Episode 15: Eliminating coolant in machining, tech institutionalization, and why we should get rid of legacy data systems.

Season 1 Episode 15, Aug 04, 2021

author

Rich Furness, Data Scientist with the Global Data Insights and Analytics Group at Ford Motor Company, talks about how he and his team pioneered dry machining, the challenges involved in getting teams to adapt to new tools and systems, and why a data revolution in manufacturing would need legacy data systems to be jettisoned.

Topics in this podcast

Machining,Lubrication,Analytics

Show Transcript

Rich:[Background music] The chips that were results of the manufacturing process, since they were contaminated with coolant, we had to pay a company that would clean the chips before the metal could be recycled. And as soon as we started dry machine, we actually were able to sell our chips for money because they can be taken directly to a Foundry remelted and used for making new castings.

Intro:[Background music] Pashi presents The Means of Production, a podcast about what it really takes to build, maintain, and scale the processes that produce the physical products that power our world. Every episode, we ask a manufacturing expert to walk us through the nuts and bolts of how they do their job, we explored how and why they got into manufacturing. Dive deep into the hardest problems they've solved on production lines and discuss their thoughts on what's broken in manufacturing today and how those things can be fixed. This podcast is hosted by me Siddhit Sanghavi, Pashi's US manufacturing operations lead, and former assembly engineer at Ford Motor Company. If you are a part of the manufacturing world and you're interested in being a guest on The Means of Production, get in touch with me at siddhit@pashi.com.

Siddhit: Welcome to season one, episode 15 of The Means of Production and with us today is my colleague and my friend from Ford Motor Company, Rich Furness. Hi, Rich, how are you?

Rich: Fantastic. Great to hear your voice Sid.

Siddhit: Awesome.I'm glad I got to catch up with you. So welcome to this podcast. Thank you for giving your time. Rich is a data scientist and advanced manufacturing process, subject matter expert in the GDIA department at Ford Motor Company. And before Rich and I had this conversation, Rich is going to read out a quick disclaimer.

Rich: Okay. Hello, everybody. Sid said my name is Rich Furnace. I currently work at Ford Motor Company, but this is my opinion and it's not the opinion of Ford Motor Company. I am not a spokesperson for Ford official or otherwise.

Siddhit: Perfect Rich, thank you so much. So Rich, firstly how are you, how are you dealing with the COVID situation? How is work? How is family and just, what are you up to these days?

Rich: Well, let me tell you the COVID experience to me started off, it was a little difficult. I've been at the company for a number of years. So the transition to working from home was a little awkward. So the first few weeks I didn't like it at all. I felt very off, you get used to habits of getting up, driving to the office, spend your day in the office, driving home, and that becomes your routine. But now over a year later, I really enjoy working from home. I think realistically it works for the type of job I currently have. And also we probably need a blend because it becomes a bit difficult to establish relationships with people you've never seen. So fortunately, most of my interactions are with people I knew before we were working from home, but we have had a couple new employees and a summer intern. And that became very difficult when you never had a chance to see people face-to-face and talk to them in the hallway and do kind of the small talk, relationship building, that's very different.Yeah. So we're work is, work is good and all things family-wise are good. My wife also is working from home and fortunately we have enough space in our household to accommodate both of us doing that. And it's worked out well. And for you?

Siddhit:That sounds great Rich and I'm so happy you are also taking it in such a positive light, despite having worked on the floor so many years, you've adjusted to it quite well. Yeah, same for me. I guess once I was at GDIA with you, just not being in the plant for a while, gave me that habit, that my job is to now sit at a desk and not walk on lines. So that kind of helped me a little bit when we started working from home. So now I'm, like you're saying, now I kind of enjoy it a little bit because we don't need to be out there all the time. But also, like you said a lot of great things happen because you meet people in the aisles and you speak to them and it may spark an idea or so. So we have to find like a balance between those two things. So I agree with what you're saying. Well, thank you for letting.

Rich: I have seen that our global collaboration, I would contend is better now that we're working remote than before because in the past we would have meetings with our colleagues in different parts of the world. And we would be sitting in the conference room and Dearborn, and the majority of the attendees were all together and there'd be one or two people calling in from India or Europe. And now we're all on equal footing. So to me, I think the relationships and the collaboration with our global colleagues is actually better.

