Shainin: A concept for problem solving
|
|
- Julia Thomas
- 8 years ago
- Views:
Transcription
1 Shainin: A concept for problem solving Lecture at the Shainin conference Amelior 11 December 2009 Willy Vandenbrande 1
2 Dorian Shainin ( ) Aeronautical engineer (MIT 1936) Design Engineer for United Aircraft Corporations Mentored by his friend Joseph M. Juran Reliability consultant for Grumman Aerospace (Lunar Excursion Module) Reliability consultant for Pratt&Whitney (RL-10 rocket engine) Developed over 20 statistical engineering techniques for problem solving and reliability Started Shainin Consultants in 1984, his son Peter is current CEO. 2
3 Dorian Shainin and ASQ 15th ASQ Honorary Member (1996) First person to win all four major ASQ medals In 2004 ASQ created the Dorian Shainin Medal For outstanding use of unique or creative applications of statistical techniques in the solving of problems related to the quality of a product or service. 3
4 Dorian Shainin Not very well known outside USA (compared to Deming, Juran) 1991: Publication of first edition of World Class Quality by Keki Bothe 2000: Second edition (Keki and Adi Bothe) Books brought attention to Shainin methods, but are very biased. 4
5 Problem Solving Focus is on variation reduction LSL USL After Before LSL = Lower Specification Limit USL = Upper Specification Limit 5
6 Problem Solving But also LSL After Before 6
7 Basic Shainin assumption The pareto principle of vital few and trivial many. Only a few input variables are responsible for a large part of the output behavior. Red X TM Pink X TM Pale Pink X TM Problem solving becomes the hunt for the Red X TM 7
8 Shainin tools Recipe like methods / statistics in the background Comparing extremes allows easier detection of causes BOB Best of Best WOW Worst of Worse Non parametrics with ranking tests in stead of calculations with hypothesis tests Graphical Methods Working with small sample sizes The truth is in the parts, not in the drawing: let the parts talk! 8
9 Preliminary activities Define the critical output variable(s) to be improved (called problem Green Y ) Determine the quality of the Measurement System used to evaluate the Green Y A bad measurement system can in itself be responsible for excessive variation Improvements can only be seen if they can be measured 9
10 variables Clue generating Components Search Multi-Vari chart Paired Comparisons Product / Process Search Variables Search Full Factorials 5 20 variables 4 or less variables Formal Doe tools Validation B vs C No interactions Interactions Optimization Scatter Plots RSM methods Control Assurance Positrol Process Certification Ongoing control Precontrol Overview of Shainin tools 10 Source: World Class Quality 2nd edition
11 General comments Gradually narrowing down the search Clear logic Analyzing Improving Controlling Not all tools are Shainin tools What s in a name? Positrol versus Control Plan Process Certification versus Process Audit 11
12 Tool details Overview of methods More info on B vs C TM and Scatter Plots in workshops Some more detail on Multi-Vari chart Paired Comparison TM and Product/Process Search Pre Control 12
13 Clue Generating / Multi-Vari Chart Objective Application Understand the pattern of variation Define areas where not to look for problems Allow a more specific brainstorm Problem type: excess variation Wide applicability Principles Sample Size Divide total variation in categories Search for causes of variation in the biggest category first Samples taken in production on current process Could be a big measurement investment Comments Very useful tool and best applied before brainstorming causes on excess variation 13
14 Multi-Vari Chart Breakdown of variation in 3 families: Positional (within piece, between cavities, ) Cyclical (consecutive units, batch-to-batch, lot-tolot) Temporal (hour-to-hour, shift-to-shift, ) 14
15 Multi-vari Chart If one family of variation contains a large part of total variation, we can concentrate on investigating variables related to this family of variation. 15
16 Clue Generating / Component Search TM Objective Find the component(s) of an assembly that is (are) responsible for bad behavior Application Principles Sample Size Problem type: assembly does not perform to spec Limitation: Disassembly / Reassembly must be possible without product change Select BOB and WOW unit Exchange components and observe behavior. Components that change behavior are Red X comp 2 = 1 BOB and 1 WOW Comments Disassembly / reassembly requirement limits application. 16
17 Clue Generating / Paired Comparison TM Objective Find directions for further investigation Application Problem type: occasional problems in production flow Principles Sample Size Select pairs of BOB and WOW units Look for differences Consistent differences to be investigated further 5 to 6 pairs of 1 BOB and 1 WOW Comments Practical application of let the parts talk 17
18 Paired Comparisons TM : method Step 1: take 1 good and 1 bad unit As close as possible in time Aim for BOB and WOW units Step 2: note the differences between these units (visual, dimensional, mechanical, chemical, ). Let the parts talk! Step 3: take a second pair of good and bad units. Repeat step 2 18
