2 Episode_3-Ken_Stocco-Ready MODIFIED 1

Ken Stokoe, a Master Black Belt in Lean Six Sigma, discusses the importance of qualitative methods in process improvement. He contrasts qualitative methods, which involve brainstorming and gathering information from the team, with quantitative methods, which rely on data and statistical analysis. Ken emphasizes that qualitative methods allow for divergent thinking and exploring all possibilities before converging on a solution. He highlights the benefits of using qualitative tools, such as increased understanding, buy-in, and ownership from the team. Some examples of quantitative tools mentioned include descriptive statistics, inferential statistics, hypothesis tests, statistical control charts, and design of experiments. Overall, Ken stresses the significance of balancing qualitative and quantitative methods for effective problem-solving and improvement. Welcome to Lean into Excellence, a Workstream Consulting Podcast. I'm Liz Crescenti. And I'm Marco Bonilla. And we will be your hosts as we embark on our continuous improvement journey. Welcome back to another episode of Lean into Excellence. I'm your host, Liz Crescenti. And I'm Marco Bonilla. And today we have our very own Master Black Belt, Ken Stokoe. We are very excited to have him on the show today. We sure are. Ken and I have been working together about five years. It's been a great pleasure getting to know him and getting to learn from him. So welcome, Ken. Yes. Welcome to the podcast, Ken. Hi. Thanks, Liz. Now, could you give the audience just a brief bio of your background and introduce yourself? Sure. My name is Ken Stokoe. I live in Duluth, Minnesota. I've been with Workstream Consulting for about five years. Prior to that, I worked in mining and mineral processing for 28 years. During that time, my responsibilities included quality assurance, managing the testing laboratory, process engineering, asset management, project engineering, and project management, as well as during that time, I got my Master Black Belt and my Black Belt certification in Six Sigma. I also served four years in the Army and 16 years in the Army National Guard and retired at the rank of major. In addition to my relationship with Workstream Consulting, I have my own consulting company, Practical Process Improvements, and I currently work as a part-time adjunct instructor for Minnesota North University. I have a bachelor's degree in chemistry and a master's in management. Thank you for that. Marco, you better be locked in today. We have a major on the line with us. Oh, yeah. He's going to make me look bad. I know. I know. I'm taking notes, though. I'm taking notes. Yes. Awesome. Thanks for coming on. So the purpose of this podcast today is to talk about qualitative methods. Ken's here because he'd like to do a deeper dive onto the framework around qualitative methods for Lean Six Sigma. Thanks, Marco. And during this podcast, I'd like to compare and contrast qualitative versus quantitative methods. But first, I'd like to kick off with just a little story to kind of show where we're going with this. This is New Year's, and everyone has New Year's resolutions, and a lot of us have a resolution to exercise more. So let's use that as an example of how we use quantitative versus qualitative methods. I start off with a qualitative idea. I'd like to exercise more. And I follow that up and say, well, I think the way I should do it is buy some home exercise equipment. And then I say, what's the best type of home exercise equipment? And I agree to say it's an exercise bike. And typically what happens then is I immediately start researching exercise bikes, looking at them based on cost, their size, the different features they have, their ratings and review. And in the end, the result is I have used quantitative methods to select the best exercise bike I can find. But by skipping over and glossing over the qualitative or brainstorming type methods, I may have the best exercise bike, but I may not have the best exercise plan, right? I left everything else off the table, things like outdoor activities, going to a gym, joining a sports league. So that's what I want to talk about today, is if we go too fast to the quantitative methods, we leave a lot of good possibilities on the table. That's excellent, Ken. Yeah, we tend to be, especially those of us who are STEM backgrounds, we tend to focus on the numbers. Right. And not on the words or the concepts. Or the reasons why, right? So this is really important. Yeah. And, you know, if we jump back to the basics, Lean Six Sigma is a team-based process improvement approach that utilizes data-driven decision-making. And we can think about the tools and methods used within Lean Six Sigma as qualitative and quantitative. And the qualitative, you know, they're based on process knowledge and the team's experience. In other words, we're gathering information from the team to help us solve the problem or make the improvement. You know, examples of this include brainstorming, process mapping, visual and graphical tools. And I'd like to add a couple of things to that, Ken, if you don't mind. In one of the classes I teach in the MBA program, we do market research. And there's a couple of extra things