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  • 1. An Overview of Six Sigma Larry Stauffer, PhD, PE College of Engineering, University of Idaho Boise
  • 2. Six Sigma Concepts Six Sigma: A data driven, problem-solving methodology for improving business and organizational performance Six Sigma performance is identified as 3.4 defects per million opportunities for a defect Six Sigma is a black hole of process improvement Engineering Management Program, University of Idaho Boise Six Sigma: A data driven, problem-solving methodology for improving business and organizational performance. There are two main Six Sigma programs: Six Sigma Improvement and Design for Six Sigma. Typically, when someone talks about Six Sigma they are talking about Six Sigma Improvement, sometimes referred to as Six Sigma Innovation. In this methodology we are looking to achieve six sigma performance by improving existing processes. Design for Six Sigma (DFSS) is a related methodology for designing vastly new processes or products that produce six sigma results. We will cover DFSS in more detail later in the course. Six Sigma is a black hole of process improvement. When you look at all of the techniques and tools available, they have been around for a long time. Six Sigma basically has pulled in the best of the best. If someone comes up with a great tool for process improvement, watch out, it will become a part of Six Sigma! 2
  • 3. Six Sigma Defined Engineering Management Program, University of Idaho Boise You know you have arrived when Scott Adams pokes fun at you. So what it six sigma? It is a lot of things, so we better take some time to explain so that we are all singing from the same page. 3
  • 4. Six Sigma Background Made popular by Motorola and GE, now gaining traction everywhere Most of the material was “borrowed” from the TQM and design methodology communities. 6σ improvements are focused on teams and projects Engineering Management Program, University of Idaho Boise
  • 5. Six Sigma Forefathers Bill Smith W. Edwards Deming Joseph M. Juran Philip B. Crosby Kaoru Ishikawa Genichi Taguchi Walter Shewhart Engineering Management Program, University of Idaho Boise William Edwards Deming was an American statistician, college professor, author, lecturer, and consultant. Deming is widely credited with improving production in the United States during WWII, although he is best known for his work in Japan. There, from 1950 onward he taught top management how to improve design (and thus service), product quality, testing and sales. Deming made a significant contribution to Japan becoming renowned for producing high-quality products. Deming is famous for his 14 Points for Management. Management's failure to plan for the future brings about loss of market, which brings about loss of jobs. "Long-term commitment to new learning and new philosophy is required of any management that seeks transformation. The timid and the fainthearted, and the people that expect quick results, are doomed to disappointment." Joseph Moses Juran was an American industrial engineer. He is known as a quality guru, making significant contributions to quality management theory. He worked in Japan at the same time as Deming though they worked separately. He is widely credited for adding the human dimension to quality management. He pushed for the education and training of managers as apposed to Deming who was more into statistics. Philip B. Crosby was a businessman and author who contributed to management theory and quality management practices. He initiated the Zero Defect program at the Martin Company and later started the consulting company Philip Crosby Association, Inc. This consulting group provided educational courses in quality management. Later he published his first business book, Quality Is Free. This book would become popular at the time because of the crisis in North American quality. 5
  • 6. Table 2: Six Sigma Cost And Savings By Company Year Revenue ($B) Invested ($B) % Revenue Invested Savings ($B) % Revenue Savings Motorola 1986- 356.9(e) ND - 16 1 4.5 2001 Allied Signal 1998 15.1 ND - 0.5 2 3.3 GE 1996 79.2 0.2 0.3 0.2 0.2 1997 90.8 0.4 0.4 1 1.1 1998 100.5 0.5 0.4 1.3 1.2 1999 111.6 0.6 0.5 2 1.8 1996- 382.1 1.6 0.4 4.4 3 1.2 1999 Honeywell 1998 23.6 ND - 0.5 2.2 1999 23.7 ND - 0.6 2.5 2000 25.0 ND - 0.7 2.6 1998- 72.3 ND - 1.8 4 2.4 2000 Ford 2000- 43.9 ND - 1 6 2.3 2002 Key: $B = $ Billions, United States (e) = Estimated, Yearly Revenue 1986-1992 Could Not Be Found ND = Not Disclosed Note: Numbers Are Rounded To The Nearest Tenth Engineering Management Program, University of Idaho Boise Although the complete picture of investment and savings by year is not present, Six Sigma savings can clearly be significant to a company. The savings as a percentage of revenue vary from 1.2% to 4.5%.. 1.2-4.5% of revenue is significant and should catch the eye of any CEO or CFO. For a $30 million a year company, that can translate into between $360,000 and $1,350,000 in bottom-line-impacting savings per year. It takes money to make money. Is investing in Six Sigma quality, your employees and your organization's culture worth the money? Only you and your executive leadership team can decide the answer to that question. 6
