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Part 1 - Lean Six Sigma Green Belt Introduction Video
1. About Excelr, Agenda For Introduction
Hello and welcome to the Lean Six Sigma Greenbelt training. This training is predominantly driven using Minitap. The concept of Lean, which is also about reducingwaste, and the concepts of Six Sigma, which isall about reducing the variation in the process whencombined together would provide you profound results. The results, which cannot be achieved by commonsense and require extraordinary reasoning, would be achieved if we implemented Lean Six Sigma. And the stepping stone to implementing Lean Six Sigma projects would be Lean Six Sigma Green Belt training. Why do we need to drive it using a tool called Mini Tab? Because Mini Tab has been de facto when it comes to most organizations' ability to analyse the data, 90% of Fortune 100 companies use Minitab. Minitab is used at more than 40 universities across the globe. That speaks volumes about Minitab. This is the reason why we'll be learning about Minitab alongside the theoretical concepts of Lean Six Sigma Green Belt. Before that, a quick introduction. That's my name, Barani Kumar. I'm an alumnus of IIT and Indian School of Business). These are the certifications that I possess. I'm a certified business analytics expert. I've gotten my Lean Six Sigma green belt and black belt from Rabqsa's registered accreditation board. Quality Society of Australasia I've also completed my Six Sigma Master Blackbilt. And hence, I'm proud to say that I'm well qualified to train you all on Six Sigma Greenwell concepts. I have also earned my certification in project management professional, which is from the Project Management Institute. Along with this, I completed my PMI RMP Risk Management Professional Certification. I've also completed my Agile Certified Practitioner Certification. This is also through the Project Management Institute, which happens to be the number one institute when it comes to the project management space. I've also done my Agile project management foundation and practitioner courses. This is my profession. I'm a data scientist and I do a lot of research in the analytics space, which is all about building prediction models. If someone applies for a bank loan, should we approve the loan or not? Would the customer be a fraudulent customer or not? Would the person pay the EMI on time or not? This is all about analytics. If you swipe a credit card or make a payment using a credit card, is it a fraudulent transaction that someone steals your car and then makes the payment or is this a genuine transaction? This is all about analytics. predicting when an employee will resign. An organization, even before an employee resigns, is all about data science. Predicting on when would a customer churn matter. If you're using AT&T Network, for example, as your telecom provider, how many days would you continue to use AT&T services even before you switched on for very long? Maybe that's what science is all about. All right, that is, in short, about my interaction. Statistics are critical for Lean Six Sigma Green Belt certification. And statistics are imperative for a data scientist. This is another reason why I believe I'm well qualified to train you all on Lean Six Sigma Greenbelt Concepts. Even before we proceed to start the program, we will first look into the introduction of XLR Solutions. XLR Solutions is headquartered in the Bengalura region of India. It has its presence across the various major cities and metros of India, not just the National Training Institute and consulting firm. XLR also has a global presence, including in developed countries such as the United States, United Kingdom, Australia, the Middle East, Europe, and so on. Until now, we have framed 100 plus participants across the globe, and the count is increasing steadily. It is increasing exponentially. thanks to the wide variety of courses that we provide. We have over 20 courses in data science, data analytics, business analytics, Six Sigma project management, quality management, service management, and the list goes on. We have over 30 corporate clients from a wide range of industries. We have clients or customers from FinancialServices, insurance industry, we have the clientsfrom Life Sciences, Healthcare, from the retailmarket, so on and so forth. And the best part is all of ourtrainers have a very solid foundation of education. All of our trainers are from the Financial Times' top 20 B schools. So you can rest assured that you are getting the best from the best. Let us discuss more about this training. Specifically, XLR has accreditation with TUCD, which is a German-based certification body. And this course that I'm training you all on is aligned with any major certification body, including the likes of the American Society of Quality, the Society of Australasia, and ASU D, which happens to be a German-based certification body. All trainings have the prerequisite readings. So we have already provided you all the training materials, so you might spend some time reading this content even before you look at the recorded videos. We have included the best practise of training—including theory—and followed that theoretical concept by looking practically at a few case studies. You would be interested in learning not about justthe theoretical concepts, but you will practically be implementingthe case studies using the tool called Minitap. As I've told you, this is the defacto tool when it comes to statistical analysis within the various organizations. The post-training support would be the wonderful thing that you might want to opt for at the end of the final program and