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6 6  An Introduction by Habib Siddiqui, Ph.D.

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2 6 6  An Introduction by Habib Siddiqui, Ph.D.

3  6 US has lost 3.3 million manufacturing jobs during March 1998-2006 (before current recession) Supply chains have lowered inventories by $4.6 trillion dollars Inquiries on your computer and phone accounts are often answered by outside call centers Your tax return is completed in India Your Jet Blue reservation is taken by Betty in her house coat and slippers in Salt Lake City Dad’s X-rays are read overseas at 2:00 a.m. while you are sleeping US share of scientific papers in the Physical Review Letters fell from 61% in 1983 to 29% in 2003 Mama-papa stores are vanishing from neighborhoods at an exponential rate. 1/3 of the US population shop in the Wal-Mart every week (Wal-Mart has 60,000 suppliers) Downstream customers and retailers are placing increasing focus on what goes upstream: how things are made, framed, packaged, transported, displayed and sold worldwide (Life Cycle Thinking) “We need to be telling our kids to hurry up and eat and get to their homework - there are kids in China and India who are starving for our jobs” - Tom Friedman, The World is Flat Have you noticed ?…

4 Competitive Realities of Our Time The Global Business Climate Fierce, global competition Accelerating pace of change New technologies Increasing customer demands - performance, quality, price, “solutions, not products,”... Quality & Performance: Higher expectations Cost: Only low cost providers will survive Speed: Short product life cycles, “e-business” mindset 6

5  6 This sentiment is echoed by a CEO… © “As we envision the environment in which pharmaceutical companies will be operating in the years ahead, we believe – and we’re hardly alone in this belief – that those companies that develop competitively priced, novel medicines that demonstrably improve the health and wellbeing of patients will prosper.” - Raymond Gilmartin, Merck & Co. [March 1, 2004]

6  6 New Initiatives to Meet the Realities Speed e-Engineering, Lean Enterprise, Streamlining SC Cost Target Costing, Globalization Quality & Performance Six Sigma, Design for Six Sigma, Lean Enterprise, Stage Gate/NPI, Risk Management Lower Cost Better Quality Quicker to Market Higher Performance

7  6 “Eighty-five percent of the reasons for failure to meet customer expectations are related to deficiencies in systems and process… rather than the employee. The role of management is to change the process rather than badgering individuals to do better.” In other words, look for the root causes, not the “root who.” Why Six Sigma? – Dr Deming

8  6 Six sigma business improvement starts with SIPOC S uppliers I nputs (Business) P rocess (Process) O utputs (Critical) C ustomer (Requirements) Defects Variation in the output from what the customer wants causes defects. Root cause analysis leads to permanent elimination of defects USL LSL CCR Output * *

9  6  All processes have variability  All variability has causes  Typically only a few causes are significant  To the degree that those causes can be understood - they can be controlled  Designs must be robust to the effects of the process variation  This is true for products, processes, services, information transfer, that uncontrolled variation is the enemy The basic Six Sigma premise...

10  6 What is Six Sigma? © Six Sigma is a powerful set of statistical and management tools and methodologies that can create dramatic increases in customer satisfaction, productivity and shareholder value for both service and manufacturing companies/organizations. © It is a disciplined methodology of defining, measuring, analyzing, improving and controlling the quality in every product, process and transaction – with the ultimate goal of virtually eliminating all defects. (Jack Welch, GE)

11  6 History of Six Sigma © Motorola (mid-’80s) © GE – under Jack Welch (mid-’90s) © Others doing it: - Dow, Witco, DuPont, Rohm and Haas - Ford, GM - Johnson & Johnson, Merck (2000-01) - Maytag - Allied Signal (now Honeywell) - 3M, Kodak, Corning - any many others. © Average six sigma project saves $250M

12  6 Six Sigma: Philosophy The Motorola School: (Show me the “Defect”) Relentless Defect Elimination –Find and Remove Existing Defects –Prevent New Defects The GE School: (Show me the “$$$”) Relentless Pursuit of Financial Opportunities –Identify High Impact Projects –Use Six Sigma Methodology to Optimize the Process –Visible Bottom Line Impact

