Robots can receive the supplier pallets, store the cases, and build every customer order. But even in highly automated warehouses, one step has held out before the dock: stretch wrapping. In this episode of Manufacturing Matters, recorded on the show floor at Pack Expo International 2026 in Chicago, Aaron Hand talks with Ciaran Donnelly, global business development manager at Lantech, the company that invented the stretch wrapper in 1972, about closing that last gap.

Donnelly takes us inside Lantech’s AMR-integrated stretch wrapping project with Block One Automation at Del Papa Distributing, where autonomous mobile robots carry order-picked pallets straight into the wrapper and on to the dock. Getting to autonomous stretch wrapping took more than good hardware. A human operator can improvise when something goes wrong, but a robot needs every pain point worked out before the first pallet moves. Donnelly is candid about what that meant in practice, from logic bugs found at installation to Friday-night phone calls and Sunday-morning software updates, and why a wrapper designed from the ground up for AMRs comes “as close to plug and play as I’ve ever seen in automatic equipment.”

If your automation plans stop short of the shipping dock, this conversation makes the case that the end of the line deserves as much attention as everything that comes before it.

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Walk the floor at Pack Expo and you’ll see fillers, conveyors, robots, and palletizers, each impressive on its own. But a collection of standalone machines doesn’t make a production line. In this episode of Manufacturing Matters, recorded on the show floor at Pack Expo International 2026 in Chicago, Aaron Hand talks with Dean Elkins, consumer products segment leader at Mission Design & Automation, about what it takes to connect the dots.

Elkins, who spent three decades at Yaskawa and has chaired both A3 and MHI’s Robotics Group, explains why the automation buying journey looks different for consumer products companies, which often lack deep in-house engineering staffs and are driven by labor shortages, scrap reduction, and SKU proliferation. He describes how rainbow packs and private labeling are pushing flexible automation upstream from end of line into primary and secondary packaging, how AI-driven vision and hygienic IP69K robots are closing the gap between processing and packaging, and how collaborative robots (cobots) are here to stay.

If you’re weighing your next automation project and trying to figure out where the gaps are on your line, this episode offers a practical integrator’s perspective from one of the industry’s most experienced robotics leaders.

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“Now tactile is an array of information that gives you insight into the contact mechanics between two objects.”

Pressure is a scalar value. Force is a vector. Tactile sensing, says Pressure Profile Systems founder Dr. Jae Son, is something else entirely — a map of contact across an entire surface, the same sense humans rely on to pick up a coffee cup without crushing it, and one that most robots still don’t have.

In this episode of “Manufacturing Matters,” TECH B2B Marketing’s Winn Hardin sits down with Dr. Son, PPS’s Dr. Charles MacLeod, and longtime robotics industry leader Stu Shepherd (Shepherd Solutions) to unpack why tactile sensing is finally getting its due — driven by humanoids and robots working in unstructured environments where fixed, pre-programmed grasps no longer cut it. The conversation covers PPS’s 65-point Tactile Glove (used both to collect human grasp data for robot training and, oddly, fitted directly onto robotic hands), and two very different medical spinouts: a radiation-free breast exam device now shown to outperform mammograms, and a new sensor for knee-replacement surgery that finally gives surgeons data instead of feel.

Shepherd brings the industry-veteran perspective: Grip force has long been over-engineered because robots couldn’t tell how they were gripping something, and tactile data opens the door to lighter, smarter grippers. Dr. Son also pushes back on humanoid-robot hype, arguing the bigger near-term opportunity is smarter automation broadly, and walks through a very literal use case: giving assembly robots the ability to feel whether a connector clicked into place — something factory-floor noise often makes impossible to hear.

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Machine vision teams will happily argue for hours about cameras, sensors, and AI models. Then they choose the light last — and wonder why the system can’t see the defect it was built to find.

Lindsey Sullivan has a demo that ends the argument. At CCS America’s hands-on lighting roundtables, she hands engineers a battery with a small dent in it. Put a coaxial light at one working distance and the dent is unmistakable — any camera, any software, even the cheapest black-and-white setup catches it instantly. Move that same light a few inches closer and the dent vanishes. No camera can find it. No filter brings it back. No amount of AI rescues it.

In this episode of Manufacturing Matters, TECH B2B Marketing’s Jimmy Carroll and Aaron Hand talk with Sullivan, technical marketing manager at CCS America, about why lighting deserves a bigger role in machine vision. She explains when standard lighting gives way to custom designs, how manufacturing environments can change the equation, and why finding the right wavelength often involves some experimentation. UV can make coatings and inks fluoresce. IR can reveal defects hidden beneath packaging graphics. And SWIR? Sometimes you just have to try it.

Sullivan also explains where artificial intelligence fits into the picture: It can sharpen the marginal calls that once tripped up machines and human inspectors alike, but it can’t recover a defect that the wrong lighting has already hidden.

Her advice to machine vision integrators is simple: Stop choosing the light last.

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Most discussions about industrial automation and analytics concern operations with hundreds of employees. This episode of Manufacturing Matters explores a less traveled quarter: Small shops with maybe a dozen people on the floor, a founder who has run the place for decades, and a spring sales bump nobody has ever been able to explain.