final recorded video, and we have provided our email ID. You're always free to drop us an email with the questions that you have while you perform your first project. As a result, we will provide you with some cushioning, some handholding as you begin your journey into the world of Six Sigma. We also provide you a certificate stating that you are a certified Lean Six Sigma Professional, provided you perform a live project and submit it as a project for evaluation. in this particular training. We have provided the training material at the end of each concept. We have quiz questions to test your learning. We have provided a lot of case studies. And finally, we have provided you with a practise project so that you get a brief understanding of how to implement your learning and put it into practice. Let us now look into the agenda, which would be covered as part of your first phase of the project, which is interaction fees. Six Sigma, by the way, is a project we will be learning about. What is lean? What is Six Sigma? How did Six Sigma and Lean evolve into the different types of Six Sigma? Methodologies and basic statistics along with the various data types And as I earlier committed, we will be discussing case studies after the end of logical concepts. Hence you'll be discussing an Internet service provider case study, and you will try to understand how to implement the basic statistics. When it comes to this case study, we will do a lot of graphical representation, including your box block and your normal distribution. You would understand what a probability distribution is, which is pivotal for starting off with your statistics. Finally, you'll be understanding the various variants of Sigma, and then we will do a quick recap just to ensure that your understanding is better. And finally, we'll start or probably stop this particular phase and get started with the defined phase and then the measure phase, analyse phase, and improvement control phase. Finally, please do not forget to look at the project, which is extremely important for you to differentiate yourself from the rest of the people who are lean. Six Sigma Greenville-certified Thank you. Let us get started in the next video.
2. Preliminaries of Lean Six Sigma
All right. Let us look into the preliminaries of lean Six Sigma. What is Lean Six Sigma? Most people keep asking me this question. What is the difference between Lean and Six Sigma? Yes, these two are different methodologies; there's no doubt about that. Lean is all about reducing waste in the process and removing the waste. It deals with waste with a strict stick. And Six Sigma is all about reducing the variation. Lean is all about reducing waste. When these two shake hands, the effect that you are going to bring to the improvement initiatives will be extremely robust. Lean and Six Sigma are not seen in isolation anymore. Lean concepts plus Six Sigma concepts are going to give you profound results. Hence, it is called lean Six Sigma. You no longer differentiate between these two. Who is the target audience and what industry is it applicable to? Anyone, from junior resources to board members and the CEO of a company CX, is expected to or is eligible to participate in this training. So, what industry is it applicable to? Oh my God! I can't tell you how many industries it applies to right now because otherwise I'd be moving on to the next one. Just discussing the industries goes to say that it is applicable to each and every industry. Financial Services and Insurance within that banking domain. Insurance domain. It's applicable in life sciences and the healthcare industry. It is applicable in energy and oil and gas), and within that, it is applicable to the pharmaceutical industry. It is applicable to the marketing industry. It is applicable to manufacturing industries. It is applicable to the consumer and industrial product industries. any industry, by the way. But let me tell you one interesting thing. It is also applicable in a crazy industry called cow milking. Have you ever thought that we can apply Six Sigma concepts to cow milking? And by the way, let me tell you one more interesting thing: Riley cows. Is Riley cows produce maximum litresofmilk per annum on an average. This goes to say that Israel is implementing Six Sigma concepts in their fullest sense. Oh yes. It can be used in the sports industry as well. You can just type Roger Federer plus six sigma. Roger Federer, by the way, is a tennis superstar. You'll be surprised to see him implementing the Six Sigma minus practice. Amazing. It is used in boxing. It is used in various other interesting industries. It is used in Formula One races. By the way, let me ask you this question. I'm pretty sure you all are aware of Formula One racing. In Formula One races, there is something called a pit stop. What happens when a pit stop driver, after going around the circuits for a few circuits, would like to fill up on fuel? He would like to replace the tires. He would like to check his brakes and then get started. Once again, how much time would it take to replace four tires, fill the fuel tank, check the brakes, and let the driver go? You'll be surprised if I say that it takes less than 2 seconds. How could they achieve this impossible task? For fewer? First, it might sound like—oh, my God, this is impossible. How can you change four tires, fill the fuel tank, check the brakes, and let the driver go in less than 2 seconds? It happened. Mark Webber, Red Bull Right. The team achieved this impossible task. I would not say it is impossible. I would say rather extremely difficult task. How old are Six Sigma tools and techniques? To answer this, let me tell you one