Episode 13: Designing Intelligent Range, powering manufacturing innovation with analytics, and how rotating employees across disciplines can build cooperation in companies

Season 1 Episode 13, Jul 21, 2021

author

Adam Kowalski, Data Scientist for the Advanced Connected Vehicles Group at Ford Motor Company, talks about how he got into a role that deals with the intersection of data analytics and manufacturing, the volume/quality tradeoff in data, and how test stands are the unsung heroes of product quality.

Topics in this podcast

Analytics, Testing, Calibration

Show Transcript

Adam: You got to think fourth dimension. I got my goals. Wanna be green, not red. I'm gonna light my paper up with green and get my next big bonus. But you know, everyone doesn't get their bonus if Ford as a company isn't achieving the best that we can.

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 13 of The Means of Production, with us today is Adam Kowalski, data scientists with the advanced connected vehicles group at Ford Motor Company. Welcome Adam.

Adam: Thanks Sid, happy to be here.

Siddhit: And before Adam and I had this conversation, he's going to read out a quick disclaimer.

Adam: I work at Ford Motor Company, but this is my own opinion and is not the opinion of Ford Motor Company. And I am not a spokesperson for Ford Motor Company official or otherwise.

Siddhit: Perfect Adam, thank you so much. So firstly, Adam it's great to connect back with you after a while. It's good to hear your voice and I want to know what's going on. How are you, how is work from home and just everything. Just tell us how you are?

Adam: Yeah, I don't think you miss me just talking directly across from you in our open office, like we used to have. I'm doing pretty good. I'm pretty busy working for the analytics team, the connected vehicle team and it's probably been about a year since I've been with the team and a big transition from the previous work that I used to do. Obviously it was all manufacturing and finally kind of seeing over the wall and getting to do something a little different.

Siddhit: Yeah, absolutely, that move was really good and I'm very happy that you're happy over there. Can you, I guess this is a little off topic, but can you, let's do the first question in which you tell us how you got to this role, but before you do that, can you speak a little bit about what you do in this group? And then you can go as far back as you want to explain to us how you got into manufacturing in the first place and then eventually this role.

Adam: Yeah. So we'll go through back then time and space, so Ford Motor Company and my sort of progression of how I got there, because it isquite the transition. And it's definitely a story in itself, but as for what my day-to-day operations are, I work on a team that is primarily research oriented, which is a deviation from kind of my previous work. I have limited experience with research teams. My project on the other hand just happens to be a little bit more tactile in the sense that you can touch it, is that we're working on cloud enhanced distanced MTU, and the marketing name for that in Ford is Intelligent Range, which has been plastered up on the side of the building for the new F-150 launch. So everyone is very nervous, but very excited to get that feature out and go.And what that feature is, is essentially it's going to calculate your range left in the electric powertrain, based on your driving habits, your weather, or the traffic. Everything is sort of surrounding the vehicle that environmentally, in the past we might've just had to deal with, sort of the nuts and bolts engineering side of it. Now we have all these, APIs and things we can kind of crunch into and really get, smart about how we're developing range.So that's mainly what I work on now.

The story of how I got there is more complicated. I originally started at Ford as an FCG, which is the Ford College Graduate program, which is sort of more of a rotational program for experience rather than some people are looking for like a leadership program.So not to be confused, but that got me into manufacturing originally. And from there, I was doing different types of things, mainly focused around industrial engineering. So the thing is that I always knew I kind of had some interest in analytics. There were some projects for my undergrad that were pretty insightful to see that was something I had some aptitude for and that I felt really comfortable kind of talking about. So I really wanted to get doing more of that kind of stuff. So that led me to test engineering, which is a department inside of Ford Manufacturing. And that has to do with sort of the process of acceptance for quality within the plants and kind of going through procedural tests in order to figure out if a product is okay to go. And there are variations on those tests, but see that's sort of building towards that idea of like, okay, this is analytics, it's all about doing your statistical quality control and things like that.

