Dan O'Lear, Vehicle Operations Manufacturing engineer at Ford Motor Company talks about resource management in launches, the intricacies of dimensional data, communication and software.
Report generation, Dimensional data, Communication
Dan: [Background music]..one of the hardest things about launch is .. having enough time and resources to get the information to make the decisions.
Siddhit [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 explore 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 Siddhit Sanghavi, Pashi's US Manufacturing Operations Lead, and former assembly engineer at Ford Motor Company.
Siddhit: Welcome to season one, episode three, our guest is Dan O'Lear. Vehicle Operations Manufacturing Engineer at Ford Motor Company, a former colleague of mine, and a good friend of mine, so welcome Dan to this podcast.
Dan: Thanks for having me on.
Siddhit: Yeah, and before we start, Dan has a quick disclaimer to read out.
Dan: Yeah, just a disclaimer, I work at Ford Motor Company, but this is my own opinion and it should not be the opinion of Ford Motor Company. I'm not the spokesperson for Ford Motor Company, officially or anything otherwise.
Siddhit: So Dan, firstly, it's great to catch up with you on such a unique format.
Dan: Yeah, it's been a while, we haven't talked in a couple of months since you left the company.
Siddhit: Yeah, since I betrayed Dan.
Dan: Yeah, that's right.
Siddhit: Left, so Dan, what's up with you at work in life, in general? I know you are so busy and all the great things you're doing, so tell us a little bit about that.
Dan: Yeah, so it's a very busy time for me right now. I'm currently launching the Ford Bronco at Michigan Assembly Plant; I work specifically in the body shop side of things. So I'm an exterior ornamentation systems engineer. So we'll work on any kind of like lighting, fixtures that goes on the car, grills, mirrors, anything that's on the outside of the body I have responsibility for dimensionally. So yeah, and right now we're kind of ramping up production and it's getting really, really busy trying to make the best product that we can and deliver to market. So that has been taking a lot of my time and then I kicked off grad school this year, so that's been quite an experience as well.
Dan: Yeah, so I'm getting a CS Masters and it's definitely kicking me in the butt a little bit. Especially with work, working at the plants like probably 10 hours a day easy, and then you have to code at night, it's kind of a grind right now, so it's been fun.
Siddhit: That is a whole lot of things on your plate Dan. So firstly, congratulations on starting that Masters degree. I think it fits you really well in your profile and what you're doing. Well, he's making the Bronco Ford, so you guys have seen what the Bronco Sport looks like, and it's a reception, which has been pretty positive. And so Dan is one of the people who is bringing the other Bronco, which some people call the real Bronco out to the market, so he's your guy for that. So Dan, let's get down to like the meat of the podcast and you described what you're doing right now to us. So how did you get into this kind of role or in this field in general, in the first place and why?
Dan: So my education, I'm an industrial engineer and so I've always been kind of drawn towards manufacturing environments. They are very interesting, looking at just manufacturing processes and then it's a more active role as like kind of an engineer, you're doing a little bit less designing and more doing which, I kind of like. You're always on your feet, problem solving every day, it's fun work. And then I got a job at Ford Motor Company and then they give me the opportunity to do a rotation program, which has been really great, I got to see a bunch of different areas of the company. And I think I'm settling on this kind of systems role and in my job, we focus a lot on dimensional quality of the vehicle and it's very data-driven, problem solving, which I like. So usually you'll find a problem and then you'll just keep drilling down step-by-step to the root cause. So for example, like I'm in the body shops, so we will build a vehicle, find an issue in final assembly, and then they will say, hey, what's going on with this? And then we'll have to investigate and we have all of these different templates with different data and you just kind of have to root cause it all the way down to stamping components or things that are being imported by the supplier. And it's really interesting, you would think that looking at dimensional data gets kind of dry, but there's a lot of stories behind how that data gets created and a lot of things that you can identify in your own manufacturing process. So I don't know if I answered your question, but I kind of.
Siddhit: No, you absolutely did, because I was always fascinated in the same manner. And by the way, go Hokies because Dan is also from Virginia Tech and so am I and their industrial engineering program is pretty good and that was also another driver of why we work at manufacturing plants. And I think you put it really relevant there with stories that every line on the body of a car has been carefully thought out by somebody and all its properties are designed through a whole bunch of processes. And it's very well thought through and to manufacture it is a whole different story also, so it kind of covers all of that and I think you summed it up pretty well.
