkjet printer, but instead of ink, it deposits liquid binder onto a powder bed. Here’s a breakdown:

• A print head selectively deposits the binder on a layer of powder.
• A new layer of powder is added on top.
• The layers adhere together, forming a 3D object.

Because binder jetting doesn’t involve heat during fabrication, it requires a post-processing phase to strengthen the model. This technique is well-suited for producing plastic or ceramic parts and is often used for creating molds, models, and some prosthetics.

5. Materials in Medical 3D Printing

A wide range of materials can be used in rapid prototyping for healthcare applications:

• Metals: Titanium alloys, stainless steel—suitable for implants and surgical instruments.
• Polymers: Nylon, polystyrene, polyetheretherketone (PEEK)—used for lightweight and durable parts.
• Ceramics and Composites: Applied in dental and orthopedic uses.

These materials are selected based on required characteristics such as strength, biocompatibility, flexibility, and sterilization capability.

6. From Models to Medical Devices

Initially, rapid prototyping was confined to creating scale models and visual aids. However, advancements in material science and printer resolution have allowed manufacturers to fabricate functional, clinical-grade devices.

As of 2015, the U.S. Food and Drug Administration (FDA) had already approved over 85 3D-printed medical devices, including:

• Orthopedic implants like hip and knee replacements
• Cranial and facial reconstruction implants
• Customized surgical instruments
• Dental restorations such as crowns and bridges
• External prosthetics for limb loss

This regulatory progress highlights the maturity of additive manufacturing in clinical contexts.

7. Personalization: The Real Disruptor

Perhaps the most transformative aspect of rapid prototyping is its potential for personalized medicine. Since every human body is unique, standard off-the-shelf implants often fall short in delivering optimal outcomes. With rapid prototyping:

• Implants can be tailored to the patient’s exact anatomical structure.
• Surgical tools can be customized for specific procedures.
• Prosthetics can match an individual’s mobility needs and aesthetic preferences.

This level of customization was unimaginable a decade ago with traditional manufacturing methods.

8. Advantages and Limitations

Advantages:

• Speed: Concept-to-object turnaround is drastically reduced.
• Complexity: Enables production of intricate geometries not possible with casting or machining.
• Cost-efficiency: Reduces the cost of iterations and material waste.
• Versatility: Accommodates a broad range of medical applications from prototyping to patient-ready devices.

Limitations:

• High upfront cost: Equipment and materials can be expensive.
• Production speed for large batches: Not as fast as injection molding for high-volume manufacturing.
• Regulatory and validation hurdles: Each device must pass stringent safety and efficacy tests.

Despite these, the benefits often outweigh the drawbacks—especially when tailored, one-off solutions are needed.

9. Future Outlook: Smart and Bio-Compatible Printing

The future of rapid prototyping in medicine is poised to become even more transformative with developments such as:

• Bioprinting: Printing with living cells to produce tissues and eventually, entire organs.
• Embedded sensors: Creating smart implants that monitor healing or detect infection.
• AI-driven design: Using machine learning to optimize device geometry for patient outcomes.

As research expands and regulations evolve, we may soon see fully customized, intelligent implants that not only fit perfectly but actively assist in patient recovery.

Conclusion

Rapid prototyping and additive manufacturing are redefining what’s possible in medical device innovation. From faster development cycles to hyper-personalized solutions, these technologies offer a future where medical devices are not just tools, but tailored extensions of individual healthcare needs.

In the evolving landscape of healthcare, where precision, personalization, and speed are critical, rapid prototyping stands at the intersection of engineering innovation and human well-being. It’s not just about printing objects—it’s about crafting better outcomes.

The post Rapid prototyping and 3D printing in medical device manufacturing – Explained appeared first on RoboticsBiz.

What Is 3D Printing?

At its core, 3D printing is an additive manufacturing process. It starts from a digital design, typically a 3D CAD model, and produces a physical object by laying down successive layers of material. This contrasts with subtractive manufacturing techniques like milling or machining, where material is removed from a solid block to create a shape.

Additive manufacturing minimizes material waste and enables the creation of highly complex geometries that are often impossible or prohibitively expensive to produce with traditional methods.

Think of it as creating a sculpture—not by chiseling away at a marble block—but by stacking thin slices of material until the sculpture comes into being.

Key Components of a 3D Printing System

A typical 3D printing setup includes the following core components:

• 3D CAD Model: This is the blueprint for the object. Designers use modeling software to create precise digital versions of the items to be printed.
• Model Conversion Software: The CAD file is converted into a sliceable format (often G-code or STL) that instructs the printer how to lay down material layer by layer.
• Material Feedstock: Depending on the printer type, this could be plastic filament, metal wire, resin, powder, or even food paste.
• 3D Printer: The main machine that translates digital instructions into physical objects using one of several techniques.
• Post-Processing Tools: Many objects require support removal, surface finishing, or curing after printing.

Types of 3D Printing Technologies

3D printing is not a one-size-fits-all process. Over the years, various methods have evolved, each suited to specific applications and materials. Here are the seven major categories of additive manufacturing:

1. Binder Jetting

This process uses a liquid binding agent to selectively “glue” layers of powdered material together. A printhead moves across a bed of powder, depositing the binder in specific areas to form each layer. It’s ideal for metal and ceramic parts, and offers a good balance between speed and detail.

Use Cases: Sand casting molds, metal prototypes.

2. Directed Energy Deposition (DED)

Commonly used for metal parts, DED involves a focused energy source—like a laser or electron beam—melting material as it’s deposited. The energy source and material feed move together, allowing for complex builds and repairs.

Use Cases: Aerospace components, turbine blade repair.

3. Material Extrusion (FDM/FFF)

The most widely known method, Fused Deposition Modeling (FDM), feeds a thermoplastic filament through a heated nozzle that moves in precise patterns, depositing material layer by layer.

Use Cases: Prototypes, tools, hobbyist projects.

4. Powder Bed Fusion (PBF)

This technique involves spreading a thin layer of powder (metal or polymer) and selectively fusing it using a laser or electron beam. Variants include Selective Laser Sintering (SLS) and Selective Laser Melting (SLM).

Use Cases: Functional parts, medical implants, aerospace.

5. Sheet Lamination

Layers of material—typically paper, plastic, or metal—are cut and bonded together. Technologies under this category include:

• LOM (Laminated Object Manufacturing)
• SDL (Selective Deposition Lamination)
• UAM (Ultrasonic Additive Manufacturing)

Use Cases: Architectural models, metal structures with embedded sensors.

6. Vat Photopolymerization

This process uses a vat of liquid photopolymer resin, which is selectively cured by UV light to form solid layers. The most common variant is Stereolithography (SLA).

Use Cases: Dental models, jewelry, highly detailed prototypes.

7. Wire Arc Additive Manufacturing (WAAM)

Primarily for large-scale metal parts, this technique uses an electric arc as a heat source to melt wire feedstock, creating metal structures layer by layer. It is often integrated with robotic arms for precision.

Use Cases: Shipbuilding, structural components.

The Most Popular Method: Fused Deposition Modeling (FDM)

FDM remains the go-to method for beginners, hobbyists, and educators because it’s affordable, easy to use, and widely supported. The filament—typically PLA, ABS, or PETG—is heated to its melting point and extruded through a nozzle that traces the object’s cross-section.

