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												Methods and Philosophy of Statistical Process Control
5Methods and Philosophy of Statistical Process Control CHAPTER OUTLINE 5.1 INTRODUCTION 5.4 THE REST OF THE MAGNIFICENT 5.2 CHANCE AND ASSIGNABLE CAUSES SEVEN OF QUALITY VARIATION 5.5 IMPLEMENTING SPC IN A 5.3 STATISTICAL BASIS OF THE CONTROL QUALITY IMPROVEMENT CHART PROGRAM 5.3.1 Basic Principles 5.6 AN APPLICATION OF SPC 5.3.2 Choice of Control Limits 5.7 APPLICATIONS OF STATISTICAL PROCESS CONTROL AND QUALITY 5.3.3 Sample Size and Sampling IMPROVEMENT TOOLS IN Frequency TRANSACTIONAL AND SERVICE 5.3.4 Rational Subgroups BUSINESSES 5.3.5 Analysis of Patterns on Control Charts Supplemental Material for Chapter 5 5.3.6 Discussion of Sensitizing S5.1 A SIMPLE ALTERNATIVE TO RUNS Rules for Control Charts RULES ON THEx CHART 5.3.7 Phase I and Phase II Control Chart Application The supplemental material is on the textbook Website www.wiley.com/college/montgomery. CHAPTER OVERVIEW AND LEARNING OBJECTIVES This chapter has three objectives. The first is to present the basic statistical control process (SPC) problem-solving tools, called the magnificent seven, and to illustrate how these tools form a cohesive, practical framework for quality improvement. These tools form an impor- tant basic approach to both reducing variability and monitoring the performance of a process, and are widely used in both the analyze and control steps of DMAIC. The second objective is to describe the statistical basis of the Shewhart control chart. The reader will see how decisions 179 180 Chapter 5 ■ Methods and Philosophy of Statistical Process Control about sample size, sampling interval, and placement of control limits affect the performance of a control chart. - 
												
												Create Your Own Lean Training
Create Your Own Lean Training Presented by Susan Weum Six Sigma Black Belt Lean Certified Continuous Improvement Leader 1 Background and Experience • Retired Senior Project Engineer and Continuous Improvement Leader from Smiths Medical. • Six Sigma Black Belt and Lean Practitioner. • Chemical and Materials Engineer, Michigan State University. • Six Sigma Green Belt Instructor since 2005. 2 Keys for Training Development • Start by Listing Tools & Concepts. • Understand your Audience. • Choose the delivery method. • Use examples and be flexible. • Ask students for examples. Engage at every opportunity. • Give targeted Homework. • Homework applies the concepts and tools. • Student presentations are optimal. • Students will use a variety of tools on their project. 3 Create Your Own Training Program Where do you start when developing training? Start with an overview…the BIG picture. • List what you specifically want to teach. • Steps • Concepts • Tools Let’s look at an example from Six Sigma… 4 DMAIC Process Overview -What’s the -Key Input & -Identify root -Develop -Develop SOPs & problem? Output Variables Causes Solutions Training Plan -What matters? -What data is -Confirm -Evaluate & Select -Process Control -What’s the available? Relationship of Solutions -Update VSM scope? -Validate Root Cause to -Anticipate Risks -Confirm Project -Who are Measurement Output -Cost/Benefit Results stakeholders? System -Prioritize Issues Analysis -Transition to -Financial/ Quality -Baseline data -Statistical -Benchmarking Process Owner Benefit collection - 
												
