The Manufacturers Network

What if every manufactured part could be identified and traced using nothing more than a photograph?

In this episode of The Manufacturers Network Podcast, Lisa Ryan talks with Roei Ganzarski, CEO of Alitheon, about manufacturing traceability, counterfeit parts, quality control, AI, automation, and technology that gives physical objects something similar to their own unique fingerprint.

Roei's background includes Boeing, aviation, software, electric aircraft, and manufacturing technology. Throughout his career, he kept encountering the same problem: manufacturers rely on paperwork, serial numbers, barcodes, QR codes, labels, and tags to prove that a physical part is the part everyone thinks it is.

But what happens when the label falls off, a serial number is copied, a QR code is moved, or a counterfeit part looks almost identical to the original?

Alitheon's technology, called FeaturePrint, uses photographs to identify individual physical items without adding a barcode, tag, sticker, RFID chip, or other marker.

Manufacturing processes produce parts within allowable tolerances, but seemingly identical parts still have tiny physical differences. FeaturePrint uses those variations to create what Roei describes as "biometrics for things."

That means a manufacturer can potentially identify whether a specific item has been seen before, trace it through production, or determine that an item isn't the one it claims to be.

Counterfeiting isn't limited to fake watches and designer bags.

Roei discusses counterfeit and gray-market products involving pharmaceuticals, automotive brake pads and airbags, healthcare products, military components, and other high-consequence applications.

For manufacturers, the risk extends in both directions.

Incoming counterfeit tooling, components, filters, or materials can affect production and quality. Outgoing counterfeit products can create safety risks, warranty problems, financial losses, and damage to brand reputation and customer trust.

Traceability becomes especially valuable when something goes wrong.

Imagine parts begin drifting toward the edge of an acceptable tolerance. If individual parts can be traced through production, leaders may be able to identify what the affected parts have in common, such as a specific machine, production line, shift, or point in the process.

The same principle applies when products come back for warranty claims or service.

Instead of wondering whether there's a quality problem across the entire operation, better manufacturing traceability can narrow the investigation and give people better information for making decisions.

As manufacturers pursue Industry 4.0, smart factories, AI, and greater automation, Roei argues that companies need visibility into more than what their machines are doing. They also need reliable information about the physical products moving through those machines.

Technology can provide data. Human beings still need to interpret it, question assumptions, investigate problems, and decide what to do.

For Roei, that makes critical thinking increasingly valuable as factories become more automated and data-driven.

Manufacturers are being flooded with AI pitches. Roei recommends asking practical questions before investing:

• Does the technology solve an expensive, high-consequence problem?

• Can the vendor demonstrate it live using your products?

• Who owns the intellectual property behind the solution?

• Is the AI proprietary technology or primarily built on someone else's platform?

• Does it solve the physical problem or only manage the data around it?

• What happens if the label, barcode, identifier, or underlying assumption is wrong?

Roei also challenges manufacturers to distinguish between protecting data and protecting physical products. Blockchain may protect digital records, for example, but accurate data can still be connected to the wrong physical item if the identifier itself is compromised.

• Manufacturing traceability without barcodes, labels, or serial numbers

• How digital fingerprints can identify individual manufactured parts

• Counterfeit and gray-market risks in manufacturing

• Quality control and product serialization

• AI-powered physical item identification

• Supply chain traceability and authentication

• Warranty fraud and product returns

• Industry 4.0 and smart manufacturing

• Human judgment and critical thinking in automated factories

• How manufacturers should evaluate AI vendors and technology investments

• Why better data can help people make better manufacturing decisions

As manufacturing becomes more automated, knowing what a machine did is only part of the story. Manufacturers also need confidence that the physical part in front of them is exactly the part they think it is.

Guest: Roei Ganzarski

CEO, Alitheon

#Manufacturing #ManufacturingTechnology #AIinManufacturing #Traceability #CounterfeitParts #Industry40 #SmartManufacturing #QualityControl #SupplyChain #Automation #HumanJudgment #ManufacturersNetwork

Your manufacturing business has insurance. But will it actually cover the risks your operation has today?

In this episode of The Manufacturers Network Podcast, Lisa Ryan talks with Michael Senderovich, President of Six Thirteen Business Insurance Services, about insurance gaps small and mid-sized manufacturers may not discover until there's a claim. They discuss equipment coverage, workers' compensation, cyber liability, AI, automation, additional insureds, employment practices liability, and the questions manufacturers should be asking their insurance brokers.

Manufacturers may have significant investments in CNC machines, tooling, inventory, and equipment that has been operating for decades.

Michael explains why understanding the difference between actual cash value and replacement cost matters. A 40- or 50-year-old machine may be essential to production, but replacing its capability after a major loss could be much more complicated than its depreciated value suggests.