simple thing. If you go to subway subway. And when you order a sub, you might have noticed that on the table on which they chop the vegetables, there's a small opening, and all the waste particles of the vegetables are simply pushed into this. And there is a waste container that collects the waste. just a simple push. Otherwise, what would happen is that you would place your dustbin somewhere here. You would pick up all the waste. You take that trash and put it here. On the way, you would drop some of the waste, and then you'd bend, pick it up again, and drop it once again. So it's a waste of time. Right. Instead, you chop the vegetables and just push them here. This concept is called "Clean to Clean." Right? And this concept was introduced by Japanese housewives way back when—I believe it was in the 16th century or so. So that is how old Six Sigma tools and techniques are. And most of the scientists have worked on developing Six Sigma tools and techniques. Or rather, they develop tools and techniques, which are now used in Six Sigma. All right. What are the various Six Sigma methodologies we'll be discussing in a short while? About two different methodologies and six sigma Greenville primarily deals with DMAIC—defined as measure, analyze, improve, and control. Just give me two minutes while we get into this. And there is another Six Sigma methodology called "define or design for Six Sigma," in which there is , improve, aWhat does that stand for? Define, measure, analyze, design, and verify. We'll delve deep into these two methodologies, but the Six Sigma green belt will be primarily driven by the Mi.
3. Six Sigma Management System
Alright, now let us look into the Six Sigma management system. Six Sigma helps organisations with the following it inthe first place, helps to improve your process. Or if you have a bunch of processes, you can improve all of them. And most importantly, you can align with the strategic objectives of the organization. Most people face problems over there. Six Sigma also helps teams focus on those projects that will have a high impact on the business goals, which will increase your brand image and increase the quality of the products, which will reduce the number of defects, which will impact your bottom line and top line. So you'll be able to focus on those kinds of projects. Six Sigma ensures that organisations achieve faster, improved business results. Yeah, you'll be able to achieve results in a much faster way, and the results will be extremely improved, right.in comparison to any other improvement initiative that you undertake. Six Sigma establishes mechanisms not just to achieve improvements but to ensure that your improvements are sustained in the long run. And that's what? 60 miles? That is why it's extremely important. It's extremely imperative just because it will help you sustain the results in the long run. All right, let's look intowhere Six Sigma is very focused. Six Sigma focuses a lot on reducing defects, improving your yield significantly, increasing your net income, and ensuring continual improvement. That's the most important part. It will help you reduce the number of defects in your products. It will help you reduce thebucks in the software applications. It will help you increase thenumber of products manufactured each hour. It will help you increase theloans which are processed against theapplication loan applications which are received. It basically improves the productivity of your employees as well by improving your processes, leading to higher net income. If you have a new shopping mall, that will help you improve the occupancy percentage. It will assist you in increasing the number of foot faults. If you are keeping an inventory of raw materials, it will help you reduce the inventory costs. And for most logistics companies, saving one person's inventory costs millions of dollars. So these are a few of the areas where Six Sigma has its focus on.Yeah. And one more important thing: when it comes to measurement, people also measure color. When you want to improve something, you can also improve the color. And colour is measured in cooking as well. I'm sure most of you all are aware of McDonald's burger, the Burger King, right? McDonald feels hungry. McDonald says this: "We don't cook, we don't bake, we don't manufacture." If you eat a burger in any of the McDonald's stores, it will taste like it is the same color and will be exactly the same. How can they achieve this? They can achieve this by measuring a small component of your cooking, which is color. So they're giving out the most important information. even the colour of your burger that you're eating, right? There are a lot of colour models. If you want, you can just explore CYMK. You have RGB colour models—red, green, blue, etc.—that you can just explore out of context. Just look into that. All right, Six Sigma also focuses on reducing nonessential, non-value-added activities in a process. We'll discuss about this in detail in value streammapping, which is by the way a lean conceptwhich finds its roots deep into Six Sigma nowadays. All right, let us look into the history and evolution of Six Sigma. Little scientists who contributed toSix Sigma significant normal curve. This is the most beautiful curve that you can see. It appears to be a very symmetrical, very beautiful normal curve. Carl Frederick has come up with "normal company." It's also called as Gaussian co. By the way, control charts were a result of Walter Schwartz's work in the design of experiments and the result of Ronald Fisher's 1935 box plot, which we'll be discussing in this particular session, which itself was an output or outcome of John To Keys's work