that we do in market research that's very qualitative and very important, is observation studies, focus groups, questionnaires, very similar tools that we can leverage here in Lean Six Sigma. Yeah, that's right. And another thing to add about qualitative, and it isn't a direct one-to-one, but for today's discussion, when we talk about these qualitative tools I listed, you could think about them as divergent thinking. In other words, what could be? What could be the cause of this problem? What could be the way that we make this improvement? And we can contrast that with quantitative methods, which generally are based on data and statistical methods. And we use these to determine what is the problem and quantify the nature of the problem or the relationship between an input and an output. Right. But, you know, the quantitative, as you said, will never tell you the why, right? It just points to a direction. Right. And using my example of the exercise bike, I could use quantitative methods to determine which is the best exercise bike to buy based off of data. But it's not exploring all the possibilities. In other words, the quantitative methods, as opposed to divergent thinking, they're convergent thinking. They're converging on one solution. And the whole idea of today's podcast is, if we move to that convergence too quickly, we may leave the best solutions behind. Right. Because numerically, that might be the best solution on paper, but it might not be the best solution for you, ultimately. Right. So we can gain a lot of benefits by increasing our focus and elevating the importance of qualitative tools. And, you know, some of the reasons for that, you know, because qualitative tools draw heavily on the team's knowledge, experience and opinions, there's no better place to gain a better understanding from those who work in the process every day. And in addition to that, drawing on the team's knowledge goes a long way in increasing buy-in and ownership of the solution when we come to it. If you go to the people that are doing the work and ask their opinions, when it comes time to make the change, they're going to be a lot more willing to embrace it and to support it. Outstanding. Yeah. Absolutely. So, Ken, tell me, you know, what tools you like to use that are quantitative. Yeah, some examples of quantitative tools that we use in Lean Six Sigma would, you know, would include things like descriptive statistics. That's where I'm looking at data that I have from a sample and I'm understanding the nature of that data. You know, where's the average or the mean? What's the variation around the mean or the average? What's the shape or distribution of the data? And then another tool would be inferential statistics, where we're gaining an understanding of the entire population based off of that sample. And then when we have inferential statistics, that allows us to do things like hypothesis tests, which is determining whether there's a true statistical difference between two inputs or two outputs. Statistical control charts, you know, a whole area of quantitative tools that fall under the category of design of experiments, where we're refining or getting a better understanding of the relationship between inputs on an output of a process. But the thing that they all have in common, as opposed to the qualitative tools, is they're all convergent. They're all converging on an answer. We're using the statistics to come to a conclusion. And that's the big distinction. You know, what I'm calling qualitative tools on this discussion are divergent. We're trying to think of everything that could be. And once we've identified everything that could be, we use the statistical tools to identify what is the problem or what is the cause or what is the solution. Ken, can you do a deeper dive in the qualitative tools in Lean Six Sigma? Sure. We talked about qualitative tools already. And to expand on them a little bit, one of them I mentioned was brainstorming or mind mapping. What this is, is you're trying to gather a lot of ideas from the people involved with the issue or the problem that you're trying to solve or the improvement you're trying to make. We're not evaluating how good the idea is or which one is best. We're just trying to get a lot of them. And this is a creative process. We're trying to get as many ideas, get the thoughts going. And you can go and search the literature and the internet and find all kinds of methods to do this. But the thing they all have in common is we're trying to get a large number of ideas or solutions or potential problems from the people who are doing the work. Another example of brainstorming that focuses on what could go wrong is called Murphy's Analysis. And in that one, we say, if this problem happened, what could be all the ways that it would be caused? If this part fails, what could be all the ways that it happens? If this report doesn't go out on time, what could be all of the things that caused that to happen? And again, it's a form of brainstorming and it's a form of getting information from people. And we're not evaluating it. We're just simply getting as much as we can. It's a judgment-free zone, pretty much. Yeah. Yeah. We're not taking time to evaluate or putting any