Cinto branco Lean Seis Sigma

1. 01 Introduction to the Course: Hello, re Hopkins here from the manufacturing Academy. Thank you so much for joining me for this skill share class titled certified Lean Six Sigma white belt. I'm so glad you joined me. I suspect you're interested in continuous improvement or heard of Lean Six Sigma somehow or another, and that's why you clicked on this class. Let me share with you a little bit about what you can expect if you go ahead and choose to take this class. Well, the Lean Six Sigma is actually the combination of two different problem-solving methodologies that had two different origins from different industries and accompanies. The first is lean manufacturing. Today it's generally referred to as lean because it can be used in service and design and other types of application. But lean manufacturing or just lean, really focuses on identifying and eliminating the various sources of waste in your process. Now some of those wastes are well established and well-known like scrap and setup time and things like that. And lean does focus on those. But there's a lot of other wastes or so-called Moody's as the Japanese call it in the Toyota production system. These wastes can be excess transportation, excess motion, excess inventory. There's a lot of subtle ways that you may not even realize that you have in your system will lean manufacturing provides a wide range of tools, both qualitative and quantitative, that will help you identify and eliminate or at least reduce those wastes in your system. As you get rid of the waste in your system, you become more efficient. You increase profitability, you increase employee morale and safety and all these other things. So there's some tremendous benefits on the lean manufacturing side of it. The other side, the six-sigma side, refers to a, another set of tools and methodology related to reducing variation. Now Six Sigma, it by itself refers to sigma is a measure of variation in statistics. It's used in mathematics and statistics. But Six Sigma, the kind of the capital S Six Sigma is a problem solving methodology that focuses on reducing variation. There's an old saying that says, variation is the enemy of quality. If you wanted to improve your quality, you minimize the variation. Now, variation itself is just inherent in nature when you're buying raw material in your way, you measure things in the wave methods that people do things well, all of those naturally have to have some sort of variation. But Six Sigma methodology focuses on identifying the sources of variation and then methods for reducing them. So there's variation in all sorts of things and you'll learn about many of those in this class. The neat part is that these two methodologies, lean manufacturing in Six Sigma, six Sigma work very well together. They compliment each other very well. So there are pretty much to people that would be taking this class. One would be someone who is newer to the continuous improvement profession. And they're looking for a stepping stone into the tools, the methods, the ideas, the concepts related to continuous improvement. Well, this class is going to be fabulous for you. This will be a great class to introduce you to the concepts and the ideas. I'm going to talk about those different ways. I'm going to talk about the so-called six amps or six sources of variation. I'm going to talk about a lot of other ideas that give you a foundation on which you can build. I'm not digging into the mathematics, I'm not digging into the statistics. I'm not digging into the, a lot of these analytical tools and there's many, many Lean Six Sigma. I'm not doing that. I just want to build a foundation for you to give you the terminology. And the idea is that you need to get started to continue your studies. The other type of person that's likely to take this class as someone who probably is not going to be a continuous improvement professional. You may be further along in your career. Maybe you're head of operations, you might even be the president of a company, or you're in a field outside the mainstream of production or the service that you offer. Maybe you're in purchasing or supply chain or IT, or something like that. But you've been invited to participate on a Lean Six Sigma improvement project. Maybe you're a subject matter expert. Well, even though you're not going to be you're not going to be a continuous improvement professional like that, Black Belt or something like that. But it would benefit you to know the terminology so you can contribute more effectively. So either one of those people are viable, excellent candidates to take this class. Then the last piece of it, so it's called certified Lean Six Sigma white belt, well, white belt. This idea of these belts is borrowed from the martial arts and it implies a novice level understanding. I think when you're taking karate class, I think a white belt means you know how to tie your belt and maybe you know how to kick something or fall correctly or something like that. It's just the basics. So I'm offering you the basics here of the Lean Six Sigma methodology. So again, thank you so much for checking out this class. I've been working as a continuous improvement professional my entire career manufacturing engineering, quality, supply chain management, all sorts of areas. And I've had the privilege of working on dozens and dozens of continuous improvement projects. So what I'm offering here is not just the theoretical stuff which I am offering. By the way, many of these tools go back decades established by quality gurus and the best in the business, so to speak. So I'm offering a lot of great conceptual ideas, but often you need experience to hands-on practical real life experienced that I've gathered. So I'm offering both the theory and the experience together in one class here on Skillshare to benefit you, to help you increase your skill set and hopefully further you in your career. So the class, I promise you, you're gonna get a ton of information at the end, I've got some downloadable resources you can print up and helps show that kind of a cheat sheets. In order to remember the basics of each of these methodologies, I'll attach those to the closing lecture. And I would highly encourage you to take this class you've already signed up for Skillshare. And if you haven't, I would recommend signing up for a trial subscription. It's a fabulous program and this, I promise you, it will get you started in your understanding of Lean Six Sigma. Thank you so much again for taking this class. I'd love to see in class, if you have any comments or questions, feel free to reach out to me through Skillshare. Thank you so much. 2. 02 What is Six Sigma: The big question that we really need to start with here is, what is Six Sigma? Sigma is the 18th letter of the Greek alphabet. And this symbol right here is the lowercase letter Sigma. It's as, it's used in mathematics, it's used in statistics. And it represents a measure of dispersion in a population. If you went through elementary school mathematics, you're probably accustomed to this word average, or sometimes it's used arithmetic mean, that's the center of the population. But a lot of people don't get exposure to the dispersion of the population, not the center, but how, how is it spread out from that average? And this Greek letter, sigma is a measure of dispersion in the population. So many of you may be familiar with the so-called bell curve. The formal title is normal distribution. This plot, it's shaped like a bell. It's meant to represent graphically the, not just the average or the center of the population, but it's dispersions. How far is it spread out and what is the shape look like? We're going to dig into this a lot more so don't feel overwhelmed. But the vertical column here is the probability or the frequency in which a particular value occurs. And you have sigma is this measure of dispersion in a normally distributed population. 