The problem gets worse when insurance policies simply renew year after year without anyone reviewing what has changed.

Michael recommends an annual insurance review even when the business appears unchanged. Manufacturers should discuss new equipment, products, employees, facilities, storage configurations, processes, and other operational changes with their broker.

As manufacturers add AI, robotics, automation, and connected equipment, their risk profile changes.

Michael identifies cyber liability as a major concern for connected manufacturing operations. Small and mid-sized manufacturers shouldn't assume they're too small to become ransomware or cyberattack targets.

AI can create additional insurance questions. Depending on how AI is being used and what happens when an automated system makes an error, traditional general liability coverage may not respond the way a manufacturer expects. Professional liability or errors and omissions coverage may need to be considered depending on the operation.

The takeaway: tell your insurance broker how you're actually using AI and connected technology. Don't assume an existing policy automatically covers a new risk.

When an employee is injured on the job, Michael's advice is straightforward: act quickly.

Take care of the employee, notify the insurance carrier promptly, document what happened, and preserve relevant evidence. Delays and poor documentation can make a workers' compensation claim more difficult.

Manufacturers should also understand whether their workers' compensation limits and policy provisions match the actual risks employees face.

Michael also explains a common source of confusion involving additional insured status.

Being listed as an additional insured does not necessarily provide the broad protection a customer, supplier, contractor, or business partner may assume it does. Certificates of insurance also need to be managed and monitored rather than filed away and forgotten.

That false sense of security can become expensive when a claim occurs.

Beyond general liability and workers' compensation, Michael recommends manufacturers discuss coverage such as cyber liability and employment practices liability insurance (EPLI).

EPLI can address claims involving employment practices such as wrongful termination or discrimination. Manufacturers need to understand where their policies leave them exposed before a claim tests the coverage.

• What exclusions in our policies create our biggest vulnerabilities?

• Is our equipment insured for actual cash value or replacement cost?

• Have changes to our products, processes, machinery, facilities, or workforce changed our risk profile?

• Does our cyber insurance reflect our use of AI, automation, and connected equipment?

• Are our workers' compensation limits appropriate for our operation?

• Are certificates of insurance and additional insureds being properly managed?

• Should we carry employment practices liability insurance?

• When was the last time our policies were reviewed against how we actually operate today?

Manufacturing insurance isn't something to put on autopilot. Your operation changes. Your technology changes. Your equipment changes. Your risks change with them.

The policy that worked five years ago may not protect the manufacturing business you're running today.

Guest: Michael Senderovich

President, Six Thirteen Business Insurance Services

#Manufacturing #ManufacturingInsurance #RiskManagement #CyberSecurity #AIinManufacturing #Automation #WorkersCompensation #ManufacturingLeadership #CyberInsurance #EPLI #ManufacturersNetwork

What happens when a manufacturing leader’s experience becomes the very thing keeping the operation from improving?

In this episode of The Manufacturers Network Podcast, Lisa Ryan talks with Jared Scott, National Training Manager at SEW-EURODRIVE and author of Edge Over Ego: Why Being Right Isn’t Enough and What Actually Makes Leaders Effective, about manufacturing leadership, workforce development, technical training, AI, and the generational shift happening across the industry.

Jared has worked in manufacturing since 1995, starting with hands-on industrial maintenance before moving into workforce training and leadership development. He has watched manufacturing change dramatically, but one challenge remains: experienced leaders can become so accustomed to how things are done that they stop noticing what could be better.

That’s where ego gets expensive.

Jared shares a story from early in his career when he found a simple way to grease tapered bearings more efficiently using a basketball needle. Instead of considering the idea, the experienced employee training him rejected it because Jared hadn’t “paid his dues.”

It’s a great example of how manufacturers can lose good ideas, efficiency improvements, and employee engagement because experience becomes a reason to stop listening.

Lisa and Jared discuss a question every manufacturing leader should consider when a new employee challenges an established process:

What if they’re right?

As manufacturers face skilled labor shortages, retiring experienced workers, AI adoption, automation, and rapidly changing technology, leaders can’t afford to confuse experience with infallibility.

Jared describes the “boiled frog” problem in manufacturing. When you’ve worked inside the same operation for years, problems can become so familiar that you stop seeing them.

The pothole everyone drives over. The inefficient process everyone works around. The machine problem everyone tolerates. The procedure nobody questions because “that’s how we’ve always done it.”

New employees may notice those problems immediately because they haven’t learned to ignore them yet.

Manufacturing leaders don’t have to implement every suggestion. They do need to create an environment where employees can question a process, offer an idea, or point out a problem without being dismissed because of tenure or job title.