19 Paired Comparisons TM : method Step 4: repeat this process with third, fourth, fith, pair until a pattern of differences becomes apparent. Step 5: don t take inconsistent differences into account. Generally after the fith or sixth pair the consistent differences that cause the variation become clear. 19
20 Clue Generating / Product/Process Search Objective Preselection of variables out of a large group of potential variables Application Problem type: Various types of problems Principles Sample Size Select sets of BOB and WOW units batches -.. Add product data / process parameters and rank Apply Tukey test to determine important parameters 8 BOB and 8 WOW units / batches Comments Tukey test is alternative for t-test Widely applicable method Problem: available data (process parameters) 20
21 Product/Process Search: example Transmission assemblies rejected for noise. Components search shows idler shaft as responsible component One of the parameters of idler shaft is out of round 8 good / 8 bad units selected and measured for out of round 21
22 Product/Process search: example Out of round good units (mm) Out of round bad units (mm)
23 Tukey test procedure Rank individual units by parameter and indicate Good / Bad. Count number of all good or all bad from one side and vice versa from other side. Make sum of both counts. Determine confidence level to evaluate significance. 23
24 Tukey test confidence levels Total end count Confidence 90% 95% 99% 99.9% 24
25 Tukey test: example Good Bad Top end count (all good) 4 Overlap region Bottom end count (all bad) 3 25
26 Tukey test: example Total end count = = 7 95 % confidence that out-of-round idler shaft is important in explaining the difference in noise levels. 26
27 Formal Doe tools / Variables Search Objective Application Principles Sample Size Determine Red X TM, Pink X TM including quantification of their effect Problem type: Various types of problems After clue generating more then 4 potential variables left List variables in order of criticality (process knowledge) and indicate good / bad level. Swap factor settings and observe behavior. Factors that change behavior (and interactions) are red X TM, Pink X TM Number of tests is determined by number of variables and quality of ordering. Comments Alternative to fractional factorials on two levels Method comparable to components search 27
28 Formal Doe tools / Full Factorials Objective Application Determine Red X TM, Pink X TM including quantification of their effect Problem type: Various types of problems After clue generating 4 or less variables left Principles Classical DOE with Full Factorials at two levels Main Effects and interactions are calculated Sample Size Number of tests is determined by number of variables k (2 k test combinations) Comments Well established method 28
29 Formal Doe tools / B(etter) vs C(urrent) TM Objective Application Validation of Red X TM, Pink X TM Problem type: Various types of problems Principles Create new process using optimum settings and compare optimum with current. Sample Size Comments 3 B and 3 C tests (each test can involve several units test of variation reduction) All 3B s must be better than all 3C s Quick validation that works well with big improvements 29
30 Optimization / Scatter Plots Objective Application Principles Fine tune best level and realistic tolerance for Red X TM, Pink X TM if no interactions are present Problem type: Variation Reduction and optimizing signal Do tests around optimum and use graphical regression to set tolerance Sample Size Comments 30 tests for each critical variable Graphical method that could easily be transformed to a statistical method 30
31 Optimization / Response Surface Methods Objective Application Principles Sample Size Comments Fine tune best level and realistic tolerance for Red X TM, Pink X TM if interactions are present Problem type: Variation Reduction and optimizing signal Evolutionary Operation (EVOP) to scan response surface in direction of steepest ascent Depends on variables and surface. Method developed by George Box 31
32 EVOP example 32
33 Control / Positrol Objective Application Principles Assuring that optimum settings are kept Problem type: all types Table of What, How, Who, Where and When control has to be exercised. Sample Size Comments Checking frequency in the When column Can be compared with a Control Plan 33
34 Control / Process Certification Objective Application Principles Eliminating peripheral causes of poor quality Problem type: all types Make overview of things that could influence the process and install inspections, audits, Sample Size Comments Checking frequency to be determined Mix of 5S, Poka-Yoke, instructions, ISO 9000, audits, 34
35 Control / Pre Control Objective Application Continuous checking of the quality of the process output Problem type: control variation and setting of the process Principles Sample Size Divide total tolerance in colored zones and use prescribed sampling and rules to control the process. Checking frequency to be determined Comments Alternative to classical SPC Traffic lights system Very practical method 35