34 times two. So 68 looks like 2% of the whole population False within plus or minus one sigma of the average. If I were to put this into practical terms, we could maybe do a study of the heights of adult males living in the United States. Maybe we line up a million adult men and measure their heights and then plot their heights along a frequency distribution. And we may find that, and I have both meters and then feet and English here. We may find that the average male living in the United States is 1.78 meters or five feet, ten inches. That's the average male. And if I say, gosh, what's the probability of someone measuring, basically, we'll say five feet, ten, it's about a point for about 40% of the population you're gonna find is right around that, right around that, that measurement. And we would find that maybe we measure, measure, measure and we just say, okay, we just ask the question, what percent of the population, what is the middle? 68.268%.3 of the population. What is the shortest and the tallest male? And we might find that it's 1.70 meters to 1.85 meters represents the middle, 68.268%.35 foot, seven to six foot one. And that sounds about right, being an American and a male, I think that's about right. But what happens is the further and further you get away from the average value or the arithmetic mean, the left, the lower and lower. The probability is that someone is that height. And this is a very normal display, the normal distribution. And the idea is that a 100% of the American males, adult American males are represented by this curve. And the probability gets lower and lower and lower and lower. But actually it keeps going on, even though it isn't drawn that way. But it keeps going on technically out to infinity. But the probability gets lower and lower and lower. So if I were to add up all these numbers, 34.113.62.1.1 times two for both sides, I would get about 99.7%. About 99.7% of the population measures. Adult male population in the United States measures between five-foot one in six foot six. And that equals plus or minus three Sigma from the average. That's what those are the characteristics of a normal distribution. Again, we're going to talk about this a lot more, but it's so important to understand this concept that sigma is a measure of dispersion about the arithmetic average. And the knee part is this bell curve or this normal distribution shows up over and over again in nature and Science in manufacturing and social studies and all sorts of areas. Studies of the population studies and Economic Studies and the heights of various trees in a particular region. Over and over again, you see this frequency distribution displaying itself in this normal or bell curve shape. What is Six Sigma? Then I'm not really talking about statistics right now. I'm talking about a data guided problem-solving methodology. It includes a lot more than just that concept of sigma in statistics. It's a broader concept, It's a problem-solving methodology, and it's got its name from the goal of this problem-solving methodology, which is that plus or minus six sigma of the process output the population in the short-term measure within the engineering specification limits. So back to our normal distribution curve. I told you that this, now this is plus or minus three sigma. That equals 99.7% of the population, of the whole population. In other words, like in our adult male example, it would be of all males living in the United States, adult males. But it doesn't have to be that it couldn't be plus or minus three Sigma could be 99.7% of the thickness of a particular feature on a product that you're manufacturing millions of. So we're looking at this big, big population. The goal of Six Sigma is a six-sigma. The program is that plus or minus not three, but plus or minus Six Sigma of the population falls within the engineering specification limits. You may have a print that calls out maybe it's whatever particular feature you have, ten plus or minus 0.20. Well, the goal of the six-sigma problem-solving methodology is that plus or minus Six Sigma, the process output in the short-term fall within the engineering limits, not just 99.7%, but 99.99999998%. Again, as the number of sigmas go up, plus or minus four plus or minus five plus or minus six. Is it number of sigmas go up from that, that center of the population that average the probability of it being there as smaller and smaller and smaller up to this plus or minus Six Sigma, which is very unlikely to occur yet it's still within the engineering specification limit. If this process is achieved, it would essentially never produce a defect. In fact, in the long-term, it would be less than four parts per million, which is very, very low. So that is a quick overview of this problem-solving methodology called Six Sigma. 3. 03 What is Lean Manufacturing: What then is lean manufacturing? Will lean manufacturing is a waste reduction methodology, essentially doing less with more. The lean approach focuses on customer value. We're process steps are either identified as adding or failing to add value to the customer. In manufacturing, waste is usually thought of as defects are setup scrap, or wasted time by employees goofing off in the lunch room or something. But, but waste from a lean manufacturing perspective comes in many different very specific categories. These are the seven Muda, as muda is a Japanese word meaning waste. These are the seven predominant wastes in the lean manufacturing system. Transportation, inventory motion, waiting, overprocessing, overproduction, and defects. In the lean manufacturing methodology. We're seeking to reduce waste in each of these categories, where by reducing transportation lead time is reduced, reducing inventory, the cost of working capital is reduced. Reducing motion, employee morale is improved if they're looking for tools all the time or bending over a lot, that can wear down on an employee. So by reducing motion, Morales improved as well. Waiting. When there's less weight time floor space is opened up because there's no need to store all this stuff anymore. When you reduce overprocessing or unnecessary steps that the customer has no, doesn't recognize or doesn't value. Your sustainability is improved, overproduction customer satisfaction is improving, and of course, defects, net earnings or improved in the end, these are just some samplings and sometimes you'll hear it in a more modern depiction of Lean Manufacturing. Some experts refer to an eighth waste, which is wasted talents, where you have these ideas and abilities pent up in your own employees that have no way of getting out. So in a lean manufacturing environment, these ideas and improvements suggestions are freely shared and where the talents and abilities of each employee are genuinely valued. 4. 