type of, this is a better idea than that one. We're trying to get as many as we can. And later on, we'll sort through them. But right now, later on, when we get into the quantitative methods, we'll sort through them. But right now, we want divergent, creative thinking to evaluate every possibility. Right. Yeah. You want to make sure that your team feels comfortable laying out any idea before it's got shot down. Right. Yeah. Yeah. We want to get every idea we can and then evaluate it. Now, Ken, why is there a heavier focus on quantitative methods over qualitative? You know, there could be several reasons for that, Liz. I think one of the biggest ones comes from just simply the way that we have to teach the different methods. When a student comes into class for Greenbelt training or Blackbelt training, they probably have not had a lot of exposure to the quantitative methods. So naturally, we spend more time teaching those methods. Because we spend more time teaching them in the class, we're maybe inadvertently making them seem like we need to spend more time during projects to use those methods. The other part of it is I think when we talk about something like brainstorming, it seems so simple and straightforward that we just say, well, that's just common sense. We really don't need to teach it, when in reality, there's a lot of things we do need to teach about it for techniques into how to do it most effectively. And I think finally, and I think what's one of the biggest ones is because of the way our certification standards are set up, if somebody wants to become a certified Greenbelt or certified Blackbelt, the exam that they take and the demonstration of tools is heavily focused on quantitative methods. So I think a lot of the ways that we treat the training and the certification make it appear as if quantitative methods are more important, when they are important, but they're not really more important than quantitative. And Ken, it's kind of interesting, because as a natural STEM, if you have a room full of STEM, it's really easy to gravitate towards the quantitative or the qualitative, right? We just want to focus on the numbers. And it's, as we know, as we get more experience, we know that the qualitative is just as important. It's just harder to get the insight. It's not as easy, but that's where the answers lie, right? Right. And the big importance is that we use both. You know, there's no one saying that quantitative tools aren't important. They are. They're very important. But they're not more important than qualitative tools. So Ken, can you give me an example of how you use both methods together, the qualitative and the quantitative? Sure. I think a good example that we could use to show how they would work together and to show the importance of starting with the qualitative and gathering all the information we can before moving to the quantitative could be, you know, if we're trying to reduce accidents or workplace injuries. You know, and that's important to everybody. And what typically would happen if we jump into the quantitative approach is we take the data that we have about injuries, such as, you know, the body part that was injured, the type of injury, maybe the time of day that the injury happened, you know, the age of the person or the years of experience that they had on the job before they were injured, and all types of data like that. And then we start analyzing it and try to come up with what are the common causes or what are the things that are happening before an accident happens. And that can be a very effective way to reduce accidents. But we could make it better by spending more time on the front end with the qualitative. And by doing that, an example of how we could do that would be, you know, hold brainstorming sessions with the employees and get the information from them. What tasks do you view as the most likely to result in an injury? Which tasks are least likely to result in injury? On a given task, how could you get hurt? Or what could be the way that I get hurt if I was doing this task? Or maybe what would be some of the near misses I've had where I didn't get hurt, but I could have? What could we do right now to reduce injuries? And once we've done that, collected that information from them, and we have this big body of brainstormed information, now we can go and bring in the quantitative tools to start to narrow down and determine what are the causes of the accidents and what action should we take to reduce accidents. See, the big difference is I'm spending time up front on the qualitative tools and really spending just harvesting all the information I can from the employees and from brainstorming and getting ideas before I open up a statistical analysis package. I think that is the way that we use the two together to get the maximum benefit. Yeah, Ken, and just to add to that, it's really important that people understand that the reason qualitative tools exist is because you can't get insight from data pulling out of machines, right? You need to get information that's, yeah, whether from experience, whether from educational knowledge, or from opinion, right? You're not going to get that from a data set that you pulled off a server, off a machinery, or