So that job kind of got me, my foot starting to go into the door. And then what happened was, is that we were going through sorta some turn in the powertrain manufacturing engineering department, as far as personnel. And the industrial engineering department, which my FCG rotations were based out of, was looking to sort of close down their head count. And they knew because I am pretty vocal about and honest about what I'm looking to do. They ended up being able to find me a role within the GDIA at Ford. And I found a role that was actually exactly what I had said on paper. I remember telling my mentor, I said, if only there was a role that was like a liaison between data and the manufacturing floor. And that's exactly kind of what the role life was brought into, was, and that was in data operations, which is in its origins, more of what we call a data steward, which has to do with sort of being a curator for the data, rather than more so, than like a very technical data scientists or data engineering, little did we know.And Sid you would know, because you fell into the same trap, just that it ended up being a lot more data engineering than you ever imagined.

And it was actually really interesting. And that's kind of what has happened in the last almost three years now, which is crazy for me to think. And it's crazy to think that I've actually been a data practitioner at Ford for the same amount, if not longer amount then I was in manufacturing, so I'm actually half and half. Now half of my time, or two, three, actually, I guess two thirds of my time in GDIA was with manufacturing. But like I said, I, I felt like every month or so that I was going to direction of data engineering. I started walking away from manufacturing, unless it was that SME experience seemed less important. So that's sort of a roundabout way. And I'm sure we'll come back to parts of this in the future, because I feel like it's sort of defined a lot of my experiences so far.

Siddhit: No that's a really good way of putting your experience to how you got there. And I guess for the audiences, what I want to emphasize here, is that this will kind of give you a picture of how fast Ford moved as a giant company, very giant legacy company, as they call it, in such a cutting edge area and Adam and myself, and some other pioneers who actually set this group up, they move really fast trying to capture how data could be utilized in a way that would ready for the future. I'm very glad that I and Adam both got to experience it. And Adam is still in the same group, albeit, in a more, as he said, research capacity and this feature the Intelligent Range. I mean, everybody knows it now. Everyone watched the F-150 Lightning. I think it was like a fantastic thing to happen at Ford, to stake the F-150 brand on something so drastically different than a gas engine car. And I'm happy Adam that you get to work on such an important feature.

Adam: It almost takes me back a little bit to manufacturing in that sense that, when I used to work on the plant floor, you had that nice feeling that was, that thing that you made or whatever that process you did, or maybe it was just improving the process, whatever you did it ended up going to a customer's hands and then when you talk to that customer, whenever you talk to that customer and you actually can really explain to them what you were talking about. Like I used to work on transmissions, it's very easy to explain to someone, at least someone who cares a lot about cars. It's easy to explain to them what you worked on and all the little nuances of it. And it's not like you're explaining trade secrets or anything like that.It's more just like, it's taking pride in the thing that you sold. I had that as an intern at Mac, and it transitions to here. When you're working on the data, sort of in the back, kind of like the troll in the cave, you don't really get that exposure. So it's not quite as fun and your customer's is internal, so they know, they're like, you better be doing your job. So it's nice to have intelligent range, which is something that you can actually, again talk. I got to talk to my dad about it. He's really excited. He's looking to buy a Mach-E here in the next few months. So it'll be really fun. I told him if he gets it, I'm going to be down there a lot, because I'm going to need it to do some more testing.

