Patrick Adams  00:00

Welcome to the Lean solutions podcast. This is the podcast that adds value to leaders by helping you improve performance using process improvement solutions with bottom line results. My name is Patrick Adams, and this season, I’ll be joined by three other amazing hosts, including Catherine McDonald from Ireland, Andy Ulrich from Australia and Shane gottfah from the United States. Join us as we bring you guests and experiences of Lean practitioners from all over the world. Hello and welcome to this episode of the lean solutions podcast led by your hosts Andy Ulrich and myself. Patrick Adams, how’s it going? Andy, good night doing well. Thanks. How are you? Oh, I’m doing great. Enjoying a beautiful day here in the state of Michigan. I know it’s bright and early in Australia, 5am right? I don’t know how many you don’t drink coffee. Do you no drink tea, though? So, yeah, one, one has been one’s gone down this morning already. So all good. It’s, it’s actually quite warm here too. So it’s we’re coming into summer. Here we go. Nice, nice. Well, I’m excited for our topic today, Andy, and from our guests that will be joining us, I think we’re going to have a really, really great conversation. We’re going to be talking about data intelligence for effective, Lean transformation. And so this will be a little different topic than what we have talked about before, but so relevant in in the work that most of our listeners are doing today. So, you know, we think about just the evolution of Lean implementation in recent years, so much is driven by technologies like robotics, automation, AI, right? There’s just so many things that are happening that are now starting to drive decision making that we’ve never really had access to in the past. So, you know, the question begs is, are these things a benefit to us? Are they not? Are, you know, the these are the types of questions that I hear like, are we? Are we too? Getting too far down that road is lean, still relevant because of all of the advancements in technology. So that’s why I’m super excited about this conversation. We’re joined by Hassan Bali, and he has got a wealth of knowledge on this topic. So Andy, can you just give us a quick introduction to Hassan? Absolutely love to yeah, really excited too about the topic. And it’s, it’s, very, very much spoken about down here, as well as around digitization and AI and all those things. So Hassam is the co founder and Managing Director of obtegrity solutions and tech M solutions, and he’s got extensive experience in Lean Manufacturing, operational excellence and supply chain management, holding a PhD and MBA, he has excelled as a fractional COO for mid market business, leading large scale manufacturing operations to drive sustainable growth. He’s certified as a lean and Lean Six Sigma Black Belt. Hassan is a proven leader in optimizing operations and achieving process consistency. His ability to coach and motivate teams has consistently delivered enhanced performance and bottom line savings. Welcome to the show. Hassam, how you going? Yeah, hey, that’s that’s awesome to be here. Thank you very much. I’m doing great. Thank you very much. Are you guys super happy? Yeah, we’re great, mate, doing great. Really excited to get here. Yeah, before I know, Andy’s got a quick question for you, but before we do, can you just tell our listeners where you’re calling in from? I think everyone knows where Andy’s at and where I’m at. Where are you at in the world? I’m calling from Wichita, Kansas. Alright, nice. The world capital of the eight, the air capital of the worlds. Because if you don’t know, we have a lot of aviation manufacturing happening in Wichita, Kansas. So,

Andy Olrich  03:46

very nice. I didn’t know that. There you go. Thanks for that. Yeah, Hassan, I yeah, really, really, really pleased to have you on here. And wealth of experiences. We just just touched on a few things. I just wondering, can you tell us a bit more about obtegrity solutions and techcam solutions and kind of the you know, where that’s come from? And,