you're not getting that insight. Exactly, Marko, and that's the big key, and the better we get at collecting data and having that data on the server, the more likely we are to skip the qualitative steps, and we get the idea in our head, all the information we need is there for us on the server. We just need to understand how to analyze it, and we'll get our answers. And that's not true. And thinking that way leads us to skip some of these important qualitative steps. Yeah, the quantitative analysis can be a lonely process, right? You don't need more than one person yourself, but when it comes to qualitative, it's a team event, right? And it's time-consuming, and that's why we have to understand we've got to put the time up front to use these tools. And to be fair, the time-consuming is not just collecting the data, but sifting through it to see what is real and what isn't, right? Because sometimes you might have someone there who's an expert, or you might have someone there who's giving you an opinion, right? This is the thing about questionnaires and focus groups. We've gone through this in class where we've had these examples where you ask them, for example, here's a qualitative thing, you ask them, do they really like XYZ, or are they going to go to the gym starting the new year resolution? But when you do observational analysis, you actually watch them in process, right? What they eat, what they do, you know? Because what they say versus the behavior doesn't match, right? So there is a lot of that, too. It depends what kind of analysis you're doing, but it's really important to understand that it takes a lot more work to get insight, very critical insight from qualitative methods than pulling data off a system and sitting alone on a computer and doing some technical analysis. Yeah, that's a great point, Marco. The example I'm giving, and it's a simple example so we can talk about it, is brainstorming. And the observation and seeing what's happened is another form of qualitative analysis that's very effective because you see what's actually happened. So, yeah, there's a lot of qualitative tools. We've talked about brainstorming as kind of the one we're using to carry through this whole thing, but it applies to all of them, is use the qualitative tools. They don't replace the quantitative tools, but they supplement and they augment and they set up for the more effective use of the quantitative tools. Absolutely. I think you would agree, because the experience we've had is sometimes you have to go to the Gemba. You have to go to the place where the work's being done and actually see, does it match what you think it matches or what you've been told, right? So again, back to that observation. We can be saying they do one thing, but out of habit or just knowledge, experience, they're doing something else, right? So, Ken, as Lean Six Sigma practitioners, what can we do to ensure that we're getting the most benefit from both methods? Well, I think the first thing is, and we spent a lot of time in our conversation today talking about it, is make sure that everybody understands the relationship between the qualitative and the quantitative. And understanding that that relationship is the results of the quantitative depend on how thoroughly we do the qualitative ahead of time. Because if the data isn't in the set that we're analyzing, it can't show up as a result. Also, you know, we talked a lot about how we do the training. I think when we're as trainers, it's important that we emphasize the importance of the qualitative tools. And even though they seem simple and straightforward and common sense, they do need to be practiced and we do need to get good at using them to get the best benefit out of them. And then I think finally is just as we're going through projects, and we're working with the project leaders and trying to arrive at a solution, ask the questions, you know, did we get everything we can out of our team early before we jump to the statistical analysis? So rather than saying, you know, what's my p-value on this hypothesis test, before we ask that question, let's ask, did we analyze the right data? Was the right data even included as a candidate to have the hypothesis test done on it? And I think if we think about all those things together and think about the whole thing as a package where qualitative leads into quantitative, divergent ends up with convergent, and we don't start with convergent thinking, I think that will make the use of the tools the most effective and give us the best results on our projects. Yeah, you're trying to cast the largest net, right? And then whittle down your catch. Absolutely. Exactly. Yeah. Great job, Ken. Thanks for coming on board. Thanks for having me. Look forward to hearing some more podcasts in the future. Yes, absolutely. Thank you so much for joining us today. As a reminder, new episodes are out every other Wednesday, and we will see you next time. Thank you. Can you envision a scenario in your operations where, despite your team's best efforts, there are persistent inefficiencies or bottlenecks that seem resistant to change? What kind of impact would resolving those issues have on your bottom line? 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