SUNY Stony Brook Quality Management EMP 517 Introduction

SUNY- Stony Brook Quality Management - EMP 517 Introduction to Six Sigma (6 ) and Lean “Lean-Sigma” Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 1

Six Sigma: 6 1. Why? 2. What is it? n n n How Six Sigma is a step forward Where it came from How it’s different 3. How to get started 4. Roles, “Belts” and Certifications 5. How to integrate Lean and Six Sigma Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 2

WHY Six Sigma? The nt e m e g na a f. M o ” e g a =$ u g n “La * from Annual Reports / Company web sites Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 3

WHY Six Sigma? The Pharma Sigma Value For most pharma companies, the process quality level was between 2 and 3 sigma in 2002 OPPORTUNITY? Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 4

WHY Six Sigma? “Quality costs are the costs associated with preventing, finding, and correcting defective work. These costs are huge, running at 20% - 40% of sales. ” - J. M. Juran Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 5

WHAT is Six Sigma ? 2 Simple Questions: 1. Are your processes predictable? 2. Are they capable? Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 6

Six Sigma: 6 A management philosophy and a disciplined problem-solving methodology backed by powerful statistical tools using a crossfunctional team approach to reduce service errors or process defects to 3. 4 ppm opportunities or less. Three distinct levels: 1. Philosophy: Variation Reduction 2. Methodologies: DMAIC , DMADV/IDOV 3. Metric: 3. 4 dpmo DFSS Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 7

The Six Sigma Philosophy What is the enemy of quality? EXCESS VARIATION ! All improvement comes from understanding THEN reducing variation If the Variation is due to: • COMMON Causes (Chance/Inherent/Random/Natural) - System “ NOISE” Then the Process is: In Control/Predictable/Stable • SPECIAL Causes (Assignable/Non-random) - “ SIGNALs” Then the Process is: Out of Control/Unpredictable/Unstable THE PROCESS MUST FIRST BE PREDICTABLE BEFORE DETERMINING IF IT IS CAPABLE! Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 8

Statistical Thinking & Six Sigma Statistical Thinking Principle Six Sigma Tools All work occurs in a system of interconnected processes Process Maps, Value Stream Mapping, SIPOC Diagrams, Cause and Effect Matrix, Failure Mode Effects Analysis (FMEA), Quality Function Deployment (QFD) Variation exists in all processes Descriptive Statistics, Measurement System Analysis (MSA), Enumerative Statistics, Multi-Vari Charts, Cause and Effect Diagrams, Box Plots, Process Capability Analysis Understanding and reducing variation are the keys to improvement and success Control Charts, Design of Experiments (DOE), Hypothesis Testing, Correlation, Regression Analysis, Pareto Charts Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 9

The Six Sigma Philosophy Variation Reduction “Predictable & Capable” WHAT ABOUT THE OPPORTUNITIES? Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 10

The Six Sigma Philosophy Variation Reduction - “The Taguchi Way” Traditional View Six Sigma View The Loss, $, due to performance variation is proportional to the square of the deviation of the performance characteristic from its nominal value: L(x) = k(x-t)2 Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 11

The Six Sigma Philosophy Outputs vs. Inputs The process output, Y, is a function of all the inputs, X Determine the “vital few” X’s AND the interrelationships that impact Y E O D Y = f(X) THEN Optimize the levels of the vital few inputs 12

Six Sigma: 6 History • early 1800’s - Carl Frederic Gauss – Gaussian Distribution = Normal Curve = Bell curve • 1920’s - Walter Shewhart – Control Charts, ± 3 (99. 73%) ; 2 Types of Variation: Common Cause and Special Cause • mid 1940’s/50’s – Deming and the war effort; Japan & SPC • early 1980’s – Process capability indices popular– Cp, Cpk • mid to late 1980’s – Six Sigma is born: Bill Smith (Engineer), Dr. Mikal Harry (Statistician) & Rich Schroeder (Executive), Motorola – manufacturing focus (“Six Sigma” – federally registered trademark of Motorola) • 1994 – “Six Sigma Academy” founded by Dr. Harry; Asea Boveri Brown (Rich Schroeder) one of first clients • 1995 – CEO Larry Bossidy of Allied Signal/Honeywell (Rich Schroeder) – enterprise focus: all business processes • 1996 – CEO Jack Welch, GE hires Six Sigma Academy • late 1990’s books, consulting, training, lectures, conferences, “Certifications” • 2001 - first ASQ Certified SSBB exam Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 13