Siddhit:: That's a very unique take on it. And I think you're right, because now nobody has this big giant conference room in the background, everyone is like fake background. And then you're pretty much able to work at times of the day when you probably would have found it very uncomfortable, like 4:30 PM. Like in the U S people are trying to run home to beat the traffic, but not anymore. So you're a little more relaxed and maybe that works well for somebody who's in Europe and it just works better. So, yeah, good point, Rich. I'm already liking this. So I think we are going to have a great conversation and to kick that off, firstly, your whole profile is so interesting Rich. For the audience I'd like to say that Rich has a PhD in mechanical engineering from our very own UFM. So he's a Michigan guy and he's been doing things like, algorithms controlling the sensors on CNC machines in the late eighties before IIoT was even a thing. So from there you've come into something where we really do a lot of IIoT day in and day out with the analytics group. So can you talk a little bit about what you do now and then how you came to here throughout your journey? So how did you get here in the first place?

Rich: Okay. So first I'll start with where I am and what I'm doing now. So I'm currently in the global data insight and analytics group at Ford. That's what the GDIA stands for. And we're applying analytics to manufacturing problems and the areas of powertrain production, stamping, and vehicle manufacturing. The focus of the projects is on, I guess the main areas you'd consider is the targets for lean manufacturing principles. We're trying to apply analytics to improve quality, to reduce downtime through actions, to identify and pending failures through a predictive and prescriptive maintenance, quality improvements. I may have mentioned the cost reductions and it gets kind of interesting that the analytics is really an outgrowth of many of the approaches that have been in place for manufacturing improvement over decades.

And analytics is a newer methodology, but the focus remains, I'd contend the same as 118 years ago when the company started, you always want to get better and better every day. And analytics provides new capabilities of doing that with the computing technology and access to large amounts of data and the ability to merge data sets to generate insights, to help people make decisions for process improvement. Okay. So you were asking, how did I arrive here? Well, I guess I'll begin with where I started. I began in the company in the research lab many, many years ago. And as you pointed out I was working in an area that is remarkably similar to analytics. I was working on adaptive control for machining. I was super, super interested in adaptive control, not so much in machining and manufacturing at the time, believe it or not. My dream job was to be applying adaptive control methods for thermal combustion engines. But, you know, everyone's career takes a little bit of a different direction than you expected.That work was really focused on applying sensors on metal cutting machines, developing dynamic models and control algorithms. And a lot of the aspects, like I said, are remarkably similar. I had to do system identification for building the input output models that we were applying for controls. We weren't using machine learning, but some of the concepts are very similar, certainly the statistical methods.

Also at the time we then moved into incorporating sensors on measuring machines for doing dimensional measurement and there was a big focus on open architecture controls. And yeah, I did that work for six or seven years and then moved into a part of the company called advanced manufacturing technology development, which was one step closer to production. And in that organization, I gave up the type of work in sensing and controls and was responsible for cutting tool improvements.And over time then became responsible for machining technologies in that group. And then later on for powertrain manufacturing technologies, both the machining and assembly and test, and in the mid two thousands that organization merged with our powertrain manufacturing engineering team and continued the development of new manufacturing process technology, there for a number of years and moved into a function called engineering methods, which was all about applying digital manufacturing tools to the workflow in our engineering organization.

And then after that, I was in a group called tooling and engaging, where the team was responsible for the design and procurement installation and launch of cutting tools, engaging systems for making engines and transmissions. And then there, it was just kind of an active chance that I then moved into the data analytics group. Yeah, I wasn't seeking that job out but it's highly interesting. And as I mentioned before it's kind of coincidental, kind of almost in some sense, made a full circle back to my roots in the company and my roots from the University of Michigan.