Filament Specs:

• Diameter: Usually 1.75 mm
• Available in a wide variety of colors and materials
• Supplied on spools for continuous feeding

From phone holders to toy figurines, FDM printers can build countless items limited only by your imagination.

Real-World Applications of 3D Printing

3D printing has grown far beyond hobbyist circles. Its impact is being felt across industries:

• Aerospace and Defense: Companies like SpaceX and Boeing are using 3D printing to manufacture lightweight yet durable parts for rockets and aircraft. The precision and speed reduce both production time and cost.
• Healthcare and Prosthetics: 3D-printed prosthetics have transformed accessibility. These custom-fitted devices can be produced much faster and at a fraction of the cost compared to traditional methods. Dental aligners and surgical models are also commonly 3D printed.
• Construction: Yes, entire houses can now be 3D printed using giant printers that extrude concrete-like materials. The results are not just livable but also structurally sound and produced in a fraction of the time it takes using traditional methods.
• Food and Fashion: Innovators are exploring edible 3D printing using chocolate, dough, or paste-based foods. Meanwhile, designers are experimenting with 3D-printed clothing and accessories that push the boundaries of wearable art.
• Manufacturing and Tooling: In factories, 3D printing is used for jigs, fixtures, and replacement parts. It’s also a crucial tool in rapid prototyping—allowing designers to iterate quickly and cheaply.
• Space Exploration: NASA is testing 3D printing for in-situ manufacturing in space. The idea is to print tools or spare parts aboard spacecraft or lunar bases, minimizing cargo needs from Earth.

Advantages of 3D Printing

• Material Efficiency: Additive process means minimal waste.
• Design Freedom: Complex, organic shapes are easy to print.
• Customization: Perfect for one-off items or personalized products.
• Speed: Rapid prototyping enables faster development cycles.
• Accessibility: Desktop printers have democratized manufacturing.

Challenges and Limitations

Despite the promise, 3D printing is not without its hurdles:

• Surface Quality: Post-processing is often needed to improve finish.
• Speed vs. Scale: Larger parts can take significant time to print.
• Material Constraints: Not all materials are compatible with all methods.
• Production Cost: For mass production, traditional methods may still be more economical.

The Future of 3D Printing

3D printing is at a crucial inflection point. As hardware improves, materials diversify, and costs come down, its adoption across sectors is set to rise exponentially. From smart manufacturing floors to remote Martian habitats, the potential is as vast as the human imagination.

With ongoing R&D and investment, we can expect:

• Greater adoption of metal printing
• Hybrid manufacturing (additive + subtractive)
• Smarter, AI-driven printers
• Bioprinting of tissues and organs

Conclusion: A New Era in the Making

3D printing is not just a technological innovation—it’s a paradigm shift. It empowers individuals and industries to design, iterate, and manufacture with unprecedented freedom and efficiency. As part of the broader Industry 4.0 movement, it exemplifies the fusion of digital and physical worlds.

Whether you’re an engineer, artist, medical professional, or just a curious hobbyist, the world of 3D printing offers a toolkit to turn your ideas into reality—one layer at a time.

The post How 3D printing works: Unlocking the future of manufacturing layer by layer appeared first on RoboticsBiz.

Rethinking the Traditional Prototyping Workflow

Until recently, prototyping a gear often required extensive lead times, custom tooling, and costly machining operations. For decades, manufacturing engineers and design teams accepted this as the status quo. Whether gears were cast, milled, or cut, creating just one functional prototype could take weeks, if not longer.

Moreover, any design revisions would trigger a new cycle of delays and expenditures. Small changes, such as adjusting the module or modifying tooth geometry, demanded time, budget approvals, and often created hesitation within development teams. This hesitation has historically slowed down innovation and limited early-stage experimentation.

Additive Manufacturing Enters the Equation

Additive manufacturing, often generalized as 3D printing, has steadily transitioned from a tool for creating concept models to a serious contender in functional prototyping. Today, engineers can produce accurate, mechanically capable gear prototypes using materials such as carbon fiber-reinforced nylon, stainless steel, and advanced polymers.

The appeal is clear. With technologies such as fused filament fabrication (FFF), selective laser sintering (SLS), and metal binder jetting, it’s now possible to go from CAD to physical part within a matter of hours or days, rather than weeks. Design teams gain the flexibility to test more iterations without being burdened by prohibitive costs or extended timelines.

Speed Is Reshaping Development Strategy

Accelerated prototyping does more than save time; it shifts how teams approach design altogether. Instead of narrowing options early in the process to reduce cost and risk, engineers can afford to explore more aggressively. Gear designers can test unconventional tooth profiles, evaluate new spacing ratios, or assess the impact of different pressure angles—all without making long-term production commitments.

For example, platforms like Markforged’s FX20 allow for carbon fiber-filled gear prototypes that maintain high stiffness and wear resistance, suitable for functional testing. Similarly, Desktop Metal’s binder jetting systems are increasingly being used to produce short-run steel gears with properties suitable for dynamic testing environments.

This ability to quickly iterate improves design quality and accelerates time to validation. Ultimately, it allows mechanical and R&D teams to make more informed decisions, earlier in the process.

Precision and Functional Testing: No Longer a Trade-off

One common critique of additive manufacturing has been its limited dimensional accuracy and surface finish compared to subtractive methods. However, this is no longer universally valid. Modern machines are consistently achieving tolerances within ±0.1 mm or better, especially when paired with controlled thermal environments and refined slicing algorithms.

For cases where tighter tolerances are essential, hybrid manufacturing approaches are gaining traction. These workflows combine additive production with post-processing techniques such as CNC machining or surface grinding, enabling both geometric freedom and precision where needed.

As a result, additive prototypes are not only suitable for fit checks but are increasingly used in load-bearing test environments, including dynamic gear meshes and real-world duty cycles.

Reducing Risk While Encouraging Innovation

Perhaps one of the most undervalued benefits of additive manufacturing is its effect on engineering psychology. When prototyping is expensive and slow, teams are understandably conservative. Mistakes can be costly. But when a design flaw can be corrected overnight and reprinted the next morning, the culture around experimentation shifts.

This freedom to test more designs, more often, significantly reduces the risk of late-stage failures and encourages a more proactive approach to problem solving. Engineers are free to try new approaches, confident that the cost of failure is manageable and that iteration cycles will remain efficient.

Moving Beyond Prototypes: Short-Run Production and Functional Use

Additive manufacturing has matured to the point where its role is no longer limited to early-stage development. Many gear manufacturers and system integrators are now using AM to produce short-run gear sets for custom machinery, low-volume applications, or pilot production builds.

Industries such as aerospace, robotics, and automotive R&D are actively employing 3D-printed gear components in real-world assemblies. These parts are not merely visual models; they perform under load, integrate into operational systems, and inform future manufacturing strategies.

Limitations Still Remain, But They Are Shrinking

It is important to acknowledge that additive manufacturing is not without its constraints. Surface finishes often require post-processing to achieve desired roughness, particularly for high-speed gears. Material limitations, especially regarding thermal stability and long-term fatigue, are still being addressed. In some cases, additive components serve only as precursors to final machined parts.

