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From Academia to Industry: A Shift in Perspective · Under Pressure: Compressor Talk

• 0:00
  SPEAKER_01: Good afternoon.
• 0:00
  SPEAKER_01: This is David Abshimer with Underprometer Podcast.
• 0:03
  SPEAKER_01: Today we have Dr.
• 0:04
  SPEAKER_01: Sharuk Irin, Business Today.
• 0:07
  SPEAKER_01: Thank you so much for joining us today.
• 0:08
  SPEAKER_01: You're welcome.
• 0:09
  SPEAKER_01: So you spent more than two decades as an industrial engineering professor before realizing that despite teaching on that subject, you hadn't actually experienced what manufacturing looked like on the factory floor.
• 0:20
  SPEAKER_01: What caused that shift in perspective for you?
• 0:23
  SPEAKER_00: In 1999, I saw that my colleague was teaching industrial uh something called lean thinking, and the course was very popular.
• 0:31
  SPEAKER_00: And uh former colleague of mine at Penn State was getting tons of funding from Ford at MIT to do this thing called lean, and I was like, whoa, this I need to get on the bandwagon.
• 0:42
  SPEAKER_00: Around that time, you know how faculty are expected to go look for funding.
• 0:46
  SPEAKER_00: So I reached out to the local Delphi plant and uh scheduled a meeting with their plant manager.
• 0:51
  SPEAKER_00: So I go for the meeting, and before that, I read the book Lean Thinking, and I was like, This is all industrial engineering, you know, what's the difference?
• 0:59
  SPEAKER_00: So I met her and she says, I'm so sorry, I can't spend too much time with you.
• 1:03
  SPEAKER_00: I have to go do a kaizen.
• 1:05
  SPEAKER_00: I said, What's a kaizen?
• 1:06
  SPEAKER_00: Yeah, we you know it's a quick event and we are gonna do something on value stream mapping.
• 1:10
  SPEAKER_00: I said, What's value stream mapping?
• 1:12
  SPEAKER_00: Haven't you read this book?
• 1:13
  SPEAKER_00: And she brought the book out, learning to see, and boom, that was it.
• 1:17
  SPEAKER_00: You know, I was like, I better learn this stuff because I don't know these terms that industry is so excited about, and I don't know things about it.
• 1:25
  SPEAKER_00: So I knew there was a connection between industrial engineering as taught in textbooks, as I had learned it, as I was teaching it, but I was like, this is very practical, and I don't know anything about it.
• 1:36
  SPEAKER_00: And you know, being a researcher, when you don't know anything, the first thing you're like, I need to learn this.
• 1:41
  SPEAKER_01: Yes.
• 1:42
  SPEAKER_00: So that's how it came about.
• 1:44
  SPEAKER_01: Absolutely.
• 1:45
  SPEAKER_01: I love that.
• 1:45
  SPEAKER_01: I love that.
• 1:46
  SPEAKER_01: So you said something that really caught my attention that being an industrial engineer without really any real industrial experience felt like a sham, and that's a pretty strong statement.
• 1:56
  SPEAKER_01: Why did you feel that way?
• 1:57
  SPEAKER_00: You know, if you ask other engineers, you know, mechanical engineers, chemical engineers, electrical engineers, they refer to industrial engineers as imaginary engineers.
• 2:06
  SPEAKER_00: And I I I didn't know that then when I went for my first job interview.
• 2:10
  SPEAKER_00: This professor who was a guru in metal cutting, you know, and machine tool design was like, What's all this BS?
• 2:17
  SPEAKER_00: Yeah.
• 2:18
  SPEAKER_00: You know, he called that mental masturbation.
• 2:20
  SPEAKER_00: Okay, oh wow, this interview is going really well, you know.
• 2:23
  SPEAKER_00: He said, What's this operations research baloney you guys do in nothing?
• 2:27
  SPEAKER_00: And you know, it hit me that our profession has too many academics with math backgrounds, statistics backgrounds, operations backgrounds, where they write a lot of complicated papers, very sophisticated in analytics.
• 2:41
  SPEAKER_00: But when you say, Sir, did you implement that research?
• 2:44
  SPEAKER_00: I published the paper, you know.
• 2:46
  SPEAKER_00: So to me, that is a slap in the face of that, you know, a lot of industrial engineering research doesn't make its way to industry.
• 2:54
  SPEAKER_00: Because, you know, if you go with your act, you know, mathematical papers and your software and and you tell people I bring you the word of God and I'm gonna make all these improvements, those managers are under the gun.
• 3:05
  SPEAKER_00: They need results in three months, and you're like, give me two years to give me this fantastic optimized model.
• 3:11
  SPEAKER_00: People have no time for that.
• 3:13
  SPEAKER_00: So either your research has been proven, you've implemented it, you're confident you can replicate it elsewhere, or else I'm sorry.
• 3:21
  SPEAKER_00: Industry looks at industrial engineers and says, You're imaginary engineers.
• 3:25
  SPEAKER_00: So I realized that.
• 3:26
  SPEAKER_00: And uh when I got funding from the Department of Defense, they said, Look, we are expecting you to adapt lean into job shops.
• 3:35
  SPEAKER_00: These forging companies which bring heavy forgings, high-strength forgings for defense manufacturers, they want results.
• 3:42
  SPEAKER_00: So do your software, do your papers.
• 3:45
  SPEAKER_00: You better implement something every year.
• 3:47
  SPEAKER_00: And uh, you know, it was hard, but like I said again, if you don't know stuff and you can't do stuff, god damn it, go do it.
• 3:54
  SPEAKER_01: Yeah, absolutely.
• 3:56
  SPEAKER_01: Go do it.
• 3:57
  SPEAKER_01: You're exactly right.