36 Pre-Control: chart construction USL 1/4 TOL 1/4 TOL ½TOL TARGET LSL 36
37 Pre-control: use of chart 1. Start process: five consecutive units in green needed as validation of set-up. 2. If not possible: improve process. 3. In production: 2 consecutive units 4. Frequency: time interval between two stoppages (see action rules) / 6. 37
38 Pre-control: action rules 2 units in same yellow zone 2 units in different yellow zone 1 unit in red zone Result of samples 2 units in green zone 1 unit in green and 1 unit in yellow zone Action Continue Continue Correct Stop and act Stop and act After an intervention: 5 consecutive units in green zone 38
39 Pre-control: example Start Correct Start Time 39
40 QS Consult Willy Vandenbrande, Master TQM ASQ Fellow - Six Sigma Black Belt Montpellier 34 B Brugge België - Belgium Tel + 32 (0) willy@qsconsult.be Website Willy Vandenbrande 40
Unit 1: Introduction to Quality Management
Unit 1: Introduction to Quality Management Definition & Dimensions of Quality Quality Control vs Quality Assurance Small-Q vs Big-Q & Evolution of Quality Movement Total Quality Management (TQM) & its
More informationSix Sigma. Breakthrough Strategy or Your Worse Nightmare? Jeffrey T. Gotro, Ph.D. Director of Research & Development Ablestik Laboratories
Six Sigma Breakthrough Strategy or Your Worse Nightmare? Jeffrey T. Gotro, Ph.D. Director of Research & Development Ablestik Laboratories Agenda What is Six Sigma? What are the challenges? What are the
More informationCommon Tools for Displaying and Communicating Data for Process Improvement
Common Tools for Displaying and Communicating Data for Process Improvement Packet includes: Tool Use Page # Box and Whisker Plot Check Sheet Control Chart Histogram Pareto Diagram Run Chart Scatter Plot
More informationTHIS PAGE WAS LEFT BLANK INTENTIONALLY
SAMPLE EXAMINATION The purpose of the following sample examination is to present an example of what is provided on exam day by ASQ, complete with the same instructions that are given on exam day. The test
More informationLean Six Sigma Black Belt-EngineRoom
Lean Six Sigma Black Belt-EngineRoom Course Content and Outline Total Estimated Hours: 140.65 *Course includes choice of software: EngineRoom (included for free), Minitab (must purchase separately) or
More informationQUIZ MODULE 1: BASIC CONCEPTS IN QUALITY AND TQM
QUIZ MODULE 1: BASIC CONCEPTS IN QUALITY AND TQM These questions cover Sessions 1, 2, 5, 6, 7. The correct answer is shown in bold A fundamental attribute of TQM is Drawing control charts Having team meetings
More informationAmerican Society for Quality (ASQ) CERTIFIED SIX SIGMA GREEN BELT (CSSGB) BODY OF KNOWLEDGE 2014
American Society for Quality (ASQ) CERTIFIED SIX SIGMA GREEN BELT (CSSGB) BODY OF KNOWLEDGE 2014 Included in this body of knowledge (BOK) are explanations (subtext) and cognitive levels for each topic
More informationInstruction Manual for SPC for MS Excel V3.0
Frequency Business Process Improvement 281-304-9504 20314 Lakeland Falls www.spcforexcel.com Cypress, TX 77433 Instruction Manual for SPC for MS Excel V3.0 35 30 25 LSL=60 Nominal=70 Capability Analysis
More informationHow To Run Statistical Tests in Excel
How To Run Statistical Tests in Excel Microsoft Excel is your best tool for storing and manipulating data, calculating basic descriptive statistics such as means and standard deviations, and conducting
More informationLearning Objectives Lean Six Sigma Black Belt Course
Learning Objectives Lean Six Sigma Black Belt Course The overarching learning objective of this course is to develop a comprehensive set of skills that will allow you to function effectively as a Six Sigma
More informationCourse Overview Lean Six Sigma Green Belt
Course Overview Lean Six Sigma Green Belt Summary and Objectives This Six Sigma Green Belt course is comprised of 11 separate sessions. Each session is a collection of related lessons and includes an interactive
More informationTotal Quality Management and Cost of Quality
Total Quality Management and Cost of Quality Evsevios Hadjicostas The International Quality movement Operator Quality Control Foreman (Supervisor) Quality Control Full-time Inspectors Statistical Quality
More informationCopyright 2010-2012 PEOPLECERT Int. Ltd and IASSC
PEOPLECERT - Personnel Certification Body 3 Korai st., 105 64 Athens, Greece, Tel.: +30 210 372 9100, Fax: +30 210 372 9101, e-mail: info@peoplecert.org, www.peoplecert.org Copyright 2010-2012 PEOPLECERT
More informationVisualizing Data. Contents. 1 Visualizing Data. Anthony Tanbakuchi Department of Mathematics Pima Community College. Introductory Statistics Lectures
Introductory Statistics Lectures Visualizing Data Descriptive Statistics I Department of Mathematics Pima Community College Redistribution of this material is prohibited without written permission of the
More informationSix Sigma Acronyms. 2-1 Do Not Reprint without permission of
Six Sigma Acronyms $k Thousands of dollars $M Millions of dollars % R & R Gauge % Repeatability and Reproducibility ANOVA Analysis of Variance AOP Annual Operating Plan BB Black Belt C & E Cause and Effects