04 What is DMAIC: So Six Sigma, the problem-solving methodology is centered around a continuous improvement model called DMAIC. Dmaic is an acronym for define, measure, analyze, improve, and control. These are the five major steps involved in this continuous improvement model. With the first step being define, define the problem in very specific and exact terms that you're trying to solve. From there, then you measure your key characteristics in your process in this problem that you're trying to solve. And then you analyze that data that you just measured to draw conclusions and make assumptions about what might be the best improvements, the best corrective actions in that area. And then once those are locked, once those improvements have been made, how do we control this? How do we lock in these improvements so that we don't revert back to some former state. Then that leads us to a new definition of what our state is, our process state or our problem state. It's not the same place that we started. But you can see this continuous improvement idea. And you'll learn very quickly that there are many different qualitative and quantitative tools used at each one of these steps. Some of the tools you're familiar with, but I'll tell you there's so many Six Sigma and Lean Manufacturing is rich with different qualitative and quantitative tools that are drawn from the sciences and from other areas of engineering and statistics and analysis. They're drawn together under this umbrella called DMAIC. And here's just a small sampling of them. So the goal of the DMAIC model in a Six Sigma project is to reduce process variation. But the DMAIC model itself is not used exclusively in Six Sigma. It's also used in lean manufacturing projects as well. Six Sigma is much larger than just demonic, but DMAIC is definitely one of the cornerstones and it's a great problem-solving methodology. It's a great approach to continuous improvement. Therefore, it's used in a lot of other areas as well. 5. 05 How Do Lean and Six Sigma Work Together: So given that the name of this class is Lean Six Sigma certification, how do these two different methodology work together? How are they similar? Common question, lean manufacturing and Six Sigma are both improvement methodologies as we've seen. They both employ this demand model, define, measure, analyze, improve control. Because again, that's an improvement model that can be applied to a lot of things. Both can be applied to a variety of processes. Manufacturing, service design, personal. I've seen many articles about people doing lean manufacturing projects in their own garage to clean the place up, get a little, a little more organized. So it can be both can be applied to a variety of processes. They both have long histories and had been established by world-class organizations, toyota, Motorola, dozens, if not thousands, of companies around the world today have employed one or both of these strategies, often in concert with each other. And subpar processes usually contain both excessive variation, which is the hallmark of a Six Sigma, and several ways which is more like a lean manufacturing problem. The two methodologies work well together and they're tools compliment each other very well. The basic idea is that they came out of two different, basically schools of thought. They came out of two different major organizations. But in fact, they do compliment each other very well. Let's just take a look at it. Just a quick idea here. Defects or an obvious wastes. And in your Six Sigma project, you may analyze your process and you may find a distribution of your process output that looks like this generic curve. There's our plus and minus sigmas again. But your specification limit is such that your processes producing a number of defects. So defects is the manual, the lean manufacturing wastes. But you see you have kind of one of these statistical problems like shows up in Six Sigma. So a corrective action might be shifting the mean, which is a very popular corrective action within the Six Sigma methodology, thereby reducing the waste, which is the goal of your Lean Manufacturing Project. Take, take another example. One of the common wastes in lean manufacturing is overprocessing. Doing more than the customers asked for one of the Pi. The happens a lot. One of the areas of overprocessing would be over inspecting, inspecting for things that the customers and even noticing themselves and not just waste time the inspection process, but then you end up calling things defects that aren't customer defects. They're just things that are variations within the customer limits. Your corrective action might be to generate some detailed and specific inspection instructions with photos and arrows and things like this. What are you doing? You're reducing the variation in the inspection process, which is a goal of Six Sigma, thereby eliminating the waste of overprocessing and the defects so-called that go along with it. So again, these two big problem-solving methodologies work very well together. And there are many tools beyond DMAIC that you'll see overlap into both methodologies. So I think you will find that these two concepts work well together. And again, when you're solving a problem, you're going to find both excessive variation and wastes. And by using these tools, using these methodologies with each other, you'll improve the process overall. 6. 06 The LSS Belt System: Now one of the obvious features of Six Sigma and Lean Six Sigma training is the use of these various colored belts. These is the concept of belts as borrowed from the martial arts, where each color represents a different level of mastery. Now in the martial arts, depending on the discipline, there can be ten or more different belt colors to indicate the various levels of mastery. Now in Six Sigma, there's generally five belt colors, white, yellow, green, black, and the Master Black Belt. Lean manufacturing and lean Six Sigma practitioners have generally follow the same a belt system. This, from a training perspective, the colored belts pretty much originate with Six Sigma, but Lean Six Sigma, Lean Manufacturing pretty much follow the same sequence. So the white belt really implies an overview level of training. A white belt class is typically about an hour long and really just explains the basic concepts like what is Lean manufacturing? What is Six Sigma? It's designed for so-called stakeholders, people that really are not involved with improvement project but would benefit from understanding some of the terminology. Now, the yellow belt implies a basic level of training. So it includes some of the major concepts, problem-solving approaches, statistical methods, graphical methods, some of the quality tools. And yellow belt professionals provide support to an improvement project interviewing operators, collecting data, making observations, contributing to the cause and effect diagram, contributing to brainstorming session. Yellow Belt training is essential really to move into the higher ranks. Now the Green Belt implies an intermediate level of training includes in-depth exposure to many of the statistical and graphical tools, as well as other related tools of presentation and problem-solving and teambuilding. The Green Belt provides critical assistance to Black Belts leading complex projects, but can lead their own less complicated projects. So