![45
DMAIC: Measure the Current Situation
Gather information on the current situation to provide a clearer focus for your
improvement effort.
1.
Determine the number of defect
opportunities per unit
O = 1
2.
Determine the number of units
processed
N = 647 = Fiscal Year End 2004 Faculty
3.
Determine the total number of
defects made
D = 64 = Faculty Terminations during 2005
D
N * O =
5.
Calculate Defects per Million
Opportunities
DPMO = DPO X 1M = 98,918
6. Calculate Yield Yield = (1 - DPO) x 100 = 90.108%
= % of Units (Faculty) which went
through the process (Fiscal Year)
without a defect (Termination)
7.
Lookup Sigma in the Sigma Table
[=NORMSINV(Yield)+1.5]
Sigma Value = 2.79 = 2005 Faculty Turnover Sigma
= 2005 Faculty Turnover (9.89%)
0.098918
Calculating Sigma Value Worksheet
DPO = =
Calculate Defects per Opportunity
4.
In Good To Great, author Jim Collins mentions the need for a BHAG
or Big Hairy Audacious Goal. Using Six Sigma as a guide, you can
measure your current performance and set a BHAG of reaching the
next level sigma.](https://image.slidesharecdn.com/pirpleansixsigma-240201121917-c8f2cba7/85/P_IRP-Lean-Six-Sigma-ppt-45-320.jpg)
![49
DMAIC: Control
Maintain gains that you have achieved by standardizing your work methods
or processes. Anticipate future improvements and make plans to preserve
the lessons learned from this improvement effort.
} Improvement
Before After
A1 A2 A3 A4 A2 A1 A3 A4
Before After
1.
Determine the number of defect
opportunities per unit
O = 1 1
2.
Determine the number of units
processed
N = 647 647
3.
Determine the total number of
defects made
D = 64 7
D
N * O =
5.
Calculate Defects per Million
Opportunities
DPMO = DPO X 1M = 98,918 10,819
6. Calculate Yield Yield = (1 - DPO) x 100 = 90.108% 98.918%
7.
Lookup Sigma in the Sigma Table
[=NORMSINV(Yield)+1.5]
Sigma Value = 2.79 3.80
0.098918
Calculating Sigma Value Worksheet
DPO = =
Calculate Defects per Opportunity
4. 0.010819
Before After
}Improvement
Target
}Remaining Gap
Good
Countermeasures
implemented](https://image.slidesharecdn.com/pirpleansixsigma-240201121917-c8f2cba7/85/P_IRP-Lean-Six-Sigma-ppt-49-320.jpg)
P_IRP Lean Six Sigma.ppt
- 1. An Introduction to Lean Six Sigma (6σ) in Higher Education Dr. Andrew Luna Director Institutional Research and Planning University of West Georgia Stan DeHoff University Project Portfolio Manager Office of Decision Support Medical College of Georgia
- 2. 2 Six Sigma - As Easy to Understand As Parking Your Car
- 3. 3 • History of Quality in Higher Education • The World We Live In • Six Sigma Defined • DMAIC • Lean Defined • Example: Using Statistical Measures for Quality Control in higher education • Example: Using Lean Six Sigma at MCG Agenda
- 4. 4 • In 1980, NBC aired “If Japan can…Why can’t we?” and the Quality movement took off in the U.S. • In 1991, IBM offered $1 million ($3 million in IBM equipment) to those colleges and universities that could adapt quality management initiatives • In 1992 all of higher education went TQM “crazy” History of Quality in Higher Education
- 5. 5 History of Quality in Higher Education, cont. • TQM failed in higher education because of lack of knowledge. • TQM lost its appeal to many business because of increased labor and documentation costs and decreased reliance on Statistical Process Control • Six Sigma was an effort by Motorola and GE to bring back statistical measurement to quality • Six Sigma is now slowly entering the halls of academe
- 6. 6 The World We Live In Sonny Perdue • Governor, State of Georgia – Changing the culture of state government • Principle-centered, people-focused, customer-friendly – Commission for a New Georgia • Best managed, growing, educated, healthy, safe “Our government needed new thinking from a fresh perspective to see better ways to manage our assets and services and map our future.”
