DOCSLIB.ORG

The Statistical Optimal Design of Shewhart Control Charts with Supplementary Stopping Rules

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Statistical Optimal Design of Shewhart Control Charts with Supplementary Stopping Rules Iowa State University Capstones, Theses and Retrospective Theses and Dissertations Dissertations 1989 The ts atistical optimal design of Shewhart control charts with supplementary stopping rules Noel Artiles-Leon Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/rtd Part of the Industrial Engineering Commons, and the Statistics and Probability Commons Recommended Citation Artiles-Leon, Noel, "The ts atistical optimal design of Shewhart control charts with supplementary stopping rules " (1989). Retrospective Theses and Dissertations. 8911. https://lib.dr.iastate.edu/rtd/8911 This Dissertation is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Retrospective Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. INFORMATION TO USERS The most advanced technology has been used to photo­ graph and reproduce this manuscript from the microfilm master. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy submitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are re­ produced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. These are also available as one exposure on a standard 35mm slide or as a 17" x 23" black and white photographic print for an additional charge. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9" black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. University Microfilms International A Bell & Howell Information Company 300 North Zeeb Road, Ann Arbor, Ml 48106-1346 USA 313/761-4700 800/521-0600 L I Order Number 8920108 The statistical optimal design of Shewhart control charts with supplementary stopping rules Artiles-Leon, Noel, Ph.D. Iowa State University, 1989 UMI SOON.ZeebRd. Ann Arbor, MI 48106 1 The statistical optimal design of Shewhart control charts with supplementary stopping rules by Noel Artiles-Leon A Dissertation Submitted to the Graduate Faculty in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Major: Industrial Engineering Approved: Signature was redacted for privacy. In Charge of—itajor Work Signature was redacted for privacy. the Major Department Signature was redacted for privacy. the Graduate College Iowa State University Ames, Iowa 1989 ii TABLE OF CONTENTS PAGE I. INTRODUCTION 1 A. The Uses of Control Charts 1 B. Control Limits and Control Rules 3 C. Selection of Control Limits and Control Rules 5 II. LITERATURE REVIEW 12 A. Semieconomic Design of Control Charts 12 B. Economic Design of Control Charts 15 C. Control Charts with Warning Lines 33 D. Simplified Designs for Control Charts 41 III, PROBLEM STATEMENT, RESEARCH OBJECTIVES AND METHODOLOGY ... 43 A. Problem Statement 43 B. A General Methodology for Determining the Average Run Length 49 C. Robustness of the Most Powerful Control Schemes 53 IV. DETERMINATION AND EVALUATION OF THE MOST POWERFUL CONTROL SCHEMES 55 A. The { Rl[l,l,x,œ], RZ[2,3,y,œ] } Family of Control Schemes 55 B. The { Hi[1,1,x,œ], R3[3,4,y,œ] } Family of Control Schemes 72 C. The { R2[2,3,x,œ], R3[3,4,y,®] } Family of Control Schemes 8.2 D. The { Rl[l,l,w,=], R2[2,3.x,»], R3[3,4,y,»] } Family of Control Schemes 93 E. Comparison of Two Control Schemes 106 V. CONCLUSIONS AND RECOMMENDATIONS 114 VI. REFERENCES 117 VII. ACKNOWLEDGEMENTS 121 VIII. APPENDIX A: PROGRAMS P2311A.BAS AND P2311B.BAS 122 IX. APPENDIX B: PROGRAMS P3411A.BAS AND P3411B.BAS 136 X. APPENDIX C: PROGRAM GENSTATE.BAS AND SUBROUTINES CHECKST.INC 150 Ill XI, APPENDIX D: PROGRAMS P3423A.BAS AND P3423B.BAS 155 XII. APPENDIX E: PROGRAMS P342311A.BAS AND P342311B.BAS 170 XIII. APPENDIX F: SIMULATION PROGRAMS AND SUBROUTINES 181 iv LIST OF TABLES PAGE Table 1. Average run length for schemes suggested by Moore (with a single control limit at M+ka) 34 Table 2. Simple designs for control charts 42 Table 3. Optimal control limits for the { RlCl,l.x,®], R2[2,3,y,®] } family of control schemes (output from P2311A,BAS). Values of process mean and control limits are given in standard units 67 Table 4. Optimal control limit for the { R2[2,3,y,®] } family of control schemes. Values of the process mean and the control limit are given in standard units 68 Table 5. Optimal average run lengths for the { R2[2,3,y,®] } family of control schemes 69 Table 6. Average-run-length comparisons between optimal and non- optimal control schemes .72 Table 7. Optimal control limits for the { R1[1,1,x,=], R3[3,4, y,=] } family of control schemes (output from P3411A.BAS). Values of control limits and process mean are given in standard units 75 Table 8. Optimal control limit for the { R3[3,4,y,=] } family of control schemes. Values of the process mean and the control limit are given in standard units 78 Table 9. Optimal average run lengths for the { R3[3,4,y,®] } family of control schemes 80 Table 10. Ratios of average run lengths: optimal control scheme { R2[2,3,y,<=] } to optimal control scheme { R3[3,4,y,™] }. 81 Table 11. Optimal control limits for the { R2[2,3,x,=], R3[3,4, y,®] } family of control schemes (output from P2334A,BAS). Values of control limits are given in standard units 88 Table 12. Optimal average run lengths for the { R2[2,3,x,®], R3[3,4,y,®] } family of control schemes 91 V Table 13. ratios of average run lengths: optimal control scheme { R3[3,4,y,»] } to optimal control scheme { R2[2,3,x,=], R3[3,4,y,»j } 92 Table 14. Control limits for the family of control schemes { R1[ 1,1 , R2[2,3,x,<»], R3[3,4,y,œ] } resulting in an in-control A.R.L. of 200. Control limits given in standard units 98 Table 15. Control limits for the { Rl[l,l,w,®], R2[2,3,x,®], R3[3,4,y,®] } family of control schemes resulting in an in-control A.R.L. of 400. Control limits are given in standard units 100 Table 16. Optimal control limits for the { Rl[l,l,w,®], R2[2,3,x,œ], R3[3,4,y,®] } family of control schemes. Values of control limits are given in standard units . 101 Table 17. Optimal average run lengths for the { Rl[l,l,w,®], R2[2,3,x,œ], R3[3,4,y,®] } family of control schemes. Values of control limits and process mean are given in standard units 104 Table 18. Ratios of average run lengths: optimal control scheme { R2[2,3,x,®], R3[3,4,y,®] } to optimal control scheme { Rl[l,l,w,®], R2[2,3,x,®], R3[3,4,y,m] } . 105 Table 19. Average run length as a function of the shift in the process mean for the control schemes SI = { Rl[l,l,3,®], R2[2,3, 2,»], R3[4,5, 1,®]. R4[8,8,0,"] } and S2 = { Rl[l,l,3.216,®], R2[2,3,1.962,=], R3[3.4,1.181,»] }. 107 Table 20. Control-scheme comparisons: Double exponential distribution Ill Table 21. Control-scheme comparisons: Cauchy distribution .... 112 Table 22. Control-scheme comparisons: A.R.M.A. process 113 vi LIST OF FIGURES PAGE Figure 1. Diagram of in-control and out-of-control states of a process 31 Figure 2. Power comparison of two control schemes 47 Figure 3. A control chart to detect a positive shift in the process mean 51 Figure 4. The { Rl[l,l,x,®], R2[2,3,y,®] } control scheme 56 Figure 5. Graph of the modified objective function vs. the inner control limit for ARLo = 200, 300, and 500, and k = 1 . 65 Figure 6. Control-limit combinations giving a fixed ARLo. Graph of X = f(ARLo, y), ARLo = 200, 300, 500 66 Figure 7. Optimal control limits for the { R2[2,3,y,®] } family of control schemes. Values of the control limit are given in standard units 70 Figure 8. The { Rl[l,l,x,®], R3[3,4,y,®] } control scheme 73 Figure 9. Graph of the modified objective function vs. the inner control limit for ARLo = 100, 200, ..., 500 and k = 1 , .77 Figure 10. Control-limit combinations giving a fixed in-control average run length, ARLo; ARLo = 100, 200, .... 500 ... 77 Figure 11. Optimal control limits for the { R3[3,4,y,œ] } family of control schemes. Values of the control limit are given in standard units 79 Figure 12. The { R2[2,3,x,"], R3[3,4,y,®] } control scheme 83 Figure 13. Optimal control limits for the { R2[2,3,x,a>], R3[3,4,y,m] } family of control schemes.