Green Belts are certainly capable of taking on lower-level projects. And many of them are actually in training, working toward their own Black Belt through additional training and the completion of their own improvement projects. So when you're a green belt, you're kind of in-between. You can lead your own projects, but you can also be assisting with larger, more complicated projects led by Black Belts. Of course, the, The Black Belt implies an advanced level of training. These people have a deep understanding of the six-sigma and manufacturing tools and approaches, including some intermediate to advanced statistical applications. They also have developed advanced project management and business management skills. This belt system, the lean manufacturing, the six-sigma is not intended to be an island unto itself. It is intended to be integrated into an organization's business systems, engineering systems, quality systems. So it's essential that it doesn't become its own thing. And Black Belts, through their training and experience, have other skills outside of the Six Sigma and Lean tool set, including the project, project management, the financial tools like return on investment analysis, business management skills and more. So the Black Belts lead complex projects. They also consult on less complex projects that are maybe led by Green Belts. And then of course they're providing training to Green Belts and Yellow Belts on the tools they need to understand the Black Belts, bottom line, our leaders, they're not just quality leaders or improvement leaders, their business leaders. Lastly, the Master Black Belt implies advanced leadership and technical acumen. These are proven leaders. They are sharp. Technically. These are the people that are driving organizational change and they serve as thought leaders and innovation leaders. They are essentially program managers overseeing a wide range of improvement activities. These people serve alongside top executives and facilitate the setting of strategy for an organization. This is certainly the top level of expertise within the Six Sigma and Lean Manufacturing worlds. Now, each of these belts can very well serve as stand-alone training. There's nothing wrong with taking your Yellow Belt training, for instance, and that serving you very well. You may be involved with other parts of the organization, other types of activities. But the Yellow Belt training provides you just what you need to participate as needed in the organizations improvement activities. But they each of the belts also serve as a stepping stone to the next level of training. So everything you need to become a green belt, for instance, you will learn in your yellow bell class and you can move up the ladder quite nicely. In addition to training though, especially as you move into the Black Belt and the Master Black Belt, it's not just training and tools, it's also the successful completion of increasingly complex projects. Anyone who claims to be a black belt that doesn't actually have, I don't know, at least five to ten successfully implemented projects. It's just a piece of paper. It doesn't really mean a lot. So there is meant to be experienced coupled with training to achieve these especially higher-level belts. 7. 07 The 7 Mudas: Now we've introduced the topics of lean manufacturing and the seven Moodle has or the seven wastes of waiting and overproduction and overprocessing, et cetera. But in this video, I want to take a much deeper dive into these concepts. You understand more of what we're talking about here. So the seven wastes are sometimes remembered with this mnemonic device would admit, it's the first letter of each of our areas of waste wood waiting, overproduction over processing defects, motion, inventory, transportation. It's just an easy way to remember it. And activities not related to adding value to the customer, our wastes. So customers pay for things like molding and machining and saw cutting and transportation to their facility, things like that. That's what they pay for. Things that are not related to those are effectively waste. So anything that's not what they're paying for in the lean manufacturing system is considered waste. The things that are paying for, of course, those are value-added activities. Everything else is a non-value added activity. We're going to dig into this in a lot more detail in the following slides. It's gonna be a little longer than normal, but I really think this video is going to give you a thorough understanding of these seven Muda is the first of which is waiting. Waiting is a waste that involves a stoppage at any process because of a delay in the preceding process. These can be caused by all sorts of things from an imbalance between processing steps and poor planning and unclear work instructions or maybe inventory is not available. There's a whole bunch of reasons, but you can certainly imagine waiting because of the preceding step. Then this wait time extends the total lead time and lowers and organizations return on investment. We're gonna talk about this in possibly some later lectures. But the ROI, the return on investment. It happens when you make an investment in the inventory and equipment and labor and things like this, and then you sell the product to pay for that back. Well, if you're not selling product, your investment in those things, equipment, labor, and materials is not. You're getting no return on that, right? So it lowers your return on investment. The next area of wastes is overproduction. Basically, what it sounds like producing more than his needed or what is earlier than needed. This is commonly done to improve these so-called localized goals. Maybe one department manager wants to get a higher efficiency for his department, for his equipment, or is trying to lower the fixed costs per piece so they run more or faster or longer than what's necessary. Well, that's simply overproduction. He or she may improve their local goal, but it diminishes your net earnings and the organizational goals. So overproduction ties up equipment, you're running these large batches that aren't necessary. It ties up the people, the utilities at ties up inventory and creates idle inventory, ends up being a waste to the organization. Next, overprocessing, this is the use of tolerances or equipment or processing step along with their associated costs that the customer hasn't requested and they're not paying for. This is caused by often I lack of communication by the true customer and the true supplier. I'm emphasizing true customer, true supplier because, because there are individuals that at, at certain organizations that determine, Okay, this is good, this is not good. This is acceptable, this isn't. Then there's the people that are doing the work that are making those decisions about accepting or rejecting or reworking or not reworking. While anyway, those two exact people, they usually are never talking. There's usually engineering and sales and purchasing and other big organizations and departments between them. So often caused by a lack of communication, lack of understanding of what the customers really desiring and overprocessing costs has all the obvious problems. Loss of net earnings, loss of sustainability, and just diminished customer satisfaction and introduces delays and unnecessary costs. Next are defects, and this is the one that most