More informationThe Importance of Project Quality Management. What Is Project Quality? The International Organization for Standardization (ISO)
Chapter 8 Project Quality Management November 17, 2008 2 The Importance of Project Quality Management Many people joke about the poor quality of IT products People seem to accept systems being down occasionally
More informationCapability Analysis Using Statgraphics Centurion
Capability Analysis Using Statgraphics Centurion Neil W. Polhemus, CTO, StatPoint Technologies, Inc. Copyright 2011 by StatPoint Technologies, Inc. Web site: www.statgraphics.com Outline Definition of
More informationSimple Predictive Analytics Curtis Seare
Using Excel to Solve Business Problems: Simple Predictive Analytics Curtis Seare Copyright: Vault Analytics July 2010 Contents Section I: Background Information Why use Predictive Analytics? How to use
More informationFour Key Elements of an Effective Continuous Process Advantage Series White Paper By Jeff Gotro, Ph.D., CMC
Four Key Elements of an Effective Continuous Process Advantage Series White Paper By Jeff Gotro, Ph.D., CMC Introduction Tough times call for bold actions. The manufacturing sector is going through a challenging
More informationLearning Six Sigma Theory
Independent Learning Pursuit FX Competency The following essay was written by a student in the School for New Learning, in support of an Independent Learning Pursuit (ILP). The student has agreed to share
More informationPROJECT QUALITY MANAGEMENT
8 PROJECT QUALITY MANAGEMENT Project Quality Management includes the processes required to ensure that the project will satisfy the needs for which it was undertaken. It includes all activities of the
More informationProjects Involving Statistics (& SPSS)
Projects Involving Statistics (& SPSS) Academic Skills Advice Starting a project which involves using statistics can feel confusing as there seems to be many different things you can do (charts, graphs,
More informationHow To Check For Differences In The One Way Anova
MINITAB ASSISTANT WHITE PAPER This paper explains the research conducted by Minitab statisticians to develop the methods and data checks used in the Assistant in Minitab 17 Statistical Software. One-Way
More informationTRAINING TITLE: CAPA System Expert Certification (CERT-003)
TRAINING TITLE: CAPA System Expert Certification (CERT-003) OVERVIEW: Medical devices, biopharmaceutical, and traditional drug manufacturing companies devote an important part of their resources dealing
More informationSix Sigma Applications in Healthcare. Muder Alkrisat PhD, MSN, RN, CSSBB, CSHA, HACP Director of Clinical Process Improvement
Six Sigma Applications in Healthcare Muder Alkrisat PhD, MSN, RN, CSSBB, CSHA, HACP Director of Clinical Process Improvement Insanity is continuing to do things the way you ve always done them and expecting
More informationUniversally Accepted Lean Six Sigma Body of Knowledge for Green Belts
Universally Accepted Lean Six Sigma Body of Knowledge for Green Belts The IASSC Certified Green Belt Exam was developed and constructed based on the topics within the body of knowledge listed here. Questions
More informationStatistical Process Control OPRE 6364 1
Statistical Process Control OPRE 6364 1 Statistical QA Approaches Statistical process control (SPC) Monitors production process to prevent poor quality Acceptance sampling Inspects random sample of product
More informationINTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA)
INTERPRETING THE ONE-WAY ANALYSIS OF VARIANCE (ANOVA) As with other parametric statistics, we begin the one-way ANOVA with a test of the underlying assumptions. Our first assumption is the assumption of
More informationApplied Business Improvement. Introduction to ABI and Statgraphics Centurion
Introduction to ABI and Statgraphics Centurion 1 Nobody gives a hoot about profits. Deming Successful Businesses Disagree.. Statgraphics Centurion TM and ABI can HELP your business INCREASE PROFIT Statgraphics
More informationComparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3)
Comparison of EngineRoom (6.0) with Minitab (16) and Quality Companion (3) What is EngineRoom? A Microsoft Excel add in A suite of powerful, simple to use Lean and Six Sigma data analysis tools Built for
More informationI/A Series Information Suite AIM*SPC Statistical Process Control
I/A Series Information Suite AIM*SPC Statistical Process Control PSS 21S-6C3 B3 QUALITY PRODUCTIVITY SQC SPC TQC y y y y y y y y yy y y y yy s y yy s sss s ss s s ssss ss sssss $ QIP JIT INTRODUCTION AIM*SPC
More informationControl Charts - SigmaXL Version 6.1
Control Charts - SigmaXL Version 6.1 Control Charts: Overview Summary Report on Test for Special Causes Individuals & Moving Range Charts Use Historical Groups to Display Before VS After Improvement X-Bar
More informationMG1352 TOTAL QUALITY MANAGENMENT UNIT I INTRODUCTION PART-A
MG1352 TOTAL QUALITY MANAGENMENT UNIT I INTRODUCTION 1. Define Quality. 2. What are the dimensions of quality? 3. Why quality planning is needed? 4. What are the essential steps of quality planning? 5.