Load more

Recommended publications
  • 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.
    [Show full text]
  • Transforming Your Way to Control Charts That Work
    Transforming Your Way to Control Charts That Work November 19, 2009 Richard L. W. Welch Associate Technical Fellow Robert M. Sabatino Six Sigma Black Belt Northrop Grumman Corporation Approved for Public Release, Distribution Unlimited: 1 Northrop Grumman Case 09-2031 Dated 10/22/09 Outline • The quest for high maturity – Why we use transformations • Expert advice • Observations & recommendations • Case studies – Software code inspections – Drawing errors • Counterexample – Software test failures • Summary Approved for Public Release, Distribution Unlimited: 2 Northrop Grumman Case 09-2031 Dated 10/22/09 The Quest for High Maturity • You want to be Level 5 • Your CMMI appraiser tells you to manage your code inspections with statistical process control • You find out you need control charts • You check some textbooks. They say that “in-control” looks like this Summary for ln(COST/LOC) ASU Log Cost Model ASU Peer Reviews Using Lognormal Probability Density Function ) C O L d o M / w e N r e p s r u o H ( N L 95% Confidence Intervals 4 4 4 4 4 4 4 4 4 4 Mean -0 -0 -0 -0 -0 -0 -0 -0 -0 -0 r r y n n n l l l g a p a u u u Ju Ju Ju u M A J J J - - - A - - -M - - - 3 2 9 - Median 4 8 9 8 3 8 1 2 2 9 2 2 1 2 2 1 Review Closed Date ASU Eng Checks & Elec Mtngs • You collect some peer review data • You plot your data . A typical situation (like ours, 5 years ago) 3 Approved for Public Release, Distribution Unlimited: Northrop Grumman Case 09-2031 Dated 10/22/09 The Reality Summary for Cost/LOC The data Probability Plot of Cost/LOC Normal 99.9 99
    [Show full text]
  • Overview of Statistical Process Control Part 1 by Christopher Henderson in This Article, We Begin to Provide a Four-Part Overview of Statistical Process Control
    Issue 117 March 2019 Overview of Statistical Process Control Part 1 By Christopher Henderson In this article, we begin to provide a four-part overview of statistical process control. Page 1 Overview of Here is the outline. First, we’ll introduce the basics of statistical Statistical Process process control, including the motivation for statistical process Control Part 1 control. We’ll discuss control chart basics, including how to set up a control chart, and how to monitor a control chart. We’ll then discuss process capability index. Page 5 Technical Tidbit We begin by introducing statistical process control. Statistical Process Control, or SPC, is a collection of problem-solving tools the industry uses to achieve process stability and reduce variability in a Page 8 Ask the Experts process. This could be a manufacturing or testing process. The primary visualization tool for SPC is the control chart. This chart was developed by Dr. Walter Shewhart, who worked at Bell Laboratories Page 10 Spotlight back in the 1920s. Why is statistical process control important? If we back up for a minute to think about the problem from a philosophical point of view, Page 13 Upcoming Courses we can’t fix problems unless we first understand them. The question then would be, “okay, how do we understand them?” We do this by taking measurements, and in particular, measurements of pertinent parameters. We may not know which parameters are important priority ahead of time, so this may require trial and error. Once we have our measurement values, what do we do with them? The next Issue 117 March 2019 step is to utilize mathematical models—in particular, statistics—to organize the data, track it, and look for changes.