Siddhit: That sounds fantastic Rich. And while you were saying, I thought, wow, this is like a lot of things that must have felt like deja VU, right? You went into a department that was doing advanced manufacturing stuff before we had its own advanced manufacturing center. You were doing something that we have a specialized function for today, but you were doing it in the early two thousands. And doing things that you would experiment on first inside a building, and then take it to a plant, but still stay connected to the practical constraints of the plant, which is the whole idea of advanced manufacturing building. And you alluded to that when you said that it wasn't like very research or very specialized stuff. It was quite one step.It was very close to what the guys on the plant did. And from there you did more like conventional stuff, like how to install like tooling, engaging for all the launches. And you did a lot of management work and a lot of project management and getting lines to their job ones, which is similar to like what I have my experience in, get the line up and running. And then just like me, we had a chance to be part of a wonderful movement at Ford, which was to go into the leading edge of big data and see how it can help manufacturing. And I'm glad you're enjoying the role. And you are giving your perspective from the floor, which is very lacking in the non-manufacturing departments, not just analytics, but it could be even product development. It could even be marketing. So they all really need perspectives from the floor and you're the guy who can provide it. So it was nice knowing how you went the full circle. So thank you for that answer.

Rich: Yeah. And I guess to add onto that, another interesting fact for me is in the very early two thousands, one of our projects involved putting a vibration sensors on all of our metal cutting machines. And now almost 20 years later, those systems are widely deployed around the globe. And what we didn't have at the time was a network solution. So each machine had its own sensor and basically what we would now call an edge device, which was doing the local processing, but there's a lot of opportunity with the current technology to now network that data and apply analytics to these sensors. So it's just another interesting piece of history on how things kind of come full circle. And 20 years ago the word analytics didn't even exist or manufacturing 4.0, but it's fascinating that we put in a technology that's still alive today. And actually it creates some sense, the foundation for future development using the sensor data that's now available in a network manner.

Siddhit: Yeah. Yeah. True. True. And I've heard that from other people who are like veterans and monitoring machines. They've been saying the same thing that we used to just call it statistics at that time. And we used to call it like machine monitoring and you also did not have, not just the edge devices, you also did not have compute power. Like the high-performance computing needed, gigabytes of data.

Rich: Exactly. Yeah. Totally different compute power and memory storage. It's totally different.

Siddhit: Yeah. And I realize that like, how big this was being in the data operations side. I just realized the enormity of what would happen if you put like a sensor for every machine on every line in every plant. Like that would be a lot of data. So yeah, it must have been very hard to manage and to like accelerate in those years. But now you're there and now you have the full power there to look at all the things that Ford wants to look at. So Rich, let's move on to the next question, which is, if you had to pick like the hardest technical challenge that you ever faced, at any point in your career, what would that be? And walk us through it. And it doesn't need to be like one thing. It could be like a series or it could be like a tough year or tough launch. It could be anything, just something that you are like, oh man, I had to really use my brain hard for this one.

Rich: Okay. And you're not thinking, it doesn't need to be restricted to data and analytics?

Siddhit: Oh no, no. It could be anything in fact, the more manufacturing the better.

Rich: Okay. So I guess the biggest technical challenge and honestly, which ended up probably being one of the biggest successes in terms of deployment was a development we started at Ford in the late 1990s. I'm dating myself here, but at the time there was the beginning of pretty significant focus on environmental issues for manufacturing. And in the machining area most metal cutting is done with coolant. So you have a cutting tool that is moving across a part and the machine is splashing coolant to remove chips and cool the parts and lubricate the cutting tool. And there are these different functions for the cutting tool. And the company had a north-star objective of, let's get rid of all the coolant, let's do dry machining. So for a couple of years, we tried machining with no coolant and we were doing this in a laboratory environment and not risking production.And honestly, we really weren't having any success.

And part of our company in Germany at the time had just begun development of a technology called minimum quantity lubrication machining. So you can think of it as much like a fuel injector. It's a small dosage of oil that sprayed through the tool to provide lubrication and support the cutting process. And we saw that and we began a pretty significant development in the US. So we actually brought the specialists from Germany to become embedded with our team in the US for a number of years, and then really March forward with the development. And the reason this was probably the biggest technical challenge, is there's just so many functions of the manufacturing process that had to be addressed as soon as you said you're going to get rid of coolant. We had to tackle issues with obviously tool design and delivery of the lubricant to the cutting zone, coming up with means to measure and calibrate the flows. So we could have consistency across machines, so all sorts of interesting developments in the tooling design.