More informationCertified Quality Improvement Associate
Certified Quality Improvement Associate Quality excellence to enhance your career and boost your organization s bottom line asq.org/certification The Global Voice of Quality TM Certification from ASQ is
More informationLean Specialist Certification Program
Lean Specialist Certification Program ADVANCED INNOVATION GROUP PRO EXCELLENCE Lean Specialist Certification Program Certification from AIGPE has upheld the highest standards in the field of quality excellence
More informationCase Study Call Centre Hypothesis Testing
is often thought of as an advanced Six Sigma tool but it is a very useful technique with many applications and in many cases it can be quite simple to use. Hypothesis tests are used to make comparisons
More informationBody of Knowledge for Six Sigma Green Belt
Body of Knowledge for Six Sigma Green Belt What to Prepare For: The following is the Six Sigma Green Belt Certification Body of Knowledge that the exam will cover. We strongly encourage you to study and
More informationSoftware Quality. Unit 2. Advanced techniques
Software Quality Unit 2. Advanced techniques Index 1. Statistical techniques: Statistical process control, variable control charts and control chart for attributes. 2. Advanced techniques: Quality function
More informationDMAIC PHASE REVIEW CHECKLIST
Project Name Project Lead Champion Kick-Off Date: _mm / dd / yyyy Project CTQ & Target D-M-A-I-C: DEFINE Project Identification: Big Y linkage identified Customer(s) & Customer type identified Voice of
More informationMinitab Tutorials for Design and Analysis of Experiments. Table of Contents
Table of Contents Introduction to Minitab...2 Example 1 One-Way ANOVA...3 Determining Sample Size in One-way ANOVA...8 Example 2 Two-factor Factorial Design...9 Example 3: Randomized Complete Block Design...14
More informationCERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE
CERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE The topics in this Body of Knowledge include subtext explanations and the cognitive level at which the questions will be written. This information will
More informationTHE SIX SIGMA BLACK BELT PRIMER
INTRO-1 (1) THE SIX SIGMA BLACK BELT PRIMER by Quality Council of Indiana - All rights reserved Fourth Edition - September, 2014 Quality Council of Indiana 602 West Paris Avenue West Terre Haute, IN 47885
More informationChange-Point Analysis: A Powerful New Tool For Detecting Changes
Change-Point Analysis: A Powerful New Tool For Detecting Changes WAYNE A. TAYLOR Baxter Healthcare Corporation, Round Lake, IL 60073 Change-point analysis is a powerful new tool for determining whether
More informationQDA Q-Management A S I D A T A M Y T E S P E C S H E E T. From stand-alone applications to integrated solutions. Process optimization tool
QDA Q-Management Q-Management is the powerful base software package within ASI DATAMYTE s QDA suite that facilitates achievement and verification of quality goals such as process control, cost reduction,
More informationThe Turning of JMP Software into a Semiconductor Analysis Software Product:
The Turning of JMP Software into a Semiconductor Analysis Software Product: The Implementation and Rollout of JMP Software within Freescale Semiconductor Inc. Jim Nelson, Manager IT, Yield Management Systems
More informationINTELLIGENT DEFECT ANALYSIS SOFTWARE
INTELLIGENT DEFECT ANALYSIS SOFTWARE Website: http://www.siglaz.com Semiconductor fabs currently use defect count or defect density as a triggering mechanism for their Statistical Process Control. However,
More informationSCHMIDT ManualPress 300 Series Manual Presses with Process Monitoring
Manual Presses with Process Monitoring Process reliability, force/stroke monitoring of the joining process and EN ISO- compatible documentation of the results are becoming the major factors for small and
More informationBasic Tools for Process Improvement
What is a Histogram? A Histogram is a vertical bar chart that depicts the distribution of a set of data. Unlike Run Charts or Control Charts, which are discussed in other modules, a Histogram does not
More informationSTUDY GUIDE FOR THE LEAN SIX SIGMA (LSS) CERTIFICATION EXAM
STUDY GUIDE FOR THE LEAN SIX SIGMA (LSS) CERTIFICATION EXAM LSSYB LSSGB LSSBB ATMAE ATMAE ATMAE NOTE: An individual can become lean six sigma black belt certified by earning an 80% or higher on this exam.