• 3:58
  SPEAKER_01: You have to have the hands-on experience to be able to implement what you say is gonna work to be successful, and I I agree a hundred percent.
• 4:08
  SPEAKER_01: That's one thing that I've learned over my career.
• 4:10
  SPEAKER_01: If you can write a paper and say this is gonna work, but if you can't go out and actually do it, then it don't work.
• 4:16
  SPEAKER_00: Yeah, and you know, an industrial engineering command, it contains the word industry, right?
• 4:21
  SPEAKER_00: So I had never worked in industry.
• 4:24
  SPEAKER_00: You know, I went from bachelor's, master's, PhD, assistant professor, associate professor, not a single year of industrial experience.
• 4:33
  SPEAKER_00: And I was teaching from textbooks written by faculty who had nothing to do with the Toyota production system.
• 4:39
  SPEAKER_00: In fact, they criticized it.
• 4:40
  SPEAKER_00: I criticized it.
• 4:41
  SPEAKER_00: But the Toyota production system and lean is kind of a counterfeit, but is basically this is industrial engineering.
• 4:48
  SPEAKER_00: It's done with the people by management who came up from the ranks, so they are able to do it on the floor.
• 4:55
  SPEAKER_00: So the missing thing is that it's too basic.
• 4:58
  SPEAKER_00: That's where we come in.
• 4:59
  SPEAKER_00: If we do good research and we have the confidence to apply it and replicate it, boom, we as academics can move industrial engineering 10 levels higher.
• 5:08
  SPEAKER_01: Absolutely, I completely agree.
• 5:10
  SPEAKER_01: For the years in the manufacturing world, has looked at to the Toyota production system as the gold standard, but you built an entire career arguing that what works for Toyota doesn't necessarily work for most American manufacturers.
• 5:23
  SPEAKER_01: Where does that disconnect come from?
• 5:25
  SPEAKER_00: It's again, people don't want to learn.
• 5:27
  SPEAKER_00: Everybody, forgive me, okay.
• 5:29
  SPEAKER_00: Uh you can burn me at the stake, but the tendency among American manufacturers is give it to us.
• 5:34
  SPEAKER_00: You know, we don't need to go through it.
• 5:35
  SPEAKER_00: So we wait, we waited for you know, General Motors waited for Toyota to come and tell them you don't need to buy robotics to you know have a good performing company, right?
• 5:45
  SPEAKER_00: No me, correct?
• 5:46
  SPEAKER_00: Toyota came and did that, and then as soon as Toyota went away, we went back to doing the same old things, right?
• 5:51
  SPEAKER_00: Oh, it's not done in America, so it but that's the mistake.
• 5:55
  SPEAKER_00: That is a mistake.
• 5:56
  SPEAKER_00: I think that you know, today we are suffering.
• 5:58
  SPEAKER_00: Oh, reshoring, oh my god, China owns all the rare metals, you know, we got to reshore.
• 6:04
  SPEAKER_00: Suddenly there's a this panic.
• 6:05
  SPEAKER_00: What have you been doing for the last however many years?
• 6:08
  SPEAKER_00: Learning nothing.
• 6:09
  SPEAKER_00: So if you don't learn, you don't master.
• 6:11
  SPEAKER_00: And if you don't master, you don't lead, you can't compete.
• 6:13
  SPEAKER_01: Yep, that's exactly right.
• 6:15
  SPEAKER_01: I I agree 100%.
• 6:17
  SPEAKER_01: So you spent the last 25 years developing what you call job shopping.
• 6:21
  SPEAKER_01: What problem were you trying to solve that the traditional lean playbook simply wasn't addressing?
• 6:27
  SPEAKER_00: So if you look at what is called the product process matrix, it's a very classical matrix that puts various types of production systems on the volume versus variety axis, right?
• 6:37
  SPEAKER_00: The Toyota system is assembly manufacturing, so that's high volume, low variety of product.
• 6:43
  SPEAKER_00: Okay, today these lines are more flexible, right?
• 6:46
  SPEAKER_00: They do mixed models, but it's still four-wheeled gizmos.
• 6:49
  SPEAKER_00: They don't make on the same line, they don't make washers, they don't make motors, correct?
• 6:53
  SPEAKER_00: Now, if you go diagonally across that matrix and you look at the bottom corner, which is high variety, low volume, that's what we call the job shops, machine shops, forge shops, furniture manufacturers, steel service centers, maintenance repair overhaul, you know, for example, compressors, you know, national compressor exchange, right?
• 7:13
  SPEAKER_00: So, what I found was that there is low mix, high volume assembly, but then there are opposites.
• 7:19
  SPEAKER_00: There's high mix, low volume parts manufacture, and they're all generally job shops, meaning they get an order, they have to produce it, they have due dates.
• 7:29
  SPEAKER_00: The next order may not be the same, they still have to flex and be able to mix the orders.
• 7:35
  SPEAKER_00: So when I worked with the forging companies who are DOD suppliers, they were all job shops.
• 7:40
  SPEAKER_00: Very high variety of parts, very low volume, variable, uncertain demand.
• 7:46
  SPEAKER_00: They were organized by function, which is the biggest killer when you're doing high mix, low volume.
• 7:51
  SPEAKER_00: I had to go in and design a system that would work for those kinds of companies.
• 7:55
  SPEAKER_00: And so I got a lot of flack.
• 7:58
  SPEAKER_01: Yes, absolutely.
• 7:59
  SPEAKER_00: If you remember if you know the thickness of my skin is quite thick, I mean, like you know, once I was confident that I knew what I was doing, I quit academia because I needed the experience and the viability that I can do this.
• 8:12
  SPEAKER_00: Once I got that ability and the track record, today, no problem.