Siddhit: That's great for your dad. I, myself am, I guess of all the vehicles that Ford has come up with; I like the Mustang Marquis the most. And you're right, you are in quite a customer facing product now, because the intelligent range is what everyone, even if they don't have range anxiety, they are going to be really focused on how accurate this feature is while they are driving the first electric pickup truck, which has always given more importance to the torque and the power and the convenience, than really the range, because we never had to worry about it. And now this is something that people will pay a lot of attention to. And it's like you said, there will be a lot of scrutiny and there will be a lot of testing to do on the range.But this really rounds up the state of manufacturing today. Folks in the audience, like from test engineering which gives you a lot of data from these industrial, within the plant processes, into statistical process control and statistical quality control. And then for you who are just coming out of college or thinking of manufacturing, these are the kinds of paths that you can take, what Adam is just on. So he went from industrial engineering to test engineering, to using that knowledge of analyzing complex, a large amount of data directly into the big data world with the analytics group, and then going into more research/customer oriented areas. Because by this time you have some subject matter knowledge about the automotive production that you can contribute to, so many diverse areas, like devising the intelligent range for a pickup truck in the electric version. So that's a great introduction, Adam, thank you for that. I'd like to take us into like our question, which is what was the most technical challenge, like hard challenge you had to face in your career? It could be any point in your career. And how did you face it? It could be one incident. It could be a month. It could be just a tough time. So just walk us through how you handle it.

Adam: I think there are many out there and I'm going to focus a little bit I think on manufacturing. One of the things that really screams out to me is, as far as like hard technical challenges, was always, for me as a test engineer, was getting the sort of, so my group was focused on launch. So we were bringing equipment in, we want to get it ready to go, so that we're able to scale up. One of the hardest technical challenges, is getting that equipment okay to go. So to get it to so it's sort of sold equipment, that it's the plant's problem in a sense. But I had that challenge a lot with the the launch of one of the 10 speed transmissions in Livonia.And it was all surrounded around getting this equipment settled and getting everything ready to go. And I had a very short period of time. I came into this program again from the rotation program. I came in in the last six months of the phase launch of the 10 speed. We had about eight of what we called the tests calibration, Test Stands. So we had 8 Test Stands and 8 Test Stands are all the same, but they were fed by all this gantries and equipment across. And they do a very slow process. That's why we had to have 8 of them, and they do a very precise process.

They have lots of transducers that have really high resolution.So you gotta make sure that they are all calibrated. You have lots of meters and flow and everything gets involved in the process and how we test everything is sort of a research project of its own.And there are people at Ford who really understand that process and are sort of like global experts on hydraulic flow and everything. So you kind of follow their guidelines, but at the end of the day, you got to get the machine to produce a product that is consistent. And that's the whole idea of these, they're essentially calibrating, so you're taking a product that isn't consistent and making it. But it's really important that your test stands don't produce its own variation. So my challenge was getting those test equipment up and running and ready to go for the launch, but at the same time needing to get that equipment okay to go as far as volume and making sure that they're running on time and then all of them not one of 8, that all eight were there.

And I can tell you that it was a very dramatic launch. There were lots of things happening, equipment that didn't work quite the way, we had to take whole pieces of equipment out that basically slowed the entire wind down. So everyone's looking at you, you have your morning meeting every day, and everyone's like, what do you got today? And you have to say, same as yesterday. It's never the most exciting conversation, but that is the reality. One of the things I found is being honest about where you're at and where you need help is one of the first things you gotta get across to people because without it you're just going to constantly fail and then they'll never know why. So that was a sort of my biggest technical challenge, was sort of getting those test stands in alignment. And if you want to go further into that, we can.

Siddhit: Thanks Adam. I just have a few things that I want to unpack for myself because I don't understand it as well. I wasn't focused on that area. And by the way, the only thing I know about test stands is what I'm going to say to the audience, is that we have like eight test stands, which is where this transmission was getting tested because they had to have eight servers, because there testing time was so much higher than what the regular stations used to do. The regular stations used to be at a 22 second cycle time, whereas the test stands were a lot more, so they needed eight of them just to get the flow and the traffic and the GPH at the same speed at the pack-out, where it's also supposed to be coming out at 20 seconds. Having said that, just because I used to handle the bottlenecks and the cycle time and stuff, Adam high-level, what does it test and do exactly?