More informationContinuous Improvement Toolkit
Continuous Improvement Toolkit Mind Mapping Managing Risk PDPC Pros and Cons Importance-Urgency Mapping RACI Matrix Stakeholder Analysis FMEA RAID Logs Break-even Analysis Cost Benefit Analysis PEST PERT/CPM
More informationIntroduction to Quality Systems
Introduction to Quality Systems An NTMA Technology Team Member Training Program Intro to Quality Quality systems are methodologies in which a manufacturer must establish and follow a system to help ensure
More informationSimple Data Analysis Techniques
Simple Data Analysis Techniques Three of the most common charts used for data analysis are pie, Pareto and trend charts. These are often linked together in a data trail. Pie Charts Pie charts provide a
More informationSimulation and Lean Six Sigma
Hilary Emmett, 22 August 2007 Improve the quality of your critical business decisions Agenda Simulation and Lean Six Sigma What is Monte Carlo Simulation? Loan Process Example Inventory Optimization Example
More informationTABLE OF CONTENTS. About Chi Squares... 1. What is a CHI SQUARE?... 1. Chi Squares... 1. Hypothesis Testing with Chi Squares... 2
About Chi Squares TABLE OF CONTENTS About Chi Squares... 1 What is a CHI SQUARE?... 1 Chi Squares... 1 Goodness of fit test (One-way χ 2 )... 1 Test of Independence (Two-way χ 2 )... 2 Hypothesis Testing
More informationSPC Demonstration Tips
Tip Sheet SPC Demonstration Tips Key Points to Cover When Demonstrating Ignition SPC Downtime In general, the SPC Module is designed with a great level of flexibility to support a wide variety of production
More information1 Variation control in the context of software engineering involves controlling variation in the
1 Variation control in the context of software engineering involves controlling variation in the A) process applied B) resources expended C) product quality attributes D) all of the above 2 There is no
More informationTHE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK
THE CERTIFIED SIX SIGMA BLACK BELT HANDBOOK SECOND EDITION T. M. Kubiak Donald W. Benbow ASQ Quality Press Milwaukee, Wisconsin Table of Contents list of Figures and Tables Preface to the Second Edition
More informationMultiplexer Software. www.elcometer.com. Multiplexer Software. Dataputer DATA-XL Software
Multiplexer Software Multiplexer Software There are two ways that data can be collected Electronically, where there is no human intervention, and Manually, where data is collected by the User with the
More informationAPPENDIX E THE ASSESSMENT PHASE OF THE DATA LIFE CYCLE
APPENDIX E THE ASSESSMENT PHASE OF THE DATA LIFE CYCLE The assessment phase of the Data Life Cycle includes verification and validation of the survey data and assessment of quality of the data. Data verification
More informationFINAL DOCUMENT. Quality Management Systems - Process Validation Guidance. The Global Harmonization Task Force
GHTF/SG3/N99-10:2004 (Edition 2) FINAL DOCUMENT Title: Quality Management Systems - Process Validation Guidance Authoring Group: Endorsed by: SG3 The Global Harmonization Task Force Date: Edition 2 - January
More informationCERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE
CERTIFIED QUALITY ENGINEER (CQE) BODY OF KNOWLEDGE The topics in this Body of Knowledge include subtext explanations and the cognitive level at which the questions will be written. This information will
More informationSix Sigma in Action. Data-driven process improvement. Process Improvement Brief February 2015 www.datamark.net
Six Sigma in Action Data-driven process improvement Process Improvement Brief February 2015 www.datamark.net Six Sigma Methodology Applied to clients business processes at our U.S. and offshore sites,
More informationWhy Is EngineRoom the Right Choice? 1. Cuts the Cost of Calculation
What is EngineRoom? - A Web based data analysis application with an intuitive, drag-and-drop graphical interface. - A suite of powerful, simple-to-use Lean and Six Sigma data analysis tools that you can
More informationbusiness statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar
business statistics using Excel Glyn Davis & Branko Pecar OXFORD UNIVERSITY PRESS Detailed contents Introduction to Microsoft Excel 2003 Overview Learning Objectives 1.1 Introduction to Microsoft Excel
More informationKSTAT MINI-MANUAL. Decision Sciences 434 Kellogg Graduate School of Management
KSTAT MINI-MANUAL Decision Sciences 434 Kellogg Graduate School of Management Kstat is a set of macros added to Excel and it will enable you to do the statistics required for this course very easily. To
More informationQuality Concepts. 1.1 Introduction. 1.2 Quality and Reliability Defined
1 Quality Concepts 1.1 Introduction Quality is perceived differently by different people. Yet, everyone understands what is meant by quality. In a manufactured product, the customer as a user recognizes
More informationCertified Six Sigma Yellow Belt
Certified Six Sigma Yellow Belt Quality excellence to enhance your career and boost your organization s bottom line asq.org/cert The Global Voice of Quality TM Certification from ASQ is considered a mark
More informationCHAPTER 1 THE CERTIFIED QUALITY ENGINEER EXAM. 1.0 The Exam. 2.0 Suggestions for Study. 3.0 CQE Examination Content. Where shall I begin your majesty?