- 7. 7 The World We Live In Erroll B. Davis, Jr. • Chancellor, University System of Georgia – Ongoing series of changes to improve System communication and institutional engagement • Reorganization, new System Strategic Plan, more unified System – Focus on accountability and quality and “Six Sigma” “I want our actions and decisions to be based upon knowledge. So that is an initial expectation; that we will focus on data-driven decision-making.”
- 8. 8 What is Six Sigma (6σ)? •Sigma (σ) is a statistical concept that represents how much variation there is in a process relative to customer specifications. •Sigma Value is based on “defects per million opportunities” (DPMO). •Six Sigma (6σ) is equivalent to 3.4 DPMO. The variation in the process is so small that the resulting products and services are 99.99966% defect free. Amount of Variation Effect Sigma Value Too much Hard to produce output within customer specifications Low (0 – 2) Moderate Most output meets customer specifications Middle (3 – 5) Very little Virtually all output meets customer specifications High (6)
- 9. 9 Customer Specification Every Human Activity Has Variability... Reducing Variability is the Key to Understanding Six Sigma Six Sigma Concept defects Target Customer Specification defects Target Customer Specification
- 10. 10 Six Sigma Concept Parking Your Car in the Garage Has Variability... Target defects defects Customer Specification Customer Specification
- 11. 11 Six Sigma Concept A 3s process because 3 standard deviations fit between target and spec Target Customer Specification 1s 2s 3s 3s Before Target Customer Specification 1s 1s 2s 2s 3s 3s 3 s Before Target Customer Specification After 1s 3s 6s 6s ! By reducing the variability, we improve the process Target Customer Specification After 1s 1s 3s 3s 6s 6s 6s ! No Defects! By reducing the variability, we improve the process
- 12. 12 Six Sigma 99.99966% Good 20,000 articles of mail lost per hour Unsafe drinking water for almost 15 minutes each day 5,000 incorrect surgical operations per week 2 short or long landings at most major airports each day 200,000 wrong drug prescriptions dispensed each year 7 articles of mail lost per hour Unsafe drinking water for 1 minute every 7 months 1.7 incorrect surgical operations per week 1 short or long landing at most major airports every 5 years 68 wrong drug prescriptions dispensed each year 3.8 Sigma 99% Good What’s Wrong With 99% Quality?
- 13. 13 Why Use Sigma as a Metric? Focuses on defects • Even one defect reflects a failure in your customer’s eye Establishes a common metric to make comparisons easier Is a more sensitive indicator than percentage or average-based metrics …
- 14. 14 Limitations of Average-Based Metrics FOXTROT BY BILL AMEND
- 15. 15 Where Did 6σ Come From? • Started at Motorola Corporation in the mid-1980’s, when the company discovered that products with a high first-pass yield (i.e., those that made it through the production process defect-free) rarely failed in actual use, resulting in higher customer satisfaction. • Popularized by former General Electric CEO Jack Welch’s commitment to achieving Six Sigma capability (realized $12 Billion savings over 5 years). "Six Sigma is a quality program that improves your customers' experience, lowers your costs and builds better leaders."