Lisa and Jared also explore what happens as artificial intelligence and automation become more prevalent in manufacturing.

AI can increase capability, but manufacturers still need people who understand equipment, processes, troubleshooting, and what actually happens on the shop floor.

At the same time, experienced workers are retiring with decades of practical manufacturing knowledge. Younger employees may enter with less experience but different technical skills, fresh perspectives, and access to new tools.

The leadership challenge is connecting those capabilities instead of allowing generations to dismiss each other.

Jared’s work in manufacturing training has taught him that theory alone doesn’t prepare someone to troubleshoot equipment, machine a component, wire a motor, or solve a real production problem.

Effective manufacturing training requires practical application and instructors who understand the work.

Employees also want to know why a new process is better, how it helps them do their jobs, and whether their experience and feedback matter. Jared shares how employee feedback has led his team to change training because workers discovered a better way to perform the work.

One of Jared’s simplest leadership practices is also one of the hardest: Don’t respond immediately when you feel challenged.

When a leader’s first instinct is to prove they’re right, pause before responding. Then get curious.

“Help me understand what you mean.”

“What made you think this would work better?”

“How could we implement this idea?”

The objective isn’t to give up decision authority. It’s to understand the information before deciding what to do with it.

In this episode:

• How ego can block continuous improvement in manufacturing

• Why experienced leaders stop noticing familiar operational problems

• The “boiled frog” effect and what it costs manufacturers

• How frontline employees uncover waste and inefficiency leaders may no longer see

• Why listening to an idea doesn’t mean you have to implement it

• Manufacturing’s generational leadership shift

• Retiring workers and the loss of practical manufacturing knowledge

• Why vocational education, mechatronics, and skilled trades matter

• AI, automation, and the continuing value of hands-on technical skills

• How ego affects listening and decision-making

• Mental health and workplace well-being in production environments

• How to design technical training that actually sticks

• Why practical experience matters when training frontline workers

Manufacturing needs experienced people. It also needs experienced people willing to reconsider what they know.

The competitive advantage may come from knowing when experience should guide the decision, when a new idea deserves attention, and when being right matters less than getting to a better answer.

Guest: Jared Scott

National Training Manager, SEW-EURODRIVE

Author of Edge Over Ego: Why Being Right Isn’t Enough and What Actually Makes Leaders Effective

#Manufacturing #ManufacturingLeadership #WorkforceDevelopment #SkilledTrades #ManufacturingTraining #AIinManufacturing #Automation #ContinuousImprovement #HumanJudgment #ManufacturersNetwork

What happens on the manufacturing floor when pressure goes up, communication shuts down, and your best employees stop speaking up?

In this episode of The Manufacturers Network Podcast, Lisa Ryan talks with leadership consultant, speaker, and applied neuroscience practitioner Kabrina Ashley about how stress and burnout affect communication, decision-making, safety, employee retention, and human judgment in manufacturing.

One of the earliest warning signs isn't always poor performance. It's silence.

An experienced employee notices something doesn't sound right. A high performer sees a potential problem but stops mentioning it. Someone has an instinct that a machine, process, or production line needs attention, but they've learned that speaking up may get them dismissed as overly cautious.

Kabrina explains how sustained pressure can narrow attention and create tunnel vision. Employees become focused on completing the immediate task, protecting themselves from criticism, and getting through the shift. That can mean missing the small warning signs that could prevent a larger safety, quality, or production problem.

Lisa and Kabrina also discuss the growing importance of human judgment as manufacturers adopt AI and automation. Technology can provide data, dashboards, and alerts, but experienced workers carry what Lisa calls a "sensory library." They can hear, feel, smell, notice, remember, and recognize patterns that may never appear on a dashboard.

The challenge for manufacturing leaders is creating an environment where people still use that judgment and feel safe enough to speak up.

The conversation also explores burnout in manufacturing and the warning signs leaders often miss. High performers may continue producing excellent work while quietly withdrawing. They stop volunteering ideas. They stop staying the extra five minutes. They stop fixing problems nobody asked them to fix. Eventually, the manufacturer risks losing an employee whose knowledge may take two or three people to replace.

Kabrina shares practical, brain-based leadership strategies plant managers and frontline supervisors can use under pressure, including slowing down before reacting, using breathing to regain composure, paying attention to physical signs of stress, and asking better questions instead of settling for "Everybody good?"

