Qcc: an R Package for Quality Control Charting and Statistical Process Control
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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. -
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. -
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. -
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. -
Run Charts Revisited: a Simulation Study of Run Chart Rules for Detection of Non- Random Variation in Health Care Processes
RESEARCH ARTICLE Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of Non- Random Variation in Health Care Processes Jacob Anhøj1*, Anne Vingaard Olesen2 1. Rigshospitalet, University of Copenhagen, Copenhagen, Denmark, 2. University of Aalborg, Danish Center for Healthcare Improvements, Department of Business and Management, Aalborg, Denmark *[email protected] Abstract Background: A run chart is a line graph of a measure plotted over time with the median as a horizontal line. The main purpose of the run chart is to identify process improvement or degradation, which may be detected by statistical tests for non- random patterns in the data sequence. Methods: We studied the sensitivity to shifts and linear drifts in simulated processes using the shift, crossings and trend rules for detecting non-random variation in run charts. Results: The shift and crossings rules are effective in detecting shifts and drifts in OPEN ACCESS process centre over time while keeping the false signal rate constant around 5% Citation: Anhøj J, Olesen AV (2014) Run Charts Revisited: A Simulation Study of Run Chart Rules and independent of the number of data points in the chart. The trend rule is virtually for Detection of Non-Random Variation in Health useless for detection of linear drift over time, the purpose it was intended for. Care Processes. PLoS ONE 9(11): e113825. doi:10.1371/journal.pone.0113825 Editor: Robert K. Hills, Cardiff University, United Kingdom Received: February 24, 2014 Accepted: November 1, 2014 Introduction Published: November 25, 2014 Plotting measurements over time turns out, in my view, to be one of the most Copyright: ß 2014 Anhøj, Olesen. -
A Guide to Creating and Interpreting Run and Control Charts Turning Data Into Information for Improvement Using This Guide
Institute for Innovation and Improvement A guide to creating and interpreting run and control charts Turning Data into Information for Improvement Using this guide The NHS Institute has developed this guide as a reminder to commissioners how to create and analyse time-series data as part of their improvement work. It should help you ask the right questions and to better assess whether a change has led to an improvement. Contents The importance of time based measurements .........................................4 Run Charts ...............................................6 Control Charts .......................................12 Process Changes ....................................26 Recommended Reading .........................29 The Improving immunisation rates importance Before and after the implementation of a new recall system This example shows yearly figures for immunisation rates of time-based before and after a new system was introduced. The aggregated measurements data seems to indicate the change was a success. 90 Wow! A “significant improvement” from 86% 79% to 86% -up % take 79% 75 Time 1 Time 2 Conclusion - The change was a success! 4 Improving immunisation rates Before and after the implementation of a new recall system However, viewing how the rates have changed within the two periods tells a 100 very different story. Here New system implemented here we see that immunisation rates were actually improving until the new 86% – system was introduced. X They then became worse. 79% – Seeing this more detailed X 75 time based picture prompts a different response. % take-up Now what do you conclude about the impact of the new system? 50 24 Months 5 Run Charts Elements of a run chart A run chart shows a measurement on the y-axis plotted over time (on the x-axis). -
Recent Debates on Monetary Condition and Inflation Pressures on the Mainland Call for an Analysis on the Inflation Dynamics and Their Main Determinants
Research Memorandum 04/2005 March 2005 MONEY AND INFLATION IN CHINA Key points: • Recent debates on monetary condition and inflation pressures on the Mainland call for an analysis on the inflation dynamics and their main determinants. A natural starting point for the econometric analysis of monetary and inflation developments is the notion of monetary equilibrium. • This paper presents an estimate of the long-run demand for money in China. The estimated long-run income elasticity is rather stable over time and consistent with that estimated in earlier studies. • The difference between the estimated demand for money and the actual money stock provides an estimate of monetary disequilibria. These seem to provide leading information about CPI inflation in the sample period. At the current conjuncture, the measure suggests that monetary conditions have tightened since the macroeconomic adjustment started in early 2004. • Additionally, an error-correction model based on the long-run money demand function offers insights into the short-run and long-run inflation dynamics. Specifically: ¾ Over the longer term, inflation is mostly caused by monetary expansion. ¾ The price level responds to monetary shocks with lags and on average it takes about 20 quarters for the impact of a permanent monetary shock to peak. This suggests the need for early policy actions to curb inflation pressure given the long policy operation lag. Prepared by : Stefan Gerlach and Janet Kong Research Department Hong Kong Monetary Authority I. INTRODUCTION Concerns have been raised recently about overheating pressures in the economy of Mainland China (see, for example, Kalish 2004, Bradsher 2004, and Ignatius 2004). -
Use Process Capability to Ensure Product Quality