												Finland—Selected Issues and Statistical Appendix
O1996 International Monetary Fund September 1996 IMF Staff Country Report No. 96/95 Finland—Selected Issues and Statistical Appendix This report on selected issues and statistical appendix on Finland was prepared by a staff team of the International Monetary Fund as background documentation for the periodic consultation with this member country. As such, the views expressed in this document are those of the staff team and do not necessarily reflect the views of the Government of Finland or the Executive Board of the IMF. Copies of this report are available to the public from International Monetary Fund • Publication Services 700 19th Street, N.W. • Washington, D.C. 20431 Telephone: (202) 623-7430 • Telefax: (202) 623-7201 Telex (RCA): 248331 IMF UR Internet: [email protected] Price: $15.00 a copy International Monetary Fund Washington, D.C. ©International Monetary Fund. Not for Redistribution This page intentionally left blank ©International Monetary Fund. Not for Redistribution INTERNATIONAL MONETARY FUND FINLAND Selected Issues and Statistical Appendix Prepared by T. Feyzioglu, D. Tambakis (both EU1) and C. Pazarbasioglu (MAE) Approved by the European I Department July 10, 1996 Contents Page I. Inflation and Wage Dynamics in Finland: A Cointegration Approach 1 1. Introduction and summary 1 2 . Data sources and statistical properties 4 a. Data sources and definitions 4 b. Order of integration 4 3. Empirical estimates 6 a. Modeling strategy 6 b. Cointegration and error correction 8 c. Model multipliers 10 4. Outlook for CPI and nominal wage inflation: 1996-2001 14 a. Baseline scenario 14 b. Alternative scenario: further depreciation in 1996 17 References 20 II. - 
												
												Ruggles, Olivia, M Title: Standardized Work Instruction
1 Author: Ruggles, Olivia, M Title: Standardized Work Instruction The accompanying research report is submitted to the University of Wisconsin-Stout, Graduate School in partial completion of the requirements for the Graduate Degree/ Major: MS Technology Management Research Adviser: Jim Keyes, Ph.D. Submission Term/Year: Summer, 2012 Number of Pages: 56 Style Manual Used: American Psychological Association, 6th edition I understand that this research report must be officially approved by the Graduate School and that an electronic copy of the approved version will be made available through the University Library website I attest that the research report is my original work (that any copyrightable materials have been used with the permission of the original authors), and as such, it is automatically protected by the laws, rules, and regulations of the U.S. Copyright Office. My research adviser has approved the content and quality of this paper. STUDENT: NAME Olivia Ruggles DATE: 8/3/2012 ADVISER: (Committee Chair if MS Plan A or EdS Thesis or Field Project/Problem): NAME Jim Keyes, Ph.D. DATE: 8/3/2012 --------------------------------------------------------------------------------------------------------------------------------- This section for MS Plan A Thesis or EdS Thesis/Field Project papers only Committee members (other than your adviser who is listed in the section above) 1. CMTE MEMBER’S NAME: DATE: 2. CMTE MEMBER’S NAME: DATE: 3. CMTE MEMBER’S NAME: DATE: --------------------------------------------------------------------------------------------------------------------------------- This section to be completed by the Graduate School This final research report has been approved by the Graduate School. Director, Office of Graduate Studies: DATE: 2 Ruggles, Olivia M. Standardized Work Instruction Abstract Mercury Marine is a world-wide manufacturing company in the marine industry. - 
												
												Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series
RESEARCH ARTICLE Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series Jacob Anhøj* Centre of Diagnostic Evaluation, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark * [email protected] Abstract Run charts are widely used in healthcare improvement, but there is little consensus on how to interpret them. The primary aim of this study was to evaluate and compare the diagnostic a11111 properties of different sets of run chart rules. A run chart is a line graph of a quality measure over time. The main purpose of the run chart is to detect process improvement or process degradation, which will turn up as non-random patterns in the distribution of data points around the median. Non-random variation may be identified by simple statistical tests in- cluding the presence of unusually long runs of data points on one side of the median or if the graph crosses the median unusually few times. However, there is no general agreement OPEN ACCESS on what defines “unusually long” or “unusually few”. Other tests of questionable value are Citation: Anhøj J (2015) Diagnostic Value of Run frequently used as well. Three sets of run chart rules (Anhoej, Perla, and Carey rules) have Chart Analysis: Using Likelihood Ratios to Compare been published in peer reviewed healthcare journals, but these sets differ significantly in Run Chart Rules on Simulated Data Series. PLoS their sensitivity and specificity to non-random variation. In this study I investigate the diag- ONE 10(3): e0121349. doi:10.1371/journal. pone.0121349 nostic values expressed by likelihood ratios of three sets of run chart rules for detection of shifts in process performance using random data series. - 
												