13  6 Defects & yield depend on –Spec width (design requirement) –Standard deviation (process variation) i.e., on the Sigma Level Reduce defects and improve yields by reducing process variation, i.e., by reducing std deviation and thereby raising the Sigma Level Reducing Defects and Improving Yield Target LSL  Defects = Area > USL Defects = Area < LSL USL

14  6 Six Sigma : Measurement Historic standard New standard 20,000x improvement

15  6 How Important is Quality? If your water heater operated at Four Sigma performance, you’d be without hot water more than 54 hours each year. At Six Sigma, you’d be without hot water less than two minutes a year! If your goal was 99% quality, you'd still have: © 15 minutes of unsafe drinking water every day © 2 unsafe plane landings per day at most major airports © 20,000 pieces of lost mail every hour © 200,000 wrong drug prescriptions per year © 5,000 incorrect surgical operations per week (Ref: Control Engineering, Jan. 1999)

16 Sigma Levels of Some Activities 1 10 100 1000 10000 100000 1000000 6

17  6 The Cost of Variation – Taguchi Loss Function Where LSL= lower spec limit Nom= target USL= upper spec limit Traditional View Cost LSL Nom USL Acceptable Correct View Cost LSL USL Nom CTQ Cost of Poor Quality – COPQ - consists of those costs which are generated as a result of producing defective material. COPQ is a financial measure of the user or customer’s dissatisfaction with a product’s performance as it deviates from a target value. $ COPQ = k (CTQ – Target) 2

18 Tangible Costs Inspection Scrap Rework Warranties Intangible Costs Lost Customers Longer Cycles The High Cost of Poor Quality Enormous opportunity Avg. US Co. World-Class Co 30% 25% 20% 10% 15% 5% 0% Sigma Level 65432 Cost of Poor Quality (% of Sales) 6

19  6 The Focus of Six Sigma: Fix Processes, Not Products Y Output Dep Variable Effect Symptom Monitor x 1, x 2, …, x n Inputs Indep Variables Root Causes Problems Fix & Control Output Y= f (Process Variables x 1, x 2, …, x n ) Many quality approaches focus on inspecting and fixing outputs (Y’s, e.g., products) Six Sigma focuses on fixing and controlling key process variables (x’s) which cause output defects

20  6 Six Sigma Methodology “Flavors” © Six Sigma Process Optimization (DMAIC)  Manufacturing process  Business/service/transaction process © Design for Six Sigma (DFSS)  new products  new processes  new services  Redesigning an existing product/service.

21  6 Six Sigma DMAIC Methodology D M I A C D efine what’s important M easure how we’re doing A nalyze what’s wrong Improve by fixing what’s wrong C ontrol to guarantee performance Six Sigma is information dependent.

22  6 The Six Sigma Filtering Effect Critical Input Variables 100+ Inputs 8 - 10 3 - 6 1 - 3 25 - 30 MEASURE ANALYZE IMPROVE CONTROL Capability Study Measurement Study C& E Matrix FMEA Multi-Vari Studies Design of Experiments (DOE) Control Plans Inexpensively, narrow in on fewer and fewer Variables, Saving more “expensive” tools for when we have fewer Variables Process Maps All Possible Variables Sustain-Certify Confirm Benefits $$ Output Y= f(x 1, x 2, …, x n )

23  6 Six Sigma Methodology Road Map  Process Maps  Cause and Effect Diagram (Fishbone)  Dot Plots  Box Plots  Scatter Plots  Control Charts  Process Capability  Measurement System Analysis  Gage R&R  P/T Ratio  Discrimination Ratio  Multiple Subjective Evaluation  Destructive Testing  Full Factorial DOE  Fractional Factorial DOE  Power and Sample Size  Taguchi DOE  Response Surface Modeling  EVOP  Attribute DOE  Mixture DOE  Solution Generation  Simulation  Piloting  TRIZ  Kaizen  Cause & Effect Matrix  Process / Product FMEA  Probability  Components of Variation  Multi-Vari Analysis  Hypothesis Testing  Confidence Intervals  Test for Equal Variance  Correlation  Linear Regression  Multiple Regression  One-Way ANOVA  Two-Way ANOVA  Chi Square  Binary Logistic Regress.  Non-Parametric Tests  Value Analysis  Theory of Constraints  Discrete Event Sim  Is-Is Not Analysis  Design FMEA  Process Control Plan  CUSUM Charts  EWMA Charts  Control Metric  ISD Procedures  COPS / 5S  Visual Factory  Poka-Yoke  Implementation Plan  PERT Diagrams  Communication Plan Define Measure Control Improve Analyze Sustain Certify  Effective Meetings  Opportunity Analysis  Cost of Poor Quality  Asset Utilization  Financial Analysis  SIPOCC  Brainstorming  Nominal Group Technique  Affinity Diagram  Check Sheets  Run Charts  Pareto  Histogram  Market Research  Survey  Focus Groups  Interviews  Kano Model  QFD  Decision Analysis Matrix  Impact-Effort Analysis  Project Charter  Gantt Charts  Project Translation  Documentation  Certification TOOLS: Statistical Non statistical

