Seven Basic Tools of Quality Control: the Appropriate Techniques for Solving Quality Problems in the Organizations
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X-Bar Charts
NCSS Statistical Software NCSS.com Chapter 244 X-bar Charts Introduction This procedure generates X-bar control charts for variables. The format of the control charts is fully customizable. The data for the subgroups can be in a single column or in multiple columns. This procedure permits the defining of stages. The center line can be entered directly or estimated from the data, or a sub-set of the data. Sigma may be estimated from the data or a standard sigma value may be entered. A list of out-of-control points can be produced in the output, if desired, and means may be stored to the spreadsheet. X-bar Control Charts X-bar charts are used to monitor the mean of a process based on samples taken from the process at given times (hours, shifts, days, weeks, months, etc.). The measurements of the samples at a given time constitute a subgroup. Typically, an initial series of subgroups is used to estimate the mean and standard deviation of a process. The mean and standard deviation are then used to produce control limits for the mean of each subgroup. During this initial phase, the process should be in control. If points are out-of-control during the initial (estimation) phase, the assignable cause should be determined and the subgroup should be removed from estimation. Determining the process capability (see R & R Study and Capability Analysis procedures) may also be useful at this phase. Once the control limits have been established of the X-bar charts, these limits may be used to monitor the mean of the process going forward. -
Image Segmentation Based on Histogram Analysis Utilizing the Cloud Model
Computers and Mathematics with Applications 62 (2011) 2824–2833 Contents lists available at SciVerse ScienceDirect Computers and Mathematics with Applications journal homepage: www.elsevier.com/locate/camwa Image segmentation based on histogram analysis utilizing the cloud model Kun Qin a,∗, Kai Xu a, Feilong Liu b, Deyi Li c a School of Remote Sensing Information Engineering, Wuhan University, Wuhan, 430079, China b Bahee International, Pleasant Hill, CA 94523, USA c Beijing Institute of Electronic System Engineering, Beijing, 100039, China article info a b s t r a c t Keywords: Both the cloud model and type-2 fuzzy sets deal with the uncertainty of membership Image segmentation which traditional type-1 fuzzy sets do not consider. Type-2 fuzzy sets consider the Histogram analysis fuzziness of the membership degrees. The cloud model considers fuzziness, randomness, Cloud model Type-2 fuzzy sets and the association between them. Based on the cloud model, the paper proposes an Probability to possibility transformations image segmentation approach which considers the fuzziness and randomness in histogram analysis. For the proposed method, first, the image histogram is generated. Second, the histogram is transformed into discrete concepts expressed by cloud models. Finally, the image is segmented into corresponding regions based on these cloud models. Segmentation experiments by images with bimodal and multimodal histograms are used to compare the proposed method with some related segmentation methods, including Otsu threshold, type-2 fuzzy threshold, fuzzy C-means clustering, and Gaussian mixture models. The comparison experiments validate the proposed method. ' 2011 Elsevier Ltd. All rights reserved. 1. Introduction In order to deal with the uncertainty of image segmentation, fuzzy sets were introduced into the field of image segmentation, and some methods were proposed in the literature. -
The 7 Quality Improvement Tools
1 Continuous Quality Improvement for Excellence The 7 Quality Improvement Tools “From my past experience as much as 95% of all problems within a company can be solved by means of these tools” Kaoru Ishikawa Kaoru Ishikawa made many contributions to the field of quality improvement, including a range of tools and techniques. His emphasis was on the human side of quality. The concept of quality improvement as a fundamental responsibility of every member of staff became a key component of the Japanese approach to QI. Ishikawa’s work focuses on the idea of kaizen (a Japanese word that can be roughly translated as ‘continuous management’). This concept developed by Japanese industry in the 1950s and 1960s, is a core principle of quality management today, and holds that it is the responsibility of every staff member to improve what they do. www.nhselect.nhs.uk Helping our members to excel since 2002 2 Continuous Quality Improvement for Excellence Flow Charts / Process Maps What is Process Mapping and How Can it Help? Processes within healthcare have evolved over many years and through many organisational changes; this means there are often many layers to pathways and complicated systems that have built up over time. A good way to review systems and/or pathways to understand where improvements are needed is to work with frontline teams to process map. This is a simple exercise which facilitates a positive and powerful opportunity to create a culture of ownership within the multidisciplinary team to focus on areas for improvement. A process map is a visual way of representing and understanding a step-by-step picture of processes, either one aspect or a whole patient pathway. -
Permutation Tests