• 8:16
  SPEAKER_00: You drop me into any high mix low volume shop, anyone, I can help.
• 8:22
  SPEAKER_00: I'm not God.
• 8:23
  SPEAKER_00: There's still a lot to be learned, but no longer will I tell people job shops cannot adopt lean, they can adapt lean, not the way you would do it at an OEM.
• 8:34
  SPEAKER_00: That's the big difference.
• 8:35
  SPEAKER_01: Absolutely.
• 8:35
  SPEAKER_01: I and I completely agree, and that makes perfect sense, especially with most manufacturers in the US today.
• 8:41
  SPEAKER_01: Just like you said, Toyota was a good example of you know, high volume, very, very limited amount of different products that they made.
• 8:49
  SPEAKER_01: So they just made the same product over and over.
• 8:52
  SPEAKER_01: And in my career younger, you know, definitely I got to experience all the different training from being working in manufacturing and learning all the different six sigmas and you know, all the getting to participate and be educated.
• 9:05
  SPEAKER_01: And that's one thing.
• 9:06
  SPEAKER_01: Now owning a job shop is more I mean, we manufacture, but it's definitely a job shop type environment, and it's a different kind of lien where you can't set up just an assembly line type process, it's very decoupled manufacturing.
• 9:20
  SPEAKER_01: And you know, always go back to you know, Toyota was the leader back, you know, 30 or 40 years ago in the, you know, and of course, when all of those things started getting implemented in the US, it was a it was a struggle.
• 9:33
  SPEAKER_01: It was it was a challenge for US manufacturing to be able to utilize what you know Toyota had brought to the stakes to you know to show us.
• 9:41
  SPEAKER_01: So I love that.
• 9:42
  SPEAKER_01: So you had an interesting perspective on AI.
• 9:45
  SPEAKER_01: You weren't dismissing it, but you were questioning whether people expect it to replace decades of hard-earned manufacturing experience.
• 9:53
  SPEAKER_01: Where do you think AI genuinely adds value and where do you think it's being oversold?
• 9:57
  SPEAKER_00: Again, I I speak from ignorance.
• 9:59
  SPEAKER_00: So my exposure to AI is uh a couple of courses on heuristic search and expert systems years ago and a generic knowledge of machine learning and large language models.
• 10:09
  SPEAKER_00: So, you know, if I'm wrong, I'm I apologize for being wrong.
• 10:12
  SPEAKER_00: But here's what I think the model of AI is based on machine learning.
• 10:17
  SPEAKER_00: Now, learning is based on the knowledge you give it.
• 10:20
  SPEAKER_00: If it starts scraping the garbage information on the internet, and that's the only source of information, and all it does is massages and mixes and matches that garbage, you get garbage, better looking, but garbage.
• 10:33
  SPEAKER_00: Now, somebody who's run a CNC machine for 30 years and can sense toolware, can sense vibration, right?
• 10:40
  SPEAKER_00: The AI will have to have acoustical signatures, it'll have to be given all these images of worn tools before it can say, Oh, I understand that this tool is about to break, you know.
• 10:50
  SPEAKER_00: That knowledge takes a lot of gathering, cleaning, organization, feeding to the AI, verifying that it has learned, and then deployment, and then feedback.
• 11:01
  SPEAKER_00: We aren't doing any of that.
• 11:03
  SPEAKER_00: We're just writing some code and saying, hey chat GPT, give me a CNC code for the simple geometry.
• 11:08
  SPEAKER_00: Well, duh, it the AI doesn't know what a tool is, what a rake angle is, what a Okuma does versus what a horse does.
• 11:17
  SPEAKER_00: So everybody like physical AI is the next thing.
• 11:21
  SPEAKER_00: Where's the human being?
• 11:22
  SPEAKER_00: It should be human-centric AI, it should be human in the loop AI, expert in the loop AI.
• 11:28
  SPEAKER_00: So the problem is that these Silicon Valley guys, all these NVDs and all these guys, they are pitching the snake oil that our software replicates intelligence gained from working on things.
• 11:40
  SPEAKER_00: That's phony baloney.
• 11:42
  SPEAKER_00: Everybody's saying there's no ROI.
• 11:44
  SPEAKER_00: Why?
• 11:45
  SPEAKER_00: Because all they could do was eliminate the soft jobs, the white collar job, right?
• 11:49
  SPEAKER_00: Because they deal with software.
• 11:51
  SPEAKER_00: So AI can replace the software people.
• 11:53
  SPEAKER_00: But the welding person, the grinding person, the machinist, even the guy who palletizes pipes, that heuristic knowledge that comes from picking pieces, you know, this is a three-dimensional NPR problem, it cannot.
• 12:07
  SPEAKER_00: AI has no clue.
• 12:09
  SPEAKER_00: I mean, like vision.
• 12:10
  SPEAKER_00: Oh my god, it takes it slices each video and then has to process each slice to make knowledge about a finger being twerked like this.
• 12:19
  SPEAKER_00: What does a finger twerking like this help me to make a product?
• 12:23
  SPEAKER_00: AI could recognize a finger.
• 12:25
  SPEAKER_00: Well, I picked my nose with this finger.
• 12:27
  SPEAKER_00: That's not productive, so I'm cynical, you know.
• 12:30
  SPEAKER_00: So let's see, Jeff Bezos and all these guys who say, you know, we are getting into physical AI.
• 12:35
  SPEAKER_00: Have you even stepped into a machine shop?
• 12:37
  SPEAKER_01: Exactly.
• 12:39
  SPEAKER_00: Do you even know the last 50 years of knowledge of what has good has been done in machine shops, what has not?
• 12:45
  SPEAKER_00: That's what I've done.