- 16. 16 Isn’t 6σ Just For Manufacturing? • No, Six Sigma is good for ANY business. – Has been successful in industries such as banking, retail, software, and medical – Has been successful in improving processes throughout operations, sales, marketing, information technology, finance, customer services, and human resources • Why? – Because every business suffers from the two key problems that Six Sigma can solve: defects and delay
- 17. 17 Six Sigma (6σ) in Academia Abraham Baldwin Columbus State Kennesaw State Armstrong Atlantic State Darton College Southern Polytechnic State Bainbridge College Georgia State University of Georgia Clayton State Georgia Inst of Tech Valdosta State USG Institutions Teaching Six Sigma Institutions which have implemented some form of Six Sigma methodology within their operations: Health Sciences: Medical College of Pennsylvania Alabama Jackson State South Carolina Medical College of Virginia Boston University Johns Hopkins South Dakota State Medical College of Wisconsin Cal Poly State Kettering Tennessee Medical U of South Carolina California Michigan Texas St. Louis U Health Sciences Center Carnegie Mellon Mississippi Texas A&M U of Michigan Health System Central Florida Mississippi State Tulane U of Tennessee Health Science Center Central Michigan NC State UNC Chapel Hill U of Texas Health Science Center Clemson Ohio Vanderbilt U of Texas Medical Branch Coastal Carolina Penn State Vermont University System of Georgia: Colorado Purdue Villanova University of Georgia Connecticut Rockhurst Washington University of West Georgia Florida Tech Rutgers Western Illinois Valdosta State University Illinois Central San Diego Western Kentucky Other:
- 18. 18 Six Sigma (6σ) Methodologies Define Measure Analyze Improve Control DMAIC: This method is used to improve the current capabilities of an existing process. This is by far the most commonly used methodology of sigma improvement teams. Define Measure Analyze Design Verify DMADV: This method is used when you need to create or completely redesign a process, product, or service to meet customer requirements. DMADV teams are usually staffed by senior managers and Six Sigma experts.
- 19. 19 DMAIC Methodology DEFINE Identify, prioritize, and select the right project(s) MEASURE Identify key product characteristics & process parameters, understand processes, and measure performance ANALYZE Identify the key (causative) process determinants IMPROVE Establish prediction model and optimize performance CONTROL Hold the gains
- 20. 20 Analysis of Variance (ANOVA) Box Plots Brainstorming Cause-effect Diagrams Correlation & Regression Design Of Experiments Graphs and Charts Histograms Hypothesis Testing Pareto Analysis Process Capability Studies Process Control Plans Process Flow Diagrams Quality Function Deployment Response Surface Methods Scatter Diagrams Standard Operating Procedures (SOPs) Statistical Process Control Six Sigma Toolbox
- 21. 21 Process Problems and Symptoms Process outputs Response variable, Y Independent variables, Xi Process inputs The Vital Few determinants Causes Mathematical relationship Y X’s Measure Analyze Improve Control Process Characterization Process Optimization Goal: Y = f ( x ) Define The right project(s), the right team(s) Project Focus
- 22. 22 30,000 Ft. – View of Entire Organization 5,000 Ft. – View of One Process Different Views of the Organization
- 23. 23 So, What is Lean? • The methodology of increasing the speed of production by eliminating process steps which do not add value – those which delay the product or service – those which deal with the waste and rework of defects along the way
- 24. 24 Where Did Lean Come From? • Lean thinking originated at Toyota with the Toyota Production System (TPS). The original ideas were formulated by Sakichi Toyoda in the 1920s and 1930s, but only made the leap to full implementation in the 1950s. • Many of the principles of lean came from a surprising source: American supermarkets where small quantities of a vast selection of inventory is replenished as customers "pull" them off the shelf.
- 25. 25 Core Ideas of Lean • Determine and create value – What does the customer want? • Use “pull” instead of “push” systems to avoid overproduction – Inventories hide problems and efficiencies. • One piece flow – Make the work “flow,” so that there are no interruptions and no wasted time or material • Eliminate the seven speed bumps (non-value adds) caused by waste • Use the “five whys?” and Six Sigma problem solving to eliminate defects
- 26. 26 The Seven Speed Bumps of Lean 1. Over production which creates inventories that take up space and capital 2. Excess inventory caused by over production 3. Waiting for the next value-added process to start 4. Unnecessary movement of work products 5. Unnecessary movement of employees 6. Unnecessary or incorrect processing 7. Defects leading to repair, rework, or scrap. Non-value added waste – is any activity which absorbs money, time, and people but creates no value.