Adam: There are many variations in test stands. In fact like you had mentioned, there is, so for my test stands there is like the final test, which is the whole transmission put together sort of a hydraulic flow test. And you're basically looking at shifting and making sure that it shifts on time and you do a bunch of statistical techniques, splining and things like that to find the optimal shift quality and make sure it's all within spec. There are other tests stands before us that are testing sort of for, if there are blockages, if there is a leaks. Helium leak test is one of the things that we'll look into, but these test stands are kind of special because they're not actually technically true test stands. They actually are a value add process.

This is something that actually creates value for the end customer in a sense that it's not a nut and bolt, but it is a calibration. And what these test stands would do was take a component of the transmission, main control and actually take all the variation. These main controls have solenoids. They have different Springs, they have different filters, and they have different suppliers for all those things. Obviously that creates a lot of variability. And if you have a bad main control, you have essentially what in the body, is a bad like brain. It's sort of the control center. And if your brain is not transmitting on its neurons, quite the way it should, your body is not going to function great. And that's actually what this calibration is supposed to do, it's basically pre-programming offsets to essentially allow for better shift quality down the road.

So these offsets are the value add that we're putting in. And that's the end goal, is basically, is to make sure that no test stand is sort of, so if you have a test stand or we'll call it a calibration stand. That is wrongly sad. Your whole formula is going to be off down the road and when it ends up happening. And this is one of those challenges that you kind of have, is if I change a setting, I may not find out about that being a problem in the plant, which is your greatest nightmare. You don't want to find how that it comes out of warranty six months after you've sold that or sent that transmission out of the stands. But that's kind of where it comes from. We ended up getting a lot of these back from either warranty claims, are very common, do self-rejecting the plant all the time, and we can fix all those things for the most part. But generally speaking these are things that end up facing the customer. So that's why it does get a lot of spectators as far as the importance.

Siddhit: That makes a lot of sense. And thank you for that explanation. I have never seen the quote unquote test stand or more accurately the calibration stand in that light. That makes like a lot of sense and I learned something new today. So let's unpack that. What you're saying is that, although it is not contributing in terms of a physical component, what it is doing, it is making sure that it's calibrated properly for use. And if it's not done properly, it can be a problem, which is pretty much what you can say for any other components. So that makes a lot of sense. And I did not know all that about test engineering. So this is a good overview of how transmissions are calibrated and tested. So thanks for that Adam.

Adam: That item I mentioned before about that idea of what I used to call like the Plant Etherspace. The no zone of I produce something. I changed the knob on my stand and did it produce a warranty problem? That was an etherspace. We are slowly making that, not an etherspace with analytics. That is sort of where analytics has increased in my own personal experience. I know of a couple of use cases now, recently being in GDIA, where the idea is that you can look at things that we've changed or sort of look at root cause. And they're using deep learning to sort of analyze at a much lower level than what we're able to do on the plant floor sometimes, because they're looking at big data, they're looking at it off the plant floor, we're not creating rejects. And they're able to find sort of patterns that are creating these warranty rejects. So that's a space where they're starting to deep dive and and really kind of get somewhere with. So I'm looking forward to sort of the advancement of that.

Siddhit: That does sound very exciting. And for the audiences, this is happening because there are so many tests that are done on that test stand. And so many like millions of data points that are created possibly everyday, if not every year and for all of these vehciles and engines and transmissions and whatnot, and the variability is so extremely high, that at some point you have to surrender this to some kind of AI, kind of deep learning or machine learning method, to help you understand what particular factor made something go off, or just ruin something. At that point it just becomes that much variability and that much data that this has to be done. So I think that's what you meant Adam by coming out of the etherspace and being a little more concrete with it, I guess.

Adam: Exactly.

Siddhit: All right. So Adam, what has been your greatest non-technical challenge? And again, it can be at any point in your career and any type of challenge, just walk us through.

