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Implementing SPC for Non-Normal Processes with the I-MR Chart: a Case Study
Implementing SPC for non-normal processes with the I-MR chart: A case study Axl Elisson Master of Science Thesis TPRMM 2017 KTH Industrial Engineering and Management Production Engineering and Management SE-100 44 STOCKHOLM Acknowledgements This master thesis was performed at the brake manufacturer Haldex as my master of science degree project in Industrial Engineering and Management at the Royal Institute of Technology (KTH) in Stockholm, Sweden. It was conducted during the spring semester of 2017. I would first like to thank my supervisor at Haldex, Roman Berg, and Annika Carlius for their daily support and guidance which made this project possible. I would also like to thank the quality department, production engineers and operators at Haldex for all insight in different subjects. Finally, I would like to thank my supervisor at KTH, Jerzy Mikler, for his support during my thesis. All of your combined expertise have been very valuable. Stockholm, July 2017 Axl Elisson Abstract The application of statistical process control (SPC) requires normal distributed data that is in statistical control in order to determine valid process capability indices and to set control limits that reflects the process’ true variation. This study examines a case of several non-normal processes and evaluates methods to estimate the process capability and set control limits that is in relation to the processes’ distributions. Box-Cox transformation, Johnson transformation, Clements method and process performance indices were compared to estimate the process capability and the Anderson-Darling goodness-of-fit test was used to identify process distribution. Control limits were compared using Clements method, the sample standard deviation and from machine tool variation. -
Introduction to Measurements & Error Analysis
Introduction to Measurements & Error Analysis The Uncertainty of Measurements Some numerical statements are exact: Mary has 3 brothers, and 2 + 2 = 4. However, all measurements have some degree of uncertainty that may come from a variety of sources. The process of evaluating this uncertainty associated with a measurement result is often called uncertainty analysis or error analysis. The complete statement of a measured value should include an estimate of the level of confidence associated with the value. Properly reporting an experimental result along with its uncertainty allows other people to make judgments about the quality of the experiment, and it facilitates meaningful comparisons with other similar values or a theoretical prediction. Without an uncertainty estimate, it is impossible to answer the basic scientific question: “Does my result agree with a theoretical prediction or results from other experiments?” This question is fundamental for deciding if a scientific hypothesis is confirmed or refuted. When we make a measurement, we generally assume that some exact or true value exists based on how we define what is being measured. While we may never know this true value exactly, we attempt to find this ideal quantity to the best of our ability with the time and resources available. As we make measurements by different methods, or even when making multiple measurements using the same method, we may obtain slightly different results. So how do we report our findings for our best estimate of this elusive true value? The most common way to show the range of values that we believe includes the true value is: measurement = best estimate ± uncertainty (units) Let’s take an example. -
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. -
Software Testing
Software Testing PURPOSE OF TESTING CONTENTS I. Software Testing Background II. Software Error Case Studies 1. Disney Lion King 2. Intel Pentium Floating Point Division Bug 3. NASA Mars Polar Lander 4. Patriot Missile Defense System 5. Y2K Bug III. What is Bug? 1. Terms for Software Failure 2. Software Bug: A Formal Definition 3. Why do Bugs occur? and cost of bug. 4. What exactly does a Software Tester do? 5. What makes a good Software Tester? IV. Software Development Process 1. Product Components 2. What Effort Goes into a Software Product? 3. What parts make up a Software Product? 4. Software Project Staff V. Software Development Lifecycle Models 1. Big Bang Model 2. Code and Fix Model 3. Waterfall Model 4. Spiral Model VI. The Realities of Software Testing VII. Software Testing Terms and Definition 1. Precision and Accuracy 2. Verification and Validation 3. Quality Assurance and Quality Control Anuradha Bhatia Software Testing I. Software Testing Background 1. Software is a set of instructions to perform some task. 2. Software is used in many applications of the real world. 3. Some of the examples are Application software, such as word processors, firmware in an embedded system, middleware, which controls and co-ordinates distributed systems, system software such as operating systems, video games, and websites. 4. All of these applications need to run without any error and provide a quality service to the user of the application. 5. The software has to be tested for its accurate and correct working. Software Testing: Testing can be defined in simple words as “Performing Verification and Validation of the Software Product” for its correctness and accuracy of working. -
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. -
Statistical Process Control for Monitoring Nonlinear Profiles: a Six Sigma Project on Curing Process
