Across 101 manufacturing podcasts and 3,182 episodes, the data shows an industry grappling with tariffs, a retiring workforce, and regulatory pressure from Europe.
- Reshoring mentions jumped 135% year-over-year
- Workforce and talent discussions climbed 32%
- Sustainability content rose 54%
Why I did this
I'm a web developer who works with manufacturers and wanted to see what the industry is actually talking about. Everyone does year-end rewinds now, so I figured manufacturing podcasts deserved one too. Take this for what it is: a curiosity project, not academic research.
What's changing
I compared 2025 episodes (3,182) to 2024 (2,768). These are the topics growing fastest:
| Topic | 2024 | 2025 | Change |
|---|---|---|---|
| Reshoring and Made in USA | 46 | 108 | +135% |
| Supply chain | 453 | 715 | +58% |
| Sustainability | 452 | 694 | +54% |
| Digital transformation | 521 | 692 | +33% |
| Workforce and talent | 941 | 1,239 | +32% |
| AI and automation | 1,995 | 2,510 | +26% |
What might be driving this
- Reshoring (+135%): Probably the tariffs. China tariffs hit 145%, imports dropped 26% YoY.
- Supply chain (+58%): Post-COVID reckoning plus tariff uncertainty. China sourcing down from 90% to 50% over the decade.
- Sustainability (+54%): Regulatory pressure. EU's CSRD took effect January 2025, California disclosure rules coming 2026.
- Workforce (+32%): Retirement wave. 11,000 Americans hitting 65 daily, 1.8M manufacturing workers expected to retire.
Sources: CNBC, Supply Chain Management Review, KEY ESG, Protected Income, KNOWRON
What they're talking about
I measured how many different podcasts discussed each topic. This matters more than episode counts because a few prolific shows can skew the numbers. When 65+ independent podcasts cover the same topic, that's the industry speaking.
| Topic | Podcasts | % of shows | Episodes |
|---|---|---|---|
| Leadership and strategy | 74 | 73% | 1,582 |
| AI and automation | 67 | 66% | 1,207 |
| Workforce and talent | 65 | 64% | 930 |
| Quality and compliance | 61 | 60% | 645 |
| Sustainability and ESG | 61 | 60% | 485 |
| Supply chain | 59 | 58% | 667 |
| Lean and operational excellence | 56 | 55% | 671 |
| Digital transformation | 47 | 47% | 386 |
| ERP/MES systems | 33 | 33% | 159 |
| Reshoring and Made in USA | 28 | 28% | 108 |
Topics identified via keyword matching on episode titles and descriptions.
Why podcast counts matter more than episode counts: Some topics have inflated episode counts due to prolific single-topic shows. For example, "robotics" appears in 370 episodes, but 51% come from Industrial Robotics Weekly alone.
Who's getting booked
I extracted LinkedIn profiles from episode descriptions to find guests who appeared on multiple different podcasts. When hosts independently invite the same person, that's a signal.
Guests appearing on 3+ different podcasts in 2025
| Guest | Podcasts | Episodes | Shows include |
|---|---|---|---|
| Andrew Crowe | 4 | 16 | Making Chips, Manufacturing Culture, Manufacturers Network |
| Jake Hall | 4 | 12 | Manufacturing Marketer, Manufacturing Unscripted, 4.0 Solutions |
| Paul Van Metre | 3 | 20 | Making Chips, Manufacturing Mavericks, Manufacturing Transformed |
| Chris Luecke | 3 | 10 | Making Chips, Manufacturing Hub, Manufacturing Unscripted |
| Meaghan Ziemba | 3 | 10 | Making Chips, Fabricator Podcast, Manufacturing Unscripted |
| Allison Giddens | 3 | 9 | Making Chips, Supply Chain Now, Manufacturers Network |
| Jeff Winter | 3 | 4 | Manufacturing Happy Hour, Manufacturing Hub, 4.0 Solutions |
| Rick Bullotta | 3 | 4 | Manufacturing Happy Hour, Connected Factory, 4.0 Solutions |
| Ann Wyatt | 3 | 4 | Manufacturing Culture, Manufacturing Unscripted, Manufacturers Network |
An additional 45 people appeared on 2 different podcasts. Full list of 1,415 LinkedIn profiles extracted available on request.
Which shows are rated highest
I scraped Apple Podcasts for ratings. These are the top-rated shows with 20+ reviews:
| Podcast | Rating | Reviews |
|---|---|---|
| Arc Junkies | 4.8 | 282 |
| Business of Machining | 4.8 | 215 |
| Gemba Academy Podcast | 4.8 | 197 |
| Within Tolerance | 4.9 | 103 |
| Manufacturing Happy Hour | 4.9 | 102 |
| Being an Engineer | 4.9 | 54 |
| The Industrial Talk Podcast Network | 4.9 | 53 |
| The Robot Report Podcast | 4.9 | 39 |
| Futurized | 5.0 | 29 |
| The Manufacturing Employer | 5.0 | 27 |
Ratings as of December 2025.
Which companies get mentioned
I used named entity recognition (spaCy) to extract organizations mentioned across all episodes:
| Company | Mentions | Context |
|---|---|---|
| Toyota | 553 | Lean/TPS benchmark |
| FDA | 213 | Pharma/medical manufacturing compliance |
| Amazon | 202 | Supply chain, logistics, automation |
| Siemens | 169 | Industrial automation, PLM |
| Ford | 139 | Auto manufacturing, EV transition |
| Microsoft | 108 | Cloud, AI, enterprise software |
| Tesla | 97 | EV manufacturing, innovation |
| GE | 87 | Industrial conglomerate reference |
Toyota's dominance reflects its status as the benchmark for operational excellence. The Toyota Production System remains the reference point for lean discussions.
Methodology and limitations
I'm not a data scientist or NLP expert. This is keyword matching and web scraping, not machine learning.
How I did it
- Source: RSS feeds from 101 manufacturing-focused podcasts
- Content: Episode titles and descriptions (not transcripts)
- Period: 2025 episodes through December 15
- Volume: 3,182 episodes in 2025; 13,379 total all-time
- Topic classification: Keyword/regex matching
- Entity extraction: spaCy NER
- Guest identification: LinkedIn URL extraction from descriptions
- Ratings: Scraped from Apple Podcasts
- Orchestration: Claude Code handled the heavy lifting: writing Python scripts, parsing RSS feeds, running analysis, and iterating on the output
Known limitations
Prolific podcast skew: Five podcasts produced 28% of 2025 episodes. This inflates episode-level metrics for topics they cover heavily:
- "Supply chain" mentions: 34% from Supply Chain Now
- "Robotics" mentions: 51% from Industrial Robotics Weekly
I use podcast counts (not episode counts) as the primary metric to mitigate this.
- Metadata only: Analysis based on titles/descriptions, not audio. Topics discussed verbally but not in metadata are missed.
- Keyword limitations: Regex matching catches false positives and misses synonyms.
- Selection bias: Podcast list curated from directories; independent shows may be underrepresented.
What this can and cannot tell you
Can tell you: What topics podcast producers choose to cover; which guests get booked across multiple shows; relative popularity based on ratings.
Cannot tell you: What topics are most important to the industry; quality of individual episodes; listener engagement or downloads.
Full podcast list (101 shows)
All podcasts included in this analysis, sorted by 2025 episode count:
Plus 51 additional podcasts with fewer than 22 episodes in 2025.
I'm sure I missed some great shows and probably included a few that don't quite fit. Let me know what I got wrong.
Looking forward to keeping tabs on the manufacturing industry in 2026!