24  6 Six Sigma: Conceptual Approach Practical Problem Statistical Problem Statistical Solution Practical Solution

25  6 When to Use Six Sigma © You have a performance gap in a process  Driven by the Business strategy  High Impact on $$ or Customers  Problems that “have withstood the test of time”  Root Cause is unknown  Solution is not known © You want a more robust solution  In the past, solutions fail to “stick”

26  6 Let’s be smart about this… IF © You know the solution to your problem / opportunity © You don’t know the solution but you suspect others do © The impact solving the problem / opportunity is small or not strategically important. THEN © Implement the Solution. © Identify Best Practice and Implement. © Cancel the project

27  6 Daily Commute Problem © The employee John Doe lives nearly 25 miles away from his workplace. He likes to come to work at 8 a.m. His average commuting time is nearly 46 minutes and depends on many factors. He rides a car that gives him approx. 20 miles/gal. With high gasoline prices (costing ~$2.80/gal), he is reevaluating how to best optimize his time and miles/gal in commuting to work. © How can we help John Doe with his problem? Define

28  6 Variable N Mean Median TrMean StDev SE Mean Commute_ 100 45.720 46.000 45.711 7.716 0.772 Variable Minimum Maximum Q1 Q3 Commute_ 24.000 66.000 40.000 51.000 Define

29  6 What are the CTQs and COPQ? © CTQs: - Commuting time (defn: time between leaving home and arriving in office) - MPG © COPQ: - Internal: $100M/yr (lose job) plus price differential in gasoline purchase - External: $1MM (company) Define

30  6 What are the variables that affect these outcomes? © Where to start to identify variables? - Process Mapping © Process Mapping involves - Identify steps - Input variables - Types of input variables (controlled or uncontrolled) - Outputs Measure

31  6 Process Map Home Activities Car Start-up Activities Road Activities Parking/Arrival Measure

32  6 Which of these 31 variables are the potentially important ones? © How important are the CTQs?  Commute Time: what is desirable? – max. 50 min  MPG: what is desirable? – 30 mpg © How do we prioritize the input variables? - Use Cause & Effect matrix to find causal relationship. - Use a scale of 0 (no), 1 (minor), 3 (moderate) and 9 (major relationship). Measure

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35  6 Potentially important Input Variables are: Measure

36  6 Further narrow down of variables © Use Failure Modes & Effects Analysis (FMEA) © Rate for severity, S (1=min, 10=max) © Rate for occurrence, O (1=least, 10=most) © Rate for current control/detection, D (10=none, 1=certain detection) © Risk Priority Number, RPN = SxOxD Analyze

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41  6 Experiment with new procedure to check hypothesis Variable N Mean Median TrMean StDev SE Mean New_Time 100 35.390 36.000 35.456 4.788 0.479 Variable Minimum Maximum Q1 Q3 New_Time 24.000 46.000 32.000 38.000 Improve

42  6 nt

43  6 Gas Mileage per Gallon © Initial gas consumption = 20 mpg © Desire to get better mileage © Key input variables for MPG are - gasoline octane rating - tire pressure - driving speed © Full Factorial DOE with 3 variables @ 2 levels would require: 2x2x2 = 8 experimental runs. Improve

44  6 Problem: Gas Mileage is 20 mpg What conclusion do you make now? MPG = f(Speed, Octane, Tire Pressure) Do we think 32 is best? Full Factorial Experiment Improve