in let me ask you this question.Who has the world's largest Six Sigma team? The answer is given here. It's the US Armed Forces. The US Army has the world's largest Six Sigma team, and they have come up with FME (failure modes and effects analysis) in the year 1940. The Aieg Automotive Interaction Group developed measurement system analysis. We'll look into attribute agreement analysis for Greenville during the measure phase, though we also have gauge R and R. But in Greenville, we'll be discussing the AAE. And then we have the Ishikawa Diagram, which is an outcome of Kauru Ishikawa in the 1960s. Ishikawa. diagram is also called a fishbone diagram. It is also called a "cost-and-effect diagram." We have Doctor Edward De Bono, who has come up with six thinking hats. And then you have Pew Matrix, which is the output of the work done by Stewart Pew. And then we have the Delphi Technique, which was an output of these two gentlemen from the Rand Corporation in the year 1916. So these are a few scientists who have contributed to fewer tools and techniques, which ultimately have their roots in Six Sigma. Now. The History of Six Sigma Okay, let us go over this in detail because this is Six Sigma's promise history. It started at Motorola with Bill Smith and Michael Harry, and it has saved them $16 billion in a span of ten years. Amazing. Bob Smith, who is also called the father of Six Sigma," has coined the word "Six Sigma." Mikhail Harry began using statistical analysis to solve problems. He was a senior staff member at Motorola Government Electronics Group. That was what it was called at that point in time. He experimented with problem-solving through these statistical techniques. Using this approach, Motorola's products were being designed and produced at a faster rate and at a lower cost. And he was the person who coined the words "black belt," green belt," and this kind of Karate Kung Fu Rite. "Bells," basically, under the leadership of Bob Galvin, who was the chairman of Motorola in Six Sigma, was started as a methodology at Motorola. That was the good part, right? And the ultimate result is that this company has saved $16 billion in ten years. And soon, other companies followed. Companies have started adopting SixSigma, a light signal. General Electric, obviously, is extremely well known for implementing Six Sigma to its fullest. Honeywell, Philips, Sony, Siguetoshi, soon, and so forth. They followed the route, right? And they soon made Six Sigma the philosophy of their lives. That is the interesting part.
4. Six Sigma Maturity Continuum
All right, let us look into the six-sigma maturity continuum. What does this mean? Six Sigma is all of these four. It is a metric, it is a methodology, it is a management system, and it is the philosophy of a life. Sigma, by the way, is a Greek letter that measures variability in data. And as I've already discussed, six-sigma means reducing defects and reducing the variability of variation in the process. Let me quickly explain how to reduce variation. I'm pretty sure most people know the game of cricket. Cricket is heavily played in India. All right, if any of you are already aware of cricket and the cricket ball, now let me ask you this question. What is the circumference of an ICC cricket ball? International Cricket Council. Cricket ball. What is the circumference of that in mm? Right? Do not worry. I'll let you know that. lower specification limit is 224 mm. Don't worry about the specification limits and all that. We'll do a deep dive on the control fees as well. The upper specification limit is 229 mm. Right? So any ICC cricket ball that is manufactured should be within these limits. assuming that this is the mean. Let us see These are the cricket balls that are manufactured. Say the circumference of the cricket ball manufacturer is 228. The second cricket ball's circumference is 225. The third cricket ball circumference is, say, 229. Exactly. The fourth one is 224, once again. The fifth one is 226. or so on and so forth. If you see that the variation in the process is high, can you guarantee that the cricket ball that I'm manufacturing will not fall outside these lower and upper specification limits the next time? Can you guarantee me your confidence? Maybe, maybe not be.But let us take another example of actually explaining the variation: Let me write this down. USL LSL ICC cricket ball circumference is 229 mm, and LSL is 224 mm. Now, let us look at the cricket balls. The first manufactured cricket ball's circumference is 226. Then it's 225.5. The third one is here. Fourth is six, seven, eight, so on and so forth. The variation is very low. Now, can you tell me with a higher degree of confidence in comparison to the previous example that the next cricket ball that is going to be manufactured will fall within the specification limits? I would agree that you would say that here in this process. I would say that the next cricketball that's going to be manufactured will most likely fall within the specification limits. I would say this with greater confidence. But in the previous example where the variation was high, you would say that with less confidence, there is a possibility that the circumference of the next cricket ball manufactured would fall within the limits. So look at the importance of tomorrow if you want to speak with your client and say, "What is the level of confidence that you'll be able to complete your project on time with good quality?" If you were to do that, you will have to ensure that the variability is reduced, which to some extent also helps you reduce the defix. Alright? What is Six Sigma? It is a metric definition of Six Sigma in most cases; is this 3.4 defects