That said, the current capabilities of additive manufacturing—especially when integrated into a broader workflow—offer a level of design freedom and responsiveness that conventional methods cannot match on their own.

Looking Ahead: From Reactive to Strategic Design

The future of gear prototyping is not just faster—it is smarter. As generative design algorithms become more accessible, additive manufacturing will play a key role in bringing unconventional geometries and lightweight structures into functional testing environments.

By reducing the cost and time barrier between concept and application, additive processes are allowing engineering teams to focus more on performance-driven design and less on manufacturability constraints. For companies specializing in advanced gear engineering, this shift opens new opportunities to test unconventional geometries, custom ratios, and innovative load-transfer methods without being restricted by traditional production timelines.

Conclusion: A Shift in Capability and Culture

For gear manufacturers, mechanical engineers, and R&D professionals, additive manufacturing represents more than a new tool. It signifies a cultural and strategic shift in how products are developed, tested, and refined. What was once considered experimental is now essential.

With shorter lead times, lower costs, and an expanded capacity for design exploration, additive manufacturing is not simply accelerating prototyping—it is redefining what’s possible in early-stage gear development.

The post The rise of additive manufacturing in industrial gear prototyping appeared first on RoboticsBiz.

This article explores the evolution and future of hyperautomation, diving deep into how organizations are leveraging self-improving bots to scale operations, enhance customer experiences, and stay competitive in a technology-driven world. From practical use cases to strategic implications, we’ll uncover how hyperautomation is redefining the automation landscape and why businesses, regardless of size or maturity, should embrace this transformative technology.

1. What Is Hyperautomation?

At its core, hyperautomation is the strategic integration of RPA and AI to create an ecosystem where digital systems can intelligently automate not just actions but also decisions. While RPA handles rule-based, repetitive tasks across graphical user interfaces (GUIs) and APIs, AI and ML bring in the ability to mimic human reasoning, learn from data, and dynamically adapt to changing conditions.

Think of hyperautomation as a Venn diagram where RPA and AI intersect. On one side, RPA excels at automating structured tasks, such as data entry, form processing, or system integrations. On the other, AI/ML models analyze large volumes of data to detect patterns, make predictions, and guide decision-making. Hyperautomation sits in the sweet spot, orchestrating a seamless flow between doing and thinking — making it possible to build systems that adapt and improve over time.

2. The Rise of Intelligent Automation Platforms

Over the last few years, we’ve seen a significant transformation in the automation landscape. Market leaders in RPA, including UiPath, Blue Prism, and Automation Anywhere, are no longer limiting themselves to pure-play RPA solutions. Instead, they are rapidly evolving into comprehensive hyperautomation platforms that bundle AI capabilities, natural language processing (NLP), computer vision, and low-code/no-code tools.

A particularly notable shift is the emphasis on cloud-native automation. Vendors are increasingly offering their solutions via the cloud, making them easier to deploy, manage, and scale. This democratizes access to automation for organizations of all sizes and technical maturities. Microsoft, for instance, has gained significant traction through its Power Automate platform and strong cloud integration, earning recognition as a leader in both the Forrester Wave and Gartner Magic Quadrant.

This expansion from toolkits to integrated platforms is not just technical—it also represents a strategic realignment. Businesses are no longer thinking in terms of automating isolated tasks but rather transforming entire processes across departments, customer journeys, and value chains.

3. From Experiments to Enterprise-Scale Automation

While early adopters initially implemented RPA in siloed business units—most commonly in finance, HR, and IT—organizations are now entering a new phase: scaling hyperautomation enterprise-wide.

The past few years were all about experimentation. Companies piloted bots for invoice processing, data validation, or customer onboarding. Now, those same organizations are looking to scale these solutions across hundreds of processes and departments. This demands a robust governance model, centralized control, and a clear automation strategy.

Crucially, scaling also means that organizational structures must evolve. Managing ten bots in a single department is fundamentally different from orchestrating hundreds of intelligent agents across functions. IT and business teams must collaborate closely to ensure consistency, maintainability, and security. Automation is no longer just a technical implementation—it becomes a core component of operational strategy.

4. Expanding Use Cases Across Departments

Hyperautomation is breaking out of its traditional strongholds and making inroads into new areas such as customer service, supply chain, sales, and compliance.

In customer care, for instance, bots are increasingly used not just to automate back-office tasks like ticket assignment or CRM updates, but also to interact with customers directly using AI-driven chatbots. These bots can understand natural language, access knowledge bases, and make decisions in real-time, delivering faster and more personalized experiences.

Similarly, in supply chain management, bots can monitor inventory levels, forecast demand using machine learning, and even trigger procurement actions without human intervention.

By integrating AI capabilities into RPA, hyperautomation is enabling businesses to go beyond mere efficiency gains and deliver measurable improvements in quality, responsiveness, and customer satisfaction.

5. The Power of Self-Improving Bots

A defining feature of hyperautomation is the emergence of self-improving bots. Unlike traditional RPA bots that follow static rules, these intelligent agents use AI models to learn from data and evolve their behavior over time.

Automation Anywhere, for example, integrates various components such as AARI (Automation Anywhere Robotic Interface), IQ Bot (for intelligent document processing), and Bot Insight (for real-time analytics). Together, these tools create bots that can not only process tasks but also evaluate their own performance, identify bottlenecks, and adapt based on outcomes.

For instance, a bot tasked with processing loan applications can learn from past approvals and rejections, improving its accuracy in identifying eligible candidates. It can dynamically adjust criteria, flag anomalies, and even suggest changes to business rules.

This shift from static to dynamic decision-making represents a leap forward. It means automation systems are no longer just executing—they are thinking, analyzing, and optimizing, driving continuous improvement without constant human oversight.

6. The Rise of the Citizen Developer

Another significant trend is the democratization of automation through low-code and no-code development platforms. Today’s hyperautomation tools are designed with user-friendly interfaces that allow non-technical employees—also known as citizen developers—to create their own automations.

This empowerment fosters a culture of innovation and agility within organizations. Business users, who understand their processes best, can now quickly automate routine tasks without waiting for IT. It also leads to faster ROI and a broader base of automation across the enterprise.

However, this decentralization brings challenges. Organizations must define clear guidelines on governance, security, and maintenance. Who owns the automation? Who ensures it complies with policies? These are critical questions that must be addressed as citizen development becomes mainstream.

7. Challenges and Considerations

While the potential of hyperautomation is immense, it’s not without hurdles:

• Integration Complexity: Combining multiple technologies—RPA, AI, ML, OCR, etc.—can introduce integration challenges, especially in legacy environments.
• Scalability: Scaling from a handful of bots to enterprise-wide automation requires robust infrastructure, governance, and change management.
• Data Quality: AI and machine learning models rely heavily on clean, structured, and relevant data. Poor data quality can significantly impair outcomes.
• Change Management: Employees may resist automation due to fears of job displacement. Effective communication and reskilling programs are crucial.

Despite these challenges, the long-term benefits far outweigh the initial barriers—provided organizations approach hyperautomation strategically.

8. Who Should Embrace Hyperautomation?

The short answer: everyone.