Adam: So one of the biggest challenges that I've encountered is sort of dealing with the offsets. And what I mean by offsets is of the sort of end goal, how do we make money as a company versus the kind of company that we want to be, and to be more concrete about it. It's all about how much product, what's put into manufacturing terms, how much product do I want to put out versus the quality of that product? The example I gave about my very technical issue, I had to do a lot with getting a product out that was in good quality. And a lot of that holdup was making sure that we met the spec and sometimes exceeded it because we knew that if we didn't, then there might be problems later down the road, and that's not something we want to leave, but at the same time, there's going to be management that really wants you just to be done.They have boxes that they need to check, and if you're not checking them, then it's a pressure point.

Those are the kinds of things, these aren't exclusive to manufacturing, this happens everywhere. In the data world, this is the equivalent of what we call sort of unstructured and like raw data, which we tend to land. And sometimes it has a structure, sometimes in a database, but sometimes these databases, even though they have structured are sort of like seemingly meaningless, unless you build sort of a good product out of it, cleaning it, making sure that all of the columns and all the dataset of the rows are in compliance and that they follow some standardization and it's not filled with empty data points and things like that. So that people one day might actually be able to use it. And that's something we ran into. We ran into how much we wanted to land or sort of copy and bring a lot of data into our data cloud, but we're not exactly bringing all the data that is high quality. So we do have to measure that and make sure that we're delivering high quality and large volumes of high quality data when we can.

Siddhit: Right and I guess what you're referring to, is are we getting this done just to get over with it, or are we doing a good job of whatever we are doing? And sometimes it may seem like it's going late and going or something else, but if you're doing it like early or on time, but it was just not correct. And it's just gonna come and bite you later and that's going to be worse. So that does require a lot of foresight. It does require a lot of patience and just being able to ignore pressure, which is hard to do especially in a large company where your voice can get lost. So yeah, that's really a good and unique answer that you gave. I'm sure many people face it, but it's glad that you're giving this as an example. And like you said, it happens in almost every company, which is, am I doing this just to finish it off and not thinking about what can come back to bite me. So that's fantastic. So thank you for that answer, Adam.

Adam: Yeah, you've got to think fourth dimensionally, you got to think in time. Not just what's in front of me, you got to think about what's going to happen to the person and what you're phrasing is exactly right because this is a problem that you have with any large company. I have no doubt it happens in a small company, but it's probably easier to confront, is the concept of like, I got my goals and these are my goals and I want to be green, not yellow, not red at all my goals. And sometimes we're setting goals that are so obscure, that they could be green, no matter what, anyway you phrase it, it will be green. So they're sort of unachievable. And then on the other hand, they'll be maybe so easy or they didn't actually challenge yourself enough, that you really aren't showing any progress. So that's one thing that, like you said, it's the idea of like, I'm going to light my paper up with green so that I can say I can get my next big bonus, but everyone doesn't get their bonus if Ford as a company isn't achieving the best that we can.

Siddhit: Absolutely, absolutely. I do see how it can get very nebulous in the data world, because it's not like they can put the data in a truck, come to your dock and start yelling. I mean, it's unusable and they might tell you, Hey, this is not something I can use, but the effect is the same. It's still wasting time and it's wasting other people's resources if the data is like unusable. Again, this would be breeding troubles for any company that is now realizing how important data is and getting into big data and trying to make sense of their operations in terms of analytics and data that is not catered or not refined, or are not finessed in some way and is unusable will do the same thing, come back and bite you with wrong conclusions. So absolutely right Adam. So Adam, you did mention seeing some of the problems that you've seen with manufacturing or with large companies in general, but if you had a magic wand to wave off something, it could be more than one thing, within reason, what would it be and why? This could be in your job, it could be in your work, it could be in the industry. It could be just something that you've seen throughout your career. And you wish you could just wave off with the magic wand if that was possible.