In this episode:

• Why silence can be an early warning sign of burnout and disengagement

• How chronic pressure affects attention, communication, and decision-making

• Why psychological safety matters for manufacturing safety and quality

• How AI and automation increase the importance of human judgment and communication

• Why experienced workers' tribal knowledge and sensory knowledge need to be protected

• How generational differences can create communication breakdowns on the shop floor

• Why leaders often repeat the management behaviors they experienced themselves

• Early signs that a high-performing employee is mentally checking out

• Why rewarding your best employees with more work can eventually drive them away

• How breathing and emotional regulation can help leaders make better decisions under pressure

• Why employee behavior that looks like a bad attitude may have another explanation

• How leaders can ask better questions to uncover problems before they become bigger problems

One practical change manufacturing leaders can make immediately: stop asking questions that make "yes" the easiest answer.

Instead of "Everybody good?" or "Did everybody get it?" ask employees what they're noticing. Ask what's working with a new machine or process and what isn't. Ask what they see that leadership may be missing.

When manufacturers invest in AI, automation, and smarter technology, human judgment doesn't become less important. The ability to notice, question, communicate, and intervene may become even more valuable.

#Manufacturing #ManufacturingLeadership #AIinManufacturing #Leadership #EmployeeRetention #ManufacturingWorkforce #Burnout #HumanJudgment #WorkplaceSafety #Automation #ManufacturersNetwork

Manufacturers are investing millions in AI, Microsoft 365, automation, and digital transformation. Yet many organizations are still struggling with the same challenge: getting employees to actually use the technology while keeping operations secure.

In this episode of The Manufacturers Network Podcast, I sit down with Denis O'Shea, Founder and CEO of Mobile Mentor, to discuss what manufacturers need to know about cybersecurity, AI adoption, Microsoft Copilot, and protecting frontline workers.

Drawing on more than two decades of helping organizations bridge the gap between technology and employee adoption, Denis explains why technology alone never solves the problem. Success depends on helping people understand, trust, and use the tools they're given.

We also explore one of the biggest security risks facing manufacturers today: shared passwords. Denis explains why eliminating passwords, moving to cloud-based security, and properly supporting frontline workers can dramatically reduce cyber risk while improving productivity.

If you're a manufacturing executive, plant manager, HR leader, IT professional, or operations leader trying to balance innovation with security, this conversation offers practical guidance you can implement immediately.

• Why manufacturers struggle to get full value from Microsoft 365 and AI investments
• The biggest cybersecurity threats facing manufacturing companies today
• Why shared passwords create unnecessary security risks
• How manufacturers can better support frontline employees using AI tools
• What Microsoft Copilot, AI agents, and workflow automation mean for manufacturing
• Why employee adoption matters as much as technology selection
• The advantages of cloud security over aging on-premise systems
• How to improve both productivity and cybersecurity without overwhelming your workforce

Technology is evolving faster than people can adopt it. Organizations often purchase powerful software but use only a fraction of its capabilities because employees lack training, confidence, or support.

Frontline workers are frequently overlooked during digital transformation initiatives. While executives receive AI training and new tools, employees on the plant floor often receive little guidance, creating both productivity and security gaps.

Manufacturers should prioritize eliminating shared passwords wherever possible. According to Denis, compromised credentials remain one of the leading causes of security breaches, particularly in environments where multiple employees share devices and accounts.

Cloud-based platforms continue to provide stronger security protections than many organizations can maintain on their own, making cloud migration an important long-term cybersecurity strategy.

As AI capabilities expand, organizations also need to monitor usage and costs. New AI assistants, agents, and automated workflows create tremendous opportunities, but they also introduce new budgeting and governance challenges that leaders should understand before scaling adoption.

Denis O'Shea is the Founder and CEO of Mobile Mentor, a global Microsoft partner specializing in helping organizations improve cybersecurity, modern workplace productivity, AI adoption, and employee technology experiences. After spending 15 years with Nokia during its rise to global leadership, Denis founded Mobile Mentor to help organizations close the gap between rapidly advancing technology and real-world employee adoption.

Learn more about Mobile Mentor and Denis's work helping organizations strengthen cybersecurity, improve Microsoft adoption, and prepare for the future of AI in the workplace.

• Manufacturing cybersecurity
• AI in manufacturing
• Microsoft Copilot for manufacturers
• Frontline worker technology
• Manufacturing digital transformation
• Cybersecurity best practices
• Passwordless authentication
• Microsoft 365 adoption
• Cloud security
• AI governance
• Employee technology adoption
• Manufacturing leadership
• Industrial cybersecurity

If you enjoyed this conversation, check out other episodes of The Manufacturers Network Podcast, where manufacturing leaders share practical insights on workforce development, leadership, technology, retention, automation, and building stronger organizations.

Keywords: manufacturing cybersecurity, AI adoption, Microsoft Copilot, manufacturing technology, industrial cybersecurity, frontline workers, digital transformation, cloud security, Microsoft 365, passwordless authentication, manufacturing leadership, cybersecurity best practices, AI governance, manufacturing innovation, employee technology adoption.