Use Process Capability to Ensure Product Quality Lawrence X. Yu, Ph.D. Director (acting) Office of Pharmaceutical Science, CDER, FDA FDA/ PQRI Conference on Evolving Product Quality September 16-17, 2104, Bethesda, MD 1 2 Quality by Testing vs. Quality by Design Quality by Testing – Specification acceptance criteria are based on one or more batch data (process capability) – Testing must be made to release batches Quality by Design – Specification acceptance criteria are based on performance – Testing may not be necessary to release batches L. X. Yu. Pharm. Res. 25:781-791 (2008) 3 ICH Q6A: Test Procedures and Acceptance Criteria… 4 5 Pharmaceutical QbD Objectives Achieve meaningful product quality specifications that are based on assuring clinical performance Increase process capability and reduce product variability and defects by enhancing product and process design, understanding, and control Increase product development and manufacturing efficiencies Enhance root cause analysis and post-approval change management 6 Concept of Process Capability First introduced in Statistical Quality Control Handbook by the Western Electric Company (1956). – “process capability” is defined as “the natural or undisturbed performance after extraneous influences are eliminated. This is determined by plotting data on a control chart.” ISO, AIAG, ASQ, ASTM ….. published their guideline or manual on process capability index calculation 7 Nomenclature Four indices: – Cp: process capability index – Cpk: minimum process capability index – Pp: process -
Mistakeproofing the Design of Construction Processes Using Inventive Problem Solving (TRIZ)
www.cpwr.com • www.elcosh.org Mistakeproofing The Design of Construction Processes Using Inventive Problem Solving (TRIZ) Iris D. Tommelein Sevilay Demirkesen University of California, Berkeley February 2018 8484 Georgia Avenue Suite 1000 Silver Spring, MD 20910 phone: 301.578.8500 fax: 301.578.8572 ©2018, CPWR-The Center for Construction Research and Training. All rights reserved. CPWR is the research and training arm of NABTU. Production of this document was supported by cooperative agreement OH 009762 from the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH. MISTAKEPROOFING THE DESIGN OF CONSTRUCTION PROCESSES USING INVENTIVE PROBLEM SOLVING (TRIZ) Iris D. Tommelein and Sevilay Demirkesen University of California, Berkeley February 2018 CPWR Small Study Final Report 8484 Georgia Avenue, Suite 1000 Silver Spring, MD 20910 www. cpwr.com • www.elcosh.org TEL: 301.578.8500 © 2018, CPWR – The Center for Construction Research and Training. CPWR, the research and training arm of the Building and Construction Trades Department, AFL-CIO, is uniquely situated to serve construction workers, contractors, practitioners, and the scientific community. This report was prepared by the authors noted. Funding for this research study was made possible by a cooperative agreement (U60 OH009762, CFDA #93.262) with the National Institute for Occupational Safety and Health (NIOSH). The contents are solely the responsibility of the authors and do not necessarily represent the official views of NIOSH or CPWR. i ABOUT THE PROJECT PRODUCTION SYSTEMS LABORATORY (P2SL) AT UC BERKELEY The Project Production Systems Laboratory (P2SL) at UC Berkeley is a research institute dedicated to developing and deploying knowledge and tools for project management. -
Stat 3411 Fall 2012 Exam 1
Stat 3411 Fall 2012 Exam 1 The questions will cover material from assignments, classroom examples, posted lecture notes, and the text reading material corresponding to the points covered in class. If there is a concept in the book that is not covered in the posted lecture notes, in class, or on assignments, I won't ask you anything about that topic. The questions will be chosen so that the required arithmetic and other work is minimal in order to fit questions into 55 minutes. Plotting, if any, you are asked to do will be very quick and simple. o See problem 6 on the posted chapter 3 sample exam questions. Make use of information posted on the class web site, http://www.d.umn.edu/~rregal/stat3411.html Stat 3411 Syllabus Notes NIST Engineering Statistics Handbook National Institute of Standards and Technology Assignments Data Sets, Statistics Primer, and Examples Project Exam Information Sample Exam Questions From the Notes link: Chapters 1 and 2 1.1 Engineering Statistics: What and Why 1.2 Basic Terminology 1.3 Measurement: Its Importance and Difficulty 1.4 Mathematical Models, Reality, and Data Analysis 2.1 General Principles in the Collection of Engineering Data 2.2 Sampling in Enunerative Studies 2.3 Principles for Effective Experimentation 2.4 Some Common Experimental Designs 2.5 Collecting Engineering Data Outline Notes Guitar Strings: Use of Experimental Design Sticker Adhesion: Use of Models Testing Golf Clubs Observational and Experimental Studies: Ford Explorer Rollovers Excel: Factorial Randomization Table B1: Random Digits The first two chapters concerned basic ideas that go into designing studies and collecting data. -
Chapter 6: Process Capability Analysis for Six Sigma
Six Sigma Quality: Concepts & Cases‐ Volume I STATISTICAL TOOLS IN SIX SIGMA DMAIC PROCESS WITH MINITAB® APPLICATIONS Chapter 6 PROCESS CAPABILITY ANALYSIS FOR SIX SIGMA © Amar Sahay, Ph.D. Master Black Belt 1 Chapter 6: Process Capability Analysis for Six Sigma CHAPTER HIGHLIGHTS This chapter deals with the concepts and applications of process capability analysis in Six Sigma. Process Capability Analysis is an important part of an overall quality improvement program. Here we discuss the following topics relating to process capability and Six Sigma: 1. Process capability concepts and fundamentals 2. Connection between the process capability and Six Sigma 3. Specification limits and process capability indices 4. Short‐term and long‐term variability in the process and how they relate to process capability 5. Calculating the short‐term or long‐term process capability 6. Using the process capability analysis to: assess the process variability establish specification limits (or, setting up realistic tolerances) determine how well the process will hold the tolerances (the difference between specifications) determine the process variability relative to the specifications reduce or eliminate the variability to a great extent 7. Use the process capability to answer the following questions: Is the process meeting customer specifications? How will the process perform in the future? Are improvements needed in the process? Have we sustained these improvements, or has the process regressed to its previous unimproved state? 8. Calculating process