Hessam Vali  04:08

sure, definitely, yeah, glad to do that. So just a little bit of a background about myself. I’ve been always in manufacturing, so just recent years, we start doing some some work in service industries. But my main background is coming from manufacturing and and I think from working in the shop for as a quality man, quality engineer, manufacturing engineer, lean engineer, all the way to like operations management and operation leadership role. So I’ve been doing it for for multiple different industries, including aviation, automotive, general manufacturing. And in 2017 I basically made my mind I want to start doing my own thing. So that was the inception of telecom solutions. And when we started Telecom, our main focus was really operational excellence. So a lot of things that it’s around, obviously. Are using a lot of Lean techniques and Lean principles and philosophies, but but tying up with some other tools and techniques like digital or business intelligence or automation, or even either process automation or industrial automation. So this, these are the services that we’re providing, but with having the the operations and the center of everything. So we are doing things to make sure that we are advancing our operations, to meet our numbers, whatever it could be, from people side all the way to Financials. So that was, that was the inception of telecom solutions. And as we are doing it, you know that in Lean environments, there are a lot of things that we are, we are teaching or coaching people to implement as simple as, Hey, make sure that you have a great success program and 5s program in place and you’ve sustained that right? Or it’s critical to have good visual work instructions or SOPs then, because that’s the basis of any improvements, as we always say, or, Hey, quality as number one. Quality source is critical for any lean systems, because you don’t want to pass quality issues. So we are helping our clients by by implementing all these principles in their day to day operations. But sometimes everything has been done manually, and it might not be an easy way to sustain those things, even if you have the right processes. But when something becomes a little bit more manual, manual, or it’s not really easy to to follow the process, then you will see that the motivation is start getting, you know, decreasing a little bit. So I’ll tell you, they came in to address that, that particular problem. So it developed some digital solutions that, hey, if you’re really, if you get to the point that you understand what visual work instructions is, what time of study is, what level loading is, how to eliminate all those waste and unbalanced operations. Now in order to just make that process simpler, faster, more more user friendly, hey, this is the technology that can help you that, and that was the inception of obtality solution. So it’s a digital solution to do a lot of things that we are doing a lean environment, but in a digital manner. But as I always say and talk with our clients, we always say that tool and take tools are the secondary things you gotta first understand what we’re doing. If they get mastered and that then tools can help you to streamline some of those processes.

Patrick Adams  07:24

Yeah, I love, I love that you said that, because that is always the question that that people ask is, well, why are we doing this? You know, on with pen and paper, why are we doing this on a whiteboard? Like, let’s go digital. And, you know, our recommendation is always the same, like, let’s, let’s figure it out with a whiteboard first, and then once we get really good at it, and we’ve we’ve figured out, you know, how to how everything works. And, you know, then maybe we look at some digital solutions, or whatever it may be, because so many companies, they spend a ton of money, and then they find out that it, it isn’t what they needed, or it’s an add on that doesn’t work with what they currently, whatever it might be. But one of the things that that came to mind as you were talking is kind of back to my introduction for our conversation. I do get a lot of people that ask, you know, well, with all the advancements in technology, is lean, still relevant? You know, they’re asking, well, we’ve, you know, you have new, new robotics. We have automation. We have AI, all these things that are now in place that were not in place, you know, X number of years ago. So how do we how has lean evolved, given these advancements? Because I think you and I both know, and we’ll answer that question of Yes, lean is still relevant. Obviously, if you have waste, if you have customers, if you’re looking to provide value to your customers, then lean principles are relevant. But how has it evolved, given all of these different things that have now been implemented into these organizations?