Artificial intelligence is everywhere. Unfortunately, so is AI hype.

Manufacturers are hearing promises about smarter factories, automated planning, and AI-powered decision making. But what actually works inside a real manufacturing operation where data is messy, production schedules change hourly, and experienced employees solve problems machines never see coming?

In this episode of The Manufacturers Network Podcast, Lisa Ryan talks with Mike Fedorov, Co-CEO and COO of Applied AI Labs and former manufacturing leader at Mars and Accenture. Together, they cut through the AI buzzwords and focus on practical strategies manufacturers can implement today.

Instead of chasing shiny technology, Mike explains why successful AI projects begin with operational pain points, involve frontline employees from day one, and focus on delivering measurable business value quickly.

If you're wondering how artificial intelligence fits into manufacturing without replacing your workforce or sacrificing human judgment, this conversation is a must-listen.

• Why more than 80% of AI projects fail before delivering meaningful results
• How manufacturers can identify the best first AI project
• Why employee buy-in matters more than software selection
• The difference between AI demonstrations and real factory floor solutions
• How AI can improve production planning and sequencing
• Why dirty manufacturing data shouldn't stop your AI journey
• How experienced employees and AI create stronger decisions together
• The biggest mistakes companies make when implementing AI
• Why resilience still matters even in highly automated operations
• How manufacturers can begin using AI without a massive technology budget

The best AI projects don't begin with technology.

They begin with the biggest frustrations employees experience every day.

Instead of asking, "Where can we use AI?" ask:

• What's slowing us down?
• What's costing us money?
• What repetitive work frustrates our people?

Those answers often reveal the highest-return automation opportunities.

AI is remarkably good at processing data.

Experienced people are remarkably good at recognizing when something doesn't feel right.

The future of manufacturing isn't AI replacing people. It's AI helping experienced employees make faster, better decisions while preserving the judgment developed over decades on the plant floor.

Many organizations delay AI because they believe their data isn't clean enough.

Mike argues that's the wrong approach.

Real manufacturing environments always contain incomplete, inconsistent, or messy data. The goal isn't perfection. It's building AI systems that can operate successfully in the real world.

Manufacturers don't need million-dollar AI initiatives.

Choose projects that are:

• Easy to implement
• Highly valuable
• Capable of delivering measurable ROI quickly

Early wins build confidence throughout the organization and create momentum for larger AI initiatives.

The strongest manufacturing organizations combine:

• Experienced people
• Practical AI tools
• Operational expertise
• Continuous improvement

Technology doesn't replace great employees.

It helps them spend less time fighting systems and more time solving problems that matter.

Mike Fedorov is the Co-CEO and COO of Applied AI Labs, helping mid-market manufacturers deploy practical AI solutions that improve operations, production planning, and business performance. Before founding Applied AI Labs, Mike spent 25 years leading manufacturing, supply chain, and consulting initiatives at Mars and Accenture.

LinkedIn: Mike Fedorov

Applied AI Labs

Email: [email protected]

Hosted by Lisa Ryan, CSP, founder of Grategy and author of Smart Plant: Aligning AI, Automation, and People, The Manufacturers Network Podcast features conversations with manufacturing leaders, innovators, and experts who are helping organizations build stronger cultures, improve employee retention, develop frontline leaders, and prepare for the future of manufacturing.

Whether you're a manufacturer, plant manager, operations leader, HR professional, or executive, each episode delivers practical strategies you can apply immediately to improve workforce performance and business results.

Keywords: AI in manufacturing, manufacturing AI, artificial intelligence for manufacturers, factory automation, manufacturing leadership, AI adoption, manufacturing operations, production planning, production scheduling, AI agents, manufacturing technology, digital transformation, frontline leadership, employee retention, manufacturing culture, operational excellence, Industry 4.0, smart manufacturing, production optimization, manufacturing workforce, manufacturing podcast, manufacturing innovation, manufacturing productivity, human judgment, manufacturing efficiency

How can manufacturers solve workforce shortages while embracing artificial intelligence, automation, and digital transformation?

In this episode of The Manufacturers Network Podcast, Lisa Ryan sits down with Bryce Carpenter, Executive Vice President and Chief Operations & Strategy Officer at Conexus Indiana, to discuss how one of America's strongest manufacturing states is preparing its workforce for the future.

Bryce shares how manufacturers, educators, policymakers, and economic development organizations can work together to strengthen talent pipelines, accelerate technology adoption, and help small and mid-sized manufacturers compete in an increasingly digital economy.

From apprenticeships and plant tours to AI use cases and workforce partnerships, this conversation is packed with practical ideas for manufacturing leaders looking to attract talent, improve operations, and future-proof their organizations.