												Approaches for Detection of Unstable Processes: a Comparative Study Yerriswamy Wooluru J S S Academy of Technical Education, Bangalore, India, [email protected]
Journal of Modern Applied Statistical Methods Volume 14 | Issue 2 Article 17 11-1-2015 Approaches for Detection of Unstable Processes: A Comparative Study Yerriswamy Wooluru J S S Academy of Technical Education, Bangalore, India, [email protected] D. R. Swamy J S S Academy of Technical Education, Bangalore, India P. Nagesh JSS Centre for Management Studies, Mysore, Indi Follow this and additional works at: http://digitalcommons.wayne.edu/jmasm Part of the Applied Statistics Commons, Social and Behavioral Sciences Commons, and the Statistical Theory Commons Recommended Citation Wooluru, Yerriswamy; Swamy, D. R.; and Nagesh, P. (2015) "Approaches for Detection of Unstable Processes: A Comparative Study," Journal of Modern Applied Statistical Methods: Vol. 14 : Iss. 2 , Article 17. DOI: 10.22237/jmasm/1446351360 Available at: http://digitalcommons.wayne.edu/jmasm/vol14/iss2/17 This Regular Article is brought to you for free and open access by the Open Access Journals at DigitalCommons@WayneState. It has been accepted for inclusion in Journal of Modern Applied Statistical Methods by an authorized editor of DigitalCommons@WayneState. Approaches for Detection of Unstable Processes: A Comparative Study Cover Page Footnote This work is supported by JSSMVP Mysore. I, sincerely thank to my Guide Dr.Swamy D.R, Professor and Head of the Department, Industrial Engineering &Management, JSSATE Bangalore and Co-Guide Dr P.Nagesh, Professor, Department of Management studies, SJCE, Mysore. This regular article is available in Journal of Modern Applied Statistical Methods: http://digitalcommons.wayne.edu/jmasm/vol14/ iss2/17 Journal of Modern Applied Statistical Methods Copyright © 2015 JMASM, Inc. November 2015, Vol. 14 No. - 
												
												Statistical Process Control Tools: a Practical Guide for Jordanian Industrial Organizations
Volume 4, Number 6, December 2010 ISSN 1995-6665 JJMIE Pages 693 - 700 Jordan Journal of Mechanical and Industrial Engineering www.jjmie.hu.edu.jo Statistical Process Control Tools: A Practical guide for Jordanian Industrial Organizations Rami Hikmat Fouad*, Adnan Mukattash Department of Industrial Engineering, Hashemite University, Jordan. Abstract The general aim of this paper is to identify the key ingredients for successful quality management in any industrial organization. Moreover, to illustrate how is it important to realize the intergradations between Statistical Process Control (SPC) is seven tools (Pareto Diagram, Cause and Effect Diagram, Check Sheets, Process Flow Diagram, Scatter Diagram, Histogram and Control Charts), and how to effectively implement and to earn the full strength of these tools. A case study has been carried out to monitor real life data in a Jordanian manufacturing company that specialized in producing steel. Flow process chart was constructed, Check Sheets were designed, Pareto Diagram, scatter diagrams, Histograms was used. The vital few problems were identified; it was found that the steel tensile strength is the vital few problem and account for 72% of the total results of the problems. The principal aim of the project is to train quality team on how to held an effective Brainstorming session and exploit these data in cause and effect diagram construction. The major causes of nonconformities and root causes of the quality problems were specified, and possible remedies were proposed. © 2010 Jordan Journal of Mechanical and Industrial Engineering. All rights reserved Keywords: Statistical Process Control, Check sheets, Process Flow Diagram, Pareto Diagram, Histogram, Scatter Diagram, Control Charts, Brainstorming, and Cause and Effect Diagram. - 
												