This is the author’s final, peer-reviewed manuscript as accepted for publication. The publisher-formatted version may be available through the publisher’s web site or your institution’s library. Statistical process control for monitoring nonlinear profiles: a six sigma project on curing process Shing I. Chang, Tzong-Ru Tsai, Dennis K. J. Lin, Shih-Hsiung Chou, & Yu-Siang Lin How to cite this manuscript If you make reference to this version of the manuscript, use the following information: Chang, S. I., Tsai, T., Lin, D. K. J., Chou, S., & Lin, Y. (2012). Statistical process control for monitoring nonlinear profiles: A six sigma project on curing process. Retrieved from http://krex.ksu.edu Published Version Information Citation: Chang, S. I., Tsai, T., Lin, D. K. J., Chou, S., & Lin, Y. (2012). Statistical process control for monitoring nonlinear profiles: A six sigma project on curing process. Quality Engineering, 24(2), 251-263. Copyright: Copyright © Taylor & Francis Group, LLC. Digital Object Identifier (DOI): doi:10.1080/08982112.2012.641149 Publisher’s Link: http://www.tandfonline.com/doi/abs/10.1080/08982112.2012.641149 This item was retrieved from the K-State Research Exchange (K-REx), the institutional repository of Kansas State University. K-REx is available at http://krex.ksu.edu Statistical Process Control for Monitoring Nonlinear Profiles: A Six Sigma Project on Curing Process Shing I Chang1, Tzong‐Ru Tsai2, Dennis K.J. Lin3, Shih‐Hsiung Chou1 & Yu‐Siang Lin4 1Quality Engineering Laboratory, Department of Industrial and Manufacturing Systems Engineering, Kansas State University, USA 2Department of Statistics, Tamkang University, Danshui District, New Taipei City 25137 Taiwan 3Department of Statistics, Pennsylvania State University, USA 4Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan ABSTRACT Curing duration and target temperature are the most critical process parameters for high- pressure hose products. -
Breathe London-NPL-Statistical Methods for Characterisation Of
Statistical methods for performance characterisation of lower-cost sensors J D Hayward, A Forbes, N A Martin, S Bell, G Coppin, G Garcia and D Fryer 1.1 Introduction This appendix summarises the progress of employing the Breathe London monitoring data together with regulated reference measurements to investigate the application of machine learning tools to extract information with a view to quantifying measurement uncertainty. The Breathe London project generates substantial data from the instrumentation deployed, and the analysis requires a comparison against the established reference methods. The first task was to develop programs to extract the various datasets being employed from different providers and to store them in a stan- dardised format for further processing. This data curation allowed us to audit the data, establishing errors in the AQMesh data and the AirView cars as well as monitor any variances in the data sets caused by conditions such as fog/extreme temperatures. This includes writing a program to parse the raw AirView data files from the high quality refer- ence grade instruments installed in the mobile platforms (Google AirView cars), programs to commu- nicate with and download data from the Applications Program Interfaces (APIs) provided by both Air Monitors (AQMesh sensor systems) and Imperial College London (formerly King’s College London) (London Air Quality Network), and a program that automatically downloads CSV files from the DE- FRA website, which contains the air quality data from the Automatic Urban Rural Network (AURN). This part of the development has had to overcome significant challenges including the accommo- dation of different pollutant concentration units from the different database sources, different file formats, and the incorporation of data flags to describe the data ratification status. -
Achieving Total Quality Management in Construction Project Using Six Sigma Concept
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 05 Issue: 06 | June 2018 www.irjet.net p-ISSN: 2395-0072 Achieving Total Quality Management in Construction Project Using Six Sigma Concept Dr.Divakar.K1 and Nishaant.Ha2 1Associate Professor in Civil Engineering, Coimbatore Institute of Technology, Coimbatore-641014 2Assistant Professor in Civil Engineering, Kumaraguru College of Technology, Coimbatore-641049 ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - A Six Sigma approach is one of the most Low Sui Pheng and Mok Sze Hui (2004) examined the efficient quality improvement processes. In this study, six strategies and concepts of Six Sigma and implemented sigma concepts were applied in construction scheduling those concepts in construction industry. A. D. Lade et.al. process to avoid delay as well as to maintain the quality of (2015) analysed the quality performance of Ready Mix the construction activities. Detailed schedule of the building Concrete (RMC) plant at Mumbai, India, using the six was analysed & also the updated schedule was verified. At sigma philosophy had been evaluated using various this stage, DMAIC (Define, Measure, Analyse, Improve and quality tools and sigma value was calculated. According to Control) principle was implemented. The variation in the the sigma level, recommendations were given for the scheduling due to delay of the activities was noted down. improvement. Seung Heon Han et.al. in their study they Delay reasons and their impacts in the whole project were developed a general methodology to apply the six sigma calculated. All delayed activities were considered as principles on construction operations rather than “Defects”. DPMO (Defects Per Million Opportunities) was construction materials in terms of the barometers to calculated. -
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 ............................................................................................................................................................................