Dan: Yeah, there's a lot of time, energy and money put into making these products, their design actually come to fruition. It's, amazing how much money it takes to build a facility, have people be employed full time, running three shifts and just all of the manufacturing equipment. It's pretty impressive when you really stand back and look at it all.
Siddhit: And this is exactly why we're having this podcast because no one knows this, right? They just get their car in a dealership and that is pretty much the extent of how much a person thinks about their vehicle and they may not know these stories. So it is a good thing to bring these out and the way you mentioned, the people, the machines, all the work that has to be done to make it perfect. So thanks Dan, that is a good way to put it. So you mentioned this, with the hardest problem question and we can take it very organically because every day in the plant is hard.
Dan: Every day is a hard problem.
Siddhit: It's hard, just walking around the line is hard, it's like a mile just to walk around to see if everything is okay. But the question is supposed to be, what is the hardest problem you faced in this field? How did you solve it? Which can be not one problem, but many or just a tough time and I know that launch is a very tough time. So I was wondering if you had any particularly hard launch experiences which could qualify as a hard bunch of problems.
Dan: Yeah, I think one of the hardest things about launch is having enough time and resources to get the information, to make the decisions. To be honest with you, so looking at dimensional quality of things, we have great technology at Ford now, we can scan things and get these great color maps of dimensional quality. There's like a probing tool that you can use basically on the go, like CMM items, if you need to, but the problem with doing all those things, if you need to conduct these long form of root cause analysis strategies it takes a long time to do and you have a limited workforce. And working in a union environment, you very much need to plan out what you need to do and sometimes you just don't have the time to properly root cause things because you need to keep moving.
Siddhit: Yeah, and before I respond just for our audiences, CMM stands for Coordinate Measuring Machine and that is a vital tool for any company that cares about quality. And by scan, what that means is that there are many imaging devices, they may be thermal imaging devices, they may be 3D devices that scan like a competent visually. And they give you a whole bunch of information in terms of where there could be stress, where there could be dislocation and stuff like that. And I link some of that in the show notes from this episode, they're pretty interesting machines to look at and operate. And yeah Dan, back to the back to the dimensional quality, I think what you said really strikes a chord because you think that you have one year and you think that is a lot, but it never is.
Dan: No, sometimes like our teams are pretty lean, I wouldn't say that like we just throw people at launches. On our team, there's a few of us that you got to look at all of the supplier data that's coming in and then once you build it up, we have like standard items that we track. But a lot of stuff comes out in the woodwork that you don't really expect and you need to take the time to do these special studies and everybody has a certain study that they want to do, now there's different aspects to it. So I work specific, just in the body shop, but we also have PD folks that will come use our resources to do different studies on their parts. There's also pre current production that's happening, that we are needing to battle with resources for as well. So that's been kind of a struggle point and it's been really interesting getting the opportunity to like lead some of the management on that. And we're definitely getting it done and I'm very happy with how things have ended up with this launch in particular. But it's definitely been challenging to just jump into it and manage all of the different work streams that are happening.
Siddhit: I completely get that, so I guess what you were referring to in the large or the broad sense is, resource allocation is extremely difficult with production or facility tools and services. Because on the one hand you have a production that is running and you don't want to steal anything away from them, it's always tabooed to do that and on the other hand, you need dedicated resources to launch something. So I completely get where you're coming from and to be able to manage that and to get a chance to do some of that resource allocation. I think it makes you a better project manager, so that's a good opportunity, always. I remember that with skilled trades, we use to always be in a bind as to who gets to use the limited, highly skilled tradesmen and tradeswomen available to those plants and for what work. So figuring out what is going to get a launched there faster is something that you got to think like a chess player or something.
Dan: Yeah, you got to kind of justify the resource and not just to yourself, but to your coworkers as well, which I think in return it just makes you kind of like a better engineer. Because I think, I ran into the problem where being a younger guy in the group, maybe not taken as seriously, so when I come to any, or if I have information or if I want to do something, I always come with support of, this is why I'm doing it, this is the result we're going to get. And then over time you develop that relationship a little bit better, but that was definitely also like kind of a struggle point earlier on.