												Cause and Effect Diagrams
Online Student Guide Cause and Effect Diagrams OpusWorks 2019, All Rights Reserved 1 Table of Contents LEARNING OBJECTIVES ....................................................................................................................................4 INTRODUCTION ..................................................................................................................................................4 WHAT IS A “ROOT CAUSE”? ......................................................................................................................................................... 4 WHAT IS A “ROOT CAUSE ANALYSIS”?...................................................................................................................................... 4 ADDRESSING THE ROOT CAUSE .................................................................................................................................................. 5 ROOT CAUSE ANALYSIS: THREE BASIC STEPS ........................................................................................................................ 5 CAUSE AND EFFECT TOOLS .......................................................................................................................................................... 6 FIVE WHYS ...........................................................................................................................................................6 THE FIVE WHYS ............................................................................................................................................................................ - 
												
												Lean Process Improvement Assessment
LEAN PROCESS IMPROVEMENT ASSESSMENT INSTRUCTIONS: Read each item, then check a box on the 1-to-5 scale to show how often this occurs in your workplace: If you checked "often" or "very often" then consider: 1 = never 2 = rarely 3 = sometimes 4 = often 5 = very often In my work area, we spend too much time looking for the items we need to do our 5S+ 1 2 3 4 5 1 jobs: files, materials, equipment, and so on. If the physical work area was better 5S+ stands for sort, straighten, shine, standardize, sustain, organized, we’d be more productive and less frustrated. and safety. When we get inputs from customers or from other sources (from paper forms or Poka-Yoke online web forms, for example), we often find that the incoming information is 1 2 3 4 5 From the Japanese term for mistake-proofing, poka-yoke is 2 incomplete or inaccurate. We have to circle back to people to get the information all about putting measures in place to prevent errors from we need. If we received complete information from them the first time around, we’d occurring as a process unfolds. be a lot more efficient. We find ourselves wondering what our customers are thinking. Are we meeting their Voice of the Customer needs and expectations? Are they happy with our work? Are there things we can do 1 2 3 4 5 When it comes to improvement, understanding the 3 to increase their satisfaction? If we had answers to these questions, we could serve customer’s spoken and unspoken requirements is essential. - 
												
												Using the DMAIC Process to Direct the Sequencing of Lean/Six Sigma Tools for Successful Project Outcomes
Using the DMAIC Process to Direct the Sequencing of Lean/Six Sigma Tools for Successful Project Outcomes Jim Ellis Certified Master Black Belt J&J Process Excellence Director – US Sales Strategic Accounts [email protected] Intent: Using the DMAIC Process to Direct the Sequencing of Lean/Six Sigma Tools for Successful Project Outcomes The purpose of this presentation is to share with the attendees my experience over the past 5 years using the DMAIC Process how to determine what Lean and/or 6 Sigma tools is the best to use when for assuring successful outcomes of your projects. Learning Objectives: 1.) Clear understanding of DAMIC Process 2.) Clear understanding of which common Lean and 6 Sigma Tools are best used in what stage of DMAIC 3.) Clear understanding of how to assess success for a project and know if you have achieved it. After Attending the attendees will: 1.) Know what the DMAIC process is and how it is applied in project planning 2.) Understand which Lean and 6 Sigma tools are used in what sequence to assure successful project outcomes © 2008 Ortho Clinical Diagnostics The Driving Forces of Change Today’s Healthcare Delivery Challenges… Declining Resources $29.9 Space Vacancy Capital Funds billion Skilled Labor Rates Medicare/Medicaid Reimbursement Laboratory Techs 5.9% Shortfall Imaging Techs 5.9% Pharmacists 8.1% RNs 8.1% Inpatient Admissions2 ED Visits2 Volume New Technology Market Pressures Increasing Pressures © 2008 Ortho Clinical Diagnostics Why are labs changing? “I need to transform my lab!” Why? • 2X Volume • ½ the - 
												