How “ 6 ” Started On January 15, 1987, CEO Bob Galvin established Six Sigma as the required capability level to approach the standard of 3. 4 DPMO. This new standard was to be used in everything, that is, in products, processes, services and administration. Motorola’s Stated Goals: “Improve product and service quality ten times by 1989, and at least one hundred fold by 1991. Achieve Six Sigma capability by 1992…achieve a culture of continual improvement to assure Total Customer Satisfaction. There is only one ultimate goal: zero defects in everything we do. ” Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 14

Why “ 6”? The Motorola Way Ensuring that process variation (as measured by ) is half the design tolerance (spec. width / process width = 2. 0) while allowing the mean to shift as much as 1. 5 standard deviations. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 15

PPM Defective As a Function of Process Off-centering (Shifting) Process Quality Level Off-Centering 3 3. 5 4 4. 5 0 2700 465 63 6. 8. 25 3577 666 99 12. 8. 5 6440 1382 236 32. 75 12288 3011 665 88. 5 1. 0 22832 6433 1350 233 1. 25 40111 12201 3000 577 1. 5 66803 22800 6200 1350 1. 75 105601 40100 12200 3000 2. 0 158700 66800 22800 6200 from: P. R. Tadikamalla, Quality Progress, Nov. ‘ 94 5 0. 57 1. 02 3. 4 11 32 88. 5 233 577 1300 5. 5 0. 034 0. 1056 0. 71 1. 02 3. 4 10. 7 32 88. 4 233 6 0. 002 0. 0063 0. 019 0. 1 0. 39 1 3. 4 11 32 In most cases, controlling the process to target is easier and less expensive than reducing the process variability. This table helps assess the trade-offs. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 16

Is 99. 9% “Good” Enough? Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 17

The 6 Metric n Traditional ± 3 sigma (99. 73%) yields: • 54, 000 incorrect drug prescriptions per year • 40, 500 newborn babies dropped each year * 2700 ppm n “Good Enough” quality (99. 9%): • 20, 000 incorrect drug prescriptions per year • 18, 250 newborn babies dropped each year * 1000 ppm n “Six Sigma quality” (99. 9996%) yields: • One incorrect drug prescription every 25 years • 3 newborn babies dropped each century * 3. 4 ppm (“Statistical” Six Sigma = 2 ppb (w/o the shift)) Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 18

The Six Sigma Methodology and “The Scientific Method” Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 19

The Six Sigma Methodology The History of Problem-Solving / Process Improvement Models Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 20

Six Sigma Methodologies DMAIC (“Corrective”) Define the customers (internal and external), project goals, scope, and deliverables using: VOC, QFD (House of Quality); Flow Charts; Process Maps; VSM, SIPOC Diagrams… Measure the process to determine current performance; id input AND output variables using: Measurement System Analysis / R&R Study; Multi-Vari Analysis; Control Charts… Analyze the results to determine the root cause(s) of the “defects” and the relationships between input and output variables using: 7 QC Tools; Regression Analysis; Hypothesis Testing; DOE… Improve the process by eliminating defects; experiment to establish cause-effect relationships; optimize the inputs using: DOE; FMEA; Mistake-proofing; Benchmarking; Action Plans… Control future process performance; placed on inputs NOT outputs (i. e. NO inspection) using: Control Plans; Mistake-proofing; TPM; SPC; Audits… Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 21

Six Sigma Methodologies DMADV (“Preventive”) DMADV (a. k. a. DFSS) – using the VOC Define the customer, the project goals and deliverables Measure to determine customer needs and specifications Analyze the process options to meet the customer needs Design (detailed) the process to meet the customer needs Verify the design performance and ability to meet customer needs Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 22

Six Sigma: 6 How is it Different…? 80 -90% of quality problems can be solved with simple tools. Six Sigma also contains more sophisticated, statistical tools for the more complex, chronic problems + = The Language of Management! Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 23

Six Sigma: 6 How is it Different…? n n n Structured, disciplined, statistical problemsolving Dedication of varied resources to the task of continuous performance improvement of ANY process Voice of the Customer (VOC) drives the business and projects are aligned n Driven from a business perspective n Quantifiable, bottom-line returns - $ Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 $$ 24

Organizing for Six Sigma (Yellow/White) Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 25

Six Sigma Getting Started - Alignment The “Voice of the Customer” (VOC) & Policy Deployment "The best Six Sigma projects begin not inside the business but outside it, focused on answering the question - how can we make the customer more competitive? What is critical to the customer's success? …One thing we have discovered with certainty is that anything we do that makes the customer more successful inevitably results in a financial return for us. ” - Jack Welch, GE’s 1997 Annual Meeting Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 26

Six Sigma Translating the “Voice of the Customer” using QFD Once customer needs are identified, preparation of the product planning matrix or "house of quality“ can begin. Quality Function Deployment (QFD) is a structured approach to defining customer needs or requirements and translating them into specific plans to produce products to meet those requirements. The "voice of the customer" is the term to describe these stated and unstated customer needs or requirements. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 27