45  6 Control Plan © During rainy and winter seasons: leave home early, watch weather report before leaving home. © In normal times: Leave between 6:40-7 a.m. © Put office badge inside wallet © Take Freeway + Turnpike whenever possible © Check tire pressure upon arrival at home and before start-up © Go to sleep before 11 p.m., use alarm clock to get up on time. © Leave trash for pick-up on Sunday evening © For best mileage: ensure tire pressure @35 psig, drive @ 55 mph, and use 91 octane rating gasoline. Control


47  6 Cost Of Poor Quality over the product life cycle (from lab to customer) Most current six sigma effort is here R&D/ Discovery, Pre- clinical Pilot plant/ Clinical I-III Production/ Commercialization Customer Defects are: Difficult to see/predict Easy to fix Easy to see Costly to fix May lose customers We ought to do here We need a paradigm shift in how we do things: from reactive to predictive mode.

48  6 » Six Sigma Practices in Manufacturing is Not Enough » Cannot Produce a Six Sigma Product via Mfg Alone Up to 4.5  4.5  5  The “5  Wall” Achievable via Mfg Improvements Law of Diminishing Returns in Mfg Requires Product Designed for 6  DFSS Customers don’t care about the Mfg Processes Customers want Product Performance, Reliability & Durability Why DFSS? D-M-A-I-C has been around for over 10 years, but…

49  6 Underlying Truism: Knowing what the customer needs We don't know what we don't know, we can't act on what we don't know, we won't know until we search, we won't search for what we don't question, we won't question what we don't measure, and hence we just don't know.

50  6 Customer Service 101: Know what is “good” to your customer … (VOC) Ichiro Ishikawa: “When I ask the designer what is a good car, what is a good refrigerator and what is a good synthetic fiber, most of them cannot answer. It is obvious they cannot produce good products. You simply cannot design a good product or service if you do not know what “good” means to the customer. The designer must create a map that moves the world of customer to the world of the designer”

51  6 Customers: how important are they? Customer loyalty for strategic partners is very important. Keeping an old customer happy is more fruitful than finding a new one. ( It is easy to retain five satisfied customers than to find a new one.) © One happy customer tells three people, 1 unhappy customer tells 20.

52  6 Voice of the Customer Customer Satisfaction = f( Perception, Expectation) Level 1 : Features and Cost Level 2 : Quality Level 3: Features, Cost, Quality, Delivery,…. Value added 1 2 3 Customer value = Higher Quality Better Service x Lower Cost x Less Time

53  6 Kano Model (a 2-d concept of quality) Satisfied Dissatisfied Good performance Poor performance With Time

54  6 And we often do …. © Insane things. Insanity (definition): Continue to do things we have always done and yet expect to get different results.

55  6 A Specific Product Design Methodology where Customer Requirements Dictate the Critical Parameters and the Variability of the Critical Parameters are Optimized for Predictive Product Performance, Manufacturability, Reliability and Durability. Define Identify DesignOptimizeValidate Critical-To-Quality (CTQ) Parameters, Set Technical Requirements & Quality Targets Concept Design, Develop Transfer Functions between CTQ’s and Design Spec’s… Y=f(x) Analyze & Optimize for Robust Performance, Predictive Manufacturability, and Reliability Test & Validate Predictions, Assess Performance, Initial Capability Studies Marketing Science/Engg. Statistical Thinking Understand/Control Variations DFSS Overview: Alternative Roadmap Customer Requirements Based on Expectations, a.k.a: Voice of the Customer (VOC)

56  6 What’s Different About DFSS? © Disciplined, comprehensive process © Line of sight from customer CTQs to all design levels © Statistical design to understand and reduce variation © “New” tools: QFD, DOE, Robust Design, DFM, statistical tolerancing, multi-variable optimization,... © Quality prediction throughout development