Permutation tests Ken Rice Thomas Lumley UW Biostatistics Seattle, June 2008 Overview • Permutation tests • A mean • Smallest p-value across multiple models • Cautionary notes Testing In testing a null hypothesis we need a test statistic that will have different values under the null hypothesis and the alternatives we care about (eg a relative risk of diabetes) We then need to compute the sampling distribution of the test statistic when the null hypothesis is true. For some test statistics and some null hypotheses this can be done analytically. The p- value for the is the probability that the test statistic would be at least as extreme as we observed, if the null hypothesis is true. A permutation test gives a simple way to compute the sampling distribution for any test statistic, under the strong null hypothesis that a set of genetic variants has absolutely no effect on the outcome. Permutations To estimate the sampling distribution of the test statistic we need many samples generated under the strong null hypothesis. If the null hypothesis is true, changing the exposure would have no effect on the outcome. By randomly shuffling the exposures we can make up as many data sets as we like. If the null hypothesis is true the shuffled data sets should look like the real data, otherwise they should look different from the real data. The ranking of the real test statistic among the shuffled test statistics gives a p-value Example: null is true Data Shuffling outcomes Shuffling outcomes (ordered) gender outcome gender outcome gender outcome Example: null is false Data Shuffling outcomes Shuffling outcomes (ordered) gender outcome gender outcome gender outcome Means Our first example is a difference in mean outcome in a dominant model for a single SNP ## make up some `true' data carrier<-rep(c(0,1), c(100,200)) null.y<-rnorm(300) alt.y<-rnorm(300, mean=carrier/2) In this case we know from theory the distribution of a difference in means and we could just do a t-test. -
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. -
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). -
Tyson Foods Presentation Template
2019 Meat Industry Food Safety Conference Chicago, IL September 4, 2019 A Practitioner’s Reflections on SQC & SPC A Practitioner’s Reflections - On The Use Of SPC/SQC Techniques; or, How much education does a microbiologist, straight out of college, have on the application of SPC/SQC in a manufacturing environment? A Practitioner’s Reflections - On The Use Of SPC/SQC Techniques; or more specifically, why: - Is USDA tagging up my hotdogs? - Are customers complaining? - Is there is so much rework? - Am I addressing the same problem over and over? Statistical Quality Control (SQC) Statistical quality control (SQC) is defined as the application of the 14 statistical and analytical tools (7-QC and 7-SUPP) to monitor process outputs (dependent variables). Statistical Process Control (SPC) Statistical process control (SPC) is the application of the same 14 tools to control process inputs (independent variables). Although both terms are often used interchangeably, statistical quality control includes acceptance sampling where statistical process control does not. ASQ.ORG (2019) 4 SPC SQC Inputs Outputs Raw materials Finished product (lot) Process steps Manufacturer Customer / Consumer Establishment USDA/FSIS, FDA Raw Materials Step 1 Finished Product Step 2 Step # 5 Frederick W. Taylor Promoted “scientific management” principles to significantly increase economic productivity in the factory, by closely observing a production process and then simplifying the process into specific, individualized tasks. Utilizing time and sequential motion studies, -
1 the Achievements and Personality of Dr. Kaoru Ishikawa Dr. Noriaki
The Premier Memorial Ishikawa Lecture Dr. Kaoru Ishikawa Birth Centenary Commemoration The Achievements and Personality of Dr. Kaoru Ishikawa Dr. Noriaki KANO Professor Emeritus, Tokyo University of Science Honorary Chairperson, Asian Network for Quality (ANQ) Honorary Member of JSQC, ASQ, and IAQ 1. Quiz for Prof Kaoru Ishikawa (originated by Acn Y. Ando, modified by N. Kano) Q1: How Do You Know Prof. Kaoru Ishikawa? A: Father of TPS B: Father of TPM C: Father of QC Circles Q2: Which country Prof. Ishikawa visited the most? A: U. S. B: Switzerland C: Taiwan Q3: What Prof. Kaoru Ishikawa used to advise his students? A: Be Proficient in English B; Learn to Hold Your Drink C: Be Good with Data 2. English translation of the book: “Kaoru Ishikawa: The Man and Quality Control”, Published by Mrs. Keiko Ishikawa in 1993, to that 172 writers including 144 writers from Japan and 28 writers from 15 other countries contributed. It is translated into English and is uploaded at: “Kaoru Ishikawa: The Man and Quality Control”, http://www.juse.or.jp/english/archives/ 3. My Personal Memory of Professor Ishikawa Why did I Join Professor Ishikawa’s Research Group! : Why did I got selected to his group? No, I did not select his group. It was the only group available because I was considered “unpromising student.” My first option professor declined to accept me and only vacant seat available was in Prof. Ishikawa’s group. I was supervised by Prof Ishikawa for seven and a half years i.e. from the time I was a senior of the undergraduate course until I obtained the Doctorate degree. -
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 ............................................................................................................................................................................ -