QReview 1 CHAPTER 1 THE CERTIFIED QUALITY ENGINEER EXAM 1.0 The Exam 2.0 Suggestions for Study 3.0 CQE Examination Content Where shall I begin your majesty? The White Rabbit Begin at the beginning, and
More informationAachen Summer Simulation Seminar 2014
Aachen Summer Simulation Seminar 2014 Lecture 07 Input Modelling + Experimentation + Output Analysis Peer-Olaf Siebers pos@cs.nott.ac.uk Motivation 1. Input modelling Improve the understanding about how
More informationWhat you measure is what you get? a novel approach for specifying and controlling acoustic quality of road surfaces
What you measure is what you get? a novel approach for specifying and controlling acoustic quality of road surfaces Ard Kuijpers & Wout Schwanen M+P consulting engineers, Vught, the Netherlands. Jan van
More informationLean Six Sigma Black Belt Body of Knowledge
General Lean Six Sigma Defined UN Describe Nature and purpose of Lean Six Sigma Integration of Lean and Six Sigma UN Compare and contrast focus and approaches (Process Velocity and Quality) Y=f(X) Input
More informationt Tests in Excel The Excel Statistical Master By Mark Harmon Copyright 2011 Mark Harmon
t-tests in Excel By Mark Harmon Copyright 2011 Mark Harmon No part of this publication may be reproduced or distributed without the express permission of the author. mark@excelmasterseries.com www.excelmasterseries.com
More informationLean Six Sigma Analyze Phase Introduction. TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY
TECH 50800 QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY Before we begin: Turn on the sound on your computer. There is audio to accompany this presentation. Audio will accompany most of the online
More informationDemo - Sales Force. Cockpit
The Sales Force integration with Memo allows your sales team to obtain information about the status of every opportunity and new leads for the new business. Memo allows access, sharing and integration
More informationAdverse Impact Ratio for Females (0/ 1) = 0 (5/ 17) = 0.2941 Adverse impact as defined by the 4/5ths rule was not found in the above data.
1 of 9 12/8/2014 12:57 PM (an On-Line Internet based application) Instructions: Please fill out the information into the form below. Once you have entered your data below, you may select the types of analysis
More informationpm4dev, 2008 management for development series Project Quality Management PROJECT MANAGEMENT FOR DEVELOPMENT ORGANIZATIONS
pm4dev, 2008 management for development series Project Quality Management PROJECT MANAGEMENT FOR DEVELOPMENT ORGANIZATIONS PROJECT MANAGEMENT FOR DEVELOPMENT ORGANIZATIONS A methodology to manage development
More informationTHE USE OF STATISTICAL PROCESS CONTROL IN PHARMACEUTICALS INDUSTRY
THE USE OF STATISTICAL PROCESS CONTROL IN PHARMACEUTICALS INDUSTRY Alexandru-Mihnea SPIRIDONICĂ 1 E-mail: aspiridonica@iota.ee.tuiasi.ro Abstract The use of statistical process control has gained a major
More informationTAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW. Resit Unal. Edwin B. Dean
TAGUCHI APPROACH TO DESIGN OPTIMIZATION FOR QUALITY AND COST: AN OVERVIEW Resit Unal Edwin B. Dean INTRODUCTION Calibrations to existing cost of doing business in space indicate that to establish human
More informationBody of Knowledge for Six Sigma Lean Sensei
Body of Knowledge for Six Sigma Lean Sensei What to Prepare For: The following is the Lean Six Sigma Certification Body of Knowledge that the exam will cover. We strongly encourage you to study and prepare
More informationIPB-IB ELECTRIC ACTUATORS FOR INDUSTRIAL PROCESS CONTROL INDUSTRIAL STEAM BOILERS. BECK VIDEO Scan w/ Smartphone
IPB-IB R ELECTRIC ACTUATORS FOR INDUSTRIAL PROCESS CONTROL INDUSTRIAL STEAM BOILERS BECK VIDEO Scan w/ Smartphone 1 Increasing Business Pressures Necessitate Boiler Control Improvements Today s industrial
More informationBasic Testing Concepts and Terminology
T-76.5613 Software Testing and Quality Assurance Lecture 2, 13.9.2006 Basic Testing Concepts and Terminology Juha Itkonen SoberIT Contents Realities and principles of Testing terminology and basic concepts
More informationSimple Random Sampling
Source: Frerichs, R.R. Rapid Surveys (unpublished), 2008. NOT FOR COMMERCIAL DISTRIBUTION 3 Simple Random Sampling 3.1 INTRODUCTION Everyone mentions simple random sampling, but few use this method for
More informationSmartDiagnostics Application Note Wireless Interference
SmartDiagnostics Application Note Wireless Interference Publication Date: May 27, 2015 KCF Technologies, Inc. Background The SmartDiagnostics wireless network is an easy to install, end-to-end machine
More informationCertified Quality Engineer
Certified Quality Engineer Quality excellence to enhance your career and boost your organization s bottom line asqmena.org/certification.php www.asqmena.org Certification from ASQ is considered a mark
More informationStatistical Process Control (SPC) Training Guide
Statistical Process Control (SPC) Training Guide Rev X05, 09/2013 What is data? Data is factual information (as measurements or statistics) used as a basic for reasoning, discussion or calculation. (Merriam-Webster
More informationAdditional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm
Mgt 540 Research Methods Data Analysis 1 Additional sources Compilation of sources: http://lrs.ed.uiuc.edu/tseportal/datacollectionmethodologies/jin-tselink/tselink.htm http://web.utk.edu/~dap/random/order/start.htm
More informationMeasurLink V7. Measurement Data Network System. MeasurLink provides total support from production line to production management and beyond.