57  6 Integrating DFSS with Stage-Gate/NPI Tollgates 1 2 3 4 5 NPI Stages DFSS Elements DFSS: Identify Design Optimize Validate Business needs Mkt reqts Alpha drawings Rev 1 drawings Rev 2 drawings CTQs Systems Eng Quality targets Updated scorecard: prediction, capability data Scorecard verification Product Z estimates Specs Detailed Design Prototype & Test Mfg Preparation Prediction Quality Design for Perf Product Marketing & Concept Design Design for Prod Customer CTQs QFD 2: Reqts to part specs CTQ estimates First piece CTQ verification Final system CTQ verification Tech feasibility assessment Alpha test performance verification Pilot run performance verification Mfg process concept Tolerancing Proc capability compatibility Tooling procurement Final mfg reqts Supplier qual Improved perf based on eng analysis & tests Improved transfer fcts: eng analysis & DOE System concept Functional block diagram Prototype confirmation Model validation System & component qualifications Initial Z allocations & scorecard: opinion, entitlement Prelim perf based on subsystem specs & initial tests Prelim mfg reqts Key process capability data CTQ flowdown Subsystem reqts & transfer functions Preliminary Design QFD 1: Customer CTQs to tech reqts Preliminary product specs Columns: Items required for tollgate review Columns: Items required for tollgate review Stage-Gate/NPI: Bus/mgt process for new product dev Rows: Progressive refinement of DFSS elements DFSS: Technical process - how product is designed

58 Lean

59  6 Chairman Cho of Toyota: Three Keys to Lean Leadership  Go See  “Senior Management must spend time on the front lines”  Ask Why  “Use the “Why?” technique daily”  Show Respect  “Respect your people.” Fujio Cho, Chairman of Toyota Motor Corp. Reproduced with permission of John Shook, Lean Enterprise Institute

60  6 Lean Methodology Specify Value Identify the Value Stream Pull Flow Perfection Reference: Lean Thinking, J. Womak and D. Jones, 1996 A Simple, Intuitive Approach!! Focus on Value from the eyes of the customer (VA and NVA activities) Eliminate waste (Muda) Reduce cycle time / increase speed In lean design – create flow and value for the customer / minimize waste

61  6 Value Add vs. Non-Value Add Type of Process StepDefinitionAction Value Add (VA)Transforms or shapes a product/service which is eventually sold to a customer Identify Non-Value Add (NVA)Take time, resources, or space but do not add value to the product or service Eliminate or redesign Business Value Add (BVA)Not seen as valuable by the customer, but required in order to complete the process Remediate or Continuously improve

62  6 What is Waste? © Activity that consumes resources without creating value for the customer © Unevenness in a process (“Hurry up and wait”) © Overburdening people or equipment

63  6 7 Types of Waste Processing Motion Defects Over- Production Transportation Inventory Waiting Time Types Of Waste Returns Incoming material rejects Multiple sign offs Over design of packaging Expired, unused promotional materials Unnecessary Warehousing Multiple handoffs Searching for information Multiple data bases Excess promotional material Over forecasting Slow decision making Late sales material TIMWOOD

64  6 Many Hand-offs Poor Quality Lack of Training Poor Access To Data Poor Scheduling Sea of Excess Transactions, Staffing, Capital Some Drivers of Waste

65  6 A Lean Roadmap Strategy Deployment ID & Select Key Processes Project Charterin g Understandi ng As-Is Plan Event / Design Future State Project Closure Link company strategy to lean initiatives Develop plan to identify key value streams to be addressed Determine the type of event and resources required Train participants Generate Current State Identify opportunities Create Future State Plan for improve- ments Track results Transfer knowledge Conduct events Drive for results Implement Improveme nts VOC VOP CM

66  6 Project Selection Guideline New Process/ Business/Product/Service? Improve Process/ Business/ Product/ Service by reducing standard deviation and shifting the mean? Reduce Waste in Process/Business/Product/Service? No Just do it! DFSS DMAIC Lean Yes Needs redesign because of reaching entitlement? Yes No

67  6 Which Problem Solving Method? – Another Look Lean Unacceptable time to get things done Large amounts of waste/wasted effort High, unexplained variation in key metrics Lack of optimal settings for process X’s DMAIC A current design that cannot achieve desired result No current design at all (clean sheet) DFSS If You Are Experiencing... You probably need...

68  6 Practical Statistics © The long-range contribution of statistics depends not so much upon getting a lot of statisticians into industry as it does in creating a statistically minded generation of physicists, chemists, engineers, and others who will have a hand in developing and directing the production processes of tomorrow. – W. A. Shewhart and W. E. Deming (1939)

69  6 Six Sigma … The right projects + The right people + The right roadmap and tools + The right support = The right results

70  6 Business Results of Using Six Sigma

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