Another main element was as soon as you got rid of the coolant from the machines, how did you get rid of the chips from the cutting area? So there was another branch of development with new methods for chip evacuations from machines and kind of redesign of the work interior surfaces. So they'd be more amenable to chip shedding. And that was also very fascinating and technical. And along with the new extraction methods of the chips coming from the machines, there were some newer concerns in terms of the emissions that perhaps may be generated from this process.So the oil was consumed, but there was concern expressed outside in the industry about the size of the metal particles and what may be coming in the air. So we did a very, very thorough job of analyzing emissions from the cutting zone and making sure we had addressed that.

And another major function for the coolant when you're doing wet machining, is cooling the part. So we had to come up with technologies to address the thermal effects by doing measurements of the part, while we were doing machining and doing compensation as appropriate through the machine controls, to maintain part quality. And so there were a variety of technical challenges that were super fascinating, but you couldn't just simply turn off the coolant and go to MQL machining. You had to have solutions that would work and high volume production that tackled all the main functions of coolant that were no longer available due to the absence of the fluid. So that took a number of years, but we did succeed. And it became the standard method for machining parts inside of Ford Motor Company.And it has been implemented in all of our new powertrain manufacturing programs since 2006. And I believe it's in facilities in all continents in the world at this point, were Ford has manufacturing plants. So a great technical challenge, super fun, and super gratifying to see that it actually got implemented in production.

Siddhit: Wow. I did not know anything about this. So thank you for describing it Rich. And I think you, and the team that you work with by now, y'all have put in so much credit with environmental measures for Ford that you can't even imagine how many cutting tools have been working since that time in so many factories on so many jobs, that everywhere, a little less coolant flows down the drains and a little less coolant goes to like water bodies thanks to you and your team. So I think that's a huge impact just from the environmental point of view, but also from the industrial point of view. You guys develop like a whole different way of doing things. The MQL or the minimum quantity lubrication, it reminds me a lot not just of the fuel injection in the engine. It also reminds me like of agriculture in which they have drip irrigation. And you just give the plant only as much as it needs instead of starting the whole thing and wasting water. So, absolutely.

Rich: Yeah. And another thing I failed to mention, this is going down memory lane, but it was quite exciting to see. At the time the chips that we manufactured or that were results of the manufacturing process, we usually had to pay to have them hauled away because they were, I don't know whether it was hazardous or scrap, but since they were contaminated with coolant, we had to pay a company that would take them and then clean the chips before the metal could be recycled. And as soon as we started dry machining, we actually were able to sell our chips for money because they could be taken directly to a Foundry remelted and used for making new castings. So that was another environmental benefit that we saw from the technology.

Siddhit: Wow,this is this really what they call a circular economy in which you're actually creating value from something that you don't need, that somebody else might need and encourages re-usability. So that is very stunning that you were able to convert something that you had to pay to get rid of into something that you could sell. So like another Testament to what can happen if you really allow people to innovate on technology and give them the time and resources to do this, and they must have moved with a lot of freedom just hiding somebody from Europe and just letting you guys experiment on whatever you thought was correct. And just trusting you and being like, Hey, you know, you got this, you do it. And it led to two results. And that is a kind of environment that you want in a company, especially in the advanced technology departments. You want these people to just experiment and keeping an eye on practicality, but give them the free reign to just think out of the box and make it work. And thinking back on what you said with the chips and all, I would assume that this was, must've been very different, you know, because now the chips are like probably flying more than they are drowned in the liquid, was there like a vacuum or something, because they must be very dry and hard, if it was like a minimum.