people are thinking of when they think of wastes, usually it's defects is the first place that they go. So these are products that don't conform to customers expectations and defects are caused generally by inadequate process designs or deviations from that design over to our right here. Here's a collection of defects of some sort of can. Showing you all the different possibilities, different ways of going wrong. Defects result in loss of the raw material, labor, utilities, everything that went into that part, and then extend the whole lead time because now purchasers and engineers and managers have to go get more material, schedule the equipment, and bring more labor into remake that product so it ends up resulting in significant losses to an organization. The next one is motion. Sometimes it's not exactly understood. There's a little confusion sometimes between motion and transportation. Motion is all the movement within a processing step that does not add value to the customer. So the common example given is that it's only the last turn of the nut that tightens the bolt. All the other turns, or just to get you to that last turn, your crank down. The customer's pain for a tightened bolt, not for all the threads leading up to it. It's a simple example, but keeping that in mind helps you understand wasted motion. Wasted motion is caused by poor product or poor process design. The picture here over on the right shows this welded flange pipe assembly, one operators kneeling down, the other ones up on a ladder. This thing is in completely the wrong spot. Nobody is working in the right spot here. So really it's a poor process design. Wasted motion can lead to injuries, repetitive stress disorders like carpal tunnel, loss of morale, diminished earnings, longer lead times. All these other problems associated with this wasted motion is, I believe probably the hidden loss. And a lot of processes, a lot of organizations, inventory, very common one. This is the cost associated with purchasing, storing, transporting, packaging, etc. Unprocessed and unshift inventory. A lot of people get really concerned when they're first leaning, learning about lean manufacturing and Six Sigma, they think, gosh, we need inventory. We have to have inventory. We use our inventory. Well, excess inventory is often added to the process to cover up other shortcomings like defects, you need inventory to make up for the defects on the products that you're scrapping. It could be poor planning. If you have a poor forecasting system, you don't know how many parts you need next week. So you overkill you make more than is necessary. It took to cover the potential there. It's also lost from damage if your products are getting hit and scratched and dropped and otherwise rendered unusable because of a poor process design? Well, yeah, you have to make more than expected to make up for those losses. And obviously inventory wastes costs, it adds costs and diminishes inventory again, there's this idea of return on investment. When you are investing, an organization is investing in labor and materials and automated equipment. They're expecting a return. Well, if those parts aren't selling because they're sitting in a rack or a big parking lot somewhere, you're not getting a return on your investment, and that has some substantial impacts on the organization. Lastly, as transportation a little bit different than motion, transportation, within a process adds no value to the customer the customer is paying for again, the molding, the machining, the forging. They're paying for these processes packaging, but they're not paying for the cost of getting it from one machine to the next. You think, Gosh, that's necessary. But what we're really talking about, wasted transportation, transportation, like a lot of these things are, in some areas are essential, but they're not value-added well, you have to transport it from one machine to the other, but it's wasted transportation that's caused by poor process design or poor facility layout. You have to transport parts. But many facilities layouts design wasted transportation into them. So reorganizing your plant or reorganizing your process can often accomplish the exact same goal with a lot less transportation in-between. Again, just like all these other things, wasted transportation adds costs and introduces more opportunity for products to be damaged. There is a rapid but thorough overview of the seven mood is the seven wastes as they relate to lean manufacturing. And I know this is a lot of material. What I'm gonna do is create a PDF out of these slides so you can download them and use them as a reference moving forward. 8. 08 The 6 M's: So we know that lean manufacturing focuses on waste reduction and therefore has the seven Muda as the seven wastes, the predominant areas where this waste occurs, six Sigma has its focus on variation reduction. Therefore has a similar to the mood as it has the so-called six M's, which are the major sources of variation. So these are the six M's. Each one of these words begins with the letter M. It helps you remember like wood mid and it kind of helps you remember the various m's, Machine Method, Man material measurement, and mother nature. So the six M's serves as a bit of a mnemonic device, a bit of a framework for brainstorming potential root causes and obviously potential sources of variation that could lead to defects and losses. So I just want to mention that I didn't invent these words, their back from the fifties and sixties a long time ago. And words like man, mother, nature are a little gender specific. In some cultures, those have been replaced with things like employee or ambient conditions. So it's not intended to be sexist, but I've gotten comments like that in the past six M's are easy to remember, but you can easily replace some of these terms. So let's step through the six amps, starting with the machine. And again, these are sources of variation. So the machine, it includes the machinery itself like this vertical mill that I have shown over my right. But it includes the software of the tooling, the fixturing, and what happens to that? The preventive and corrective maintenance and the facility, the floor of the beams, the electrical feed to it. It includes all of that equipment that is used to make your parts. So this also includes, by the way, the capacity of the equipment and the capability of the equipment. Sometimes the piece of equipment itself is fine. It's just there's not enough of them or it's ill fitted to the application. It shouldn't be used for your specific application. Next area is the method. And just having taught this for many years, there's sometimes confusion between machine and method. The method is the so-called process recipe. The feeds and speeds, the visual aids, the error proofing devices, the signaling are. And on devices, it's not the hardware, so to speak. It's more like the software, the instructions, and how part is being made. So that's the method. And then man or of course, employee or people factor. That includes the training, experience, the habits, the empowerment, What are your people allowed it to do? So this could, this could certainly involves policies and things like this. The technical proficiency awareness, all of these things, ergonomics, big area, all these things fit into the so-called man. And it certainly includes the