- 27. 27 The Antidote to Waste: The 5 S’s 1. Sort – Keep only what is needed 2. Straighten – A place for everything and everything in its place 3. Shine – Clean systems and work area to expose problems 4. Standardize – Develop systems and procedures to monitor conformance to the first three rules. (Six Sigma’s Define and Measure phases) 5. Sustain – Maintain a stable workflow. (Six Sigma’s Analyze, Improve, and Control phases)
- 28. 28 Synergy of Lean and Six Sigma # of Steps ±3s ±4s ±5s ±6s 1 93.32% 99.379% 99.976% 99.999% 7 61.63% 95.733% 98.839% 99.997% 10 50.08% 93.96% 99.768% 99.996% 20 25.08% 88.29% 99.536% 99.993% 40 6.29% 77.94% 99.074% 99.986% Lean reduces non-value-add steps Six Sigma improves quality of value-add steps Source: Motorola Six Sigma Institute
- 29. 29 The Birth of “Lean Six Sigma” • Six Sigma improves effectiveness by eliminating defects (improves Quality) • Lean improves efficiency by eliminating delay and waste (improves Speed) • Most Six Sigma efforts are incorporating the principles of Lean. Therefore, Six Sigma is often called Lean Six Sigma.
- 30. 30 Pareto Chart in Residence Halls 0 50 100 150 200 250 M i s c . N o i s e V i s i t a t i o n F i r e S a f e t y A l c o h o l V a n d a l i s m T h e f t H a r r a s s m e n t M e d i c a l M e c h a n i c a l Count 0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00% Cumlative Percentage Residential Life Incident Reports – 2 Years
- 31. 31 Using Pareto and Trend Analysis Trend Analysis
- 32. 32 Control Chart for Hot Water in Residence Hall Problem • Survey found that most residents in a female hall were unhappy with the bathrooms • Subsequent focus groups found that residents were upset over the quantity and quality of hot water • Define – Hot water variability in high-rise residence hall • Measure – Record temp. of hot water on high, med., and low floors for two weeks, three times a day • Analyze – Plot hot water on X-Bar/R Control Chart
- 33. 33 Control Chart for Hot Water in Residence Hall, Cont. 100 110 120 130 140 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 21 2 2 2 2 2 2 2 2 3 31 3 3 3 3 3 3 3 3 4 41 0 5 10 15 20 25 30 35 40 45 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2 21 2 2 2 2 2 2 2 2 3 31 3 3 3 3 3 3 3 3 4 41 X - Bar R Means Ranges Run Trend Hugging of the Mean Periodicity Exceeding Control Limit
- 34. 34 Control Chart for Hot Water in Residence Hall, Cont. • Improve – After understanding the process and the control chart, the team offered suggestions to control variability • Control – A new control chart was run after changes to the system and the process was found to be in control • Money – The changes decreased utility costs and increased student retention in the hall
- 35. 35 Regression Analysis • Multiple Regression was used to explain variability in academic departmental budget allocations • Credit hours, professors, degrees, market of the discipline, and majors were used to predict budget allocation • Predicted allocations were compared to actual allocations and significant discrepancies were addressed.