Rich: Yeah. So it turns out that heat really isn't so much of an issue, but they are dry. And if anybody has seen wet machining, when you peer through the window of a machine, you can't see anything, coolant is splashing everywhere. So it's like looking at a river, right. You just can't see anything. So with this technology, you can peer through the window and you can see everything that's happening. So your hypothesis on one of the methods for moving chips is correct. The very first machines that we put in did use a vacuum extraction. So instead of having a mechanical chip conveyor there was in the bed of the machine, it was kind of designed like a funnel. So the chips would fall into the funnel and they would be vacuumed to the back.And then through the roof of the machine, there was a very low pressure down flow of air to direct the chips into this funnel. That system was very effective. And then later on we went to machines that had a combination of a mechanical conveyor, along with some vacuum assist for the smaller particles. So the chip evacuation problem was solved. Now thinking about it even more, the work environment is also very different. So if anybody that's been in a high volume powertrain manufacturing facility, or anywhere where they do machining, very often you'll experience that there's some coolant on the floor. The floors could be slippery in places, but there's none of that in our plants. They're really world-class facilities with exceptional levels of cleanliness because there's no coolant to contain and worry about contaminating the floors.

Siddhit: Brilliant. Not being from the machining world, I found this very, very fascinating and very interesting, and I'm really proud of that team that you were in was able to make such a great impact on all future launch programs. And it just makes a lot of sense in every way, environmental wise, technology wise, money wise, it's just like a win-win win. So congratulations Rich. I found it very exciting, I'm sure to talk about it with people more. And so this was like a technical problem Rich. Now you've been a manager, you've been in project management kind of roles and you've taken programs to fruition. What is your hardest non-technical challenge? And sometimes people find those harder than the technical challenges, whereas others find the others easier, whatever it is, but that's why, you know, we ask both of these questions. So what was your hardest non-technical challenge at any point in your career?

Rich: Yeah. So funny you mentioned, some people find the non-technical challenges more difficult. That's been my experiences as well. Many, many times would joke with colleagues that the technology piece of our projects was easy. The people piece was more difficult and I'm not saying that people are difficult. It's just the implementation and the change resulting in introducing a new process. It requires people to change their behaviors. And that's tough. It's very, very hard. People are used to doing things a certain way and to modify how they do their jobs to a different way is very hard. And the manufacturing parts of companies; especially like Ford tend to be kind of conservative. You don't want disruptions for manufacturing. You want things to be stable. You want smooth, stable flow that ensures quality. So chaos is the enemy in manufacturing. So people become a little hesitant to change because there's things that are proven and working, and there's always risk when you alter that.

What was the most difficult non-technical problem? That was when I was the manager for engineering methods. And we were charged with introducing computerated manufacturing, doing our digital manufacturing tools, whatever you want to call it, into our workflow. And this was very, very difficult. So we were a group in the company that were using these tools. And we were trying to spread them beyond use in our own small team. So we didn't have any direct authority to tell people what to do. And even if you told them what to do, that's not gonna work. So you were in a mode of trying to show them that using these tools was better than the way they had been doing their work. And I don't think it would be any shock to hear that, you know, at Ford, one of the tools, common tools for engineers is Excel.

You know, lots of people did their work with spreadsheets, whether it was coming up with process plans or doing tolerance analysis. Excel was kind of a predominant tool. And so introducing, in the end they use AutoCAD. I'm not suggesting that they didn't use other sophisticated engineering tools, but to say, okay, go away from the current way you're doing process planning to now we're going to do it in a computer based environment. And you're going to pull in CAD of the machine tool and CAD of the part, and we'll generate these process plans and do simulations. It was a big stretch. And people certainly could see the benefit, but changing the way people did their work was difficult. And management had a pretty lofty expectations rightfully so and that they had seen these types of technologies being used at other companies, and really wanted to see these be used at Ford, but it just takes time.

So you have to have some level of patience, a lot of perseverance. And also what I'd say a dose of persuasion to influence the way people do their jobs. And honestly Sid, I'm no different, if someone says, Hey, rich we don't want you to use outlook for email anymore. And we're going to get rid of email. The new way of communicating is going to be some new system, slack or something. It's like, Hey, I'm comfortable with this, why should I change? I think you can relate. So you develop a certain comfort, a certain familiarity and a certain skill set with doing your job one way. And then there's this outside group that's saying, Hey, there's a better way. And even if the new method is better, you just have to have this patience, perseverance and persuasion to introduce it. So to me, that was the biggest challenge in my career. I was one in a series of managers that tried to affect that change and everyone who made an impact. Hopefully I made an impact, but it wasn't going to get done during one person's tenure. This is something that's going to take I'd say a generation at a company like Ford to really become the new way of doing work.