Organizations with mature, well-documented processes were the first to adopt RPA. However, the COVID-19 pandemic showed that even newly formed, unstructured processes—especially in the public sector—can be automated quickly. Whether you’re a startup exploring your first automation or a large enterprise with a mature RPA setup, hyperautomation offers valuable opportunities.

For newcomers, it’s advisable to begin by experimenting with different technologies, understanding their strengths, and building internal capabilities. For veterans, the focus should shift to expanding use cases, scaling operations, and embracing AI to drive next-generation efficiency.

Hyperautomation is not a one-time initiative—it’s a journey of continual learning, adaptation, and innovation.

Conclusion: Future-Proofing Your Business with Hyperautomation

Hyperautomation is more than a technological trend—it’s a strategic imperative. By combining RPA with AI and machine learning, organizations can build intelligent systems that not only automate tasks but also enhance decision-making, improve customer experiences, and scale operations dynamically.

As platforms evolve, capabilities expand, and use cases multiply, businesses must be proactive in embracing hyperautomation—not just as a toolkit, but as a philosophy of continuous improvement.

Whether you’re looking to boost operational efficiency, improve service quality, or future-proof your organization, hyperautomation is your gateway to a smarter, more agile enterprise.

The post Hyperautomation: How self-improving bots are revolutionizing business operations appeared first on RoboticsBiz.

Unlike traditional rotary motors, which rely on spinning motion, linear motors offer direct, precise linear movement—ideal for industries where speed, accuracy, and minimal maintenance are paramount. In this article, we’ll explore how linear motors work, why they are superior to traditional pneumatic systems, and why they are poised to be the future of automation.

Understanding Linear Motors: A Shift from Rotary to Linear Motion

When we think of motors, the typical image is one of a spinning shaft. However, many industrial applications—from packaging to CNC machining—require linear motion. This is where linear motors come in.

At their core, linear motors operate based on the principle of a linearly traveling magnetic field. Picture a traditional electric motor, unrolled and laid flat: instead of a rotor spinning inside a stator, the motor generates force along a straight path, moving an object in a linear direction.

The basic structure involves two main components:

• Primary (Stator): A stationary section containing coils energized by a three-phase AC supply.
• Secondary (Slider): A moving part, often a rod or block, that is propelled by the magnetic fields generated in the primary.

Through precise control of current in the stator coils, a magnetic field travels along the stator, dragging the slider along with it. It’s a brilliant yet simple mechanism that eliminates the need for intermediary mechanical systems like gears or belts.

The Science Behind the Motion: Linearly Traveling Magnetic Fields

The concept of the linearly traveling magnetic field is the heart of linear motor operation. Here’s how it works:

• When three-phase AC current is applied to the coils arranged in the stator, the currents create alternating magnetic fields.
• These alternating fields combine to form a moving magnetic wave along the length of the stator.
• If a conductive block, such as aluminum, is placed within this moving field, eddy currents are induced in it according to Faraday’s Law.
• According to Lenz’s Law, these induced currents generate a magnetic field opposing the change, resulting in a force that propels the block along with the magnetic wave.

However, the block never quite matches the speed of the traveling field, a phenomenon known as slip. This slip is necessary to maintain continuous eddy current generation and force.

An important feature of this system is the ability to reverse direction simply by changing the order of the three-phase power supply, enabling reciprocating motion easily—a significant advantage for many industrial processes.

Real-World Applications: From CNC to Maglev Trains

Linear motors are not just theoretical marvels—they are being used in a wide variety of applications, including:

• CNC machines: For ultra-precise cutting, milling, and engraving tasks.
• Packaging industries: Where fast, accurate, and repeatable movement is critical.
• Automatic sliding doors: Providing smooth, quiet, and reliable operation.
• Roller coasters: Enabling rapid, high-acceleration launches without complex mechanical systems.
• Ropeless elevators: Where vertical and horizontal movement can be combined seamlessly.
• High-speed trains: Technologies like maglev trains use superconducting magnetic levitation powered by linear motors for ultra-fast travel.

Each of these applications leverages the core strengths of linear motors: speed, precision, and reliability.

Common Examples of Linear Motion in Action

Here are some typical examples where linear motion plays a crucial role:

• Robotic Arms: Robotic arms use linear motion to extend, retract, or adjust vertically. This smooth, steady movement helps with precision tasks like assembly, painting, and welding.
• Pick-and-Place Machines: In electronics and food packaging, these machines move items from one point to another quickly and directly, reducing errors and boosting efficiency.
• 3D Printers: Building objects layer by layer requires precise motion along the X, Y, and Z axes. Even slight inaccuracies can cause failed prints, making smooth linear motion vital.
• CNC Machines: CNC machines shape and cut materials by moving cutting tools exactly where needed, enabling the creation of intricate and finely detailed designs.
• Packaging Systems: Linear motion ensures that trays, fillers, or sealing devices move into place at precisely the right time, maintaining consistent, high-quality packaging.
• Conveyor Systems: Some conveyors use linear motors for fast item movement or direction changes, keeping production lines smooth and efficient.
• Inspection Systems: Cameras or sensors move linearly to scan products for defects, ensuring thorough and accurate inspection processes.
• Material Handling Systems: Linear motion aids in lifting, sliding, or positioning heavy objects safely across workspaces.
• Medical Lab Equipment: In labs, machines often use precise linear motion to handle delicate test samples, maintaining accuracy in small, controlled environments.
• Laser Cutting Machines: Laser heads must move smoothly across materials like metal or plastic to ensure clean, accurate cuts, with no vibration or jagged edges.
• Printing Machines: Large-scale printers rely on linear motion to guide print heads and paper feeds, maintaining color alignment and sharp print quality.
• Textile Machines: In fabric production, linear motion helps cut or sew materials precisely, reducing waste and ensuring smooth cloth handling.

Each of these systems depends on smooth, reliable movement in a straight line—exactly what linear motors are designed to deliver.

Why Linear Motors Are Outpacing Pneumatic Systems

In many factories, pneumatic actuators have been the go-to choice for linear motion. They are inexpensive to purchase and relatively simple to set up. However, pneumatic systems have significant downsides that linear motors address elegantly.

1. Energy Efficiency

Pneumatic systems rely on air compressors, which consume vast amounts of energy—most of which is wasted in heat and leakage. In contrast, linear motors consume energy only during motion. Studies have shown that linear motors use a fraction of the energy compared to pneumatic systems, leading to significant operational savings over time.

2. Precision and Control

Pneumatic actuators are binary: they extend or retract fully, with little control over intermediate positions. In contrast, linear motors offer full positional control with exceptional repeatability—up to 5 microns standard, and down to 1 micron when equipped with high-resolution encoders.

Such precision is critical in high-end manufacturing, semiconductor fabrication, and robotics, where even tiny deviations can be catastrophic.

3. Reduced Downtime

Downtime in manufacturing means lost profits. Pneumatic systems are prone to leaks, wear, and compressor failures, leading to frequent maintenance and costly stoppages. Linear motors, by contrast, are designed for extremely long operational life with minimal maintenance—typically just periodic cleaning and re-greasing of the slider.