08:57

Sure, sure. That’s a great question. Well, let, let me start this way. You already, you’re, you’re the head and nail. But what you just said, but waste is not going to go anywhere, right? We as we as a business owners or or operations leaders, we always gotta deal with wastes. It’s just it may move from one point to another point, but it’s not going to go away. I mean, we will identify them, we reduce them, we eliminate them, but but the concept is going to stay, because as you get better and your processes, then you identify more opportunities to improve. So I want to, I want to address that question by by talking about the philosophy first. So what is the philosophy of lean, right? It’s two things, if you think about like the Toyota way and how Toyota has applied all the Lean principles, talking about two things, talking about respecting people and understanding that people has knowledge and has has expertise that you got to use and continually improve processes. These two pillars. Right? So I don’t think that we get any time in in the next at least as long as I’m going to be alive, that we get to the point that we say people are going to are not needed for businesses or or continuous improvement, not needed. So I think that these two are going to be relevant all the time. So that’s why, even if you start looking at some some newer technologies as as a company who implement new technologies in some some of our clients, when you look at it, you see that we’re talking about Lean robotics now. And why is that? Why we are talking about Lean robotics as a as a point, because there are way too many examples that the robotic Solutions has been implemented on the wrong place. I mean, I just again, if I want to just talk a little bit about the literature, if you think about the book, the goal, right? The prime example was that, hey, you had a robotic X, X, a 10 that is just producing parts and generating generating inventory where the problem is not there, right? So I think that is going to stay it is relevant, and it’s going to stay relevant, because if we do not understand where we are applying technology, the likelihood that we missed the point is going to be very high. So from philosophy perspective, it’s not going to go anywhere from adoption and implementation and execution. If we do not understand where we are applying the technology, then it’s gonna, it’s gonna, it’s gonna be more cost than benefit, in my opinion,

Andy Olrich  11:27

agree, and we, there’s lived examples right here and now where we’ve jumped in with with the automation, or the robotics, for example, in the spirit of trying to accelerate and and Improve and reduce waste and all those things. But yeah, without that, you know that proof of value that you can do with the whiteboards and the traditional manual processes, but yeah, changing the thinking it it doesn’t go sideways, and it turns it, yeah, from something that’s supposed to be an enabler and a help into it’s constraining, and it’s consistently wasteful. It’s, I saw a fantastic quote on somewhere on LinkedIn where the person was talking about someone that spoke to Motorola, and they said it was around cleaning up the process before you go in and automate all the robotics. And it was, it was something around, if you, if you automate your processes, without first removing the waste, you’ll be able to screw things up at a rate that you never thought possible. And that was just such a great way to put it. And, and, yeah, it is. I there’s some traditional thinkers out there, you know, that’s only to be on paper and that’s it. And stay away with the digital. However, like most things, you know, there’s a there is a time and a place for all these new technologies where they can collaborate and help. So again, it’s such a great topic that we’ve got on here and and when I’m sort of talking about those and those challenges or perceived threats and things that come from it, like, what, when we’re talking about, you know, we’ve got all this data now, or we’re trying to gain more data. You’re in it, and you’re, you’re, you’re working with clients now, with robotics, automation, AI and what, what are you seeing is that is the main challenges with opportunities that you see are really valid for that particular organization and lead transformation? What are the, what are the main challenges that you are coming up against,

13:21

sure. So let me, let me talk. Just start with some stories, right? So we work with a client. I hope this did listen to this, this podcast, but, but they don’t get a very personal so we work with clients that the basically, the decision of where to put the technology was not very well thought of, meaning that they came up with the technology first and then they decided how this technology is going to fix the problem before they define the problem first. So I remember, this is a very this is an example that we just recently worked with it. So we are talking with a packaging organization. They’re talking about putting some some automated line with some robotic solution for palletizing, right? So at the surface, you would say that that’s a great solution, right? You’re doing the palletizing on a regular basis. So why? Why not? So let’s put a couple of robots, and we just do the pilotizing robotic but using, using technology that’s great at the surface, it looks great. So the very first thing that we did was we did a full blown time, full blown time studies on the entire line to understand what’s happening. So what we understood was the downstream part of the process, which is feeding to the robotic line. The the palletizing process had a lot of unevenness in that process. So as a result, the input to the to the robotic cell was not consistent, and it had multiple different reasons, from from the speed. Of the hopper machine on the beginning of their line, the way that the people are running that machine. So it resulted in any unlevel load into the robotic. So even though we spend about they spend huge amount of money on implementing that robotic cell, but it was not successful. So after seven months, they had to remove that robotics from that cell, because even though the robotic was working, but everything coming from downstream was absolutely unpredictable, so because of that, the robotic cell didn’t do anything. So one challenge that I see constantly is jumping to the solution before really understanding what the problem is and and assuming that the technology is going to fix everything from the beginning. So on the other hand, they have examples of going through the process really understanding what the constraints of bottleneck is, after selecting that, after fixing the supplier and the customer for that operations, then we implemented some robotic solutions, and it’s been absolutely successful. So I would say, not really been data driven to make the decision what the problem is, and this quickly jumping the solution. Probably that’s these are the most the the common theme I see when the technology is not successful and and when it when it comes to the adoption