number of people available, just like we were talking about with our equipment. It's certainly includes that as well. The total available labor. Next m is the materials, the raw materials that are better an input to the process. So it's the physical properties, the quantity of purchase materials, labeling, storage, perishability. How long is your materials lasting? Chemicals, chemistry, makeup like these, steel bars, steel, aluminum, brass, metallic components like that have a wide range of chemical properties that vary how they feed through machining processes are forging or rolling processes. The physical property in chemical makeup of these materials. The next M is the measurement system. How are we actually measuring the parts, the gauge types, the design, how often the sampling size and frequency, and then things that fall into the quality of the measurement like bias and stability, stability and linearity of the measurement system. The measurement records the data collection system, anything to do with the measurement system. Sometimes believe it or not, you may think apart is defective, but it's actually not. It's your measurement system that's giving you bad information. So certainly variation, perceived output variation can be caused by the measurement system itself instead of the part that you're actually producing. Then the last one is so-called mother nature or the ambient conditions in the facility or the workplace that you have. The temperature, humidity, dust, vibration, noise, light, all of this feeds into potential sources of variation. Just like I did for the seven moodiness of Lean Manufacturing, I'm gonna generate a PDF slide deck for you covering the six amps. So you can download them and use them as a reference. Moving forward, I've gone through this video kind of fast, but maybe a month from now, two months from now you're actually working on a Six Sigma project or a lean manufacturing project, or trying to share this information with some of your colleagues. I think it'd be nice to have these handouts available for you to reference. 9. 09 An Introduction to the Seven Quality Tools, Part 1, Rev 2: Now I want to give you an introduction to the so-called Seven Quality Tools. It's a fundamental set of measurement and visualization and analyzing tools that will take you very deep into the Lean Six Sigma methodologies. They are used throughout all five phases of the DMAIC process, define, measure, analyze, improve, and control. It. You'll find these seven quality tools and other areas as well, possibly through maybe you're taking a certification exam or you're using them in other types of data analysis visualization. They're very common in the gurus of quality decades ago kinda brought them under one umbrella called The Seven Quality tools are used commonly in quality engineering, but they're used in a ton of other areas as well. So I wanted to give you an introduction to each of these tools. We're not digging that deep into them, but I want to show you the basics of each one. I think this will be rewarding for you. The first of those seven tools is called the histogram. I wanted to just show you how a histogram works and then we can think about it a little bit more. But Let's say you have a set of raw data. This is our data right here. I want to plot it in such a way that I can visualize the data. Well, long time ago they used to actually do it this way. So I have a, I have an XY diagram here. So for every time I see a point like right here, 1.7. So I'm gonna put the, I'm gonna put an X right here between the one and the two. This I might call a bin between 12 that contains all the numbers that fit in-between there. So you can see I have several bins here. What's my next number? 4.4. So that would fit into my bin between 45. So I'm gonna put an X right there. And then what else do I have? 3.9. You can see I'm filling in the next bin, 1.6 and all here's another one in this spin, 0.41.3, there's another 15.21.1. Imagine if I were to put an equal size acts in each bin for each time it came across another value that fit into there. Well, what would happen over time as these bins began to fill up is I would start getting a sense of what this data looked like. So over here I would have a frequency, if you can imagine, that would look like this, 1234, etc, all the way up. And then what I can do is now visualize what the data looked like. So what happens here is practically speaking in, say you're using Excel or some statistics program. You're not going to have x's there. You're going to have these vertical bars. Each bar is proportion to the number of values found in that bin. And forgive my crooked lines here, but you get the idea that a histogram is a vertical bar chart with the bars proportion to the number of values inside that been. What a lot of beginners in data analysis or quality engineering maybe don't realize, is that this then becomes the basis for what's called the, the frequency distribution. Distributions are common in statistics and use widely there where this can give you this histogram can give you a hint at what the underlying, we'll call it a probability distribution looks like. So certainly that's a very valuable tool. Now let's take a look at this next tool, which I'm calling an ordered histogram. But realistically and it is a histogram. But the more common term is the Pareto diagram. Maybe you've heard of Pareto analysis or the Pareto diagram. This works in a lot of different ways here. So instead of having necessarily just raw data like this, now we have more qualitative data. So let's just say you're analyzing the defects that may be in a customer complaint and these are the defects that you received over the last month and you have five different categories of defects. I didn't fill these in, but in your industry you could imagine five different types of defects that can occur. So each defect has a frequency defect, a happened three times, B, 41, C7. You get the idea, the so-called Pareto principle. Sometimes it's referred to as the 8020 principle, implies that the bulk of your problems happen because of a small number of causes. The back to the gurus of quality. I can't remember if it was. Joseph Jeran are one of the originators of the quality movement in the United States, termed the phrase the significant few or the trivial many. Here I have five defects, but I could have 50 different defects with each with a different defect frequency. So the idea of the ordered histogram or the Pareto diagram is to put these in a particular order by magnitude. So what I could see here is that b is the largest with 41 defects, so I can have a bar here. And again, my drawing skills are kind of choppy, but imagine I have a vertical bar here. Proportion to 41 units on my y-axis. My next is E at 26. So e, and I could draw another bar right about there for 26. And then my next largest, Let's see, a C at 21 or I'm sorry, see it's 17. That's right about there. Then let's see. I got this. Okay. D is next right here at nine. So it looks probably about like that. And my last one is a at three. And that's very small, something that looks like this. So this, I just drew a histogram using my raw qualitative data. The idea of the Pareto analysis is if you can draw a histogram, ordered histogram ranking your problems from largest in magnitude to smallest magnitude. It would help you understand where