4. Environmental Benefits

Beyond operational savings, linear motors also contribute to environmental sustainability. By reducing reliance on compressed air (which is energy-intensive to produce) and increasing efficiency, manufacturers can significantly lower their carbon footprint.

Key Features That Make Linear Motors Ideal for Automation

Linear motors bring a suite of benefits that position them as a game-changing solution for modern automation:

Full Range of Control

Unlike pneumatic cylinders that are either fully extended or retracted, linear motors offer complete control throughout the motion range. They can stop precisely at any point along their travel path, offering unprecedented flexibility in design and operation.

Multi-Position Capability

Linear motors can perform multiple discrete movements within a single cycle. This enables more complex and efficient workflows, reducing the need for additional actuators or repositioning systems.

Minimal Maintenance

With fewer moving parts and no requirement for compressed air systems, linear motors require significantly less maintenance than traditional options. Routine maintenance typically involves only cleaning and re-lubricating the slider components—tasks that can be completed quickly without specialized skills.

High Durability

Many linear motors are rated for over a billion cycles, ensuring long-term reliability even in high-demand environments. Their robust design makes them ideal for industries where downtime is not an option.

Compact and Flexible Integration

Because they eliminate the need for mechanical linkages like belts, screws, or gears, linear motors can be seamlessly integrated into compact machine designs, offering greater flexibility and innovation in product development.

The Future of Automation: Why Linear Motors Matter

As industries face increasing pressure to produce faster, cheaper, and greener, traditional technologies are reaching their limits. Pneumatic systems, while historically effective, cannot match the performance, efficiency, and precision that modern manufacturing demands.

Linear motors represent the next step forward, offering:

• Dramatic energy savings
• Incredible positional accuracy
• Longer service life with minimal maintenance
• Flexible, programmable movement profiles
• Greater environmental sustainability

By embracing linear motor technology, manufacturers can future-proof their operations, drive higher profits, and meet the growing demand for eco-friendly production.

Conclusion

The shift to linear motors is not just an incremental upgrade—it’s a transformational leap. By replacing outdated pneumatic systems with highly efficient, precise, and durable linear motors, industries across the globe are stepping into a new era of manufacturing excellence.

Whether it’s improving the precision of CNC machines, enabling smoother packaging lines, or driving the transportation systems of tomorrow, linear motors are poised to be the cornerstone technology that powers the future of automation.

The post Why linear motors are the key to the future of automation appeared first on RoboticsBiz.

This article explores a human-centric, step-by-step roadmap to adopting warehouse automation successfully, based on insights from supply chain experts and operations leaders. It’s a practical playbook for organizations navigating this transformation, balancing innovation with inclusivity, and preparing teams for the warehouse of tomorrow.

1. Start with a Clear Vision: Define Goals and Metrics for Automation

The first and arguably most important step in implementing warehouse automation is aligning leadership and stakeholders around a shared vision of success. Automation initiatives can easily become mired in complexity without clear direction.

That’s why organizations must begin by identifying Key Performance Indicators (KPIs)—the benchmarks that will determine the value and impact of automation. Whether it’s reducing order cycle time, increasing pick accuracy, lowering labor costs, or improving throughput, defining these goals up front gives everyone—from the C-suite to the warehouse floor—a common target.

But alignment isn’t just strategic—it’s cultural. To avoid resistance, leadership must communicate clearly and often, framing automation as an enhancement rather than a replacement. This fosters buy-in across all levels and sets a foundation for success.

2. Plan for Physical and Digital Transformation

Warehouses were historically designed for people, not machines. Retrofitting them to accommodate automation isn’t as simple as plugging in a robot.

Automation-ready environments often require:

• Wider aisles for robotic navigation
• Charging stations and power infrastructure
• High-speed wireless connectivity
• Advanced software platforms that integrate AI, robotics, and warehouse management systems (WMS)

For existing facilities, this may involve significant adjustments—sometimes even full-scale reconfiguration. Companies must assess whether to retrofit or build new, considering factors like floor load capacities, ceiling heights, and network readiness.

It’s essential to recognize that automation isn’t just a hardware upgrade—it’s a transformation that affects the physical layout, the software ecosystem, and the operational mindset of the organization.

3. Empower the Workforce: Human-in-the-Loop Design

One of the most impactful shifts in modern automation is the adoption of human-in-the-loop systems. These models acknowledge that while robots excel at repetitive tasks, they still rely on human judgment for exceptions and nuanced decision-making.

Rather than eliminating jobs, automation redefines them:

• Robots handle mundane, repetitive actions.
• Humans oversee exceptions, intervene when needed, and provide contextual intelligence.

For example, if a robot encounters a damaged or crumpled label, a human can intervene, resolve the issue, and the AI can learn from the interaction—creating a closed feedback loop that improves performance over time.

This dynamic partnership ensures that automation remains adaptable and continuously improves, while also reinforcing the value of human roles in an increasingly digital environment.

4. Develop Tailored Training and Upskilling Programs

The integration of robots into warehouses necessitates a comprehensive, role-specific training strategy. Success depends on how well employees adapt to the new tools and processes—and that starts with education.

A robust training process includes:

1. Identifying a core team—those who will work directly with the automation.
2. Collaborating with automation vendors to create personalized training modules.
3. Incorporating real-time feedback from the workforce to fine-tune training.
4. Ongoing learning opportunities, including cross-training and upskilling.

Upskilling existing employees ensures that the organization doesn’t just bring in automation experts from outside, but elevates internal talent who already understand the business. It also sends a strong message of investment in people, which can drive retention and morale.

Additionally, training should address both technical competencies (e.g., robotics maintenance, AI systems handling) and soft skills (e.g., adaptability, communication, collaboration). As roles evolve, so must the people filling them.

5. Prepare for Disruptions: Build a Resilient Support System

No matter how well-designed a system is, things will go wrong. Robots can malfunction, AI algorithms can misfire, and software platforms can crash. What separates successful automation projects from failures is the preparedness to handle exceptions.

A well-crafted support system includes:

• Defined troubleshooting protocols and escalation paths.
• Routine drills and scenario planning, akin to fire drills.
• Clearly assigned responsibilities in case of failures or breakdowns.
• Remote diagnostic capabilities to reduce downtime.

Having these in place ensures that when things do go sideways—and they will—the team isn’t scrambling but responding with confidence and clarity.

This level of foresight isn’t just about minimizing disruption. It builds organizational resilience, helping teams learn from failure, refine systems, and continuously improve.

6. Commit to Continuous Learning and Evolution

Warehouse automation is not a one-and-done initiative—it’s a journey. The industry evolves fast. New sensors, better AI models, faster robots, and smarter software emerge constantly. Organizations must treat automation as a dynamic capability that is always being optimized.

This mindset requires:

• Regular system evaluations to identify performance gaps.
• Employee feedback loops to uncover usability challenges or improvement opportunities.
• Software updates and hardware upgrades to keep pace with innovation.
• Learning and development programs that evolve alongside the tech.

Continuous learning also ensures that teams remain engaged, future-ready, and motivated to adapt. It encourages curiosity and experimentation—qualities that will be increasingly valuable as automation matures.