  • 7. Critical to Quality CTQ refers to a characteristic that is critical to quality Mail end up in my mail box Coffee the temperature that I like Pants the length that fits me Target value Performance scale Engineering Management Program, University of Idaho Boise Life is full of characteristics that relate to quality. When we think about our mail, we want it in the mail box; not on the street, in a neighbor’s box, lost in the ozone – in our box. If I drink a cup of coffee, I want it hot but not too hot; I want it the way I want it. I want pants that fit me. Not too long and not too short. Other examples: •To fill the customer’s order, I must ship 5 units every week for the next 18 months. •To stay competitive, we must process a loan application in 14 days. •From now on, we will not have an incorrect bill leave this hospital. If we look at these performance characteristics, there is a certain value in mind. But what we get is often not the target value. Why? Variation. 7
  • 8. Variation You can not hit the target value all the time, no matter how hard you try Why? Reality. Every product, every service, every transaction has variation Target value Engineering Management Program,scale Performance University of Idaho Boise Variation is everywhere. Any time a characteristic comes out different there is variation. In reality there is a reason, due to cause and effect. The problem is that we don’t totally understand anything so we can not predict what will happen. What if I pour a little bit of water onto the top of your hand. Where will it go before it flows to the edge and down to the floor? It is anyone’s guess. We think of this variation as having no cause. Not because it doesn’t but because we don’t know what it is. It appears random. In this case the variation is represented by a normal distribution or the classic bell shaped curve. 8
  • 9. Deming, “Variation is like a disease: you get it from your suppliers and you give it to your customers.” Engineering Management Program, University of Idaho Boise 9
  • 10. Sigma, σ Sigma is the standard deviation It represents variation (spread) of the data about some average value Sigma capability indicates how well a LSL USL process is performing w.r.t. limits defective Engineering Management Program, University of Idaho Boise Sigma denotes the standard deviation of a set of data. The standard deviation is a measure of the variation or spread that a set of data has about some mean. The greater the variation, the greater the standard deviation, and vise versa. The capability indicates how well a process is performing. In other words, how capable is the process for meeting customer requirements? This capability is relative to some lower specification limit and some upper specification limit. The upper process in this slide is capable; the lower one is not. 10
  • 11. Six Sigma in Perspective Traditional quality calls for +/- 3σ which means 99.73% of the population is good But this also means that 0.27% of the population is bad. Is this ok? Depends on your perspective. Engineering Management Program, University of Idaho Boise Traditionally, we operate at 3 sigma. Traditional control charts are plus or minus 3 standard deviations. A normal curve will capture 99.73% of the population within plus or minus 3 standard deviations from the mean. 11
  • 12. Sigma Level and Defects 2 308,537 3 66,807 4 6,210 5 233 6 3.4 99.999966% good σ process capability defects per million opportunities Engineering Management Program, University of Idaho Boise
  • 13. How Good is That? 99% (3.8 σ) 99.999966% (6σ) 20,000 lost pieces of 7 lost pieces of mail per mail per hour hour 5,000 surgery mistakes 1.7 surgery mistakes per week per week 2 long or short airplane 1 long or short airplane landings at a major landings at a major airport per day airport every 5 years 200,000 wrong drug 68 wrong drug prescriptions per year prescriptions per year Engineering Management Program, University of Idaho Boise
  • 14. Customer Driven Quality Less than 1 failure for Why do we every 2,000,000 flights tolerate one and not the other? Over 240,000 pieces of luggage from 700 million passengers never find their owners again Engineering Management Program, University of Idaho Boise All of this is really driven by the customer. Think about air travel. Current travel represents less than a half a failure per million flights – better than six sigma. Customers consider this an acceptable risk. But what about the luggage. This operation is worse than 3 sigma. There are 700 million passengers boarding US flights a year. While we don’t know the number of bags checked, there were about 30 million lost in 2005. 240,000 were never returned to their owners! What happens to them? The airlines sell them to stores that resell the suitcase and the contents to the public. For example, there is one store in Scottsboro, Alabama that is bigger than a city block which stocks more than 7,000 items every day. 14