Andy Olrich  16:17

you talked about, the principles remain. I mean, isn’t that? Isn’t that the problem from day dot, isn’t that why we do Lean and Lean Six Sigma. It’s to stop us from from jumping to solution, you know, taking the time to step back and have a look. And yeah, it’s again that it’s just, it’s kind of here. But yeah, the digitization piece can be something shiny and bright that people want to jump straight into again without taking the time to clean up. Thank you for that example. Patrick, I’ll throw to you. Mate,

Patrick Adams  16:45

yeah, no, I appreciate that as well. And obviously the you know, we we talk about the importance of allowing data to drive our decisions, you know, in everything that we do to try to take out, take the emotion, take the feeling out of it. And let’s, let’s let data really kind of point us in the right direction to make the the proper decision. And in the past, for many companies that we’ve worked with, you have the it’s like the loudest, the person who’s yelling the loudest in the Kaizen event is going to get their way, or the person who complains the most, you know, gets gets the floor. And, you know, again, we try to really push people towards, let’s, let’s get away from that, and let’s allow data to drive those decisions. So just kind of bringing this back to my question, and there’s tons of data out there, you know, we can pull, we could, we could pull all kinds of data and but, but what is the right data in a Lean transformation, what type of data should we be looking at, and what is, where do we, you know, where? I guess it probably depends. But I want to kind of ask you and your experience, is there data that we shouldn’t be looking at. Or, you know what is, what’s the right business data to look at? What are your thoughts? Well,

18:08

again, talking about a just going back to the philosophy of lean, we all know that lean as a very customer centric operating system, right? And we always talking about when, when we talk about customer. You’re always talking about internal customer and external customer. So what we don’t to start what don’t we start from, from that and in the center. So let’s look at customer. And it could be again, and nowadays, the customer is not necessarily the end user of the product, right? Even if you’re talking about the shareholders, they’re a customer of the business. If you’re talking about the internal customer, those are, those are the the customers that we need to look at. So I always try to look at it from the customer perspective, right again, enter on external and see what makes the most sense, and start in front there and then. Always try to look at it from both lagging and leading perspective that what we need to measure today in order to prevent issues from happening in tomorrow. So from that perspective, and if you look at it from the value stream perspective, we have all these functions within the value stream. So always try to look at and say, who is the customer for this function and what matters the most for that function, this customer to this particular process, and then try to define the KPIs from that perspective. So if you look at it from that perspective, always talking about some, some lagging KPIs, like, hey, customer is receiving what they want on time. Are we providing the highest level of quality that we could provide to them and all the way to the are we making enough money, and are we? Are we a healthy business, from the financial perspective, that we can keep continue providing service to our customers? Sure. So these are some leadership and executive type KPIs, but when it comes to production level or the shop floor level, what KPIs now we need to look at from that perspective, that’s where. Technology could be very helpful, because now, with the newer technologies there is, it gets much easier to collect data from day to day operations out there in the shop floor. I think that this is, this is what we can scale the lean and data together, and then we really uncover a lot of opportunities from, from from that perspective. Hey,