Human-Centric Automation: The Real Competitive Advantage

At its core, warehouse automation is not about robots replacing humans—it’s about robots augmenting humans. When properly deployed, automation frees people from monotonous, physically taxing work, enabling them to focus on higher-order tasks like:

• Analyzing operational data
• Troubleshooting AI behaviors
• Designing better workflows
• Managing inventory strategies

This shift unlocks new job categories, such as:

• AI integration specialists
• Automation workflow analysts
• Digital warehouse coordinators

It also highlights the increasing value of human creativity, problem-solving, and collaboration in a tech-driven environment.

Addressing Equity and Accessibility in Automation Adoption

As companies implement automation, they must be vigilant against the risk of creating a two-tiered workforce: one thriving in high-tech roles, the other left behind in outdated positions. This transition must be equitable.

Leaders should focus on:

• Broadening access to upskilling programs, regardless of educational background or job title.
• Providing support for digital literacy, particularly for older or underserved workers.
• Designing training in multiple formats to suit different learning styles.
• Promoting internal mobility, so that warehouse associates can grow into automation roles.

Fairness and inclusion aren’t just ethical imperatives—they’re strategic advantages. A more inclusive workforce is a more resilient, innovative, and loyal one.

The Ripple Effect: Automation Beyond the Warehouse Walls

Warehouse automation doesn’t just benefit operations—it reshapes the entire supply chain. With more efficient and responsive systems, companies can achieve:

• Faster delivery times
• Reduced shipping errors
• Lower operational costs
• Improved customer satisfaction

Consumers feel the difference, whether they realize it or not. As automation becomes more pervasive, its impact will ripple across retail, manufacturing, logistics, and even sustainability goals (via optimized energy use and reduced waste).

Final Thoughts: Automation as a Catalyst for Progress

As we stand on the cusp of a new era in logistics, one thing is clear: the future of warehousing will be a hybrid of human ingenuity and robotic precision. Automation, when implemented thoughtfully, can create better jobs, stronger supply chains, and more resilient operations.

But to realize these benefits, companies must go beyond simply installing machines. They must commit to:

• Clear strategic planning
• Thoughtful design and integration
• Equitable training and upskilling
• Strong internal communication
• A culture of continuous learning

Automation isn’t just a tool—it’s a transformation. And in this transformation, the most important asset isn’t the robot on the floor—it’s the person guiding its purpose.

The post 6 essential steps for successful warehouse automation appeared first on RoboticsBiz.

In this article, we dive into the human side of the automation revolution, drawing insights from the recruitment trenches of proactive Global, one of the leading staffing firms specializing in automation and robotics. From shifting salary dynamics to the strategic importance of university partnerships, we unpack the industry’s current challenges and future pathways—highlighting what it takes to build and sustain the workforce behind the machines.

1. The Talent Engine Behind Automation: Who’s Hiring and Why

Warehouse automation isn’t just about shiny new robots—it’s about people. While AI, AMRs (Autonomous Mobile Robots), and goods-to-person systems get the spotlight, it’s the behind-the-scenes talent—from project managers to software engineers to customer success reps—that keep these systems humming.

Recruitment firm proactive Global, operating for over 16 years, has carved out a niche focusing solely on this specialized talent pool. Their clients span the automation ecosystem:

• OEMs and system integrators like Vanderlande and KNAPP.
• Robotics startups and established players such as Geek+ and GreyOrange.
• End-users like logistics companies implementing automation for the first time.
• Venture capital firms seeking promising automation ventures.

This broad perspective gives proactive Global unique visibility into industry-wide hiring trends—and what they see is both exciting and sobering.

2. The State of the Labor Market: From Overheating to Correction

Just a couple of years ago, the automation sector was sizzling hot. Salaries were inflated, talent was hopping between firms, and employers—flush with contracts from Amazon and other retail giants—were paying premiums to poach experienced candidates.

That era came to a halt around 2023. Amazon scaled back its investments. Greenfield projects slowed under the weight of high interest rates. Hiring cooled. Layoffs, even among big players, became common.

Now in 2024, the industry is recalibrating. Salaries, while still strong, are adjusting to more sustainable levels. A role that fetched $200,000 a year in 2022 might now be offered at $170,000. There’s a mismatch between candidate expectations and what employers are willing to pay—leaving some highly skilled professionals on the sidelines.

However, there’s optimism that this “bottoming out” phase may soon give way to growth, especially in 2025. Many projects deferred in 2023 are expected to come online, potentially creating a fresh wave of hiring and investment.

3. Retooling for Brownfield: Why Retrofit Projects Are Reshaping Hiring

As the frenzy for Greenfield mega-warehouses tapers, Brownfield automation—retrofitting and upgrading existing facilities—is taking center stage. This shift requires a different type of talent.

In the Greenfield phase, hiring focused on high-level project managers and engineers capable of building massive operations from scratch. Now, companies need people who can integrate automation into legacy systems, often in facilities that must remain operational during upgrades.

This transition is fueling demand for:

• Controls engineers with expertise in PLCs and software retrofits.
• Operations-savvy project managers who understand both human workflows and robotic integration.
• Customer success professionals skilled in helping clients navigate the complexity of automation rollouts.

4. Talent is Circulating, But That’s Not Enough

One of the biggest challenges is the circulation trap—automation companies tend to hire talent from within the existing ecosystem. Engineers jump from one integrator to another. Salespeople are prized for their contact books. It’s efficient, but it’s not sustainable.

To truly scale, the industry must grow the pie, not just shuffle it. That means pulling talent from adjacent industries and nurturing newcomers with training and exposure.

The companies best positioned for long-term success are those investing in talent development programs, such as:

• Rotational programs for recent grads, like the one offered by Fortna.
• Internships and university partnerships, particularly with supply chain schools like Georgia Tech.
• Reskilling programs that help warehouse managers and engineers from other industries transition into automation.

5. End-User to Integrator: A Career Path That Needs Nurturing

One underutilized source of talent? The people already working in warehouses. Managers, engineers, and operations professionals at 3PLs and retailers often develop hands-on experience with automation systems—but face barriers when trying to cross over to the vendor side.

This transition isn’t always linear. A warehouse operations director might be a perfect fit for a customer success leadership role. A solution design engineer could move into sales engineering. Yet, few companies have structured pathways to support this shift.

The solution lies in bridging the language gap between skill sets and job roles, something that specialized recruiters—and increasingly, forward-looking HR departments—are starting to address.

6. Global Expansion: Eyes on Europe and APAC

As U.S.-based automation startups mature, many are turning their eyes abroad. Europe, in particular, is seen as fertile ground for growth, thanks to its openness to pilot new technologies and often more agile procurement processes.

In contrast, the Asia-Pacific market presents a more challenging terrain, often dominated by low-cost, hardware-driven competition. Still, established players like Locus Robotics and SoftBank Robotics are pushing into the region, indicating there’s opportunity—especially for differentiated, value-driven solutions.

This globalization trend means recruiters and talent professionals must think internationally. Cross-border roles, multilingual candidates, and culturally attuned onboarding will only become more important.

7. AMRs: Cool Tech Is Not Enough Anymore

The days of selling automation based on novelty are over. AMRs (Autonomous Mobile Robots), once the poster children of innovation, are at risk of commoditization—especially when hardware differentiation becomes marginal.