• 12:46
  SPEAKER_00: I've I've basically gone back to the 60s.
• 12:48
  SPEAKER_00: I was born in 61.
• 12:50
  SPEAKER_00: They had fixed machine shops then without AI, without algorithms.
• 12:55
  SPEAKER_00: We don't even know that.
• 12:56
  SPEAKER_00: We haven't even read those books.
• 12:58
  SPEAKER_00: So that's my difficulty.
• 13:00
  SPEAKER_00: That where is that knowledge, the real knowledge being given to these AI systems to learn that they can tell manufacturing folks, you this is how you should, you know, program the CNC code for this particular geometry.
• 13:14
  SPEAKER_00: I don't see that.
• 13:15
  SPEAKER_00: I I just I don't see that.
• 13:16
  SPEAKER_01: Yeah, absolutely.
• 13:17
  SPEAKER_01: One of the things that you said that I really love is garbage in and garbage out, right?
• 13:22
  SPEAKER_01: So and that's one thing, just like you said, you can't take the knowledge from the operator of a CNC machine that's done it for 30 years and try to replace it with AI.
• 13:32
  SPEAKER_01: It's just impossible.
• 13:34
  SPEAKER_01: And it goes back to the welding and the machining and the even the mechanic working on the engine.
• 13:42
  SPEAKER_01: AI cannot replace the physicality part of the work and the listening and the sound and the touch and the vibration, there's no way that it can replace a human being.
• 13:52
  SPEAKER_01: And I agree it's gonna replace a lot of white-collar jobs for sure, especially on the data side of things.
• 13:58
  SPEAKER_01: But you know, the American workforce, the blue-collar folks that are still making things, it's definitely light years away before it ever could touch anything there.
• 14:09
  SPEAKER_00: You know, if I may throw in something, you know, I have never heard any of these AI talking heads, the Altmans and the Nadellas and you know, Tucker Bug, oh my god, and the must bow, you know, whatever.
• 14:21
  SPEAKER_00: None of them have been saying we are gonna upskill with AI, we're gonna upskill the workforce.
• 14:26
  SPEAKER_00: Never, nobody has said we're gonna upscale, like we'll make manufacturing attractive in this country.
• 14:32
  SPEAKER_00: Manufacturing is like, oh, who wants that child?
• 14:34
  SPEAKER_00: Oh my god, do you know that if you went into a machine shop of today, with the sophistication of the machines and their software and the opportunities for heuristic optimization using machine learning and data science and tribal knowledge?
• 14:48
  SPEAKER_00: I mean, we aren't even selling that to these kids.
• 14:50
  SPEAKER_00: No wonder they say, yeah, machining is a dirty job.
• 14:53
  SPEAKER_00: No, it's just a trade skill job.
• 14:55
  SPEAKER_00: No, you can do analytics stuff, you can work with college kids, you can work together.
• 15:00
  SPEAKER_00: We are not selling that.
• 15:01
  SPEAKER_00: We are fooling ourselves.
• 15:03
  SPEAKER_01: Yeah, absolutely.
• 15:03
  SPEAKER_01: I completely agree.
• 15:05
  SPEAKER_01: So, you also challenge the term tribal knowledge, saying it really experim experimental knowledge deserves far more respect.
• 15:14
  SPEAKER_01: Why do you think preserving that expertise has become such an important issue?
• 15:18
  SPEAKER_00: Simply because um, you know, I don't know if you I'm old.
• 15:22
  SPEAKER_00: Uh if you remember the movie Fahrenheit 451, it was like a dystopian world where people burned books, books were evil.
• 15:30
  SPEAKER_00: So the movie ends with this police guy who was in charge of burning all the books going and joining the dark side because he read the books and he was like, My god, these are classics.
• 15:40
  SPEAKER_00: So the last scene was where they have this camp which is hidden away in the forest, and this host says, That is Huckleberry Finn, that is Moby Dick.
• 15:49
  SPEAKER_00: There's an older man with a child in toe, and the child is being taught the book and its understanding and its meaning from the old man.
• 15:57
  SPEAKER_00: So when he passes away or she passes away, the knowledge is passed on to the youngster, and the libraries become the people are moving libraries, they are moving books, right?
• 16:06
  SPEAKER_00: To me, that's tribal knowledge.
• 16:07
  SPEAKER_00: You know, we say, Oh, you know what, they're gonna retire and walk away.
• 16:11
  SPEAKER_00: What are you doing?
• 16:12
  SPEAKER_00: What are we doing?
• 16:13
  SPEAKER_00: If we want today's camp programs to be smart, if we want today's robots to understand when to change the gripper, when to sense a hit versus a part, right?
• 16:24
  SPEAKER_00: Where are we capturing that knowledge from the tactile of the human being?
• 16:28
  SPEAKER_00: So, you know, to me, this tribal thing is you know, survivor running around nude and all that stuff.
• 16:33
  SPEAKER_00: I hate that word tribal.
• 16:35
  SPEAKER_00: I would call it experiential knowledge.
• 16:36
  SPEAKER_00: And we don't, I don't see anybody, everybody's like, hoo-ha, you know, hoo-ha this, you know, we need to improve the war base, this, that.
• 16:45
  SPEAKER_00: Our munitions are overpriced, goddammit.
• 16:47
  SPEAKER_00: I mean, you know what?
• 16:48
  SPEAKER_00: You know, we can't afford them.
• 16:50
  SPEAKER_00: Have we ever worried about making them affordable?
• 16:52
  SPEAKER_00: Have we ever developed our supply chain?
• 16:54
  SPEAKER_00: What people are saying is because the defense establishment is so hard to work with, manufacturers have moved away.
• 17:00
  SPEAKER_00: So, so where are we doing what is needed?
