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Manufacturing the Future

Manufacturing the Future

Epicor

Manufacturing the Future is dedicated to helping manufacturing leaders future-proof their operations. Each episode features interviews with innovative manufacturing executives, subject matter experts, and thought leaders who share actionable insights, tips, and best practices to embrace technology so they can streamline operations, prepare for what lies ahead, and continue to keep the world turning.

64 - Harmar's Stephen Hightower on the data problem hiding behind AI in manufacturing
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  • 64 - Harmar's Stephen Hightower on the data problem hiding behind AI in manufacturing

    "They have a data problem that's wearing an AI costume. Because if you don't have accurate data, it doesn't matter what you do, you just do it faster." – Stephen Hightower

    A sharp wake-up call from Stephen Hightower, Chief Technology Officer at Harmar, who put a moratorium on development until his team fixed the data underneath. The problem he's solving isn't just data quality, it's the operational waste that builds up when three integrated enterprise systems don't agree with each other, manual workarounds become tribal knowledge, and spreadsheets quietly replace the systems people no longer trust. When your data is broken, AI doesn't fix it. It just does the wrong thing faster.


    In This Episode:

    Stephen walks through how he's rebuilding data confidence at Harmar, a manufacturer of mobility and accessibility solutions. He describes launching a Master Data Initiative using CRUD and RACI matrices to assign clear ownership for every critical data element across engineering, ERP, and customer systems. He explains why cycle time, quality, and unit cost are the only three metrics that matter in manufacturing, and how tracking integration error rates across systems exposes where data breaks down.

    Stephen also shares how his Lean Six Sigma background, starting in the late 90s at Lockheed Martin, shaped his approach to treating broken tech stacks as waste to be eliminated through root cause analysis and corrective action. He describes putting monitoring tools in place to gain insight the ERP system wasn't providing, deploying a digital worker to handle customer care calls so his team can move up the value chain, and using the financial close cycle time as a diagnostic for how well a business is really running. He's also candid about AI's limitations: he uses Claude for data analysis and engineering cycle time problems, but stresses that every AI output requires human verification because it will, in his words, "lie to you all day long."


    Topics:

    Why most AI failures start with a data problem wearing an AI costumeRunning a master data initiative using CRUD and RACI matrices for clear ownershipTracking integration error rates across enterprise systems to expose wasteApplying Lean Six Sigma to broken technology stacksFinding the hidden spreadsheets that signal system waste and broken trustUsing monitoring tools to gain real operational insight the ERP wasn't providingDeploying a digital worker for customer care operationsWhy financial close cycle time reveals operational healthThe "go find the spreadsheets" test for any manufacturing business


    A conversation with Stephen Hightower of Harmar about why most manufacturers have a data problem wearing an AI costume, and how master data management and Lean Six Sigma applied to technology fix it from the inside.


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    Thu, 13 Aug 2026 - 36min
  • 63 - Tyler Madsen and Jason Bassett on cutting spec review from 2 hours to 15 seconds with AI

    "What used to take me two hours takes me about 15 seconds now." – Tyler Madsen

    A striking before and after from Tyler Madsen, Director at Madsen's Millwork & Custom Cabinets, who rebuilt their estimating workflow by feeding drawings and specifications into an AI bot that strips out irrelevant data and returns only what's needed to quote a job.

    The broader problem he and IT & Asset Manager Jason Bassett are solving isn't just speed, it's the operational drag that builds up when a manufacturing shop is still running on paper drawings, manual data sorting, and an IT person who becomes the bottleneck every time someone needs an answer.

    When information lives on paper, you get version control failures, field installers working off outdated drawings, and a team spending its time managing data instead of acting on it.

    In This Episode:

    Tyler and Jason walk through how Madsen's Millwork has digitized its day to day operations, from estimating to the shop floor to field installation.

    Tyler describes feeding specs and drawings into an AI bot that strips out irrelevant data for quoting.