Six Sigma Translating the “Voice of the Customer” using QFD The House of Quality Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 28

Getting Started: Six Sigma and the Product Life Cycle Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 29

Lean Thinking n A focus on the removal of waste in every step/task/activity of a process. n Waste is defined as anything not necessary (nonvalue added) to produce a product or service. n Lean is a process improvement strategy that facilitates an organization’s ability to become highly responsive to customer demand while producing top quality products or services in the most efficient manner possible. The goal is to achieve perfection through the total elimination of waste in the value stream. Lean uses incremental improvement to constantly expose waste to balance operational and standard workflows. Most notable examples are the supply chains established by Toyota and Honda. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 30

Lean Thinking – “TIM WOODS” Eliminate anything which does not add value (8 Wastes): • • Transportation - unnecessary Inventory - obsolete Motion – unnecessary; layout Waiting - delays Overproduction – see inventory Over processing / over specifying Defects – scrap; rework… Skills – underutilization… Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 31

The Evolution of Six Sigma and Lean OR Six Sigma? Lean Six Sigma “Lean Sigma” Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 Kaizen Blitz 32

Integration of Six Sigma and Lean Leadership, Creativity, Innovation LEAN Focus on Improvement Knowledge of Tools • Waste, Non-Value Add • Speed, Cycle Time • Standardization • Inventory Reduction • Logistics Cost Reduction • Kaizen • Value Stream Mapping • Pull Systems / 1 -Piece Flow • Kanban / Work Cells • Visual Controls • 5 S • Setup Reduction • TPM SIX SIGMA • Complex Problems • Variation Reduction • Stability, Predictability • Process Capability • Defect Prevention • Design Excellence • Process Mapping • Statistical Methods • FMEA • MSA / Gage R&R • Cp & Cpk Analysis • ANOVA • DOE Closed Loop Performance Teaming and Employee Involvement Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 33

The Integration of Six Sigma and Lean Six Sigma Green Belt Certification Body of Knowledge B. Lean principles in the organization 1. Lean concepts and tools Define and describe concepts such as value chain, flow, pull, perfection, etc. , and tools commonly used to eliminate waste, including kaizen, 5 S, error -proofing, value-stream mapping, 2. Value-added & non-value-added activities Identify waste in terms of excess inventory, space, test inspection, rework, transportation, storage, etc. , and reduce cycle time to improve throughput. 3. Theory of constraints Describe theory of constraints. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 34

Lean Sigma What is the enemy of quality (productivity, efficiency…)? NON-VALUE ADDED WORK and EXCESS VARIATION ! Statistical Thinking Principles: 1. All work occurs in a system of interconnected processes 2. Variation (and waste) exists in all processes 3. Understanding and reducing variation are the keys to improvement and success All improvement comes from understanding THEN reducing WASTE and VARIATION in a process Lean Sigma A management philosophy and disciplined problem-solving METHODOLOGY backed by powerful tools using a crossfunctional team approach to eliminate waste and reduce service errors / process defects to 3. 4 ppm opportunities or less (“world -class”). Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 35

Lean Sigma: Expected Results Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 36

Is Lean Sigma for You? What are your company’s “brutal facts”? (specifically in the areas of quality, costs and delivery) Quality, price and delivery are controlled by process capability. Process capability is greatly limited by variation and waste. Decreasing process variation and non-value added work leads to a decrease in defects, costs and cycle time. Define a current problem (or opportunity) that needs the rigor of a Lean Sigma project… Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 37

Lean Sigma Project Cycle Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 38

Six Sigma: 6 More Info: www. isixsigma. com sixsigmaforum. com (ASQ) sixsigmaexchange. com industryweek. com Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 39

Six Sigma: 6 Opposing views: http: //money. cnn. com/2006/07/10/magazines/for tune/rule 4. fortune/index. htm http: //www 4. asq. org/blogs/financial-services-sixsigma/2007/01/even_the_wsj_doesnt_understand. html Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 40

“Progress” Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 41

Lean Sigma Case Study – Health Care n Project Selection “Cardiac catheterization labs represent a significant capital investment for many hospitals. Realizing a ROI is increasingly challenging, given the introduction of advanced technologies and limitations in reimbursement. To meet the challenges…hospitals are pursuing strategies such as Six Sigma and Lean techniques to improve throughput, maximize equipment utilization and increase efficiency. New York-Presbyterian Hospital recently embarked on a comprehensive initiative aimed at improving throughput in their cardiac catheterization labs…” - source: www. isixsigma. com Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 42