Process Optimization Methods
Process optimization methods 1 This project has been funded with support from the European Commission. This presentation reflects the views only of the auth or, and the Commission cannot be held responsible for any use which may be made of the information contained therein. LLP Leonardo da Vinci Transfer of Innovation Programme / Grant agreement number: DE/13/LLP-LdV/TOI/147636 Contents Contents .................................................................................................................................................. 2 Figures ..................................................................................................................................................... 2 Introduction ............................................................................................................................................. 4 Definition of Process optimization .......................................................................................................... 4 Process optimization methods- Approaches ........................................................................................... 5 BPR – Business process reengineering ................................................................................................ 5 Lean Management............................................................................................................................... 5 Kaizen (CIP) ......................................................................................................................................... -
Shinyitemanalysis for Psychometric Training and to Enforce Routine Analysis of Educational Tests
ShinyItemAnalysis for Psychometric Training and to Enforce Routine Analysis of Educational Tests Patrícia Martinková Dept. of Statistical Modelling, Institute of Computer Science, Czech Academy of Sciences College of Education, Charles University in Prague R meetup Warsaw, May 24, 2018 R meetup Warsaw, 2018 1/35 Introduction ShinyItemAnalysis Teaching psychometrics Routine analysis of tests Discussion Announcement 1: Save the date for Psychoco 2019! International Workshop on Psychometric Computing Psychoco 2019 February 21 - 22, 2019 Charles University & Czech Academy of Sciences, Prague www.psychoco.org Since 2008, the international Psychoco workshops aim at bringing together researchers working on modern techniques for the analysis of data from psychology and the social sciences (especially in R). Patrícia Martinková ShinyItemAnalysis for Psychometric Training and Test Validation R meetup Warsaw, 2018 2/35 Introduction ShinyItemAnalysis Teaching psychometrics Routine analysis of tests Discussion Announcement 2: Job offers Job offers at Institute of Computer Science: CAS-ICS Postdoctoral position (deadline: August 30) ICS Doctoral position (deadline: June 30) ICS Fellowship for junior researchers (deadline: June 30) ... further possibilities to participate on grants E-mail at [email protected] if interested in position in the area of Computational psychometrics Interdisciplinary statistics Other related disciplines Patrícia Martinková ShinyItemAnalysis for Psychometric Training and Test Validation R meetup Warsaw, 2018 3/35 Outline 1. Introduction 2. ShinyItemAnalysis 3. Teaching psychometrics 4. Routine analysis of tests 5. Discussion Introduction ShinyItemAnalysis Teaching psychometrics Routine analysis of tests Discussion Motivation To teach psychometric concepts and methods Graduate courses "IRT models", "Selected topics in psychometrics" Workshops for admission test developers Active learning approach w/ hands-on examples To enforce routine analyses of educational tests Admission tests to Czech Universities Physiology concept inventories .. -
Lecture 8 Sample Mean and Variance, Histogram, Empirical Distribution Function Sample Mean and Variance
Lecture 8 Sample Mean and Variance, Histogram, Empirical Distribution Function Sample Mean and Variance Consider a random sample , where is the = , , … sample size (number of elements in ). Then, the sample mean is given by 1 = . Sample Mean and Variance The sample mean is an unbiased estimator of the expected value of a random variable the sample is generated from: . The sample mean is the sample statistic, and is itself a random variable. Sample Mean and Variance In many practical situations, the true variance of a population is not known a priori and must be computed somehow. When dealing with extremely large populations, it is not possible to count every object in the population, so the computation must be performed on a sample of the population. Sample Mean and Variance Sample variance can also be applied to the estimation of the variance of a continuous distribution from a sample of that distribution. Sample Mean and Variance Consider a random sample of size . Then, = , , … we can define the sample variance as 1 = − , where is the sample mean. Sample Mean and Variance However, gives an estimate of the population variance that is biased by a factor of . For this reason, is referred to as the biased sample variance . Correcting for this bias yields the unbiased sample variance : 1 = = − . − 1 − 1 Sample Mean and Variance While is an unbiased estimator for the variance, is still a biased estimator for the standard deviation, though markedly less biased than the uncorrected sample standard deviation . This estimator is commonly used and generally known simply as the "sample standard deviation".