Patrick Adams  20:21

everyone, I am sorry to interrupt this episode of the lean solutions podcast, but I wanted to take a moment to introduce you to our company, lean solutions. We exist to empower and equip people for positive change. We do this through our three pillars, which include training and development, coaching and consulting and talent solutions, whether you have specific areas for improvement, or you’re not really sure where to start, we can build tailored solutions and provide resources to meet your needs. Send us an email at office, at finally solutions.com to begin your journey towards transformation. Now, back to the show. Yeah, just a follow up question on that. So you know, we have, I would say there’s probably a very large percentage of our listeners that are in the manufacturing space, so that would resonate, and they would understand that. And, you know, maybe we would look at just to guide them, sqdc, safety, quality, delivery, cost, might be a good starting point again, to just ask yourself, from the customer standpoint, internal or external. You know, from a safety standpoint, what’s important to the customer. You know, what’s important to us, what’s important to the customer, from a quality standpoint, from a delivery from a cost standpoint. So that’s a good again, a good guide, obviously. But what about those that are not in manufacturing? Do you have any any advice or thoughts, you know, I just think about, there’s people that are listening in that are working in the government space, state and local government. There’s people that are listening in that are in higher ed or healthcare construction. Where do they start? And how can, how can digitalization, and, you know, all of the advancements that have happened in AI and robotics and different things, how can those help us to identify the right KPIs, no matter what industry we’re in? Sure,

22:15

sure, that’s a that’s a great question. Again, I can, I can probably come up with some some stories about that. Right now, we are working with government on a project that is all about, it’s in the that’s in the social service sector. So probably that’s our second project or engagement that we have that it’s not in manufacturing. So in that particular case, we are this particular organization, which is a nonprofit organization, is providing services to kids that have some some foster care needs. So it’s absolutely service, right? It’s nothing and manufacturing. So just, just, let’s start with that example. So when I, when we start talking about it again, we said that, who is the customer for that service, right? For that, for the to start the conversation. And we said that the customer in that, in that entire operations, the kids and the families for those kids, right? So when you think about it, what matters the most for those, for that customer, a couple things come to we had that very that was a Kaizen event. We had a Value Stream Mapping Kaizen events in that organization, and all these questions start coming up. So the team is start talking about it. What matters the most is how long the kids are staying in the foster care system, right? So the time from the point that the kids are removed from for whatever reason until the point that they’re back to the family. So when you think about it from that perspective. All right, the total cycle time in the system is a great KPI to start with. And when we did our value stream mapping, we realized that that number could vary between different cases. Could vary anything from 17 months to 27 months. And we said, hey, it is really, really long. We want to shrink that now we are going down deep into the value stream and identified not only the non value added activities happening resulting complete time to grow, but also within the value added activities. What’s happening inside those that we possibly can shrink using technologies like hey, the information is just the flow of information is really clunky. It’s very manual. How can we use some technologies to to just clean up the the information and flow the information within the value stream faster? We came out with great ideas how to do that, and we’ll implement some of those ideas, and we already got the the results. So that’s an example of service sector and technology can just again merge together to identify what is non value added, what’s wasteful in the value stream, and how we can improve that particular KPI related to that particular problem.

Andy Olrich  24:52

Love it. Great example. So Hassan. One of the famous quotes that I learned very early about Taichi I know, was he said. Data is, of course, important in manufacturing, but I place greatest emphasis on the facts. I’m really interested to hear a little bit and maybe an example of how when you’re deploying some of this advanced technology, in particular, AI machine learning, the machine, in a way, even though it’s designed by humans, is starting to learn and tell us things, what’s, what’s some of the things that you look out for, or what some of the approaches that we you go, hang on. We still need to, you know, still have that person or that team validate what the machine’s telling us. Can you give me a couple of insights into that? It’s always something that fans have terminated, to always look at Skynet and go like, how far is too? Far, too soon, and those sorts of things. It’s like, Yeah, can you walk us through a couple of those things?