Siddhit: Yeah, that makes perfect sense, it's getting you prepared to talk to even more stringent management. You talk to a very busy CMM guy and he has no time, but you give them compelling results and they have to listen to you, about why you need that machine at that point. So at some point in time in the future, when you're talking to even bigger executives, you are now prepared to give the result, like what you need and what it will get you really fast instead of dilly-dallying about the stuff they don't care about.
Dan: It's really amazing how much communicating in a manufacturing environment, how long that'll work out for you. So like, if you have bad rapport with somebody, or you're not communicating well with like the broader team, it can cause you a lot of problems and just kind of hamstring you from like, doing your work really. But if you develop those relationships and you continually make like a positive effort, it can just make a world of difference in this type of working environment.
Siddhit: Oh yeah, especially when people don't even like to talk much because they have no time.
Dan: Yeah, sometimes you got to like walk and talk, even if you get five minutes while walking and talking, that's like enough.
Siddhit: I remember that.
Dan: It was just kind of crazy, you don't sit down for meetings, you're always moving, so it's definitely different.
Siddhit: So if anyone of you listening is not from manufacturing, like standup meetings in manufacturing are actually stand up. I just wanted to put it out there, so whiteboard meetings are, are basically where you have a giant whiteboard, but it's not in an office it's right next to the machine on the floor and that is what Dan is talking about. We just gathered around it and we are just told, this is an issue that needs to be solved now or yesterday and none of these meetings are even 15 minutes long. So that dynamic of it is the reason why so many people just like that kind of environment, it's very fast and you were saying, communication of something that's very concise is very important.
Dan: Absolutely, and I think it's been interesting with the pandemic and everything, even while being at work, a lot of those standup meetings are how they used to be. You get the whole team; you gather around a whiteboard. I think manufacturing starting to go away from that a little bit, just because it's like a lot of people in one space, which is good and bad. One positive, I think I've found from it is that you don't have to run literally across the two miles of the plant to go to a meeting.
Dan: But, I mean, we still have like the same work streams and the same people would be dealing with stuff, but you lose a little bit of an element of being in person. And maybe those conversations after the whiteboard of like, before, if I've had this issue, we've done this maybe you should talk to this person or try to like, do that. That conversation kind of leaves and it's more like, okay, I will handle it and then you have to kind of find your own path after the meeting is over, if that makes sense.
Siddhit: Yeah, that makes perfect sense, there's a lot that happens on the aisles, right?
Siddhit: You talk to people on the aisles, you meet some tradesman or tradeswoman who comes in and gives you the status update of a ticket or a manager you just bump into and all of that is gone. And unlike other fields, it's so crucial for manufacturing, because you can just walk up to the machine, you talking about mid walk and just say, hey, let's take a detour go that way, look at machine number 300 and just figure it out. So a lot of that is lost and like you said, you have to trace all of that back, find out what they were talking about exactly and when you get a chance to go there in person, that's when you really understand it. So yeah, absolutely, I think you nailed it.
Dan: Definitely, a blessing and a curse though, like you lose that part of it, but it allows you a lot more freedom to be anywhere in the plant and take these meetings, so we'll give and take.
Siddhit: Yeah, I think those are good things you mentioned as, what is hard about what you're doing right now. So that brings us to the last of the three questions that I always like to ask my guests, which is, if you had a magic wand to change one thing about how your job works, what would it be? Obviously within reason, so what would that be Dan, for you?
Dan: I hate to say it, but unlimited time and resources. If I could change it, the crazy thing is I will end up sometimes like working seven days a week and it's still feels like there is stuff that is not completed, or there's always something that I can be kind of working on. I also think if I could communicate with everybody a lot easier that would help in the job, it seems like between these teams and working with all these different people, sometimes one hand doesn't know what the other's doing.
Dan: So if communication could be clearer and if things would just work out the way everybody would expect them to. I think it would make my job a lot easier, but there's a lot of variability, whether it be with the communication with people or in our machines that we use every day or in the sheet metal, that's worth processing.
Siddhit: Yeah, I think you're broadly going with, do I have enough resources for what I need to do in terms of time or money and the other is once we have those resources is the business plan being communicated. And it looks like you neither have the resources, nor is it easy to distribute the right tasks to the right people and get information about those tasks from those people, from your response. So yeah, that is something and technology is helping with that and I know that we've worked on some kind of solution for that and you're working on it. So hopefully that gets better and it gets easier and makes it easy to give the tasks to people and hopefully stay more on plan than not.