  • 15. The Hidden Factory Who can actually achieve 6σ? Example: We need to clean and prime the steel. Then mix the correct pigments in the paint and spray it on evenly. What is the temperature today? Hopefully the paint job doesn’t get damaged between now and shipment. Will there be any rock damage in route? Will the fork lift operator use care in unloading? Engineering Management Program, University of Idaho Boise If any one of these processes doesn’t operate smoothly or something just goes wrong we have problems. How hot or cold is it today? Did we take care of the parts before the paint booth or did rust develop? Are the spray nozzles adjusted properly? Did they get properly cleaned last shift? Some things are out of our control. What if the truck hits a snow storm or an area where they are doing road work. Did we wrap the cargo well enough? It is extremely difficult to manage all of these events. Error propagates throughout. All of this error leads to rework, corrective actions, extra measurements and inspections. We spend enormous amounts of time fixing problems, reworking parts, touching up, “bird dogging” problems, in order to get good product out the door. All of this activity is known as the hidden factory. Six Sigma seeks to attack the hidden factory; getting back wasted time and money. 15
  • 16. Six Sigma Projects What makes a good Six Sigma project? Gap in performance: pain! Potential impact is large Solving the problem would make a lot of people happy The solution is not obvious Bottom Line: You are improving an existing process. Engineering Management Program, University of Idaho Boise What makes a good Six Sigma project? There are a number of things. Here are some of the most important criteria. Gap in performance: pain! You will often hear the question, “Where is the pain?” What is the gap in performance that is creating stress on the organization. Potential impact is large: It is often said that a Six Sigma project should result in at least a $100,000 improvement. Don’t take this as gospel, but the idea is right. Six Sigma can result in some pretty big impact. If you see an opportunity to save a lot of money or time (which is money) it could make a good Six Sigma project. Solving the problem would make a lot of people happy: Sometimes the financial impact may not be huge but if the problem impacts a lot of people and can create great satisfaction, go for it. This is the case with many internal processes: travel reimbursements, work order approvals, time to finalize records, etc. They may not seem like $100,000 projects but they certainly irritate a lot of people. The solution is not obvious: Sometime the solution is obvious or perhaps there is a general knowledge of what needs to be done but there is not the leadership or management in place to make it happen. Don’t waste your time with Six Sigma. Attack the real problem head on. Bottom Line. The best Six Sigma projects, especially to start, are for improving an existing process. Focus on the word “improvement” and projects will become obvious. 16
  • 17. Good Projects for Six Sigma Improve forecasts of customer demand Reduce time to finalize an order Reduce engineering change orders Reduce scrap Increase number of good parts Increase on-time shipments Engineering Management Program, University of Idaho Boise 17
  • 18. Poor Six Sigma Projects Install MRP system Train workers to read blue prints Develop the ability to take orders over the web Implement a program for workplace organization Create SOP’s for every workstation Engineering Management Program, University of Idaho Boise Note, these may be outcomes of a Six Sigma project. For example, you may be investigating all of the shortages of parts trying to reduce the number of times work stops because you don’t have what you need to assemble product. After your investigation you determine you need to install a particular MRP system. 18
  • 19. DMAIC Define: What are the customer’s expectations for the process? Measure: How often do the defects occur? Analyze: When, where, how do the defects occur? Improve: How do we improve the process to eliminate defects? Control: How do we hold the gains? Engineering Management Program, University of Idaho Boise
  • 20. Define Define the project CTQ’s (critical to quality; dependent Y variables) Develop team charter Map the process (high level) Engineering Management Program, University of Idaho Boise
  • 21. Measure Map the process (SIPOC, VSM, …) Define performance standards Characterize CTQ’s Define a data collection plan Engineering Management Program, University of Idaho Boise
  • 22. Analyze Determine process capabilities Set defect reduction goals Identify sources of variation (independent, X variables) Engineering Management Program, University of Idaho Boise
  • 23. Improve Screen potential causes Characterize X variables Establish operating tolerances Engineering Management Program, University of Idaho Boise
  • 24. Control Develop mistake-proof plans Implement control charts Implement process controls Engineering Management Program, University of Idaho Boise
  • 25. Six Sigma Improvement Define Measure Analyze } Plan Do Check Improve { Act Control Refinement of TQM’s Deming Cycle, focused on improving processes. Engineering Management Program, University of Idaho Boise
  • 26. Six Sigma Tools Taguchi SIPOC Engineering Management Program, University of Idaho Boise
  • 27. Project Charter Key elements of a charter include: 1. Business case 2. Problem and goal statements 3. Scope 4. Milestones 5. Roles of team members The team charter communicates the project direction to all members of the team Engineering Management Program, University of Idaho Boise Now we begin to look at the project charter. It has several elements as shown above. 27