per million opportunities? Think about this in this way. Suppose I'm manufacturing a mobile phone. What are the various components that would actually complete my mobile phone? I need to have a screen, I need to have a camera, I need to have the keypad, I need to have the battery, I need to have the motherboard within that, I need to have the chipset, so on and so forth. Each component of this particular mobile phone is called an opportunity. So if, within a mobile phone, in order to manufacture and assemble a mobile phone, suppose there are ten components, then each mobile phone has ten opportunities. If I have 100,000 such mobile phones, and if each mobile phone has ten opportunities, ten components, or ten features, that's ten parts that I have to assemble, right? Then the total count would be 1 million. Out of these 1 million opportunities, there should be no more than 3.46. That is when it is called following a Six Sigma process." It's extremely, extremely difficult to achieve such high levels. All right, let me tell you this. Let me tell you this. Very interesting. Pay attention. a process that operates at a three-sigma level. Yeah. And suppose this is an electricity generation company, right? And if it is operating at three sigma level, youwould not have electricity for 7 hours per month. If the same electricity generating company operates at a six-sigma level, then you would not have electricity for only 1 hour in 34 years. Astounding amazing. Look at the difference from three sigma to six sigma. There is a significant improvement. improvements, which no one can expect. If a power generation company is operating at three sigma, you would not have electricity for 7 hours per month. If the same company is going to operate at Six Sigma, there won't be electricity for 1 hour every 34 years. Oh my God! Amazing. Do this for cow milking. Example which I've given earlier is Riley cow onan average produces 10,000 litres of milk per annum. In the US and UK, it is approximately 6000 liters. Each cow can produce per annum litres of milk. In India it is three to 4000. Think about that. What is the kind of difference that you can bring to your organisation and your firm? All of a sudden, you'll become extremely relevant in the industry. All of a sudden, your profits are going to increase like crazy. And this is the kind of breakthrough methodology that Six Sigma is. Yes, Six Sigma is a methodology as well. It uses DMAIC primarily. We'll be going to discuss DMAIC in Greenville. And then there is a design for SixSigma as well, which we'll discuss shortly. It is a management system and a way of life. It is a philosophy. All right, here is a DMAIC. D stands for define. M stands for measure, a for analyze, I for improve, and C for control. It is a structured and repeated process improvement methodology, which you do again and again. You need to keep improving your standards, so you keep doing this. This is a repeated process. It is a structured process. Follow this in sequence. Define, measure. Analyze. Improvement. Control. In that sequence, it focuses on defect reduction. So whenever you have a defect, identify it and try to fix that. So it's a kind of reactive process. By the way, methodology It's a reactive methodology. DMIC primarily focuses on improving existing products and processes. Let us look into DMAD. On the other hand, DMAD is part of the design for Six Sigma. And DMEDB is not the only methodology within design for Six Sigma. But this is by far the best-known methodology. You have various other methodologies such as Idov, which is also designed for the Six Sigma concept. Right? Identify, design, optimize, and then validate insurance. But Design for Six Sigma (DFS) arose through the new product development plans of companies such as General Electric as well as NASA and the US Department of Defense. Right? Look at where the design for Six Sigma and Six Sigma methodologies are being implemented. All right, let us understand Dmedv. In brief, define, measure, analyze, design, and verify—this is the abbreviation of DMA. DV. Its strict focus is on design. It completely focuses on the design of the products to ensure that they meet and exceed the customer's expectations. It focuses primarily on defect prevention. It will not let the defect happen. It helps develop new products or processes, or it will also help you reengineer existing products or processes. Even. Before we proceed further, let me ask you this question about DMA de.Primarily, have you undergone one day of USB Foundation training and two days of practitioner training? All of you IT professionals, software developers, and laptop users, I'm sure you understand what USB stands for. But did you know that there is training for one day for foundations and two days for practitioners? very important, extremely important training. I'm sure you haven't even heard of such a course. The reason is extremely simple. Such training does not exist. Such training does not exist. Why do you need training to insert a USB device into the USB drive? Try putting your USB device into any other slot, such as your land slot, VGA slot, power slot, or some other slot. Can you do that? Now, a USB device only fits into a USB port. That is called a design for Six Sigma. If you heat your iron box in orderto iron your clothes, if you heat yourinbox, does it get heated beyond the temperature? No, it stops automatically if it reaches a particular temperature. That is called "designed for Six Sigma." It is not letting the damage exceed a certain limit. design for Six Sigma. Isn't that amazing? That is what design for Six Sigma is all about, or the well-known design for Six Sigma methodology DMAD.