Siddhit: I agree And the funny thing is that, in the tail end of your 10 year as engineering methods manager, this was right when I came into Ford. And by that time, I think a lot of the work you and other people, like you were pushing CAD for program management and program launch, your work had really, I think made an impact because when I came in the Process Simulate by Siemens, this was quite accepted in the assembly side, at least. And probably for assembly, it gave like a little more bang for the buck because of just the way assembly is as opposed to like a box inside, which you have like something getting cut. So it was a lot of use for me to see. Is there a column in the middle of my line, is this ergonomically fine? Is this, you know, fluoride? Like, is it going to work or is it going to be just very awkward? It helped me a lot and I'd say I spent hundreds of hours doing virtual assembly and that probably saved like a thousand hours of like actual work on the floor. So I think the efforts that you and other people like Brad Joseph, for example, you know, would have really helped pushed this by the time I came in,around 2015 or so.

Rich: Yeah, yeah. It took a lot of groundwork to create the capability. And I guess I touched base with some of our colleagues from the past and the journey is continuing. So I think they're continuing to make steady, steady progress. And over the years, I consulted with some more senior people on how to affect the change, they shared stories of the transition in the company from drafting to using CAD and lots and lots of similarities. One point in the company and we didn't have CAD systems. We had a bunch of designers that were working on boards and the transition to having people use the computers for doing CAD instead of drawing on boards was difficult. And didn't happen overnight, so it was a great analogy.

Siddhit: Yeah, absolutely. I learned drawing myself on the board and everyone in my bachelor, we're probably one of the last millennials to learn with drafters, but I can see how that can get very slow, I think a good base in learning how to draft with paper and pencil is good, but then you have to quickly move on to using technology such as CAD to make it very easy to change and be nimble. And not get into what you were saying, like we get comfortable in it and then we don't want to change. So it happens to everyone. Love that answer too Rich, it was a really good answer. A few people on this podcast have mentioned change management as one of the things that every big manufacturer has struggled with.Probably every big company in general, and other answers have been factory floor issues. Working with operators, others have mentioned, interdepartmental silos. So these are all the issues that we have to face and come together as a company and it's getting better. I think Ford and others are rising to the challenge. And the current slew of Ford products is a reflective of that. They're fantastic. I've been following all the launches and everyone is doing great work at Ford.

So Rich here's a funny question. So if you had a magic wand and you had such a long career, so you've had so many instances. If you had a magic wand and you could wave off something about your job or your industry, or about the auto industry, or just anything that you've worked with that would make a lot of difference, what would that be and why?

Rich: Okay, so I'm gonna use the magic wand for my current job Sid. One of the difficulties with the field of analytics and I'm going to lump this together with IIoT, which is the industrial internet of things, and which is also known by manufacturing 4.0. So there is an awful lot of interest in this technology area. And some may say it's hype and I actually see the potential. So I'm definitely on board with the direction, but one of the challenges that we have, is the fact that the data, even though we have large, large seas of data that we've been collecting for a number of years. And as you know, Sid you were a part of this. Ford and many other large manufacturers probably had manufacturing 4.0 systems in place a number of years ago, or at least manufacturing 3.5. And we had databases collecting and gaging information, birth history, cost data, you name it, equipment states for uptime tracking, all sorts of data. And now we're trying to leverage this data and apply analytics to it and bring ourselves to a full manufacturing 4.0 state. One of the challenges is the data isn't always as good as we'd like it to be. So if I had a magic wand, it would be to kind of wipe away the legacy data systems and start from scratch.

Siddhit: This was the best magic one answer ever. Seriously. This was fantastic. Go on, go on rich.