Hessam Vali  25:47

Sure, sure. Again, I love everything by by example. So I just basically a few, a couple samples that, if I understood your question. Andy, I I’ll just some some examples. If not, we can let me know. But so if you’re talking about interaction of human with the machine, and then let the machine to understand what’s happening and provide, provide some, some ways to improve, continue to improve, a process, I have a I have a good example for you. So previously, when you’re talking about, let’s talk about the quality improvement initiatives and activities, right? Just very briefly, what was the process in the past? Hey, we put some some checklists. We have some, this checklist that the product goes through the to the manufacturing process, or, if it’s a service environment, the information going through the value stream. And there are, you have these quality at the source gates, that you have different places, and they’re doing the check against your standard, whatever the standard is defining. And if you have any non conformity, or any inconsistency between what’s built, what’s the standard saying, Hey, you start collecting some data. The data could be, I see the problem at this point. This is, this is what the issue is. This is what what I’m measuring versus what has to be measured. So you’re collecting this information right previously, what’s happening is now you have this giant XL, if you’re advanced enough, it’s Excel. Otherwise, it’s going to be just on on a piece of paper that someone needs to just go ahead and manually enter it somewhere. But you have now that’s information. And what we’re doing most likely as we are going if we go through, like, some, some sort of the make approach, we look at it and say, Hey, let’s look at the parade to off this and say, This recent code is repeating the most for this time frame. Now let’s go ahead and start doing some Fishbone and some some some root cause analysis to identify what the cause is. And go ahead and after implementing some, some countermeasures, right? So now, if you look at that in the context of the New World, how does that work? Look like number one, we can make, if we want to go really, really crazy on that. So we can use some some vision technologies that that’s detecting the inconsistencies, as opposed to the people doing it right in many automated lines, you can do it, but still, we have a lot of many manual manufacturing processes that’s really difficult, unless you give everybody an AR HoloLens headset that they look at things. It’s not going to happen anytime soon. So, but what you can do is you can provide the users with a very simple platform to identify issues and log it to the system, right? So this is, this is what we are doing, actually, in one of our applications on the activity. So we have these tablets that the users at different points in manufacturing processes, they’re detecting issues and unlock it to the system. So what happens is, now we have all these data, and the AI is looking at data sets that we have and try to identify connections and start providing some insights. And as the information is just growing within the system, within the data sets, they get trained better and better and better, and then they provide better solutions. And it’s not necessarily solutions. They’re telling you, Hey, I see that every time that this is happening, I see that two, 510, whatever work center later, I see that this issue is happening as well. So we start getting some insights out of information in the system, and that’s how AI can help us. Again, if you take that in the context of Lean implementation, it just helping you to get to the root cause of a problem faster, adopt waste elimination tools and techniques to improve the to improve the process to the next level. So was that? Was that what you were referring to? Okay,

Andy Olrich  29:32

yeah, and how we can validate that with the team? So for example, in your example, where you said, like the data is actually being driven from the person where the work is happening. So that’s, yeah, it’s not just something that is just looking at patterns and algorithms in the background. And it’s actually that there’s still that manual interaction, still an activity for the worker to get going around. Hey, this is a and they’re saying it’s a problem, right? Or there’s an issue. Yeah, I think what I like about some of the early AI, that’s that I’m seeing around the. Places. It’s giving us those warnings and those insights before, you know, we hit our upper limits, or things like that, and then it’s a problem. So I really find it powerful there. But yeah, there is always that, that bit of a risk around, I think, around how, you know, going too far too soon, and it’s not being validated by the worker that we still need. As I say, robot can’t give you a hug like you need to. It’s, it’s collaborative robots and collaborative AI and tech, instead of just, how do we get rid of these people? And what’s that little machine over there that will do that for me? It’s, yeah, thank you for providing that example. I