    Jason covers the physical changes on the shop floor: big screen displays at every workstation now pull live drawings, replacing paper that created double data sets and made version control nearly impossible. He also gives field installers the same live job data on site, so a mid job change reaches them in real time, and describes building knowledge banks inside Epicor Prism pre-loaded with the questions his team most commonly brought to IT, so employees self-serve instantly instead of waiting on him.

    They're also candid about what isn't solved yet: getting skilled trades workers to trust and adopt AI day to day remains an open challenge, and they also flag hallucinations as a real operational risk that requires experienced human oversight to catch.

    Topics

    Cutting spec review from two hours to 15 seconds using an AI bot

    Building Epicor Prism knowledge banks to eliminate the IT bottleneck

    AI owns data retrieval; humans own judgment, their workflow decision framework

    The mutual checking model: humans verify AI output, AI challenges human decisions

    Replacing paper drawings with live digital displays at every shop workstation

    Field installers accessing real-time job drawings remotely during installation

    Using client AI renderings as the engineering starting point for custom projects

    Why getting skilled trades workers to trust AI remains their biggest unsolved challenge

    Hallucination risk in manufacturing operations and how to stay vigilant

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    Thu, 30 Jul 2026 - 15min
  • 62 - Mike Wargocki on how Framebridge pushed gross margin from 27% to 40% during peak season

    "Everything is basically a fact-finding mission. You're trying to figure out what you can do, how fast you can do it, and how efficiently you can do it." – Mike Wargocki


    Manufacturing leaders who scale fast tend to break their own operations without realizing it, not through a single bad decision, but through a string of reasonable ones: reinvesting in an existing plant one quarter too late, forcing identical equipment into buildings that were never built for it, or promoting an early team member into a role they were never suited for. The trouble is that most of these metrics and mistakes don't show up on a standard dashboard. Mike Wargocki has spent his career finding the operational truths that hold across food, biotech, and custom consumer manufacturing, and figuring out which numbers actually predict performance versus which ones just look good in a report.Much of this conversation draws on Mike's time at Framebridge, a company rethinking how custom framing is designed, produced, and delivered. Framebridge built a direct-to-consumer model that turned a traditionally complex, expensive process into something far more accessible, and has produced over two million custom frames as a result. The company has continued scaling quickly, bringing new manufacturing facilities online across the US to support both e-commerce and retail growth.


    In This Episode:

    Mike Wargocki, VP of US Operations at DINGS' Motion USA, walks through the operational lessons he built over a career spanning research chemistry, food, biotech, and custom consumer manufacturing. He explains why OEE stops being a useful metric once production gets custom, and why he shut down a limestone cave facility outside St. Louis despite its near-free refrigeration, once quality started slipping. He breaks down the real cost of forcing identical equipment across a multi-site network, how to time a new facility build against a real growth plateau instead of a projected one, and how his team planned peak season staffing at Framebridge without blowing the annual budget. He also shares where he sees manufacturing leaders waste money without noticing, why early employees aren't always the right fit for the next stage of growth, and how his team has used AI tools to help non-native English speakers write difficult workplace communications.


    Topics discussed:

    Why OEE fails as a metric in custom manufacturingThe real cost of forcing equipment uniformity across facilitiesTiming new facility builds against a growth plateauBalancing peak season staffing without blowing the annual budgetClosing a newly built facility over a hidden quality trade-offApplying a chemist's fact-finding approach to operations leadershipRight-sizing early team members as a company scalesUsing AI to help non-native speakers write difficult communications


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    Thu, 16 Jul 2026 - 33min
  • 61 - Daniel Topp on why ERP projects fail at training, not technology

    "We're common where possible and unique where necessary."

    A deceptively simple principle from Daniel Topp, VP of IT at TASI Measurement, that gets very complicated when you're managing ERP across 16 autonomous business units and integrating newly acquired companies on a rolling basis. The real question is how you operationalize it so it doesn't collapse under the weight of competing business unit priorities and leadership requests for customization.