Adam: I think the easiest thing and well, disregarding your statement on, is it possible, if people could lie or exaggerate that would be a great start, maybe enforcing that in a theoretical level. It would be really good because it's like, I wish there was more emphasis on just honest performance. I need the sort of gunning attitude and the sort of, I got to just make sure I'm okay. And that's a department thing, that's a team thing, and it's also a personnel thing. It can be at any level, it's, Hey, GDIA is going to be looking good compared to this other group or, Hey my team is outperforming this process team on things like that. And the question is, are you actually outperforming? Because if you are, I'd love to see the sort of the number. So I guess it's sort of that idea of like enforcing honesty at a certain level. I know that's kind of probably difficult to sort of concrete, but I do have a magic wand and I want to do some magic stuff.

Siddhit: That is again, a very unique answer. I think you touched upon a great point. How do you differentiate between like two organs of a body or, you know, mom, your score is higher than dad's like, what are you doing or something. It's hard to just have that kind of a comparison.

Adam: And I don't want to come across as I don't think competition is good because I completely disagree with that statement. I think competition is great. I just think competition is much better if there's like ground rules in the game that everyone follows. I don't want people cheating their way through a thing. And to me, like I talked about before, setting goals that are either on achievable, low ball is essentially cheating the system. And it's like that whole concept of under promise and over deliver or vice versa. Just be upfront about what you can do. I want you to be upfront about what you're not good at, because I want to backfill that, I want to find P, their personnel. I want to find the product. I want to find the software that really helps with that.

Siddhit: That's a great answer. And again in a very large company, it's highly likely that there are two groups whose work is overlapping, and then they do come in some sort of competition. So that's absolutely true. And if you do want to see it as a competition, then at least have some ground rules, but otherwise you're working for the same company, so healthy competition is good if it doesn't matter if one of you is ahead. Ultimately the one was behind will, like you said, you need to see what they're bad at and fix it, or all of you are going collectively suffer anyway because it's on the same ship.

Adam: I have another one with that, you've kind of jogged my mind now. Actually something recently I've worked through. I was a part of a workshop with sort of an IT perspective on a rotation program. And that's something that we talked about and it's sort of that idea. I think that it would be nice, and if I wave my magic wand, I would enforce some sort of rotation program that emphasized movement. That allowed people from factoring and product development, research and finance and marketing and all over the place. As long as they have the skill sets that are acceptable within the jobs, I would love to see them moving quicker and easier because the more time you spend in someone else's shoes, the easier it is to become and understand.

It was easy in manufacturing to say Hey, why is PD giving us these ridiculous specs? Well, that's because most of us have never built a spec. It is easy to be a data scientist that says, Hey, this data is not good, but speaking as someone who's been on both sides, there may be a reason why it's not good. And then on the other hand, you might complain, those people might complain about, Hey, my IT is really slow, but guess what, when you become IT, you start to understand what's really slow. So I would love to see more rotation. I'd love to see more skilled cross pollinization. I'm a big fan of sort of discipline diversity, as an addition to any other diversity in your workforce, I feel like it's a very important attribute.

Siddhit: That's a great second answer. I really like it, Adam, and especially I'm guilty of saying, I was guilty of saying why is PD behaving this way? And yes I do thank the way I came into Ford Motor Company, coming into manufacturing first. And I do believe that if I had not been in manufacturing, I would have said, why is our manufacturing quality not the best? It's not the best because we haven't all collectively seen what their problems are, gone to the floor and seeing where are they really struggling at and try to help. So, yeah I absolutely think that is necessary. The FCG program is one of the ways to do it, which is reminder for the audiences, it's just a rotation program basically, but this should be across like all stages, all employee types. And even the senior employee should be able to rotate more freely, like you were saying discipline diversity and be able to contribute with fresh ideas and fresh outlooks.