												Root Cause Analysis of Defects in Automobile Fuel Pumps: a Case Study
International Journal of Management, IT & Engineering Vol. 7 Issue 4, April 2017, ISSN: 2249-0558 Impact Factor: 7.119 Journal Homepage: http://www.ijmra.us, Email: [email protected] Double-Blind Peer Reviewed Refereed Open Access International Journal - Included in the International Serial Directories Indexed & Listed at: Ulrich's Periodicals Directory ©, U.S.A., Open J-Gage as well as in Cabell‟s Directories of Publishing Opportunities, U.S.A ROOT CAUSE ANALYSIS OF DEFECTS IN AUTOMOBILE FUEL PUMPS: A CASE STUDY Saurav Adhikari* Nilesh Sachdeva* Dr. D.R. Prajapati** ABSTRACT Quality can be directly measured from the degree to which customer requirements are satisfied. Some problems were reported by the customers of the automobile company under study in the fuel pumps; which is used in an automobile to transfer the fuel from fuel tank to fuel injection system after filtration.This paper presents the implementation of Quality Control tools– Check Sheet, Fishbone Diagram(or Ishikawa Diagram), ParetoChartand 5-Why analysis tools for identification and elimination of the root cause/s responsible for malfunctioning of the fuel pump in customers‟ cars. From the Check sheet and Pareto analysis, two major defects were identified which accounted for more than 80% of the problems being reported. The root causes of these two defects affecting the product quality of the company were then further analyzed using the 5- Why analysis. Keywords: Quality Control Tools, Ishikawa Diagram, Pareto Chart, 5-Why Analysis * Undergraduate Student, Department of Mechanical Engineering, PEC University of Technology, (formerly Punjab Engineering College), Chandigarh ** Associate Professor& Corresponding Author, Department of Mechanical Engineering, PEC University of Technology (formerly Punjab Engineering College), Chandigarh 90 International journal of Management, IT and Engineering http://www.ijmra.us, Email: [email protected] ISSN: 2249-0558Impact Factor: 7.119 1. - 
												
												Using Six Sigma and Lean Principles to Improve Laboratory Operations
Using Six Sigma and Lean Principles to Improve Laboratory Operations Big Bang Makeover of the Clinical Laboratory: Key Lessons for Work Flow, Analyzers, and Service Improvements Atlanta, GA Purpose/Objectives • Purpose: – To provide an overview of using Six Sigma and Lean Principles in chartering, designing and implementing a full scale Lean project in a large laboratory setting. • Objectives: – Understand how each phase of DMAIC was used to execute a full scale Lean design effort – Understand which key Lean tools were used in measure/design/implementation – Understand how interactions of physical, process, technological, and organizational changes contribute to a large scale Lean effort. 1 Outline • Who we are • Why Improving Lab Operations is Important to Quest Diagnostics • The Journey • Key Learnings Who we are • Focus: – Patients – Growth – People • Vision: – Dedicated People Improving the Health of Patients Through Unsurpassed Diagnostic Insights • Company Background – 40,000+ Employees – 900+ Clinical Scientist PhDs/MDs – Patient Service Centers – Rapid Response to Esoteric Laboratories – 145 million patient encounters annually • Values: – Quality, Integrity, Innovation, Accountability, Collaboration, Leadership • Six Sigma/Lean Journey – 2000?2008 2 Why Improving Lab Operations is Important to Quest Diagnostics PATIENTS 1. Reduce Patient Anxiety Time 2. Reduce tests not performed PEOPLE 1. Shortage of technical workforce GROWTH 2. Staffing of night shift 1. Improve equipment utilization 3. Improve technical skills 2. Improve productivity 4. Reduce potential blood exposure 3. Improve supply utilization 5. Reduce ergonomic Injuries 6. Improve staff involvement The Journey 3 The Journey – Integrating Six Sigma Design DMAIC DMADV Lean Three Major Improvement Six Sigma Process Methodologies Management Measurement Systems Integration Complex project required adequate training/development Using DMAIC & Lean to Define Project PATIENTS 1.