    [Show full text]
  • Chapter 15 Statistics for Quality: Control and Capability
    Aaron_Amat/Deposit Photos Statistics for Quality: 15 Control and Capability Introduction CHAPTER OUTLINE For nearly 100 years, manufacturers have benefited from a variety of statis­ tical tools for the monitoring and control of their critical processes. But in 15.1 Statistical Process more recent years, companies have learned to integrate these tools into their Control corporate management systems dedicated to continual improvement of their 15.2 Variable Control processes. Charts ● Health care organizations are increasingly using quality improvement methods 15.3 Process Capability to improve operations, outcomes, and patient satisfaction. The Mayo Clinic, Johns Indices Hopkins Hospital, and New York-Presbyterian Hospital employ hundreds of quality professionals trained in Six Sigma techniques. As a result of having these focused 15.4 Attribute Control quality professionals, these hospitals have achieved numerous improvements Charts ranging from reduced blood waste due to better control of temperature variation to reduced waiting time for treatment of potential heart attack victims. ● Acushnet Company is the maker of Titleist golf balls, which is among the most popular brands used by professional and recreational golfers. To maintain consistency of the balls, Acushnet relies on statistical process control methods to control manufacturing processes. ● Cree Incorporated is a market-leading innovator of LED (light-emitting diode) lighting. Cree’s light bulbs were used to glow several venues at the Beijing Olympics and are being used in the first U.S. LED-based highway lighting system in Minneapolis. Cree’s mission is to continually improve upon its manufacturing processes so as to produce energy-efficient, defect-free, and environmentally 15-1 19_psbe5e_10900_ch15_15-1_15-58.indd 1 09/10/19 9:23 AM 15-2 Chapter 15 Statistics for Quality: Control and Capability friendly LEDs.
    [Show full text]
  • Quality Control I: Control of Location
    Lecture 12: Quality Control I: Control of Location 10 October 2005 This lecture and the next will be about quality control methods. There are two reasons for this. First, it’s intrinsically important for engineering, but the basic math is all stuff we’ve seen already — mostly, it’s the central limit theorem. Second, it will give us a fore-taste of the issues which will come up in the second half of the course, on statistical inference. Your textbook puts quality control in the last chapter, but we’ll go over it here instead. 1 Random vs. Systematic Errors An important basic concept is to distinguish between two kinds of influences which can cause errors in a manufacturing process, or introduce variability into anything. On the one hand, there are systematic influences, which stem from particular factors, influence the process in a consistent way, and could be cor- rected by manipulating those factors. This is the mis-calibrated measuring in- strument, defective materials, broken gears in a crucial machine, or the worker who decides to shut off the cement-stirer while he finishes his bottle of vodka. In quality control, these are sometimes called assignable causes. In principle, there is someone to blame for things going wrong, and you can find out who that is and stop them. On the other hand, some errors and variability are due to essentially random, statistical or noise factors, the ones with no consistent effect and where there’s really no one to blame — it’s just down to the fact that the world is immensely complicated, and no process will ever work itself out exactly the same way twice.
    [Show full text]
  • Control Charts and Trend Analysis for ISO/IEC 17025:2005
    QMS Quick Learning Activity Controls and Control Charting Control Charts and Trend Analysis for ISO/IEC 17025:2005 www.aphl.org Abbreviation and Acronyms • CRM-Certified Reference Materials • RM-Reference Materials • PT-Proficiency Test(ing) • QMS-Quality Management Sytem • QC-Quality Control • STD-Standard Deviation • Ct-cycle threshold QMS Quick Learning Activity ISO/IEC 17025 Requirements Section 5.9 - Assuring the quality of test results and calibration results • quality control procedures for monitoring the validity of tests undertaken • data recorded so trends are detected • where practicable, statistical techniques applied to the reviewing of the results QMS Quick Learning Activity ISO/IEC 17025 Requirements Monitoring • Planned • Reviewed • May include o Use of Certified Reference Materials (CRM) and/or Reference Materials (RM) o Proficiency testing (PT) o Replicate tests o Retesting o Correlation of results for different characteristics QMS Quick Learning Activity Quality Management System (QMS) The term ‘Quality Management System’ covers the quality, technical, and administrative system that governs the operations of the laboratory. The laboratory’s QMS will have procedures for monitoring the validity of tests and calibrations undertaken in the laboratory. QMS Quick Learning Activity Quality Management System (QMS) Quality Manual may state: The laboratory monitors the quality of test results by the inclusion of quality control measures in the performance of tests and participation in proficiency testing programs. The laboratory
    [Show full text]
  • CHAPTER 3: SIMPLE EXTENSIONS of CONTROL CHARTS 1. Introduction
    CHAPTER 3: SIMPLE EXTENSIONS OF CONTROL CHARTS 1. Introduction: Counting Data and Other Complications The simple ideas of statistical control, normal distribution, control charts, and data analysis introduced in Chapter 2 give a good start on learning to analyze data -- and hence ultimately to improve quality. But as you try to apply these ideas, you will sometimes encounter challenging complications that you won't quite know how to deal with. In Chapter 3, we consider several common complications and show how slight modifications or extensions of the ideas of Chapter 2 can be helpful in dealing with them. As usual, we shall rely heavily on examples. So long as the data approximately conform to randomness and normality, as in all but one example of Chapter 2, we have a system both for data analysis and quality improvement. To see whether the data do conform, we have these tools: • The run chart gives a quick visual indication of whether or not the process is in statistical control. • The runs count gives a numerical check on the visual impression from the run chart. • If the process is in control, we can check to see whether the histogram appears to be roughly what we would expect from an underlying normal distribution. • If we decide that the process is in control and approximately normally distributed, then a control chart, histogram, and summary statistical measure tell us what we need from the data: • Upper and lower control limits on the range of variation of future observations. • If future observations fall outside those control limits -- are "outliers" -- we can search for possible special causes impacting the process.