- 36. 36 Reference Our Master Improvement Story Vision Long-Term Objectives Annual Objectives Measures Targets The Medical College of Georgia will become one of the nation's premier health sciences universities. I - Enhance Educational Environment and Update Educational Programs II - Enhance the Research Enterprise Improve Program Effectiveness * Number of applications * Enrollment * Number of Degrees conferred * Passage rate ___ ___ ___ ___ Improve Student Performance * Grade point averages * Standard examination scores * Fulfilled requirements * % retained * % promoted * % graduated * % certified/licensed ___ ___ ___ ___ ___ ___ ___ Improve Research Productivity * Amount of external funding * NIH funding * Comparative ranking ___ ___ ___ Improve Research Outcomes * Number of new grants * Dollar amount of new grants * Number of research studies * Number of publications * Presentations per Faculty ___ ___ ___ ___ ___ III - X A Master Improvement Story links key measures to improvement efforts. This linkage helps leaders and employees focus on the customer / stakeholder and align all of their actions to achieve desired outcomes. a.k.a., Balanced Scorecard
- 37. 37 Definition: Number of full-time instructional faculty (FTI) who left during a fiscal year (July 1 - June 30) divided by the total number of FTI faculty present as of June 30 of the prior fiscal year. DMAIC: Define the Project Define the project’s purpose and scope. Collect background information on the process and your customers’ needs and requirements. As an example project, let’s focus on the Full-Time Instructional Faculty (FTI) Turnover Rate metric … Source: IV - Continuously Enhance the Quality of Faculty and Staff Improve Recruitment Improve Retention * Incentive packages * Time to fill open reqs * Competitive salaries * Tenure status * Turnover rate ___ ___ ___ ___ ___
- 38. 38 DMAIC: Measure the Current Situation Gather information on the current situation to provide a clearer focus for your improvement effort. Most problems can be easily expressed as a line graph showing the current trend. MCG Faculty Turnover Rate 0 5 10 15 20 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 % Turnover MCG Turnover Trendline
- 39. 39 DMAIC: Measure the Current Situation Gather information on the current situation to provide a clearer focus for your improvement effort. A Control Chart is used to detect and monitor variation over time. This chart tells us that the process is unstable.
- 40. 40 DMAIC: Measure the Current Situation Stop! Wait a minute! We had an early retirement program in 2001 and 2002, where we planned to have a high faculty turnover rate. What if we were to flag those years as “special causes” and remove them from our measurement? Okay, let’s see … Gather information on the current situation to provide a clearer focus for your improvement effort.
- 41. 41 DMAIC: Measure the Current Situation Gather information on the current situation to provide a clearer focus for your improvement effort. 1991-2005 Faculty Turnover Rate (excluding early retirement years 2001-2002) 0 5 10 15 20 1 9 9 1 1 9 9 2 1 9 9 3 1 9 9 4 1 9 9 5 1 9 9 6 1 9 9 7 1 9 9 8 1 9 9 9 2 0 0 0 2 0 0 3 2 0 0 4 2 0 0 5 % Turnover MCG Turnover Trendline If we remove the “special cause” early retirement program years of 2001 - 2002, our trend is actually downward. But is the process stable?
- 42. 42 DMAIC: Measure the Current Situation Gather information on the current situation to provide a clearer focus for your improvement effort. The Control Chart still indicates an unstable process with points too close to the Upper and Lower Control Limits. But is the process capable of meeting specifications?
- 43. 43 DMAIC: Measure the Current Situation Gather information on the current situation to provide a clearer focus for your improvement effort. A Histogram measures the process’s capability of meeting the customer’s specifications. Our process is not capable, as there is too much variation. The Target and Customer Specification values are examples based on peer reports.
- 44. 44 DMAIC: Measure the Current Situation Now that we have seen that our Faculty Turnover process is both unstable and incapable of meeting specifications, let’s take a closer look at the year 2005… Gather information on the current situation to provide a clearer focus for your improvement effort.
- 45. 45 DMAIC: Measure the Current Situation Gather information on the current situation to provide a clearer focus for your improvement effort. 1. Determine the number of defect opportunities per unit O = 1 2. Determine the number of units processed N = 647 = Fiscal Year End 2004 Faculty 3. Determine the total number of defects made D = 64 = Faculty Terminations during 2005 D N * O = 5. Calculate Defects per Million Opportunities DPMO = DPO X 1M = 98,918 6. Calculate Yield Yield = (1 - DPO) x 100 = 90.108% = % of Units (Faculty) which went through the process (Fiscal Year) without a defect (Termination) 7. Lookup Sigma in the Sigma Table [=NORMSINV(Yield)+1.5] Sigma Value = 2.79 = 2005 Faculty Turnover Sigma = 2005 Faculty Turnover (9.89%) 0.098918 Calculating Sigma Value Worksheet DPO = = Calculate Defects per Opportunity 4. In Good To Great, author Jim Collins mentions the need for a BHAG or Big Hairy Audacious Goal. Using Six Sigma as a guide, you can measure your current performance and set a BHAG of reaching the next level sigma.