Lean Sigma Case Study – Health Care DEFINE Improving first case start time was selected as a project by the Children's Hospital of New York since it contributed to a significant amount of lost productivity. The failure of the first case to start on time was delaying subsequently scheduled cases and contributing to staff, physician and patient dissatisfaction. The Project Charter included: Business Case and Problem Statement – Baseline data indicated that 62 % of the first cases were not starting on time representing 267 hours of lost staff productivity and unused capacity annually. Project Scope –The start point of the cycle was patient's arrival at the hospital and the end point of the patient's entrance into the cath lab. The charter also described areas outside of the team's scope, such as lab turnaround time, which was the focus of another team. Goal Statement – A goal of 80 percent on-time starts was established. Team Members – The team for the project included the cath lab director, staff, cardiologists and anesthesiologists. The vice president of operations served as project sponsor and oversaw the work of the team. Timeline – A timeline including frequency of meetings, dates and times was agreed upon at the team's first meeting and proved essential to keeping the project on track. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 43

Cont’d Lean Sigma Case Study – Health Care DEFINE - The project charter provided the team with focus and direction. The team then developed a map describing the current process… The process map pointed to one opportunity for immediate improvement – streamlining the nursing assessment. Discussion during the process mapping revealed some redundancy in the information gathered during the phone call the day before and the nursing assessment completed the day of the procedure. The team agreed that initiating the nursing assessment during this phone call would eliminate duplicate data collection, and shorten the time needed to complete the assessment the day of the procedure. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 44

Lean Sigma Case Study – Health Care MEASURE The team brainstormed using a fishbone diagram to identify all the potential contributors to delaying the start of the first case. Some of the factors identified included: Patient arriving on time; Registration process; Transportation; Timeliness of patient prep; Completion of assessments by the cardiologist, anesthesiologist and nursing Data was then collected to identify those factors having the most significant impact on delaying the start of the first case. Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 45

Lean Sigma Case Study – Health Care ANALYZE Regression analysis, a statistical tool used to model and predict the relationship between variables, revealed that the time it took to complete the cardiology assessment was a key driver in whether the first case would be completed on time. The R-sq adjusted value showed that it accounted for about 60 percent of the variation in the process. Here is a table showing the “first case start” statistical analysis: Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 46

Lean Sigma Case Study – Health Care ANALYZE X Test Results Statistically Significant? Nurse Test for Equal Variances p=. 725 No Nurse Moods Median p=. 583 No Nurse Regression p=. 762 No Latest Assessment Time Moods Median p=. 432 No Latest Assessment Time Test for Equal Variances p=. 132 No Latest Assessment Time Regression p=. 177 No Anesthesia Yes/No Moods Median p=. 710 No Anesthesia Yes/No Test for Equal Variances p=. 318 No Oral Pre-Med Yes/No Test for Equal Variances p=. 981 No Oral Pre-Med Yes/No Moods Median p=. 288 No Anesthesiologist Moods Median p=. 389 No Anesthesiologist Test for Equal Variances p=. 013 Yes Anesthesiologist Regression p=. 625 No Patient Arrival Test for Equal Variances p=. 909 No Patient Arrival Moods Median p=. 615 No Difference vs. Card. Assessment Regression p=. 042 Yes Time Patient on Table vs. Card. Assessment Regression p=0. 00 Yes Difference vs. Anesthesia Yes/No Regression p=. 532 No Difference vs. Nursing Assessment Regression p=. 658 No Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 “highly” significant 47

Lean Sigma Case Study – Health Care IMPROVE The team used this information to discuss and develop plans to ensure the cardiology assessment could be completed in a timelier manner. For example, since the cardiologist typically initiates the cardiology assessment, the director of cardiology explored other responsibilities and obligations that might be interfering with timely completion of the assessment. As part of developing a revised process, the team also completed a new process flow map indicating a target completion time for each step in the process that ultimately would lead to the desired case start time. As shown in the table below, re-measurement of the process indicated a dramatic improvement in the number of first cases starting on time and a reduction in variation. Data Categories Baseline Data On-Time First Case Start Improve/Control Data 38 Percent 83 Percent 1. 44 2. 47 13 Minutes 0 Minutes Mean 38. 24 Minutes 6. 33 Minutes Standard Deviation 55. 62 Minutes 22. 4 Minutes Baseline Z Median Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 48

Lean Sigma Case Study – Health Care CONTROL “Process control mechanisms were implemented to ensure the changes could be sustained, and that the gains achieved from improvement activities would not be lost over time. The Control Plan outlined the procedure for monitoring the critical X (completion of cardiology assessment) as well as the number of on-time first case starts. Regular reporting to the project's executive sponsor reinforced the importance of the initiative and insured that changes would become imbedded into the organization's culture. ” Presented by: Joe Labas, ASQ CSSBB – May 8, 2008 49