    [Show full text]
  • Control Charts for Means (Simulation)
    PASS Sample Size Software NCSS.com Chapter 290 Control Charts for Means (Simulation) Introduction This procedure allows you to study the run length distribution of Shewhart (Xbar), Cusum, FIR Cusum, and EWMA process control charts for means using simulation. This procedure can also be used to study charts with a single observation at each sample. The in-control mean and standard deviation can be input directly or a specified number of in-control preliminary samples can be simulated based on a user-determined in-control distribution. The out-of- control distribution is flexible in terms of distribution type and distribution parameters. The Shewhart, Cusum, and EWMA parameters can also be flexibly input. This procedure can also be used to determine the necessary sample size to obtain a given run length. Simulation Details If the in-control mean and in-control standard deviation are assumed to be known, the steps to the simulation process are as follows (assume a sample consists of n observations). 1. An out-of-control sample of size n is generated according to the specified distribution parameters of the out- of-control distribution. 2. The average of the sample is produced and, if necessary for the particular type of control chart, the standard deviation. 3. Based on the control chart criteria, it is determined whether this sample results in an out-of-control signal. 4. If the sample results in an out-of-control signal, the sample number is recorded as the run length for that simulation. If the sample does not result in an out-of-control signal, return to Step 1.
    [Show full text]
  • Application of the Generalized Lambda Distributions in a Statistical Process Control Methodology
    Application of the generalized lambda distributions in a statistical process control methodology Benjamin Fournier, Nicolas Rupin, Maxence Bigerelle, Denis Najjar, Alain Iost To cite this version: Benjamin Fournier, Nicolas Rupin, Maxence Bigerelle, Denis Najjar, Alain Iost. Application of the generalized lambda distributions in a statistical process control methodology. Journal of Process Control, Elsevier, 2006, 16 (10), pp.1087-1098. 10.1016/j.jprocont.2006.06.009. hal-01315201 HAL Id: hal-01315201 https://hal.archives-ouvertes.fr/hal-01315201 Submitted on 12 May 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Application of the generalized lambda distributions in a statistical process control methodology B. Fournier a,*, N. Rupin a,1, M. Bigerelle a,b, D. Najjar a, A. Iost a a LMPGM Laboratoire de Me´tallurgie Physique et Ge´nie des Mate´riaux, CNRS UMR 8517, Caracte´risation et Proprie´te´sdelaPe´risurface, ENSAM, 8 Boulevard Louis XIV, 59046 Lille cedex, France b Laboratoire Roberval, FRE 2833, UTC/CNRS, Centre de Recherches de Royallieu, BP20529, 60205 Compie`gne, France Abstract In statistical process control (SPC) methodology, quantitative standard control charts are often based on the assumption that the observations are normally distributed.