Adam: That was actually a huge part of it, was one, we didn't want a program, essentially the workshop, and when I say we, didn't want a program that was just for either new employees or people coming off, maybe an MBA program, they wanted something that was for everybody. And at any point in time in their career, because there comes times when it makes sense to make a pivot. And it also, does a lot to tell you about your workforce. It sort of broadens perspectives. So to me, like I said and that's one of the things I would tell anybody looking at rotation programs, is to make sure that that rotation program satisfies your sort of wants and needs as far as the broadness. Is it diversity in different types of work?Is it different disciplines altogether, for example, electrical versus mechanical engineering and things of that nature? So those are always things to keep an eye out for as we go into the sort of new way of working, which is going to be post COVID, people have sort of opened their eyes to sort of more progressive work systems. And one of the things that it seems to be very popular or that everyone wants to be a tech company, but you kind of have to work like a tech company to be one. And they are very flexible about what they work on. They don't work typically in large departments, they typically work in teams focused on a product. And you move product to product. So that's something that we're starting to see, sort of the seedlings in Ford and I hope it kind of matures and takes on, but something to keep an eye out for.

Siddhit: Absolutely. Absolutely. And I think companies like Ford are at an advantage because they still have that rigor of their plant floor work and their manufacturing work. Plus they get the good things from these tech companies of having as they said, the team that is a size of like a pizza meal. There are two or three people working really fast in a very nimble manner. That's possible in some cases, whereas in manufacturing, you need, like a lot of people to handle a very large line. So they have so much goodness from each method to incorporate into the other and truly become like a tech enabled manufacturing company. So I think it's on the right path and it just needs more people like you. And like all the other folks who were trying to make the best use of their rotation. So great answer, Adam. That's a good conversation for sure. So moving on we have, before we close, a fun surprise question for you, which is, if this was 2051, what would the factory of 2051 look like? Or if you could go in time to 2051, what would manufacturing look like to you?

Adam: That's a great question. I think it will look drastically different, no different than if you were in a time machine in 1950 and you came to 2021. What would it look like for their perspective? I don't see any difference for us. I think you will see one, obviously whatever the term we want to use this week, internet of things, connectivity will be very prevalent, probably outdated. There'll probably be some better way of networking, whether it's wireless or something like that. But I think you'll see because the products we'll be making will be a lot more say environmentally friendly and conscious. There will be a lot more emphasis on that, I think in the future. I think one of the thing you'll see, I have no doubt that additive manufacturing will be very commonplace.I would be shocked if, for example, there weren't components of the vehicle being built that way in the future. What else? Like I said I think there'll be management done by AI. I think a lot of like the sort of decision planning and everything will be handled by an AI.

That being said, I don't see there being no people. There's been a lot of advancement in the world and everyone seems to think that a robot is going to take their job. I think even the best robot sometimes is never the best answer because there are a lot of jobs that are great because a human can be trained and be flexible, where a robot cannot. And I don't see AI quite being there in 2051. So I think we're safe on that one. And that's probably the biggest change is. I expect a very open floor plant. I don't know if there'll be so many conventional lines and if there is a line it'll be kind of passed, the way that there'll be passed will be more open so that there's flexibility built in. So it's more like a modular based line construction.

Siddhit: All good concepts, Adam. I really like all of the concepts, especially the open floor plant, because to me it seems like with electrification, which could become the majority of the vehicles in 2051, there are fewer components. Maybe we don't even need several plants, like one axle line and one transmission line and one engine plant and so-and-so. It could all be very, very compact, yet very open because the number of parts in that car, or just they probably dropped down by 10 times, and that makes it a lot simpler and you'll have a lot more configurations for all of the features that will come in the future at that time. And even some of the things that a driver needs might be completely absent because they might be autonomous by that time. So great simplification is on the way, and that would allow like open factories, fewer processes, and I guess more robots handling the hard, dirty, or even dangerous stuff. And humans, like you said, doing things that are more creative and things that require rapid relearning.