to concentrate your efforts. Here. I can know that, okay. B and E defects is where I want to concentrate my resources, my continuous improvement activities. I want to I want to do my investigations there. I have a limited amount of time and budget money and personnel. I might as well focus it on the problems that are going to give me the biggest payback. A lot of times it's just as much work to solve problem a as it is problem B. If you're gonna put the workout, why don't you skip a and go to B, go to E When you get b and e solved, well then create a new Pareto diagram and maybe see an arrow at the top of that one or something like that. So it's a great tool, powerful tool and displays a really common underlying phenomena amongst so many things. You'll see this show up in not just quality engineering, but in production supply chain analysis and even outside manufacturing like economics in nature and all sorts of how wealth is distributed. So many areas, It's amazing how often the so-called Pareto distribution shows up. The Pareto diagram super-helpful to, uh, one of the tools I've used the most in my quality career. Let's keep moving to the rest of our quality tools. Here is a simple flowchart. You've probably seen this before, but the flowcharts are really fantastic. In lean manufacturing, you'll want to define your problem, your process. You might go out there and observe, how does a part get from raw materials through finished goods? How does it actually flow? What are the real steps it takes getting through our process? Flowcharts a great way to visually portray that process. For instance, maybe your first step is going to change colors. It may be your first step is CNC machining. That's the first thing you do. Then the next step you do is inspect your parts. Maybe you use some gauging or visual aids somehow you inspect these parts. And this is a decision point after your inspection or the part's good or are they not good? Well, if they're good, maybe these go right to packaging. And if they're not good, maybe you, maybe you record the non-conformance and then you rework the parts, and then they go to packaging. This is obviously a very simple flowchart, but these right here are symbols like this. Rectangular shape implies a process. The diamond is a decision point, decision variable, you might say, and here's where this is a data i, o, if you have to put in data input or get data. And there's a handful of these other symbols, maybe five or six more, There's probably a couple dozen more, but truthfully five or six more. And you'll have a full range of symbols that you need to chart most manufacturing processes. So flowcharts, simple but powerful for depicting your process. 10. 10 An Introduction to the Seven Quality Tools, Part 2, Rev 2: The next quality tools, the checklist, this is the most unassuming, the simplest, the easiest is some people even wonder why is this even a tool? Well, I'll tell you what. It's pretty powerful. Airline pilots, surgeons, technicians of all sorts of types use checklists because when you get involved with any process of even reasonable complexity, it's really easy to forget or accidentally skip a step. A checklist, among other things, is a great way to verify that all the steps are being completed as designed. So you may have a process that has four steps. Well, as each one of them is completed, maybe I initial, I put my initials there and then gosh, if I'm reading through my checklist, this might be the final verification. Oh, you know, I may have skipped step three. I can go back. Make sure step three is done and sign off that each step along the way it's done. This is a great tool in the control step. It's not uncommon that a checklist would be added as a means of helping prevent somebody from skipping a step in a process. So it can be used in a lot of areas. It's simple, but I promise you that it's very powerful. So moving on to our last three quality tools here. So the next one I want to talk about is the run chart. Run chart is pretty simple. I'm gonna talk about a control chart in a second, but just, let's just talk run chart. And for right now, let's just say you have a process. Maybe you're working in injection molding or sheet metal fabrication. And there are some critical characteristics that are important to your customer to the fit and function of the part that you're making. So each hour you pull a subgroup of parts. So let me get another color here. You pull a subgroup of parts at the first hour, and maybe you pull a subgroup, let's just say n equals five. In other words, I pull five samples. Then I take the arithmetic average of those, which I abbreviate as x bar. Each individual point might be an X, but X bar bar implies average. I might have the arithmetic average of the five samples that I pulled out of production. And then I plot that point here. And then I don't show the y-axis, but these are, these are measurements of the part that I'm measuring here. So these are different values of the measurement. Every hour I pull five more, five more, five more. And I'm plotting my x-bars along this chart. That basically right there is a run chart. One along the x-axis, the horizontal axis, I have an equal amount of time, maybe one hour, two hour, three hour, four hour, or maybe every 1000 pieces, one thousand, two thousand, three thousand. These are equal spaces on the horizontal axis. And then I do the same thing. I pull five pieces, and then I take the arithmetic average and I plot the point. Well, right here we already have a super powerful tool. Because what I can do is visualize the center line. You can almost see kind of a center line here. If you use your imagination, I can kind of see what that might look like. Where are the processes generally. I can kind of see a max and a Min and sometimes you see some patterns. Maybe it's trending up, trending down. You can see different patterns and locations of your data. As a visualization tool, run charts are fabulous. You can see things when you plot them graphically that you can't see when it's just a bunch of raw data. The run chart is a fabulous visualization tool. Well, what the control chart does, a control chart is a run chart with the addition of some statistical tools called upper and lower control limits. So given that our data is dispersed in a particular way, there are some formulas that are beyond the scope of this introductory video that would allow me to add a couple more lines and I'm gonna change my color to white here. So I can add a couple more lines using some statistical tools. These lines I'm going to call the upper control limit and the lower control limit. These aren't the same as the upper and lower specification limit. So these are about control. These I'm going to change the color or about specifications. Specification limits are what you find on a blueprint or an engineering specification. These are what designers and engineers specify that the part has to measure between a low limit of whatever and an upper limb upper limit of whatever that is lead times you'll see specification limits yet it'll say 50 plus or minus one. And this is something you would find on a blueprint. Control limits are entirely different. Control limits have nothing to do with specification limits. Control limits are derived statistically. And they describe the typical, the normal, upper and lower limits of the