• Why workforce development remains manufacturing's biggest challenge and biggest opportunity
• How AI is helping reshape the image of modern manufacturing careers
• Why apprenticeships and work-based learning outperform traditional recruiting methods
• How manufacturers can partner with schools to build long-term talent pipelines
• Why digital transformation creates higher-paying jobs instead of replacing workers
• How manufacturers are successfully introducing AI without creating employee resistance
• Why sharing best practices across manufacturers accelerates innovation
• The value of plant tours, peer networking, and manufacturing associations
• How Conexus Indiana collected more than 100 real-world AI manufacturing use cases
• Why collaboration between industry, education, and government is essential for workforce success

• Workforce development
• Manufacturing workforce shortages
• AI in manufacturing
• Digital transformation
• Advanced manufacturing
• Manufacturing leadership
• Apprenticeships
• Work-based learning
• Career and technical education (CTE)
• Community colleges
• Employee retention
• Workforce recruitment
• Manufacturing technology
• Industrial automation
• Cobots
• Manufacturing associations
• Economic development
• Manufacturing innovation
• Supply chain development
• Manufacturing careers

Manufacturers everywhere are competing for skilled workers while simultaneously navigating artificial intelligence, automation, and rapid technological change.

Bryce explains that success isn't just about adopting new technology. It's about building partnerships that connect manufacturers with schools, community colleges, policymakers, and each other.

The companies that thrive won't necessarily be the ones with the biggest budgets. They'll be the organizations that embrace collaboration, invest in people, and create cultures where technology helps employees do more meaningful work.

For manufacturers wondering how to recruit younger workers, introduce AI successfully, or build stronger workforce pipelines, this episode offers a practical roadmap.

Bryce Carpenter is the Executive Vice President and Chief Operations & Strategy Officer at Conexus Indiana, where he helps strengthen Indiana's advanced manufacturing and logistics ecosystem through workforce development, digital transformation, economic strategy, and collaboration between industry, education, and government.

Conexus Indiana works with more than 130 manufacturing and logistics partners to advance innovation, workforce readiness, and competitiveness across one of the nation's largest manufacturing economies.

Learn more about Conexus Indiana:

https://www.conexusindiana.com

Connect with Bryce Carpenter on LinkedIn.

Connect with Lisa Ryan:

https://www.LisaRyanSpeaks.com

Learn more about Smart Plant: Aligning AI, Automation, and People

https://www.SmartPlantBook.com

Listen to more episodes of The Manufacturers Network Podcast:

https://www.TheManufacturersNetworkPodcast.com

• Manufacturing CEOs
• Plant Managers
• Operations Leaders
• HR Leaders
• Workforce Development Professionals
• Manufacturing Executives
• Community College Leaders
• Economic Development Organizations
• Manufacturing Associations
• Career and Technical Education (CTE) Leaders
• Industrial Engineers
• State Manufacturing Partnerships
• Government Workforce Agencies

What happens after your product leaves the factory may determine your profitability just as much as what happens inside it.

In this episode of The Manufacturers Network Podcast, Lisa Ryan talks with Phil Rees, founder of ShipMax and a supply chain strategist with more than 20 years of experience helping manufacturers, retailers, and global brands improve fulfillment, warehousing, customs compliance, and international logistics.

Phil explains why many manufacturers focus heavily on production efficiency while overlooking logistics decisions that quietly increase costs, delay shipments, and create unnecessary risk. He also shares how artificial intelligence is transforming warehousing and fulfillment, why companies should rethink inventory strategies, and what changing global trade regulations mean for manufacturers expanding into international markets.

If you're responsible for manufacturing operations, supply chain management, distribution, procurement, or business growth, this episode offers practical strategies for building a more resilient, efficient supply chain.

• The biggest logistics mistakes manufacturers make after products leave the factory
• Why HS (Harmonized System) codes have a major impact on customs clearance and import costs
• How manufacturers can reduce supply chain costs by minimizing border crossings
• When reshoring, nearshoring, or regional inventory makes financial sense
• How AI is improving warehouse management, fulfillment, inventory forecasting, and logistics planning
• What manufacturers should know about tariffs, customs regulations, and global trade changes
• Why supply chain strategy should be reviewed continuously instead of treated as a one-time project
• Practical ways manufacturers can begin using AI immediately to improve operations

• Manufacturing supply chains
• Global logistics
• Warehousing
• Fulfillment
• Distribution strategy
• Customs compliance
• HS Codes
• International shipping
• Tariffs
• Reshoring
• Nearshoring
• Fourth-party logistics (4PL)
• Warehouse automation
• Robotics
• Artificial intelligence in manufacturing
• AI for supply chain management
• Inventory optimization
• Manufacturing operations
• Global trade

Manufacturers spend enormous resources improving production efficiency, yet many overlook opportunities to optimize what happens after products leave the factory.