Rich: Yeah, because we have these legacy systems and there's a lot of effort that's involved in tying the legacy systems together, as you know Sid that was one of your jobs at Ford. And then the data quality in these legacy systems, it's not as good as some people would think, I'm not trying to say it's bad, but analytics really requires good data, good clean data. And then when you don't have good clean data, you spend a lot of time doing data cleaning, which is what we do right now for some of our projects. And the fact that we have these legacy systems and not always clean data, it sometimes feels like it would just be so much easier if we could just start with modern systems and start filling up the data basis correctly, versus trying to deal with cleaning up data and legacy data systems.

Siddhit: No. Agreed.

Rich: So that's my magic wand.

Siddhit: It's a fantastic use of a magic wand because with some effort, this magic wand can be like a real wand. It's not like impossible to do. It is very, very difficult to do. And I completely hear you because the team I worked for was right at the gateway of receiving that data. And I mean, I kid you not, like Adam Kowalski with whom I recorded the episode before this, he and I went to so many people talking about dirty data and talking about how we should change the way many factory floor apps, and this could not just be a Ford problem. It could be a problem everywhere, wherever you have some legacy systems that the front end has to be improved drastically for the data to be clean otherwise you just have a lot of jibberish coming in and then there's nothing anyone can ever do no matter what kind of AI or algorithm, they have, you just can't. So you either put in money to like improve their interfaces, or like you said just go on with a simple and modern system.

I don't usually talk about Pashi in podcasts, but you mentioned something very relevant, which is, there has to be some balance between one app trying to do everything and the app being super specialized. So I get where Ford is coming from. You have one app just for quality. You have one app just for throughput, you have one app just for maintenance, and there must be good reasons for that. But somewhere somehow they have to link together and make sense. If they don't make sense. And if one app data cannot be related with the other app data, that's a huge problem. So I completely get what you're saying.

Rich: Yeah, those systems that we put in are also kind of reflective of the technology state at the time. In a lot of ways, it's similar to what we were talking about earlier with the vibration sensing with the computing power. So at the time we put in some of these maintenance, quality, productivity systems, there were different limitations on memory compute power, and the notion that you could have one system do everything probably was a bit of a fantasy. Well now everything's changed. The cloud wasn't even a thing back in those days, it wasn't even an option. Everything had to be this on-prem servers. And so now the world has changed dramatically, but we have a big tie with these legacy systems and that just presents a challenge.

Siddhit: Absolutely. So like I said Rich, I loved your answer because it's at the nexus of big data technology and of manufacturing, because as manufacturing operations grow and we get like more connected vehicles and we get different parts and we go into all these new energy sources, there's going to be a lot of change in how we measure the plant health or the process health, and maybe the legacy systems will need to evolve really quickly to be able to a lot of that.So for example,Test Stands, you know what are they going to test? They have to test something completely different now, like there's no engine to test. It's completely different, you'll have to test batteries. So how do we consolidate all that? And how do we change all that fast enough for the decision-makers in the offices to be able to know that this program is going well, so agreed.

Rich: And then even with the connected vehicle capabilities, there's a lot of interest in how you can utilize that to support manufacturing. So you can have additional data streams and sources you never could consider before. And as you mentioned, test stands, you'd bring the product to a certain station and hookup a system that would do measurement. Well, maybe now the measurements can kind of come from sensors that are already embedded in the vehicle itself.Who knows, right.

Siddhit: On that note, when you said, who knows, here's a great closing question. And this was not in the email, so it's a complete surprise. You know, the fifth question is always a surprise, which is, if this was 2051, what do you think the factory of 2051 would look like

Rich: So let's see, 30 years from now. Well, that's a tough one. It's fascinating. I'm thinking back to what the factory looked like when I started the company, which was slightly over 30 years ago, and thinking about the changes that have occurred and trying to project into the future, what may happen. There were some serious changes and manufacturing strategy that happened over that 30 year, time period. When I started in the company, everything was transfer line production, lots of vertical integration. And as I mentioned before, everything was wet machining. A lot more labor than we have now. The assembly process was totally different. The work environment was very different. So given, 2021 manufacturing capability, I guess there's most likely going to be some fundamental shifts in the overall, I guess the production layout and manufacturing system design, I think is a way to describe it.