• 17:02
  SPEAKER_00: Which is this is what we need to cut metal, you know.
• 17:06
  SPEAKER_00: The Okumas and the MASACs, they've got all these manuals.
• 17:10
  SPEAKER_00: Who's asking them, hey, help put that into a national database that can teach somebody who's running a MASAC, hey, this integrex should be operated this way?
• 17:19
  SPEAKER_00: I don't see that groundswell, that that foundational effort.
• 17:24
  SPEAKER_01: Absolutely.
• 17:24
  SPEAKER_00: That's tribal knowledge.
• 17:26
  SPEAKER_01: That that's yeah, no, I I yeah, I agree a hundred percent.
• 17:29
  SPEAKER_01: You you definitely don't see that anymore.
• 17:31
  SPEAKER_01: So you've worked on both sides of the academic and industrial worlds, and you don't seem completely satisfied with either one.
• 17:38
  SPEAKER_01: What's preventing those two communities from learning from each other?
• 17:41
  SPEAKER_00: Again, I I can only speak for myself.
• 17:44
  SPEAKER_00: When I was an academia, I I was basically told you better spend your time publishing papers and getting grants or else you're fired.
• 17:51
  SPEAKER_00: And so, where was the time to go work with industry and say, sir, is my research useful to you?
• 17:58
  SPEAKER_00: Correct?
• 17:58
  SPEAKER_00: Then the you go to industry and you have all these executives sitting and saying, Hey, you know what, that that stuff I don't understand what you're doing, so it won't work.
• 18:06
  SPEAKER_00: But sir, have you would you even listen to a couple of lectures from me to teach you what I'm doing?
• 18:10
  SPEAKER_00: No, I don't have time for that.
• 18:12
  SPEAKER_00: I want results.
• 18:12
  SPEAKER_00: But sir, the results won't come for six months until you give me the data.
• 18:17
  SPEAKER_00: Where's the proof?
• 18:18
  SPEAKER_00: But I have to work with you.
• 18:19
  SPEAKER_00: So there is this industry people think that everything is magic, and they think that things like 5S and value stream mapping and seller production is all that they need to know, you know, morning huddles and RARA good people thing is enough to run these small companies which are complicated job shops.
• 18:34
  SPEAKER_00: That that they refuse to learn.
• 18:36
  SPEAKER_00: So here's me in the middle saying, Okay, it took me 22 years to figure out this research.
• 18:42
  SPEAKER_00: I've got the software, I can change any shop, any job shop I can change, but they have to let me in.
• 18:48
  SPEAKER_00: No, I am I can't let you do that.
• 18:50
  SPEAKER_00: But unless you allow me to show you what needs to be unless you learn where the challenge is, unless you support, you know, hiring an intern or hiring a full-time industrial engineer or a data science or a computer science person.
• 19:03
  SPEAKER_00: No, you should do that.
• 19:04
  SPEAKER_00: But I want to buy scheduling software.
• 19:06
  SPEAKER_00: But sir, buying the scheduling software will get you zilch.
• 19:10
  SPEAKER_00: You need a good scheduler who's technically savvy and able to work with shop people to make that scheduling software get its ROI.
• 19:18
  SPEAKER_00: Oh, I don't have time for that.
• 19:19
  SPEAKER_00: Well, it's hard.
• 19:20
  SPEAKER_00: Academia punishes most faculty by saying, if you go to industry, your research is not very sophisticated.
• 19:27
  SPEAKER_00: But I have to work in industry to get results, and it takes time.
• 19:31
  SPEAKER_00: Industry people say, I don't have time to learn what you're doing.
• 19:34
  SPEAKER_00: I don't have patience for mistakes.
• 19:36
  SPEAKER_00: But sir, you're a job shop.
• 19:37
  SPEAKER_00: Nobody's figured out a job shop.
• 19:39
  SPEAKER_00: We'll have to adapt solutions for you.
• 19:42
  SPEAKER_00: I want the cookie cutter.
• 19:43
  SPEAKER_00: You know, that's the difficulty.
• 19:46
  SPEAKER_00: That industry has to learn enough, academia has to do enough, prove enough, and they have to talk to each other.
• 19:52
  SPEAKER_00: Patience.
• 19:53
  SPEAKER_00: I don't need I don't mind you saying I need this metric to be shown, and I need you to show me progress towards this metric.
• 20:00
  SPEAKER_00: I'm fine with that.
• 20:01
  SPEAKER_00: But then hire the intern, hire the full-time industrial engineer.
• 20:05
  SPEAKER_00: That's been my success.
• 20:06
  SPEAKER_00: That I have basically taught my students my stuff, and then I go out of my way to place them at local companies or around the country.
• 20:15
  SPEAKER_00: They may start as interns, many of them get hired full-time.
• 20:18
  SPEAKER_00: That's success because then they take over, they do their own great stuff.
• 20:22
  SPEAKER_00: But we need that, we need industry to support that.
• 20:25
  SPEAKER_01: Absolutely, I completely agree, and that's so important for America's manufacturing, you know, livelihood is being able to adapt those types of things and implement them for companies to be successful and be profitable.
• 20:38
  SPEAKER_01: I think that's one thing that we forget about in the U.S.
• 20:41
  SPEAKER_01: a lot when we talk about manufacturing.
• 20:44
  SPEAKER_01: There's reasons that these companies left the United States.
• 20:48
  SPEAKER_01: And I think too, that's one thing that we forgot.
• 20:50
  SPEAKER_01: And like you mentioned, you know, that's just been forgotten in when wages got ridiculous high, the workforce got limited, and we didn't have the workforce to support America's needs for manufacturing.
• 21:03
  SPEAKER_01: These big, you know, Fortune 500 companies moved out of the US.