As tariffs evolve, customer expectations change, and artificial intelligence reshapes logistics, manufacturers need supply chains that are flexible, data-driven, and globally competitive.

Phil explains why resilient supply chains depend on more than transportation. They require smarter inventory placement, better customs planning, AI-powered decision-making, and continuous adaptation to changing global markets.

Whether you're shipping across the country or around the world, these strategies can help reduce costs, improve customer experience, and strengthen your competitive advantage.

Phil Rees is the founder of ShipMax, where he helps manufacturers, retailers, and growing brands optimize fulfillment, warehousing, logistics, and international distribution. With more than two decades of experience in global supply chain management, Phil specializes in helping organizations simplify complex logistics while preparing for future growth through automation and artificial intelligence.

Learn more about ShipMax:

https://www.shipmax.co.uk

Connect with Phil Rees on LinkedIn. 🎯Phil Rees | LinkedIn

Connect with Lisa Ryan:

https://www.LisaRyanSpeaks.com

Learn more about Smart Plant: Aligning AI, Automation, and People

Amazon.com: Smart Plant: 9798995174707: Ryan, Lisa: Books

Listen to more episodes of The Manufacturers Network Podcast:

https://www.manufacturers-network.com/

• Manufacturing CEOs
• Operations Leaders
• Supply Chain Directors
• Plant Managers
• Logistics Managers
• Procurement Professionals
• Distribution Leaders
• Warehouse Managers
• Manufacturing Executives
• Business Owners
• Industrial Engineers
• International Trade Professionals

Why do so many manufacturers struggle to attract younger workers while companies with strong apprenticeship programs have candidates lining up?

In this episode of The Manufacturers Network Podcast, Lisa Ryan sits down with Sarah Morgan, Toolmaking Apprentice at R&D Leverage (an Adler Company) and the 2026 Apprentice of the Year recognized by the Mold Technologies Division.

Sarah shares how a high school manufacturing tour changed the direction of her career, why apprenticeships are one of manufacturing's strongest recruiting tools, and what employers can do differently to attract, train, and retain the next generation of skilled trades professionals.

Whether you're a manufacturing executive, HR leader, plant manager, educator, or parent exploring career options, this conversation offers practical insights into workforce development, mentorship, and succession planning.

• Why high school manufacturing tours influence career decisions
• How apprenticeship programs help solve the skilled labor shortage
• What Gen Z employees look for in manufacturing employers
• Why visibility and outreach matter more than ever for recruiting
• The importance of mentorship and knowledge transfer before experienced employees retire
• How reverse mentoring strengthens relationships between experienced workers and younger employees
• Why manufacturing careers offer multiple career paths without requiring a traditional four-year degree
• How tuition reimbursement and continuous learning improve employee retention
• Ways manufacturers can partner with local schools to build future talent pipelines

• Manufacturing apprenticeships
• Workforce development
• Skilled trades careers
• Toolmaking
• Plastic injection mold manufacturing
• Manufacturing career pathways
• Employee development
• Succession planning
• Knowledge transfer
• Reverse mentoring
• High school career exploration
• Manufacturing education
• Recruiting younger workers
• Gen Z workforce
• Manufacturing leadership
• Employee retention
• Manufacturing workforce shortages

Manufacturing companies across North America are facing an aging workforce, increasing retirements, and a growing shortage of skilled workers. Sarah's story demonstrates that attracting younger talent isn't simply about higher wages or better technology. It's about visibility, mentorship, career pathways, and creating opportunities for people who may never have considered manufacturing.

Her experience also highlights the importance of structured apprenticeship programs, partnerships with schools, and transferring critical institutional knowledge before experienced employees retire.

Sarah Morgan is a Toolmaking Apprentice at R&D Leverage, an Adler Company, and the recipient of the 2026 Apprentice of the Year Award from the Mold Technologies Division. She is currently completing her Toolmaking degree while working full-time and serves on her local ITEC advisory board, helping strengthen partnerships between manufacturers and schools.

Learn more about R&D Leverage:

https://www.rdleverage.com

Connect with Lisa Ryan:

https://www.LisaRyanSpeaks.com

Learn more about Smart Plant: Aligning AI, Automation, and People

https://www.amazon.com/dp/B0H2FVPZZ5

Listen to more episodes of The Manufacturers Network Podcast:

https://www.manufacturers-network.com/

• Manufacturing CEOs
• Plant Managers
• HR Professionals
• Operations Leaders
• Manufacturing Executives
• Workforce Development Professionals
• Technical Education Leaders
• Career and Technical Education (CTE) Instructors
• Apprenticeship Coordinators
• Manufacturing Association Leaders
• Students exploring manufacturing careers
• Parents and educators

AI pilots look great in a controlled environment. Then they hit the real shop floor and quietly fall apart. In this episode, Lisa Ryan talks with Russell Halper, founder and managing director of Insight Kitchen, about why so many manufacturing AI initiatives stall after deployment and what leaders can do differently before they ever write the first prompt.