So going back 30 years ago, we would install leased for powertrain, the systems to make huge numbers of engines and transmissions, transfer lines that were designed for several hundred thousand or just massive numbers. And then we went to CNC based flexible machine lines with much smaller increments of capacity. So I'd say that's probably going to be some sort of a trend going in the future is, there'll be some breakthrough and having cost-effective much more scalable modules of capacity. And I guess this could coincide with what could happen in the market. It could be an era where we're moving away from high volume vehicles to more niche vehicles or with lower volumes and for the auto industry, anything less than a hundred thousand units per year is kind of a low volume vehicle.

So probably production systems that are much more scalable in terms of their system design. And then that would have all sorts of ramifications for material flow. And I think by 2051 all these visions of manufacturing 4.0 will have been implemented and replaced probably by manufacturing 5.0. I think what we view as kind of far reach right now, will be viewed as archaic in 2051. So I'd say there's going to be a couple major steps between now and 2051. So we shouldn't be satisfied thinking, oh, it's going to be people running around with their smart devices and looking at data on their phones and smart devices, making decisions and running production. That'll be long in the past, you know, smart devices weren't even around when I started in the company. And so that technology, for sure, I guess I shouldn't say for sure, is how long is the lifespan of that technology? So something else is most likely going to come along and supersede what we view as high-tech right now.

And I think the manufacturing labor will probably continue to be stressed. I wouldn't say things are going to be fully automated. I just think there's probably a limit to how much you could rely on computers and automation to take over, but the workforce jobs will probably change dramatically. And those also have shifted quite a bit over my career.There used to be people that would load parts into metal cutting machines. Now we never do that. You know, in high volume production, the parts are automatically loaded. And many of the steps in assembly that were manual have been automated. For sure there's quite a bit of human involvement there because it's very difficult to automate a lot of the delicate assembly processes and powertrain manufacturing, but there will probably be capabilities that take over more and more of the manual work over time, but there will be a need for different levels of employees with different skills to keep these systems running.

AndI'd say the March towards environmental friendliness is never gonnastop. So I expect there will be continued focus on energy conservation, waste reduction, all these things that are very, very important for the environment will be impacting, kind of the physicals of plants in 2051. And who knows what's going to happen with the global footprint that also was a major change over the 30 year horizon. And there are lots of outside factors that will shape that Sid. Technology is one of them, but there are all sorts of geopolitical factors that could influence where production is done. And I think the current chip shortage is one instigator right now, where some companies are reconsidering, whether there should be some degree of vertical integration because of the impact of chip shortages had on their facilities.

Siddhit: Yeah. All great.That was a good span in every different aspect of what the factory of the future could look like. And let's not forget that this is how Henry Ford started. He was a firm believer in vertical integration and that could very well be coming back full circle to Ford, if all of these components are completely replaced into more electrical components that are very mineral heavy. And they're very charged with geopolitical factors. So, absolutely. So Rich that was a great answer, thank you so much. And this whole conversation was really good. I enjoyed it. I hope you did. And your answers were very informative and articulate. I learned a lot about machining, especially, and how you were doing a lot of the work that we do in IIoT, or we call IIoT today and we call virtual manufacturing today in the days when they were not even cool. So it was great to know of your contributions. So I hope to meet with you and speak with you again in the future and until then take care and have a fantastic weekend and 4th of July.

Rich: Well, great. You as well Sid, it was great catching up with you. Thanks for extending the invitation and I look forward to contacting you in the future and wish you continued success wherever you go.

Siddhit: Thanks Rich.

Outro: [Background music]: If you enjoyed this conversation, please subscribe to The Means of Production podcast, for more stories from people behind all the manufactured goods we use, love and depend on. This episode was made possible by Pashi the operating system for manufacturing. Pashi unifies the entire production process for any product and combating operator instruction and data interpret interfaces, stage logic and parameter thresholding. Machine interfacing and configuration. Robot programming and coordination and stage to stage to production flow control, into a single Pashi program. Check us out@pashi.com. And until we meet again, have a fantastic day and take care.

Music from Uppbeat: "Falling" by Zayner. License code: SYFMAQN9WWBAPCSU


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