Our guest, Kyle Haworth, built his career building large-scale enterprise data systems before co-founding GuildHall to bring that same discipline to manufacturers under 30 employees and under $10 million in revenue. He notes that small shops often buy advanced technology before their processes are ready to absorb it, which is how some of them end up using a tenth of the ERP system they paid for.

In this episode, TECH B2B Marketing’s Dan McCarthy and Aaron Hand also talk with Haworth about the readiness of small shops to handle artificial intelligence. Asking what AI enables is more productive than asking what AI replaces, Haworth argues. He shares a working example of using AI to connect siloed accounting, ERP, inventory, and quality systems to let owners ask plain-English questions and get a dashboard on the fly.

Haworth’s closing advice for owners who assume they can’t afford outside help: Understand the problem and its inputs first. The tooling is now the cheap part of the solution, but it won’t do much good until owners first understand their processes.

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“You can have a high speed industrial robot, any type of robot really. And it doesn’t need fences, it doesn’t need safety devices. Everything is there. So it adjusts in real time to its environment.”

A fence is usually the price of speed: put a high-speed industrial robot on a plant floor and you cage it off, or slow it down into a cobot and accept the productivity hit. Mantis Robotics founder and CEO Gerry Vannuffelen built the company to close that gap — fenceless robots that run at full industrial speed and adjust in real time to people working around them, reserving a hard stop for the rare moment someone actually reaches out and touches one.

In this episode of Manufacturing Matters, host Wynn Hardin sits down with Vannuffelen at Automate 2026 in Chicago to unpack how Mantis certifies its fenceless robots to the same ISO 10218 and ISO 13849 standards as traditional industrial robots — not through power-and-force limiting like a cobot, but through speed-and-separation monitoring that lets the robot see people and avoid them rather than collide or stop. The conversation covers the real-estate cost of a traditional automation project, why cobots often can’t match a person’s throughput, and how Mantis’s newest dual-arm robot splits the difference between a caged industrial arm and an expensive, slower humanoid — mountable on a pedestal, a cart, or an autonomous mobile robot.

Vannuffelen also digs into Mantis Studio, the no-code deployment platform and real-time digital twin that — unlike most simulation tools — models the people working alongside the robot, not just the work cell. He makes the case for “physical AI” as the layer that closes the gap between AI’s sandbox reasoning and real-world manipulation, why a deterministic safety layer has to sit underneath any AI model that isn’t 100% reliable, and why he expects mobile manipulators — arms on wheels — to keep gaining ground on humanoids in warehouse and plant-floor settings.

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“It’s really a needle in a mountain of needles and you just want to find one.”

For most manufacturers, the hard part of AI isn’t the model — it’s the knowledge locked in the heads of veteran engineers. In this special Automate 2026 episode of Manufacturing Matters, TECH B2B Marketing’s Jimmy Carroll sits down with ReshapeX’s co-founder and CEO Juan Aparicio and Novanta’s Ajaiey Sharma (ATI Industrial Automation) for a candid case study of how the two companies turned decades of tribal knowledge into a working AI agent.

Novanta’s ATI tool changers can be configured millions of ways — the team pegs a single product line at roughly 7 million combinations — and pinpointing the one right configuration used to take days of back-and-forth with application engineers. Sharma and Aparicio walk through how they codified that expertise into an “agentic application engineer” that returns the right answer in minutes, why they set out to be the 5% of enterprise AI projects that succeed rather than the 95% that fail, and the build-versus-buy calculus that led Novanta to partner with ReshapeX.

The conversation digs into the unglamorous work behind a reliable agent: gamified, week-over-week testing with real customer queries, a human-in-the-loop feedback loop that moves the system from probabilistic to deterministic, a knowledge graph that reasons on relationships instead of semantics to reach 99.99% precision, and continuous evals to guard against model drift. They close with a playbook for other manufacturers — define the real problem, secure executive cover, get your solution fit right, and treat it like a high-risk project — plus why codifying knowledge before it walks out the door may be the most important thing an established company can do.

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“The value is moving from the metal to the code.”

As manufacturers add AI and software to their products, the old one-time hardware sale is running out of runway when it comes to improving AI models for manufacturing. For Thales’s Dale Hopkinson, the real opportunity — and the real risk — sits in how that software and AI actually get monetized: not as a bolted-on price tag, but as a flexible, recurring business built to change as fast as the technology does.

In this episode of Manufacturing Matters, TECH B2B Marketing’s Winn Hardin sits down with Hopkinson, Senior Product Manager for Cloud Services at Thales, to unpack why software is decoupling from hardware, what that means for OEMs retasking and repackaging existing equipment, and why Bain projects AI, software, and data will add roughly $70 billion to the market within five years.

The conversation covers the organizational groundwork companies need before they can monetize AI — P&L ownership, dedicated staff, and roadmaps that survive personnel changes — plus the shift from subscription tiers to usage-based and hybrid pricing, the operational lift of managing renewals, entitlements, and channel relationships, and why protecting AI models and training data on the edge is now a board-level conversation.


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