Russell's path here is unusual. He started a PhD in mathematics under a founder of chaos theory, moved into operations research (one of the earliest forms of data science), worked at UPS, then went in-house as an internal consultant for a major consumer goods manufacturer. From there, he spent eight years at a boutique Palo Alto consulting firm, helped grow it from 16 to 100 people, saw it acquired by a large global consultancy, and ran its West Coast Generative AI practice. Two years ago, he left to found Insight Kitchen, a boutique data science and AI consultancy focused on operational AI in manufacturing and supply chain.

A pilot is not a proof point; it is a rehearsal for scale. Most organizations design pilots to prove AI works in a controlled setting. Russell argues the real work is designing the pilot to surface the change management, data, and complexity issues that will show up at full scale, before you ever roll it out plant-wide.

A 95 percent solution can be a catastrophic failure. In operational environments, the last few percentage points of accuracy often carry the most real-world risk. Before adopting an AI tool, define what "good enough" actually means for that specific decision, because the threshold changes by use case.

Watch for compounding error, not just error rate. If an AI model is 99 percent as good as your best people but making decisions much faster, small errors can compound quickly. Ask whether faster, slightly less accurate decisions are actually creating value or just creating a bigger mess sooner.

Usage is your earliest warning sign. If the people on the floor are not acting on the AI's recommendations, you already have your answer. A model nobody trusts is not a decision-making tool, no matter how sophisticated it is.

Your senior operators know things your data never captured. A 40-year machine operator who says "something's off with this machine" is picking up on signals that never made it into a sensor feed. When leadership overrides that instinct because "the dashboard is green," they lose the tribal knowledge as retiring workers walk out the door.

"Clean data first" can be a stall tactic. Russell has seen organizations spend endless cycles perfecting data before doing anything with it. His advice: work the actual business problem and pull the data along with you. You will often learn more about your data by using it than by polishing it in isolation.

Start with the business problem, not the technology. The mechanics of building a financial case for an AI project look almost identical to the case for any other software investment. Start with the outcome you want (efficiency, reduced reject rate, faster throughput) and work backward to whether AI is the right tool, not the other way around.

Mid-market manufacturers may have a real speed advantage right now. Fewer legacy systems, more focused operations, and lower internal liability friction mean mid-sized manufacturers can often move faster on AI adoption than large enterprises, which are weighed down by legacy tech and internal politics.

As AI gets better, ask whether your business is getting better with it. Russell's framework: track whether your organization's capability is compounding alongside the technology, or whether you are just layering new tools onto the same old process.

• Where are we chasing a "bright, shiny" AI tool instead of a defined business outcome?
• What does our frontline team know that never makes it into our systems, and how are we capturing it before they retire?
• If usage of a tool dropped to zero tomorrow, would we notice, and would it matter?

Russell Halper is the founder and managing director of Insight Kitchen, a boutique data science and AI consultancy that embeds AI directly into the processes that run supply chain, manufacturing, and revenue management operations. He holds a PhD in Applied Mathematics and brings over 20 years of experience at the intersection of applied mathematics, operations research, and AI, having advised startups, private equity-backed SaaS companies, and Fortune 500 organizations across retail, consumer goods, manufacturing, high-tech, and logistics. Before founding Insight Kitchen, Russell was a partner at End-to-End Analytics (acquired by Accenture) and later led Accenture's West Coast Generative AI practice as Managing Director.

Connect with Russell:

• Insight Kitchen: insightkitchen.ai
• LinkedIn: linkedin.com/in/russellhalper

Lisa Ryan, CSP, MBA, is the founder of Grategy® and Chief Appreciation Strategist. She hosts the Manufacturers Network Podcast and speaks to manufacturing, industrial, skilled trades, and healthcare audiences on employee retention, workplace culture, and the human side of AI and automation. Her newest book, Smart Plant: Aligning AI, Automation, and People, explores how manufacturers can adopt AI without losing the judgment and tribal knowledge their people bring to the floor.

Book Lisa Ryan for your next event, conference, or leadership training on AI, culture, and workforce retention:

• Website: LisaRyanSpeaks.com
• Email: [email protected]

• Listen to more episodes of the Manufacturers Network Podcast
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