What separates winners from the rest today is:

• Robust fleet management software
• Operational integration capabilities
• Demonstrable ROI

For recruiters, this changes the type of talent in demand. Software engineers, system architects, and customer support teams that understand the warehouse floor are now more critical than flashy roboticists.

8. The Candidate Experience Is Evolving, Again

During the hiring surge of 2021–2022, companies moved fast—sometimes making offers in days. By 2023, the pendulum swung the other way: multi-round interviews dragged out for months as budgets tightened and approvals slowed.

Now, in early 2024, the pendulum is swinging back toward a candidate-driven market. Talented professionals have options again, and companies that drag their feet risk losing out.

For employers, this means:

• Shorter, more efficient interview processes
• Clear communication on compensation and role expectations
• Reputation matters—candidates talk, and slow or unclear hiring practices can hurt a brand

9. Making Warehousing Sexy: The Gen Z Challenge

Despite all the investment and innovation, warehouse automation still struggles with a perception problem. Among younger professionals and students, warehousing remains the least appealing link in the supply chain.

That’s changing—slowly—thanks to the “cool factor” of robotics. But companies need to do more:

• Showcase technology-forward brand identities (GXO is a standout in this regard)
• Publicize internships, not just job postings
• Engage directly with universities, not just job boards
• Celebrate diversity and purpose, values important to Gen Z

Bringing automation to life with storytelling, social content, and in-person events can help attract the next generation of talent—and ensure that the field doesn’t just grow, but thrives.

Conclusion: The Road Ahead Is Human-Centered

Warehouse automation is advancing at a blistering pace, but its success still hinges on one thing: people.

From engineers and project managers to operators and software developers, the future of logistics will be built by teams who understand both machines and the human systems they serve. For employers, this means investing not just in tech—but in pipelines, partnerships, and programs that develop the talent to deploy and support it.

And for job seekers, it’s a call to action: whether you’re in operations, engineering, IT, or logistics, there’s a place for you in the automation ecosystem—if you’re willing to learn, adapt, and connect.

As 2025 approaches, one thing is clear: automation may be mechanical, but its momentum is powered by people.

The post How to recruit top talent for warehouse automation? appeared first on RoboticsBiz.

In this in-depth guide, we unpack the four defining characteristics of flexible robotic storage automation, distilling key insights from leading automation experts. Whether you’re an enterprise managing a vast network of distribution centers or a third-party logistics (3PL) provider navigating short-term contracts, this article offers a roadmap to ensure your automation investments are future-proof, scalable, and people-friendly.

1. Beyond Buzzwords: Understanding True Flexibility in Automation

Before diving into what to look for, it’s crucial to understand what flexibility in robotic automation actually entails—and how it differs from being merely “changeable.”

Traditional systems like shuttle-based automation or rigid racking infrastructures are often changeable but at a high cost. Scaling them involves significant labor, infrastructure overhauls, or months-long downtimes. In contrast, flexible robotic automation is about ease of change—systems that can adapt with minimal disruption, cost, or time investment.

This distinction matters because warehouse environments are inherently dynamic. Whether it’s seasonal peaks, new product lines, or changing fulfillment models (B2B to D2C, for instance), flexibility allows companies to respond quickly without being hamstrung by their own technology.

2. Scalability Without Downtime: The Power of Modular Design

A hallmark of flexible robotic storage systems is their ability to scale seamlessly—not just upward, but also laterally and even temporarily. In older models, accommodating a 30% spike in volume might mean adding new aisles, rebuilding shelves, or hiring dozens of temporary workers. Flexible automation flips this narrative.

Solutions like autonomous mobile robots (AMRs) or automated guided vehicles (AGVs) are designed with modularity in mind. Need to scale during peak season? Just “plug and play” additional robots. Want to extend storage? Add more shelving modules without pausing operations.

This model is especially useful in industries where demand varies not only annually but weekly. Think food distribution, fast fashion, or online retail, where Mondays and Tuesdays may require double the capacity of Fridays. With flexible systems, businesses can scale operations in real time, reducing over-investment during off-peak times.

An emerging trend reinforcing this agility is the availability of “robotics-as-a-service” (RaaS) models. These allow businesses to rent or lease robotic equipment, minimizing capital expenditure while staying operationally nimble.

3. Facility-Agnostic Automation: Portability Across Locations

Another defining trait of a flexible system is its ability to function across multiple facilities. Whether you’re a 3PL managing short-term contracts or a retailer expanding your footprint, the capacity to redeploy automation tech between locations is game-changing.

Imagine a network of 30 distribution centers (DCs). If labor shortages or cost optimization strategies force you to consolidate or shift operations, rigid systems become deadweight—expensive to move and even more costly to replace. On the other hand, facility-agnostic solutions, especially those using mobile and grid-based systems, can be moved, reconfigured, and reinstalled quickly and with little technical friction.

This portability is vital for:

• Third-party logistics providers, who frequently change sites or clients.
• Retailers building micro-fulfillment centers (MFCs) closer to urban centers.
• Fast-scaling brands, whose operational needs evolve quarterly.

What was once a 10-DC network may now become a 50-node micro-distribution strategy. Your automation must be ready to travel that journey with you.

4. Empowering Employees: People-First Automation

Amid all the talk of technology, one critical component often gets overlooked: the human element. The best automation systems don’t replace people—they empower them.

Flexible robotic systems do this by minimizing stress and uncertainty in warehouse jobs. For example, a system that adapts to daily volume swings means employees don’t need to work double shifts one day and fear job loss the next. Predictability improves job satisfaction.

Moreover, modern robotic picking systems are ergonomically designed, reducing repetitive strain injuries and physical fatigue. Gone are the days of employees walking 10 miles per shift or lifting awkward totes. Instead, they can stay in one zone while robots bring the goods to them.

This people-centric design has real-world implications:

• Lower turnover rates, in an industry plagued by high attrition.
• Higher productivity, since trained employees stay longer and perform better.
• Better recruitment, as companies can advertise automation-enhanced, safe, and steady roles.

Employee surveys consistently show higher satisfaction scores in automated facilities compared to manual ones—not just for the physical comfort, but also because automation signals an investment in their workspace and future.

5. Adaptability for Peak Season Without Starting from Scratch

Perhaps the most immediate test of flexibility is how well an automation system handles peak season workflows. Traditional fixed systems often require temporary labor influxes, overtime, and clunky workarounds to meet Black Friday or Cyber Monday surges.

Flexible systems, however, are designed to adapt. Here’s how:

• Component separation: Storage modules, robots, and workstations operate independently, allowing each to be scaled or modified without affecting the others.
• Multi-format compatibility: Systems that can handle not just standardized totes but also full cases or mixed-load containers can accommodate broader SKU mixes without requiring a parallel setup.
• Reduced infrastructure dependency: Solutions that don’t rely on floor leveling, ceiling height constraints, or massive IT investments can be installed faster and at lower cost.

For example, an operation that once struggled with full-case fulfillment during peak season can now handle it within the same system—if their automation isn’t overly restrictive. The key is to design for variability upfront so that unexpected spikes don’t cripple throughput.