• 21:06
  SPEAKER_01: And, you know, I I was part of that transition where I've seen, you know, I work for a company where I've seen tons of jobs leave the United States and go to different countries.
• 21:16
  SPEAKER_01: And, you know, it's sad to see, but it's definitely reflects it's a reflection of the of the being able to adapt to change and be able to learn and implement and you know be efficient.
• 21:27
  SPEAKER_01: So I I completely agree.
• 21:28
  SPEAKER_01: Now, you said one of the biggest contributions to your career was challenging unconventional thinking and telling the lean community that Toyota's methods weren't universally applicable.
• 21:40
  SPEAKER_01: Was it difficult taking a position that you knew would be unpopular?
• 21:43
  SPEAKER_00: I was unpopular for taking a difficult position.
• 21:47
  SPEAKER_00: Okay.
• 21:48
  SPEAKER_00: And and you know, so I I started the JS Lean online group and I hope to start it again before I die.
• 21:55
  SPEAKER_00: I mean, there's a after this year's conference, Job Shoblin conference, I do uh Have people saying let's start that group again.
• 22:02
  SPEAKER_00: So here's the thing that when I began that online group on Yahoo, it's defunct now.
• 22:07
  SPEAKER_00: I would love to start it again.
• 22:08
  SPEAKER_00: I didn't have industry experience.
• 22:10
  SPEAKER_00: So I was truly talking through my you know where.
• 22:13
  SPEAKER_00: Okay, I was.
• 22:14
  SPEAKER_00: I I agree with that.
• 22:15
  SPEAKER_00: My fault.
• 22:16
  SPEAKER_00: But a lot of people were giving me reasons, these lean gurus, you know, who had drunk the Toyota production system Kool-Aid, right?
• 22:24
  SPEAKER_00: They were talking nonsense.
• 22:25
  SPEAKER_00: They were giving me reasons to say that the Toyota production system is equally atypical to job shells without even understanding the basic capabilities of the job shop.
• 22:34
  SPEAKER_00: Okay.
• 22:35
  SPEAKER_00: So yes, I was unpopular and I and I was wrong.
• 22:38
  SPEAKER_00: I was stubborn.
• 22:39
  SPEAKER_00: Then when this grant came from the Department of Defense 2004 to 2012, and I was forced.
• 22:45
  SPEAKER_00: My sponsor said, Look, if I don't see your work being implemented every year at at least one Ford shop through an internship or a workshop or consulting, you're gone.
• 22:56
  SPEAKER_00: Your funding's gone.
• 22:57
  SPEAKER_00: I'm not funding you anymore, right?
• 22:59
  SPEAKER_00: So I realized then that I need work, and my sponsor told me, Look, Sharu, you've got everything.
• 23:05
  SPEAKER_00: You've got the book, you've got the software, you've got the conference, you're holding workshops and seminars, bloody bloody blah.
• 23:11
  SPEAKER_00: You have not implemented job shopping.
• 23:14
  SPEAKER_00: And you're writing a book about it.
• 23:16
  unknown: Hmm.
• 23:16
  SPEAKER_00: That was it.
• 23:17
  SPEAKER_00: I said, and then Herbiger Corporation came around.
• 23:19
  SPEAKER_00: Herbiger is a compressor manufacturer, right?
• 23:22
  SPEAKER_00: Herbiger, right?
• 23:22
  SPEAKER_00: So Hannes came and said, You talk a lot on LinkedIn and Yahoo, right?
• 23:27
  SPEAKER_00: Yes.
• 23:27
  SPEAKER_00: Yes.
• 23:28
  SPEAKER_00: Can you prove it?
• 23:29
  SPEAKER_00: Yes.
• 23:30
  SPEAKER_00: So okay, take your big mouth and come work for us.
• 23:33
  SPEAKER_00: So I became the director of industrial engineering research, and he said, You're too controversial.
• 23:38
  SPEAKER_00: I'll send you down to Houston where we have a smaller facility.
• 23:41
  SPEAKER_00: So at this facility, what we did was we built the rings, the compressor rings, and the basic uh I I'll forget the technical word, but it was a basic compressor unit, right?
• 23:51
  SPEAKER_00: So the extrusion side made the soft rings and the machine shop made the hard rings, the metallic rings.
• 23:56
  SPEAKER_00: Both were job shops.
• 23:58
  SPEAKER_00: I moved everything from the warehouse to the shipping department in those two years.
• 24:03
  SPEAKER_00: Once I had that experience, I didn't like corporate.
• 24:06
  SPEAKER_00: I just I didn't fit there.
• 24:07
  SPEAKER_00: So I said, now what do I do?
• 24:08
  SPEAKER_00: I don't like academics research, I don't like corporate.
• 24:11
  SPEAKER_00: Okay, we'll become a consultant.
• 24:15
  SPEAKER_00: That was where, and I said, you know what, I'm not gonna be this guy who writes a report and gives it over.
• 24:20
  SPEAKER_00: I'm gonna get my skin, you know, put my skin in the game.
• 24:23
  SPEAKER_00: That was it.
• 24:25
  SPEAKER_00: Once I gained the confidence that I can do it, and yes, sometimes you hit people who don't want you.
• 24:30
  SPEAKER_00: It's like you are exposing the skeletons in my closet, get out of here.
• 24:34
  SPEAKER_00: I don't want you around, right?
• 24:36
  SPEAKER_00: But once I had that experience, okay, now it's like I told you, drop me anywhere.
• 24:41
  SPEAKER_00: If you make a high mix of product, I will have something to offer.
• 24:44
  SPEAKER_00: Then will come the cultural thing, sir.
• 24:47
  SPEAKER_00: Are you willing to learn?