    TASI Measurement is a global industrial measurement holding company with more than 1,000 employees, headquartered in Largo, Florida, operating through a highly decentralized structure where each business unit runs independently under the broader group.

    In This Episode:

    Daniel walks through the specific systems he's built to keep ERP standardization from becoming a constant negotiation. That includes a formal customization approval process requiring sign-off from key stakeholders before any deviation from off-the-shelf is permitted, and a 30-60-90 day acquisition integration playbook that separates non-negotiables like IT security tools from ERP decisions, which get evaluated through a risk and value heat map. He explains why data migration is where repeat ERP transformations actually improve, and why building a dedicated headquarters-level migration team, rather than relying on business unit staff, is what makes the process repeatable and scalable. He also makes the case that locking in structural decisions like chart of accounts early in a project is the difference between finishing on time and losing months at the end. On AI, his position is direct: having it available inside your ERP without a structured rollout plan is a liability, not an advantage.

    Topics:

    Why ERP projects fail at training and change management, not implementation

    Formal customization approval process requiring stakeholder sign-off

    30-60-90 day acquisition integration playbook and what's non-negotiable from day one

    Risk and value heat map for sequencing ERP integration priorities across acquisitions

    Gold standards center of excellence and how it serves newly acquired businesses

    Why locking in decisions like chart of accounts early can make or break a project timeline

    Building a dedicated, headquarters-level data migration team as a repeatable capability

    Unstructured AI rollout inside ERP as an organizational liability

    Translating IT efficiency into quantified dollar savings to shift IT from cost center to strategic partner

    Meta Description:

    A conversation with Daniel Topp, VP of IT at TASI Measurement, about how he built repeatable systems for managing ERP standardization and acquisition integration across 16 autonomous business units, and what it actually takes to position IT as a strategic partner in a decentralized global organization.

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    Thu, 18 Jun 2026 - 14min
  • 60 - JetStor’s Jim Gallagher on Why 'Good Enough' Infrastructure Costs More Than You Think

    "Every company is becoming both a data company and a bank. If you're not doing this stuff and not keeping an eye on it, other people are." 


    A lasting warning from Jim Gallagher, CEO at JetStor, who argues that although manufacturers are generating more data than ever, most haven't built the infrastructure to actually use it. The common mistake isn't a lack of storage; it's treating storage as a flat, one-size-fits-all decision rather than a tiered architecture matched to how data gets used. Without that foundation, companies can't run real-time analytics, can't prepare for AI workloads, and are quietly accumulating technical debt that compounds over time.

    Jetstor is a US-based enterprise storage company with more than 30 years in the business. The company designs and manufactures scalable storage systems built for demanding workloads, including virtualization, high-performance computing, media production, and data-intensive manufacturing environments. 


    In This Episode: 

    Jim walks through how manufacturers should be structuring their data strategy, starting with a three-tier classification framework: tier 1: for real-time, latency-sensitive workloads; tier 2: active archive for data that still needs to be accessible; and tier 3: deep archive for long-term retention. He explains why staying with legacy infrastructure isn't actually "free.” Jim closes with a concrete challenge for manufacturing leaders: when was the last time your team actually tested your backups? 


    Topics

    Why storage is not a flat ecosystem, and the performance-cost trade-offs that actually matter

    Three-tier data classification: real-time, active archive, and deep archive

    The "data lake" trap: why unstructured data hoarding happens and what it actually costs

    Why training workloads and inference workloads need entirely different architectures

    The 1-2% annual hardware failure rate and what that means for legacy infrastructure planning

    How the DevOps movement in IT foreshadows the IT/OT convergence coming to manufacturing

    Why "when did you last test your backups" is the question manufacturing leaders should be asking right now

    Ransomware as a business risk, data insurance products, and what underwriting requirements actually look like

    Why manufacturers that have been gathering data for decades may be sitting on unexpected revenue streams 


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    Thu, 16 Apr 2026 - 1h 03min
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