Final Thoughts: Flexibility as a Strategic Advantage

While some critics argue that flexibility in automation is just a buzzword, the industry reality paints a different picture. Companies investing in adaptable robotic systems aren’t just preparing for “what’s now” but building the infrastructure for “what’s next.”

Flexibility doesn’t have to come at the expense of cost efficiency. Today’s modular robotic solutions are often cost-competitive on traditional ROI metrics—volume, speed, and storage density—while offering the added bonus of adaptability. That’s not fluff. That’s future-proofing.

And flexibility isn’t just about how much a system can scale or how fast it can move. It’s also about how easily you can start. Entry-level flexibility—being able to begin with a two-robot setup and scale as you go—lowers barriers to automation adoption across businesses of all sizes.

In a world where supply chains must be agile, warehouses must be smarter, and labor must be empowered, flexible robotic storage automation isn’t a luxury—it’s a necessity.

The post 4 essential traits of truly flexible robotic storage automation appeared first on RoboticsBiz.

As cryptocurrency continues to shape the financial future, many individuals are looking for entry points into the world of blockchain. While buying Bitcoin on an exchange is the most common path, a lesser-known but equally exciting alternative is mining Bitcoin yourself—even from your own computer.

The truth is, most modern computers—especially gaming rigs or high-performance laptops—are already equipped with the necessary tools to mine. With the help of beginner-friendly platforms like NiceHash, you can start generating Bitcoin without needing technical know-how or expensive mining hardware.

This article walks you through the complete process of mining Bitcoin using your home setup. It’s designed for complete beginners and tech-savvy users alike. Whether you’re a student with a gaming laptop, a parent with a desktop collecting dust, or simply a curious investor, this guide is your first step into the world of crypto mining.

Why Mine Bitcoin at Home?

You may be wondering—why even bother mining Bitcoin at home when giant industrial-scale mining farms dominate the landscape?

Here’s why:

• Low Barrier to Entry: No need for specialized ASIC hardware.
• Passive Income: Mine while your computer is idle.
• Educational Experience: Learn how blockchain and cryptocurrency work.
• Scalable Opportunity: Once confident, you can expand your setup or offer services to others.

Now let’s break down the step-by-step guide to transform your computer into a personal Bitcoin mining machine.

Step 1: Set Up the System

a: Ensure You’re Running Windows 10 or 11

First things first, your system should be running Windows 10 or 11. Most users already meet this requirement. If you’re unsure how to install or upgrade, there are dozens of helpful guides and video tutorials online.

Tip: Windows is preferred due to its compatibility with most mining software like NiceHash.

b: Connect Your Computer to the Internet

A stable internet connection is essential for mining. You can use either a wired Ethernet connection or a reliable Wi-Fi setup. Surprisingly, mining doesn’t use much bandwidth—just a basic connection (5 Mbps down/5 Mbps up) is sufficient.

C: Disable Screensaver and Hibernate Mode

This step is often overlooked but critical. If your computer goes to sleep while you’re away, it halts the mining process.

Here’s how to fix that:

• Search for “Screen Saver” in your Windows Start menu.
• Set the screensaver to None.
• Click Power Settings → Change plan settings.
• Set all options for “Turn off display” and “Put the computer to sleep” to Never.
• Hit Save Changes.

This ensures your system stays awake and keeps mining, uninterrupted.

d: Close All Open Programs and Applications

Before you launch the mining software, free up your system’s resources. Close:

• Browsers with multiple tabs
• Resource-heavy applications like Photoshop or games
• Any unnecessary background apps

This maximizes CPU and GPU efficiency for mining tasks.

Step 2: Create a NiceHash Account

Visit NiceHash and sign up for a free account. NiceHash simplifies the mining process by:

• Automatically selecting the most profitable coin to mine
• Paying users in Bitcoin regardless of the mined coin
• Managing wallet addresses and performance tracking through one dashboard

Once signed up, you’ll gain access to your NiceHash wallet—a key element in collecting your earnings.

Step 3: Download and Install the NiceHash Miner

Now that you have an account, it’s time to install the NiceHash Miner:

• From your NiceHash dashboard, click on Mining → Download Miner.
• Choose the version labeled NiceHash Miner (white and orange logo).
• Follow the installation steps: accept the terms, click through the prompts, and finish the setup.

When prompted, paste in your NiceHash wallet address to link your software with your account.

Step 4: Launch and Configure Your Miner

With NiceHash Miner installed, launch the software and follow these final steps:

• Accept any additional prompts (language, miners to install, security pop-ups).
• Paste your mining address manually if not auto-detected.
• Allow firewall exceptions when prompted.

Benchmark your system’s GPU and CPU—this helps the software choose the most efficient algorithms. Benchmarking may take a while. Let it run as long as needed to ensure optimal performance.

If you want to mine with both GPU and CPU, enable them under the “Devices” tab in the NiceHash software. The system will now use both components to maximize mining profits.

Step 5: Start Mining Bitcoin

Once benchmarking is complete:

• Click the Start or Play button in the dashboard.
• Sit back and watch your PC go to work!

Your system will now begin solving complex cryptographic puzzles, earning fractional amounts of Bitcoin as compensation. You can monitor performance, earnings, and temperature statistics in real time.

Tip: Don’t attempt to use your computer heavily while mining—especially for gaming or video editing. Mining consumes a lot of processing power, and trying to multitask can slow down both activities.

Step 6: Add Your Personal Bitcoin Wallet for Withdrawals

While NiceHash provides a built-in wallet, serious miners should eventually withdraw funds to their own secure Bitcoin wallet.

One recommended hardware wallet is the Tangem Wallet—a secure, offline solution that gives you full control of your cryptocurrency.

Once you’ve set up your wallet:

• Go to the Withdraw section in your NiceHash dashboard.
• Enter your external wallet address.
• Withdraw your mined Bitcoin manually or set up automatic transfers.

You’re Officially a Home Bitcoin Miner

Congratulations! You’ve just joined the ranks of cryptocurrency miners worldwide—right from your bedroom, dorm, or office. Whether your setup includes a high-end gaming rig or a modest laptop, you’re now part of the decentralized network that keeps Bitcoin running.

Your PC will now passively generate small fractions of Bitcoin. Though you won’t strike it rich overnight, you’ll steadily earn while gaining hands-on experience in blockchain technology.

Join the Mining Community

The journey doesn’t stop here. The Hobbyist Miner Discord community welcomes newcomers eager to learn more about:

• Mining strategies
• GPU and hardware optimization
• Scaling mining operations
• Troubleshooting performance issues

Joining a community adds tremendous value, offering support, updates, and insights from people with similar setups and goals.

Final Thoughts: Is Home Mining Worth It?

Let’s be honest—home mining won’t make you a millionaire overnight, especially with rising competition and hardware constraints. However, it offers several advantages:

• Low upfront cost (no expensive ASICs)
• Educational opportunity to understand crypto firsthand
• Passive income during idle hours
• Scalability if you choose to expand into GPU rigs or hosted mining services

If you’ve got a reasonably powerful PC or laptop and a bit of curiosity, mining Bitcoin from home can be both rewarding and educational.

And who knows? Today’s side project could become tomorrow’s full-blown mining business.

The post How to start mining Bitcoin with your home PC or laptop appeared first on RoboticsBiz.