• 24:49
  SPEAKER_00: Because, see, like you own this company, if you did not support me, if you weren't on the floor on the days when I'm not there, you know, handling resistance, coaching people about what I'm trying to do, an outsider would never succeed.
• 25:03
  SPEAKER_00: So I think that was a big thing that once you implement your stuff, and I didn't have that with Yes Lean, it was my fault.
• 25:10
  SPEAKER_00: I I I know it today.
• 25:12
  SPEAKER_00: I know back off an argument, just be objective.
• 25:15
  SPEAKER_00: So I disagree with you.
• 25:17
  SPEAKER_00: Here's why I disagree.
• 25:18
  SPEAKER_00: Rather than say you ass, you mow on, you dumb shit, you know, you know, you grow up.
• 25:22
  SPEAKER_01: Yeah.
• 25:23
  SPEAKER_00: So I think it's very doable.
• 25:26
  SPEAKER_01: Yeah, absolutely.
• 25:27
  SPEAKER_01: No, I completely agree.
• 25:28
  SPEAKER_01: And that's one thing, too, that I know from experience.
• 25:32
  SPEAKER_01: You know, I worked, I went to go work for a company when I was younger.
• 25:35
  SPEAKER_01: I left one manufacturing company and went to another one and realized that they would not let me implement the lean manufacturing things that would work.
• 25:44
  SPEAKER_01: And they was not open-minded enough, open-minded enough to change and evolve and let us implement the things that we needed to implement, like Kan Bonds and you know, different types of things.
• 25:55
  SPEAKER_01: And it was a struggle, but you know, trying to get it through, and management didn't want to support folks like us that were coming in with these new fresh ideas, and it wasn't gonna, it wasn't gonna work, it wasn't gonna be successful because nobody wanted it to work, right?
• 26:11
  SPEAKER_01: And so just like you said, when you're in that position where you're wanting to evolve and grow and change and make things better and improve lead times and reduce down times and all those things, it's hard to understand that and and explain that to people who's never done it, and you know, getting them to implement it.
• 26:29
  SPEAKER_01: It's extremely tough and frustrating.
• 26:31
  SPEAKER_01: But like you said, I appreciate your opinion where you said just be objective, right?
• 26:35
  SPEAKER_01: So sometimes that's that's that's the better way to handle it, right?
• 26:39
  SPEAKER_00: And show me the money, you know.
• 26:40
  SPEAKER_00: I I feel that see, I feel that so when I began with McQueen, you know, I've been working with them since 2024.
• 26:46
  SPEAKER_00: How did I begin?
• 26:48
  SPEAKER_00: When they brought me in, they said that they had basically pulled back bonus for the guys because they wanted to make the profit and loss, they'd run a loss, they took the bonus money and they covered up the loss.
• 26:58
  SPEAKER_00: Okay, people were pissed, the shop floor was pissed, and management said, Show me the money, we don't want to make mistakes again.
• 27:05
  SPEAKER_00: So I said, Okay, these are two goals.
• 27:07
  SPEAKER_00: I I will make you the money without job loss.
• 27:10
  SPEAKER_00: We will do it with the people you have, and we will give the bonus back.
• 27:14
  SPEAKER_00: And they said, Okay, prove it.
• 27:16
  SPEAKER_00: So I said, Sir, sponsor my courses, you know.
• 27:18
  SPEAKER_00: So I would would teach two courses, and the projects would be done at the company.
• 27:23
  SPEAKER_00: I said, please hire two interns full-time.
• 27:26
  SPEAKER_00: I will work with them, they will help me run the course.
• 27:29
  SPEAKER_00: One of the interns left, Sultan retained, and he's going great guns.
• 27:33
  SPEAKER_00: He took them from about 600 to 900,000 a month in shipments to 1.2 million a month in shipment in about one and a half years.
• 27:42
  SPEAKER_00: And he did it with the people, with the shop guys, to the extent that the deal was if we make monthly targets, we celebrate in the end of the month.
• 27:51
  SPEAKER_00: You know, he literally says employees were coming and saying, Where are we at?
• 27:55
  SPEAKER_00: Are we meeting our monthly goal?
• 27:56
  SPEAKER_00: Bring us in more work.
• 27:58
  SPEAKER_00: So, you know, once you have people like that, you know, full-time, you know, they've drunk the job shopping Kool-Aid, they are good people themselves, they're good people, people.
• 28:08
  SPEAKER_00: Respect, you know.
• 28:09
  SPEAKER_00: I would never work for a client who would say, I need you to show a headcount reduction.
• 28:13
  SPEAKER_00: I said, Sir, don't hire me.
• 28:14
  SPEAKER_00: I'm out.
• 28:15
  SPEAKER_00: I would never work for a client where the top person would say, I don't have time for you, just make it happen.
• 28:20
  SPEAKER_00: If they don't put skin in the game, if they don't go put good industrial engineers on the floor, there's nothing they'll make happen.
• 28:26
  SPEAKER_01: Yeah, no, I I completely agree 100%.
• 28:29
  SPEAKER_01: So, one comment you really made stuck with me.
• 28:32
  SPEAKER_01: You said it's okay to admit you're ignorant as long as you're willing to do the work to overcome it.
• 28:36
  SPEAKER_01: Looking back on your own career, what was that mindset that served you so well?
• 28:40
  SPEAKER_00: I think that is that you know, if somebody says you're dumb or you're stupid, you don't know, fine.
• 28:45
  SPEAKER_00: Tell me what I should know.
• 28:46
  SPEAKER_00: I I realized that there's no harm in not knowing, there's no harm.
• 28:51
  SPEAKER_00: Just admit it.