Dan: Well, I think it will, I mean, the way the industry is kind of going right now, it seems like more data quicker, faster communication, and just all around improved connectivity around our plants. So I have to imagine it will get better, but there will always be the element of it needs to be done and it needs to be done now in manufacturing, just got to always moving.
Siddhit: Yeah, yesterday I want it yesterday.
Siddhit: But Dan, since you're a special guest, because you're involved also not just in manufacturing, but a very unexplored niche of software in manufacturing, I had a couple of more questions for you.
Dan: Yeah, go for it.
Siddhit: The first is what role do you think software plays in your work? So let's start with that first.
Dan: Yeah, so right now, I use software to help me manage my workload and put my thoughts together. That's typically what I use software for, whether that'd be kind of like OneNote, I use that every day to track what I do and like keep a daily log, but it also is where I put all my meeting notes, just like all of my analysis notes are in there as well. Like I said, we look at dimensional data every day and so I can either use it to identify problems or I actually, use PowerPoint a lot to put together my thoughts and look at things systematically. There's a lot of content you need to boil down into a few key items that you need to focus on as far as quality improvements and that always helps me. And then communication with the team too, whether it be email or instant messenger that's typically what I use, as far as software goes.
Siddhit: I think you hit upon one point that I want to expand on, which is many people might think that manufacturing work is mainly like just numbers and number crunching, but there is a whole bunch of annotation and note taking and reminders, or just general notes that people write, a lot. And like you said, you use OneNote for it, I've seen people use paper and pencil for it, there's just a lot of things.
Dan: Yeah, I have a notebook too.
Siddhit: Yeah, and on the floor that's not uncommon, so many people think that it's just completely analytical. But it's also a lot about noting down something you saw that was odd, or reminding yourself to go and check on this, or check on that or what you wanted to communicate to the manager who's running the program, or one of your reports or your colleague that is not really captured analytically. It could be very qualitative or it could be very opinionated and there's a lot of, all of that stuff that isn't really well known, like you need to communicate a lot. And I used to write, they are called, nightly emails, right?
Dan: The night letter.
Siddhit: The night letter, yeah. So the night letter is another thing that often goes out at the end of the shift to your launch manager or the production manager. So just wanted to touch upon that as to how people in this line of work. So and the other question was that what other ways that software has helped with manufacturing tasks and projects and just the whole line of work and what are the ways that you think it hasn't yet and you'd like to see it help with? And this is like a broad, wide ranging industry-wide kind of question, right?
Dan:Yeah, so I guess I'll start with how software helps; I think we have developed softwares that allow us to access what we need right when we need it. So for instance, when we talked about this CMM machine, Coordinate Measuring Machine, let's remind everybody. So that goes directly to a web service that dumps data into all of these templates that help us make our decisions. So say I come up with, or I see an issue on the floor. I go to my laptop and I can pull it up or I can go to my phone and I can pull it up within five minutes and kind of directly point to what could be causing the issue. So it's really amazing how in manufacturing, we are using these tools to just get a lot quicker turnover. We also have a lot of ways to, but just different softwares to pull in different design packages or we have a lot of tools now to help do analysis on our different studies that we'll do out on the floor.
Dan: So I think that it's pretty cool being able to have all that at our fingertips. I think that there is a little bit of issue, which tool in my toolbox should I be using to get there the fastest. There's all these different things that you can do, which one's the right one and to use right now and that kind of just comes with experience, to be honest and second part of the question you said.
Siddhit: Like what does it not do yet and you would like it to do? Like you think, oh, this should be done by software.
Dan: There's not a lot of predictive analytics put into manufacturing yet. I think it's starting to go the way of predictive analytics for like machines specifically, looking at like motors and time between repairs and stuff like that. But it's a very manually driven quality process right now, where I think that we build things retroactively, collect data, find an issue, and then go back and try to identify what was the issue and fix it.
Siddhit: It's very reactive.
Dan: It's very reactive, yeah, where it'd be really nice if this is the pie in the sky, who knows if we could ever do this, but if you could have sensors on your robots.
Dan: That would help improve quality almost in real time. Now the issue is we work in millimeters, so a millimeter is a mile in our side of the business. So you got to have a lot of really tight precision and really good ways of measuring things in real time to actually do anything like that. In other industries, it might be more easily implemented, but it'd be really great to have more predictive analytics tools for quality in the future.