process. Control limits have everything to do with the process. Specification. Limits have everything to do with the engineering blueprint or the specification. They are entirely independent of each other. So this concept of control limits and specification limits being compared to each other is really the basis for a much more involved set of analytical tools called process IT capability. This is a little more advanced and outside the scope of the introductory video, but process capability analysis is all about comparing the specification, the control limits. So anyway, there's a lot more to learn there. Again, this is just an introductory video. Let's talk about our last of the seven quality tools. The scatterplot. A scatterplot is, is about depicting another visualization tool depicting the relationship between two different variables. And those two variables are commonly considered an independent and the dependent variable, let me explain. An independent variable is in the case of a Lean Six Sigma project. There's other explanations outside this field. But in the case of a Lean Six Sigma project, in independent variables is typically a machine setting. It's something that you can dial in or set. If you're a technician, a setup technician, a machine operator, a manufacturing engineer, this is something that you can establish outside of manufacturing. These could be policies or points that happened at different service type industries where you can establish rules to follow. These are independent variables. So in the machining world, in manufacturing world, let's just pretend and an independent variable that we're considering is the feed rate. This is really how fast material is fed through a CNC machine. I can set it, I can dial that in wherever I want. And maybe it again, I'm just making up numbers 12. You get the idea where a bigger number is faster, okay, so I can make the machine faster, faster, faster, as fast as I want. Now, the dependent variable I cannot set, I can't enter some number into derive. This is something that happens as the result of the independent variable. So maybe in, again, in machining, if you're familiar with this, maybe something like the roughness of the part. So you can imagine as a machine gets faster and faster, the roughness gets higher and higher. So there's a lot of measurements here if you're not familiar with it, if you can picture in your head, what I'm talking about is if my feed rate is really slow, I'm cutting material off very, very, very slowly. I'm going to end up with a relatively smooth part and roughness. The bigger the number, the rough ER the part is. So maybe down here around my lower feed rate, I would have a lower roughness as well. As my feed rate increases, I may get rougher and rougher parts, something like this. Now, there's other variability that maybe isn't explained simply by feed rate. But you can see here as I plot the relationship between different points, I can see that, well, as I set, let's just say a feed rate of three, my roughness is gonna be something in this neighborhood here, which is different than a feed rate of five, which is going to have a roughness, hire a rougher part. So whether you're talking about CNC machinery or something else, the whole point here is that you can connect variables together to establish a on a scatterplot to depict this relationship. So what you can do here, and this is a scatterplot by itself. But what I want to draw on top of this here, I'm going to switch the color is, you can imagine some sort of best fitting line, just an average line that kind of defines and that's not supposed to be crooked. But you can imagine a line that basically defines the relationship. There's other variables that feed into roughness besides feed rate, but there's clearly a relationship here. The scatterplot is the basis for two really important statistical tools. The first one is called correlation and the second is called regression. Or more formally, this regression analysis or linear regression, is really where this starts, where you have a line that depicts us linear regression analysis or regression analysis. These are all in the same family of statistical tools. Correlation is another one that, that the scatterplot is the foundation of. So by learning about the scatterplot, you are then set to learn about these more sophisticated tools like correlation and regression analysis. Okay, I am done with the introduction to the seven quality tools. And like I've shared in other aspects of this class, There's so much more to learn. We're talking about the fundamentals right here. Can't encourage you enough. Take some of these concepts into Microsoft Excel, take them into a statistics handbook or quality engineering book. There's so many more things you can learn about these tools. And they are powerful in defining, measuring, analyzing, improving, and controlling the process that you're interested in. 11. Conclusion to the Course: Well, this is the end of the Skillshare class titled certified Lean Six Sigma white belt. I certainly hope you found this to be a rewarding experience. I know I enjoyed making it and genuinely, I hope that you learn some skills and ideas that you didn't know previously. Ideas that are gonna help you in your career. As a quality professional at or as a continuous improvement professional or someone who helps with continuous improvement projects. I wanted to just mention two things to you. Feel free to reach out to me anytime through Skillshare if you have questions or comments, I don't want to take one student for granted. I love hearing from students if there's something I can help you with along the lines of these lectures, I'd be glad to do what I can and respond to you through Skillshare. Secondly, I want to let you know that I've included a couple of downloadable resources for you. One of them is about the Six Sigma six M's that I talked about. And another one is about the seven Muda as the wastes of Lean Manufacturing. I included the slides as things that you can download kind of a cheat sheets there in the PDF format. If you want to train others, if you want to use them at your workplace, download them, and those are yours free as part of this class. And then the last thing I just want to emphasize before we go, learning is great head knowledge is fabulous. You got to learn things, but it'll really become real to these concepts, these ideas as you get involved, as you apply them, as you look for opportunities to put these into practice at your job, your projects, the things that you're working on. So take some of those concepts maybe about wastes, looking for wastes in your system are looking for variation in the method or the measurement system, or the raw materials or the machine, etc. Look for ideas. And again, this is a white belt level class, I understand that, but it's amazing once you learn some new concepts, you see the world in a different way. You see your workplace, your equipment, your production facility, whatever. You'll see those in different ways. So anyway, I can't encourage you enough to put this stuff in practice, even if it's in simple ways. So anyway, thank you so much again for joining me for this class. It's been a privilege to serve you by sharing these ideas with you. I genuinely hope you enjoyed them and I genuinely hope you have a wonderful day. Thank you so much. Goodbye.