Small Tool Instruments and Data Management Measurement Data Network System MeasurLink V7 Catalog No. E12028 MeasurLink provides total support from production line to production management and beyond. MeasurLink
More informationSix Sigma Application in Health Care
Six Sigma Application in Health Care Expediting Nursing Home Discharges in a Community Hospital Long Island Chapter of American Society for Quality Carolyn Sweetapple, R.N., C.P.A. Six Sigma Master Black
More informationIntroduction to Statistical Quality Control, 5 th edition. Douglas C. Montgomery Arizona State University
Introduction to Statistical Quality Control, 5 th edition Douglas C. Montgomery Arizona State University 3 Learning Objectives 4 1-1 Definitions and Meaning of Quality 1-1.1 The Eight Dimensions of Quality
More informationJump Start: Aspen HYSYS Dynamics V7.3
A Brief Tutorial (and supplement to training and online documentation) Glenn Dissinger, Product Director, Aspen Technology, Inc. Julie Levine, Associate Product Marketing Professional, Aspen Technology,
More informationWhy Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization. Learning Goals. GENOME 560, Spring 2012
Why Taking This Course? Course Introduction, Descriptive Statistics and Data Visualization GENOME 560, Spring 2012 Data are interesting because they help us understand the world Genomics: Massive Amounts
More informationCopyright 2010-2011 PEOPLECERT Int. Ltd and IASSC
PEOPLECERT - Personnel Certification Body 3 Korai st., 105 64 Athens, Greece, Tel.: +30 210 372 9100, Fax: +30 210 372 9101, e-mail: info@peoplecert.org, www.peoplecert.org Copyright 2010-2011 PEOPLECERT
More informationBusiness Statistics. Successful completion of Introductory and/or Intermediate Algebra courses is recommended before taking Business Statistics.
Business Course Text Bowerman, Bruce L., Richard T. O'Connell, J. B. Orris, and Dawn C. Porter. Essentials of Business, 2nd edition, McGraw-Hill/Irwin, 2008, ISBN: 978-0-07-331988-9. Required Computing
More informationControl CHAPTER OUTLINE LEARNING OBJECTIVES
Quality Control 16Statistical CHAPTER OUTLINE 16-1 QUALITY IMPROVEMENT AND STATISTICS 16-2 STATISTICAL QUALITY CONTROL 16-3 STATISTICAL PROCESS CONTROL 16-4 INTRODUCTION TO CONTROL CHARTS 16-4.1 Basic
More informationProcessing of Insurance Returns. An EMC Lean Six Sigma Project. Author: Aidan Trindle. Editor: Mohan Mahadevan
Processing of Insurance Returns An EMC Lean Six Sigma Project Author: Aidan Trindle Editor: Mohan Mahadevan EMC Corporation is the world leader in systems, software, services, and solutions for building
More informationNHA. User Guide, Version 1.0. Production Tool
NHA User Guide, Version 1.0 Production Tool Welcome to the National Health Accounts Production Tool National Health Accounts (NHA) is an internationally standardized methodology that tracks public and
More informationBlank Project Management Templates. Saving Time! Saving Money! Saving Stress!
www.projectagency.co.uk Blank Project Management Templates Saving Time! Saving Money! Saving Stress! Please feel free to copy any of the attached documents. You can alter any of them to suit the needs
More informationProject Quality Management
Project Quality Management Study Notes PMI, PMP, CAPM, PMBOK, PM Network and the PMI Registered Education Provider logo are registered marks of the Project Management Institute, Inc. Points to Note Please
More informationIntroduction to Project Management
Introduction to Project Management Quality Management Quality Management Learning Objectives Develop a quality management plan. Perform quality assurance. Apply quality tools. 2 What is Quality? Institute
More informationSoftware Process Improvement TRIZ and Six Sigma (Using Contradiction Matrix and 40 Principles)
Software Process Improvement TRIZ and Six Sigma (Using Contradiction Matrix and 40 Principles) Garikapati Pavan Kumar Email: pavan.garikapati@patni.com ABSTRACT This paper proposes an innovative application
More information