• 28:51
  SPEAKER_00: Some people will accept that, they'll accept it, especially if you say I came back.
• 28:55
  SPEAKER_00: You know what?
• 28:55
  SPEAKER_00: You said I didn't know this, I know this now.
• 28:57
  SPEAKER_00: Here's the answer to your question.
• 28:59
  SPEAKER_00: I have no issues with that.
• 29:00
  SPEAKER_00: And and again, if some people will say, Well, too bad.
• 29:04
  SPEAKER_00: If you don't know the answer, get out.
• 29:05
  SPEAKER_00: Don't need to work with you.
• 29:06
  SPEAKER_00: No problem.
• 29:07
  SPEAKER_00: No problem.
• 29:08
  SPEAKER_00: But job shops are hard.
• 29:10
  SPEAKER_00: I have maybe you know, I feel today maybe I won't complete this journey.
• 29:15
  SPEAKER_00: No job shop is hard to fix now, not a single one.
• 29:19
  SPEAKER_00: I have found a solution, as did the theater production system for OEMs.
• 29:23
  SPEAKER_00: I know how to fix any job shop anywhere.
• 29:26
  SPEAKER_00: Is it hard?
• 29:27
  SPEAKER_00: Absolutely.
• 29:28
  SPEAKER_00: Can I do it alone?
• 29:29
  SPEAKER_00: No.
• 29:30
  SPEAKER_00: Do I have all the answers?
• 29:31
  SPEAKER_00: No.
• 29:32
  SPEAKER_00: I know how to get the answers, but it cannot be done without top-level support from the job shop owner and good people, the industrial engineers being brought in, the data scientists being brought in.
• 29:44
  SPEAKER_00: If they don't invest in those people, it won't happen.
• 29:47
  SPEAKER_00: So, you know, I I just feel that it all began with my realizing I didn't know industrial engineering, I had not worked in industry, I had no right to be saying things on the online group.
• 29:59
  SPEAKER_00: You know, so okay, I don't suffer fools easily, also.
• 30:03
  SPEAKER_00: So, you know, when you bring some lean-thinking guy and he puts baloney on LinkedIn, I will of course say, Sir, I don't agree with you.
• 30:09
  SPEAKER_00: So I'm not innocent and you know, whatever.
• 30:12
  SPEAKER_00: I'm not, okay.
• 30:13
  SPEAKER_00: I'm not holier than that.
• 30:14
  SPEAKER_00: But I also realize that don't don't you don't have to put people down to to become greater.
• 30:20
  SPEAKER_00: And if you don't know something, there's an answer out there, you know, like X-Files.
• 30:24
  SPEAKER_00: The answer is out there, you know.
• 30:26
  SPEAKER_00: You know, uh that's what I found that the answer is out there.
• 30:30
  SPEAKER_00: For example, if you read the literature on group technology and cellular manufacturing from the 1900s, all the books have the answers.
• 30:37
  SPEAKER_00: Nobody has read them.
• 30:38
  SPEAKER_00: Nobody I've got Xerox copies of all those books.
• 30:41
  SPEAKER_00: I haven't read all of them.
• 30:43
  SPEAKER_00: But man, anytime I have a question, somebody in the 70s had the answer.
• 30:47
  SPEAKER_00: I just have to pick up on their work and bring it forward to today.
• 30:51
  SPEAKER_00: That becomes the answer for today.
• 30:53
  SPEAKER_00: So it all begins with I don't have the answer, but I'll find it.
• 30:57
  SPEAKER_00: X Files, I'll find it, you know, that way.
• 31:00
  SPEAKER_01: Absolutely.
• 31:01
  SPEAKER_01: Well, thank you so much today, Dr.
• 31:03
  SPEAKER_01: Iren, for jumping on and sharing your experience and your real-world knowledge.
• 31:08
  SPEAKER_01: Uh, thank you for you know just taking the time out of your busy schedule and being part of the show.
• 31:12
  SPEAKER_01: We definitely are trying to encourage young engineers to be able to pick up, pick up on the show with the wisdom and knowledge of engineers like yourself and pass it on to the next generation.
• 31:23
  SPEAKER_01: So thank you for everything that you've done.
• 31:26
  SPEAKER_01: Thank you for sharing your real life experiences.
• 31:28
  SPEAKER_01: And one of the things we like to do for guests that came on the show is just when you get a moment, if you can shoot me an email with your home address or wherever you'd like something mailed to, we're gonna send a care package.
• 31:39
  SPEAKER_01: I have ladies uh that sent care packages out for me to all our guests.
• 31:43
  SPEAKER_01: Just a little thank you saying thank you for coming on and taking time out of your busy schedule.
• 31:48
  SPEAKER_01: And uh look forward to hearing from you in the future.
• 31:50
  SPEAKER_01: And thank you again so much for jumping on today.
• 31:53
  SPEAKER_01: Sure.
• 31:53
  SPEAKER_00: And do you have a website, Midwest Compressor.com?
• 31:56
  SPEAKER_01: Yes, sir.
• 31:56
  SPEAKER_01: Yeah, feel more than free to jump on our website at Midwest Compressor Systems.com.
• 32:01
  SPEAKER_01: And then, of course, too, we'll reach out to you when we publish the podcast.
• 32:05
  SPEAKER_01: And once we post it on LinkedIn, all your friends can be able to listen as well, and we'll share it on our page as well.
• 32:11
  SPEAKER_00: So thank you very much for the online.
• 32:13
  SPEAKER_01: Thank you so much.
• 32:14
  SPEAKER_01: Well, you have a great rest of your weekend.
• 32:15
  SPEAKER_01: Thank you.
• 32:16
  SPEAKER_01: Bye.
• 32:17
  SPEAKER_01: Bye bye.