5. Introduction to Elementary Statistics
Interaction to elementary statistics In this section, we will learn how to make inferences based on data provided to us. And of course, we'll be making use of a statistics tool called Minita in actually accomplishing this, right? Statistics is broadly divided into two branches. One is descriptive, one is inferential. Descriptive statistics focuses on how to collect data, how to analyse the data, and how to present and describe the data set that is provided to you. And what is "inferential statistics"? Inferential statistics is all about making decisions about a larger set of data, which is called a population. You make inferences about the population based on a small set of the data, which is called a sample. Think about Apple conducting a survey to collect what the bad features of the iPhone 6 are. Suppose this is what Apple wants to do. Can Apple go and approach each and every iPhone 6 user and collect the data? And what are the features they do not like the most? It will be extremely cumbersome. It would take another five years to collect the data, right? It's extremely difficult. So what do you do? Instead of approaching all iPhone 6 users, select a subset, a section of this population, and begin collecting data on which features they dislike. Once you collect the data, you analyse the data and make statements that will be applicable to the entire population.
So using a small sample, you are going to make statements about the population that are inferential statistics. All right. We will also explore the statistical tools and techniques that enable decision making, which would include what are the data types, what are the measures of central tendency, what are the measures of dispersion, and what is the probability distribution? There's also beta distribution, gamma distribution, binomial post, and so on. But we'll be primarily discussing normal distribution in this section and its applications, basically. And also, we look into how to graphically represent the data so that it's extremely relevant for us. Rather than crunching a huge set of numbers, there is a famous saying that a picture speaks ten words. As a result, we will investigate a few graphical representations such as box plots, etc. Let us look into the two main data types. The first data type is attributed, which is also called categorical data. What can be done using categorical data? You can count it, you can rank it in order, or you can sort it in ascending or descending order. But you cannot perform any of the mathematical operations on it. You cannot add; you cannot subtract; you cannot divide, right? So these kinds of operations would not be possible with categorical data or attribute data. attribute data or categorical data. Here is an example. It's also called discrete data or deficit data, right? Alright, that's fine. So, discrete data: number of cars I can see that I have one car, or I have two cars, or three cars, or four cars, or five cars, but can I say I have 1.5 cars? Or can I say I have 2.5 cars, or can I say I have 3.6 cars? Does it make sense? It's not going to make any sense, right? You can have one car or two or three or four or five cars, but you cannot have zero cars in decimal format. So the moment I divide the number, it's not making sense for me.
And that is a way to identify tribute data, categorical data, or discrete or deficit data. Or it can also be something like this I have so many red balls and so many blue balls. Gender, male or female, though there are genders, is not something we will discuss, good or bad. So these kinds of things are called attribute or categorical data. On the other side of the spectrum, we have continuous data, which is also called variable data, which can be measured on a scale and can be divided nightly into parts. And it's still going to make sense if you have the examples. Wait, I can go to any accuracy level. I can say that I weigh 80 gauges, or I can say I weigh 80.5 gauges, or I can say I weigh £160, or I can say I weigh 160 points at £.9, or I can say I weigh 170 points at £68. I can go to any accuracy level possible andit's still going to make sense for me. I can say I arrived office in 1 hour, orI can say I arrived office in 60 minutes, Ican say I've arrived office in 3600 seconds. It's still going to make the same sense for me, right? You can do as much wide continuous data as you want, and it will still make sense. unlike your attribute and categorical data.
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