  • 28. Six Sigma Project Charter Project Title Project Leader Project Sponsor Project Facilitator Six Sigma Expert Business Case Problem Statement Goal Statement Scope CTQs Baseline Goal Business Metrics Baseline Goal Milestones Dates Start date Define complete Measure complete Analyze complete Improve complete Control complete Project completion date Team Members Name Signatures Engineering Management Program, University of Idaho Boise 28
  • 29. Pareto Analysis An effective way to represent data. Categories are ranked highest to lowest. Their value (usually frequency) is noted on the left vertical axis and the accumulative effect is shown on the right vertical axis Test Scores by Weight 9 120.0% Separate the vital few 8 100.0% 7 6 80.0% Frequency from the trivial many 5 60.0% 4 3 40.0% 2 20.0% 1 0 0.0% 100 95 105 90 110 115 85 80 120 Weight Categories Engineering Management Program, University of Idaho Boise A Pareto Analysis is one of the most often used tools in Six Sigma. It is simple yet powerful. The Pareto chart helps to see problems by category and set priorities for which to tackle first 1. Determine the categories to be included in the graph. 2. Select a time interval over which to collect data. It should be long enough to represent typical performance. 3. Determine the total number of occurrences for each category. Also calculate the grand total. If there are several categories that have very few occurrences, lump them into an other category. 4. Compute the percentage represented by each category. The total should sum to 100%. 5. Rank order the occurrences from highest to lowest in term of percent of occurrences. 29
  • 30. Histogram Another graphical means of representing data. Typically, a histogram presents the raw amount of a categories of data. frequency of w eight A histogram differs from a 9 8 Pareto in that it is 7 6 frequency 5 organized by some 4 3 Series1 scheme other than rank 2 1 order (time, position, 0 1 2 3 4 5 6 7 8 9 occurrence, etc.) w eight ranges Engineering Management Program, University of Idaho Boise Sometimes you want to depict data where there is another scheme that is more important than frequency from greatest to least. For example age, date, months, etc. Suppose we wanted to show the frequency of records that were misfiled by the length of service at the company. Or you want show natural gas usage by month. In these cases, a histogram would make more sense. 30
  • 31. Process Mapping Process mapping is done at a high level during the Define phase. Maps are also used at various levels in the other phases. We will cover just a few of the many types SIPOC Cross-Functional Customer Contact Cycle Value Stream Mapping Flow charting Engineering Management Program, University of Idaho Boise These mapping techniques will be explained in the pages that follow. Our goal is to describe the current state of the process. What is going on and why. Typically, these maps are at a fairly high level. Later in the DMAIC activity we may map again at a somewhat deeper level. Again, to see what is happening at a more detailed level. Mapping is a simple and powerful tool. 31
  • 32. Descriptive Statistics A set of tools that organize, summarize, and display information Mean, median, dispersion, histograms, pie charts, box plots, etc Population => all data under consideration Sample => random subset of the population Engineering Management Program, University of Idaho Boise Of critical note is that the sample is a random subset of the population. If at any point the subset is not randomly assembled, it ceases to be a valid sample of the population. 32
  • 33. Inferential Statistics Uses sample data to make estimates (inference) about the population from which is was drawn Sample statistics are sample mean, sample median, etc. Sampling error is the result of using sample data instead of the entire population to calculate a population parameter Engineering Management Program, University of Idaho Boise There is a convention that is typically used in statistics. Sample statistics are typically written in Latin and population statistics in Greek. Why? I don’t know; its Greek to me. sample population size (quantity) n N mean x μ standard deviation s σ 33
  • 34. Correlation •In the scatter plot we have two random variables, x1 and x2 x2 •The scatter plot 16 14 shows correlation but 12 10 not necessarily 8 x2 causation 6 4 •Could be a lurking 2 0 variable 0 5 10 15 20 25 30 35 Engineering Management Program, University of Idaho Boise What is a lurking variable? Just because two random variables have a strong correlation does not mean one causes the other. This plot could be relating two variable; liquor sales and the pay of Idaho school teachers. Could we say that increased liquor sales are the result of increases in the pay of Idaho teachers? Perhaps the increase in liquor sales has created more taxes which in turn enabled the legislature to pay teachers more. Or perhaps there is a lurking variable, like increase in GDP that is increasing both liquor sales and teacher’s pay. Lesson: correlation does not equal causation. 34
  • 35. Questions and Discussion Engineering Management Program, University of Idaho Boise