Patrick Adams  30:34

appreciate it. Yeah. That’s great. Yeah. It makes me also think about Andy just engagement too, not just validation, but engagement of the operators with the data, right? And, you know, obviously, you know, you mentioned some about root cause analysis, but I also was just thinking way back I was at a company this was this, this. Think we were a little ahead of our time, to be honest, but we wanted we had that the operators were complaining about. Well, we were doing hour by hours, and then the data from the hour by hours was getting candy over to an engineer, and then they were complaining because they had, you know, these stacks of hour by hours, and they’re manually entering this and manually generating these paretos and and so the team kind of challenged themselves to, like, how do we eliminate the the manual time of sitting behind the computer and entering all this data in? And so I remember someone from it came up with this idea of putting a an hour by hour, they were doing part counters. So it was really easy to tie the hour by hour data to the part counters, and then at the top of every hour, it would just pull the data from the part counters and put a number on the hour by hour. The piece that we didn’t want to miss, though, was the interaction of the team members with the tool. We didn’t want them to just be running and not paying attention, or, you know, kind of fully removed from that. So anytime that they the count, they, let’s just say, for even numbers, they had to get 100 pieces in an hour. If they got 101 or 120 or 130 if they were over the goal, it would be green. And then if they were below the goal, and they only got 80 or 60 or 70, it would actually make a noise and blink red until they typed in the reason why. Or we actually again, maybe ahead of our time at this time, but we had created a barcode scanners with reason codes, and then we had them scan it, and it would automatically put it the right reason code up into there. Then from there, they would upload all the information, it would take that and spit out paretos, and then we would do root cause analysis with the team members on that. So this was way back, too, and it just this is all bringing memories back to me when we were doing this, I still remember going to the red tag area, and I found a an old fan stand and a flat screen computer screen that we just threw together with some nuts and bolts and put it out on the line so they could see it. But anyways, I digress going back to what we’re talking about here the interaction piece, right? So if you’re talking to a potential client or somebody, let’s just say our listeners who are out there and they’re going, well, I don’t have any we’re not using automation. We’re not using robotics or AI or anything. At this point, what would your advice be to them on how to get started, and how do we keep the engagement of the people with the data or the tools or whatever it might be, yeah,

Hessam Vali  33:48

well, I think that data, and, like some some of the advanced technologies, I mean, you know, let’s, let’s just start with this, even though a lot of organizations are not using in New York technologies, right? But at least a lot of organization that we engage with, they’re using some business, business management systems, right? And some, some sort of ERP system that they’re using for different processes, some sort of, some sort of applications to track their quality defects, doing their cap on root cost analysis. So the data exists. In many cases, the question is, how comfortable or how confident are you with your data? Can you say that, hey, I have set of data, but my data is that trustworthy? Is that something that I can take actions on it? So I would say that probably again, back to what we just talked about a few minutes ago, start with what matters the most for for the customers across the entire value stream, right? And sometimes, when you talked about safety, I always, when I, when I engage with our customers, they say that your customer, for your. Your family is your customer, right? You’re providing something that they use, so you have to be safe in order to provide what your family needs. So safety is also a customer driven KPI a metric, but it’s not an end user, customer, maybe directly, but your family is your your customer in that extent. So I just wanted to put that out, but again, back, going back to what matters the most for the business from the customer’s perspective. So when we start defining those, then we gotta start thinking about when at what information needs. Need to start tracking. Sometimes, if we just to start with it could it could be. And I think there should be piece of paper and pen and paper, easy way for people to just track their information as we progress through that model. Then we kind of stop using technologies, even though, if it’s not a robotic but as you said, nowadays, there are some end on systems that has just have the end on system, and they have like 10 different buttons based on what reason code is. So as soon as something goes wrong, you push the button to say, hey, I have a problem. The red light, light goes off, and you say, I have this problem because I have this maintenance problem, maintenance issues, right? So the information is also, again, it’s a it’s interaction between the users and data. But we can use some simple after we define what needs to be measured after we define where and how we need to measure that and who needs to measure that. Then at when we’re advanced in that process, then we can start bringing technology, little by little, to streamline and make make those processes more user friendly, especially from the analysis and decision making part of it. Still, I think that there is a journey, a data journey, that we need to go through. But at the end the everything that we’re talking should be with that mind that we are improving our KPIs to Our customers. You.

Patrick Adams  45:22

Music. Thanks so much for tuning in to this episode of the lean solutions podcast. If you haven’t done so already, please be sure to subscribe this way. You’ll get updates as new episodes become available. If you feel so inclined, please give us a review. Thank you so much. You.