    [Show full text]
  • Performance Excellence in the Wood Products Industry: Statistical Process Control
    Archival copy. For current information, see the OSU Extension Catalog: https://catalog.extension.oregonstate.edu/em8726 EM 8726 • June 1999 $2.50 PERFORMANCE EXCELLENCE IN THE WOOD PRODUCTS INDUSTRY Part 1: An Introduction S. Leavengood and J. Reeb The objective of this series is to help improve the competitiveness of Oregon’s wood products manufacturers. This can be achieved by fostering a companywide focus on and dedication to continuous quality improvement. W. Edwards Deming (1982) described the “chain reaction of quality.” 1. Quality is improved. 2. Costs decrease due to more efficient use of machines and materials and less scrap, rework, errors, and downtime. 3. Productivity increases as a result of cost decreases. 4. The company can capture the market with better quality and a lower price. 5. Competitive position improves, and the company stays in business. 6. Jobs are retained, and jobs are added as the company grows. Given our objective, you may be wondering why we have chosen to focus solely on Statistical Process Control (SPC) and not on all quality improve- ment tools. As you will discover in future publications in this series, we do not, in fact, limit our focus solely to SPC. We also discuss other tools such as Pareto analysis, flow charts, cause-and-effect diagrams, and histograms. The focus, however, is on SPC because we believe that, in the continuous- quality-improvement tool kit, SPC is the primary tool for monitoring, con- trol, and diagnostics. For this reason, we have chosen to discuss other quality-improvement tools in the context of how they support implementa- tion and use of SPC.
    [Show full text]
  • A Practical Guide to Selecting the Right Control Chart
    A Practical Guide to Selecting the Right Control Chart InfinityQS International, Inc. | 12601 Fair Lakes Circle | Suite 250 | Fairfax, VA 22033 | www.infinityqs.com A Practical Guide to Selecting the Right Control Chart Introduction Control charts were invented in the 1920’s by Dr. Walter Shewhart as a visual tool to determine if a manufacturing process is in statistical control. If the control chart indicates the manufacturing process is not in control, then corrections or changes should be made to the process parameters to ensure process and product consistency. For manufacturers, control charts are typically the first indication that something can be improved and warrants a root cause analysis or other process improvement investigation. Today, control charts are a key tool for quality control and figure prominently in lean manufacturing and Six Sigma efforts. Variables Data – With over 300 types of control charts available, selecting the most appropriate one Measurements taken on a for a given situation can be overwhelming. You may be using only one or two types of continuous scale such as charts for all your manufacturing process data simply because you haven’t explored time, weight, length, height, further possibilities or aren’t sure when to use others. Choosing the wrong type of temperature, pressure, etc. control chart may result in “false positives” because the chart may not be sensitive These measurements can enough for your process. Or there may be ways to analyze parts and processes you have decimals. thought weren’t possible, resulting in new insights for possible process improvements. Attribute Data – This guide leads quality practitioners through a simple decision tree to select the right Measurements taken control chart to improve manufacturing processes and product quality.
    [Show full text]
  • Control Charts As a Productivity Improvement Tool in Construction Jospeh Hubert Ault Purdue University, [email protected]
    Purdue University Purdue e-Pubs College of Technology Masters Theses College of Technology Theses and Projects 4-4-2013 Control Charts as a Productivity Improvement Tool in Construction Jospeh Hubert Ault Purdue University, [email protected] Follow this and additional works at: http://docs.lib.purdue.edu/techmasters Part of the Construction Engineering and Management Commons Ault, Jospeh Hubert, "Control Charts as a Productivity Improvement Tool in Construction" (2013). College of Technology Masters Theses. Paper 79. http://docs.lib.purdue.edu/techmasters/79 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. Graduate School ETD Form 9 (Revised 12/07) PURDUE UNIVERSITY GRADUATE SCHOOL Thesis/Dissertation Acceptance This is to certify that the thesis/dissertation prepared By Joe H. Ault Entitled Control Charts as a Productivity Improvement Tool in Construction Master of Science For the degree of Is approved by the final examining committee: James Jenkins Chair Randy Rapp Joseph Orczyk To the best of my knowledge and as understood by the student in the Research Integrity and Copyright Disclaimer (Graduate School Form 20), this thesis/dissertation adheres to the provisions of Purdue University’s “Policy on Integrity in Research” and the use of copyrighted material. Approved by Major Professor(s): ____________________________________James Jenkins ____________________________________ Approved by: Mark Shaurette 04/04/2013 Head of the Graduate Program Date CONTROL CHARTS AS A PRODUCTIVITY IMPROVEMENT TOOL IN CONSTRUCTION A Thesis Submitted to the Faculty of Purdue University by Joe Ault In Partial Fulfillment of the Requirements for the Degree of Master of Science May 2013 Purdue University West Lafayette, Indiana ii TABLE OF CONTENTS Page LIST OF TABLES ............................................................................................................
    [Show full text]