Siddhit: Yeah, and I know what you mean, sometimes you would think that the sensors capable of doing this are either too costly or they're just not there and if they are there, then they're still not providing the kind of data you want to predict things. So all the pieces haven't fully come together and some industries and companies have demonstrated something like this. But this is not as widespread as you think it would be, that's a great magic wand answer.
Dan: Yeah, it is and actually to expand on that a little bit, where there has been some progress and some real promise is in vision systems. So vision systems are getting really good at like being on a line and you could run a vision system and it'll take all these different points on a vehicle and it will give you a really good idea where your quality is at. I think where the big key would be, how can you dynamically change your tools to match the quality improvement? So the tools that I especially use are extremely robust all steel and very hard, but it would be really cool if you could have these different tools actually change physically to react to whatever quality data that you're receiving. That would be the biggest thing that would be a big future, so we'll see if we can ever get to that.
Siddhit: I think you expanded on it, like really well, with that example. And at this point, I guess I want to tell the listeners that, firstly vision systems are pertaining to like cameras and you might not realize this, but every assembly line has like 500 cameras or something they're just everywhere. So that's not what people imagine an assembly line to be, but every assembly line has so many cameras looking at so many things, there is a lot of work going on with computer vision systems. The other aspect of what Dan touched on is where I would want to talk about Pashi a little bit, which is, in Pashi, you can set parameters of the machines, like you can set the press force and it'll drive the PLC to make these changes. We are not there yet with where you can predictively have the parameter being set automatically based on the reaction from a sensor, that it has learned something. So that is where we want to be and that is what Dan is also alluding to that if you detect something on a vehicle from a machine, can it automatically learn and just adjusted settings again, without having us to program it and reset those parameters. So absolutely, I think that's the ideal state for a very human less factory.
Dan: Yeah, and I honestly, don't know how feasible it really is, I think there will always be a human element of reaction because there's so much variation in the system. Whether it be in just the design of, I mean, you can say anything really in manufacturing, there's always variation in the design and there's always variation in the tools that are building it. So I think wrangling that in and making it really robust and limit the variation is a lot tougher than anybody can really think.
Siddhit: Yeah, agree, I think you paint the more realistic picture of where we'll be at with human involvement. So absolutely the variability is extremely high right now and you know what, this makes the last fun question, very easy for you Dan. Which is if this was 2051 or in 2051, if your grandchild went to the factory, what would it be like?
Dan: I imagine it will be clean, I imagine it'll be very clean and I think that there will be cameras everywhere and people are trying to just make the quality with just as best as they can, I think it'll be a lot of quieter as well.
Dan: I think technology is trying to use, or I think the manufacturing tries to use different technologies, that'll be more robust and less like damaging over time, if that makes sense.
Dan: Like, you don't want these machines breaking down on you all the time, so yeah, I think that'd be it.
Siddhit: Yeah, that's a great answer because you touched on some of the more human aspects and more behind the scenes aspects. People generally talk about the more attractive things like AR, VR, or robotics. But I think you reminded us that there's still going to be some people maintaining the robots that run the factory and they can't be breaking down.
Dan: Yes, very well, they will.
Siddhit: I also want to rewind back to earlier when you mentioned repairs for the audience. What Dan was talking about was the meantime to repair and the meantime between failures and I link that in the show notes as well. These are two very important metrics for uptime and maintenance, so those are a couple of good reads also. So Dan, thank you so much, I think you gave some fantastic answers.
Dan: Thank you.
Siddhit: I think we learnt about the exterior body side, of the vehicle and how all these different variables play into it, how important it is for you to look at dimensional data. And just some of the pain points that any engineer in any factory would be facing and how technology could help them. So I really enjoyed this, I hope you did too and I hope the listeners did too. So thanks a lot.
Dan: Yeah, no problem.
Siddhit: Until we meet again, maybe again on this podcast.
Dan: Yeah, thanks for having me on, this has been fun.
Siddhit: Yeah, all the best with your Masters and keep in touch, man.
Dan: Thank you, absolutely will. Good luck.
Siddhit: You too, bye-bye.
Dan: Alright, bye.
Outro: 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. Through board programming and coordination and stage to stage production flow control into a single Pashi program. Check us out at pashi.com and until we meet again, have a fantastic day, and take care.
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