- 46. 46 DMAIC: Measure the Current Situation Gather information on the current situation to provide a clearer focus for your improvement effort. 2005 MCG Faculty Turnover 19 14 12 6 4 3 3 2 1 30% 52% 70% 80% 86% 91% 95% 98% 0 8 16 24 32 40 48 56 64 V o l u n t a r y C o l l e g i a t e E m p l o y m e n t E l s e . . . I n v o l u n t a r y N o n R e a p p o i n t m e n t V o l u n t a r y O t h e r V o l u n t a r y T r a n s f e r r e d t o C l i n i c a l S t a t u s V o l u n t a r y T e m p o r a r y V o l u n t a r y R e s i g n a t i o n s V o l u n t a r y R e t i r e m e n t s I n v o l u n t a r y D e c e a s e d V o l u n t a r y P r i v a t e P r a c t i c e Reasons Terminations 0% 20% 40% 60% 80% 100% A Pareto Chart helps you break down a big problem into its parts and identify which are the most important. “Voluntary Collegiate Employment Elsewhere” caused 30% of the Faculty turnover, and “Involuntary Non- Reappoint- ment” caused 22%. Pareto Principle: 80% of the problems are caused by 20% of the contributors.
- 47. 47 DMAIC: Analyze to Identify Causes Identify the root cause of defects. Confirm them with data. An Ishikawa (Fishbone) Cause-and-Effect diagram is used to identify potential causes of the problem. Process/Methods Resources Technology People Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? During 2005, "Voluntary Collegiate Employment Elsewhere" accounted for 30% of Faculty Turnover. Problem Statement
- 48. 48 DMAIC: Improve Develop, try out, and implement solutions that address the root causes. Use data to evaluate results for the solutions and the plans used to carry them out. A Countermeasures chart is used to identify potential solutions and rank them for implementation. Problem Statement: Root Cause Countermeasure/ Proposed Solutions Feasibility Specific Actions Effectiveness Overall Action (Who?) Value ($/period) 0 0 0 0 0 0 0 0 0 0 Feasibility: 1-low, 5-high Effectiveness: 1-low, 5-high 1-Expensive & Difficult to implement 1-Not very effective 5-Inexpensive and easy to implement 5-Very Effective During 2005, "Voluntary Collegiate Employment Elsewhere" accounted for 30% of Faculty Turnover.
- 49. 49 DMAIC: Control Maintain gains that you have achieved by standardizing your work methods or processes. Anticipate future improvements and make plans to preserve the lessons learned from this improvement effort. } Improvement Before After A1 A2 A3 A4 A2 A1 A3 A4 Before After 1. Determine the number of defect opportunities per unit O = 1 1 2. Determine the number of units processed N = 647 647 3. Determine the total number of defects made D = 64 7 D N * O = 5. Calculate Defects per Million Opportunities DPMO = DPO X 1M = 98,918 10,819 6. Calculate Yield Yield = (1 - DPO) x 100 = 90.108% 98.918% 7. Lookup Sigma in the Sigma Table [=NORMSINV(Yield)+1.5] Sigma Value = 2.79 3.80 0.098918 Calculating Sigma Value Worksheet DPO = = Calculate Defects per Opportunity 4. 0.010819 Before After }Improvement Target }Remaining Gap Good Countermeasures implemented
- 50. 50 To Recapitulate Six Sigma • Define – Choose a significant process • Measure – Track the output of that process • Analyze – Determine the causes of variability within the process • Improve – Minimize the variability • Control – Stabilize the process Remember: Minimize variability, increase quality. Increase quality, decrease costs!
- 51. 51 QUESTIONS?