Adam: Exactly, I think like you said having the human-based jobs and having them be able to just kind of move around that sort of open floor, is going to be really interesting. And I think that like, you know what's happening because in the time that, for example, when I first walked into the floor in Lavonia, there were still these ugly gray machines in the middle of the floor because they hadn't moved the four speed out of the plant yet. And as we built on and built on everything is cleaner and whiter looking rather than gray. And that's because they're like these sort of more open free spaces. And I think that's going to translate, continue to translate in manufacturing and become more developed and sort of in that way, where it's this fully flexible line, so that if I want to change the product I can. And one thing I'll also say is you mentioned that there are less components, which is 100% the case, but there's also probably a hundred times more the complexity because of the wiring and the battery. So I think with that addition, that's why you really do have to rethink sort of your process. You can't just assume that the straight line production system is going to be the production system of the future.

Siddhit: Great point. Yeah, yeah, absolutely great point. There'll be a lot more choices. It'll be pretty much like a shoe or a t-shirt, well not that much, but a lot more commoditized and simpler than it is right now. So just plug something in, take something out, like Legos. So absolutely right. Well thank you for all these answers. They were very good. They were very informative for me. I learned something about test engineering. I learned a lot about how you are using data to help the F-150 Lightning become better and the other electric electrified vehicles and your technical challenge and your non-technical challenge were also very interesting to hear about. And your ideas for the magic wand were pretty great too. So all in all, I really enjoyed this and I really enjoy talking to you again. We used to banter a lot, Adam and I about stuff. And Adam he loves buying vehicles. So he used to start this monologue about which car is better.

Adam: Are you sure you really want this podcast to be about this? Because we can just talk about that, if you want to just talk about that. We can make a car. Not even, it's not even a gear head podcast. It's like a stock podcast, but only talking about buying and selling of cars. We're going to hold on to this fancy Corvette and we're going to sell this electric car or whatever, we can get there. And it still translates here. And our contentions are still very much on the table because you'll note that I refer to it as the Maki. And you constantly insist on calling it the Mustang Maki.

Siddhit: Yes. And well, that's a little, thanks for bringing that up. That was actually kind of a bone of contention. Like I was pretty worked up because I felt like Ford lending the Mustang name to this new vehicle was the right thing to do. And Adam was like, oh no, I don't, this is a separate thing. It's, not a Mustang. It's something else. And they have a million names to choose from. And it's an SUV. It's not even like a sports car. Why are you calling it the Mustang Maki. And I just thought it was the best thing that ever happened to the Mustang. So I don't think we still agree on it, but you know, we'll probably leave it at that

Adam: Recycling the F-150 lightening name though. Now that was lightning in a bottle. Insert drum roll.

Siddhit: I will insert a drum roll there. I've never done that on the podcast, but you know what, you're the first person who suggested it.

Adam: First person to tell adad joke, has your dad joke repertoire increase now that you know, you've had all, you're almost a year or so now, right?

Siddhit: My daughter is one and a halfin fact, she's almost two. She's going to be two in August.

Adam: That's crazy.

Siddhit: Yeah, it is crazy. It's crazy. I was sharing information about my parental leave not long ago and now she's two years old. So that's how fast time goes. Well, thank you so much, Adam, all the best with your work in the advanced connected vehicles group. It sounds like an exciting place right now, the way Ford is going about with its products. It has a great new lineup and things are looking absolutely great the way it's going. So, yeah, good job on the intelligent range, the whole team, and you keep doing what you're doing,

Adam: Appreciate it Sid. Keep going with the podcast, it's really interesting. And it's cool to see you kind of getting to do all this different stuff that is really interesting. And then kind of hopefully it's building sort of a portfolio for you guys of interest.

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 encompassing operator instruction and data interfaces, stage logic and parameter thresholding, machine interfacing and configuration, robot board programming and coordination and stage to stage to production flow control into a single Pashi program. Check us out at pashi.com. Until we meet again, have a fantastic day and take care.

Joke drum sound from Orange Free Sounds

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


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