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Quality Improvement Papers

2017 Papers

• Telling the Story of Your Process with Graphical Enhancements of Control Charts
  Ransdell, Bucky; SAS Institute, Inc. 2017

  This paper explains how you can use the SHEWHART procedure in SAS/QC software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore within-subgroup variation with box-and-whisker plots, and add information that improves the interpretability of the chart.

2016 Papers

• Improving Health Care Quality with the RAREEVENTS Procedure
  Ransdell, Bucky; SAS Institute, Inc. 2016

  The RAREEVENTS procedure, which is new in SAS/QC 14.1, produces rare events charts. This paper presents an overview of PROC RAREEVENTS and illustrates how you can use rare events charts to improve health care quality.

2015 Papers

• Catering to Your Tastes: Using PROC OPTEX to Design Custom Experiments, with Applications in Food Science and Field Trials
  Pereira, Clifford; Oregon State University; Tobias, Randy; SAS Institute, Inc. 2015

  This paper reviews the features of PROC OPTEX and shows them in action using examples from field trials and food science experimentation. PROC OPTEX is a useful tool for all these situations, doing the designing and freeing the scientist to think about the food and the biology.

  Click here to view the %RBDEVAL macro used in this paper.

2014 Papers

• Accelerated Tests as an Effective Means of Quality Improvement
  Gutierrez, Roberto G.; SAS Institute, Inc. 2014

  This paper is about AFT models and it guides you through building AFT(accelerated failure time) models that predict time to failure.

2013 Papers

• Good as New or Bad as Old? Analyzing Recurring Failures with the RELIABILITY Procedure
  Gutierrez, Roberto G.; SAS Institute, Inc. 2013

  This paper describes how you can use PROC RELIABILITY to estimate and compare MCFs.

2012 Papers

• It's All About Variation: Improving Your Business Process with Statistical Thinking — Revised 2012
  Rodriguez, Robert N.; SAS Institute, Inc. 2012

  This paper explains how statistical thinking and statistical process monitoring, which have been practiced in manufacturing for the past thirty years, are proving valuable for process improvement in business environments that range from health care to financial services. Basic examples drawn from real scenarios introduce the statistical concepts and show how to get started with SAS/QC software.

• Monitoring the Variation in Yout Multivariate Process: An Introduction to the MVP Procedures
  Christian, J. Blair; Ransdell, Bucky; SAS Institute, Inc. 2012

  This paper, using an example from social media sentiment analysis, illustrates how the MVPMODEL, MVPMONITOR, and MVPDIAGNOSE procedures work together and demonstrates the power of the methods for discovering and diagnosing unusual variation.

2010 Papers

• It's All About Variation: Improving Your Business Process with Statistical Thinking
  Rodriguez, Robert N.; SAS Institute, 2010

  This paper explains how statistical thinking and statistical process monitoring, which have been practiced in manufacturing for the past thirty years, are proving valuable for process improvement in business environments that range from health care to financial services. Basic examples drawn from real scenarios introduce the statistical concepts and show how to get started with SAS/QC software.

• Statistical Process Control for Health Care Quality Improvement Using SAS/QC Software— Revised 2010
  Rodriguez, Robert N.; Ransdell, Bucky; SAS Institute

  This paper provides examples that explain the use of SAS statistical software to analyze health care data with u charts, p charts, control charts for individual measurements, methods for discovering trends over time, basic forecasting methods, comparative histograms, analysis of means for rates and proportions, and model-based adjustments of mortality rates.

2003 Paper

• Using the SAS System to Construct and Operate Control Charts with Randomized Control Limits
  Wludyka, Peter; University of North Florida, SUGI Proceedings, 2003

  Attribute control charts have discrete jumps in the quantities plotted on the charts, which means that the chart designer has only a discrete set of unique control limits to choose from when designing a chart. These charts have a small set of in-control Average Run Lengths (ARLs) from which to determine the appropriate control scheme. In a chart with randomized control limits the operational control limit in an epoch is determined by a random choice from among a set of control limits. The resulting control chart will then have a predetermined in-control ARL. These charts can be thought of as ones with optimal ARL properties. SAS programs for designing and operating these charts will be provided.

2001 Papers

• New Methods for Modeling Reliability Using Degradation Data
  Ramírez, José G.; W.L. Gore & Associates, Inc.; Johnston, Gordon; SAS Institute, SUGI Proceedings, 2001

  This paper describes two methods for the analysis of degradation of product performance or material properties over time. The first uses the SAS/QC RELIABILITY procedure with pseudo-failure times to predict a given percentile of the failure time distribution, and the second uses the SAS/STAT NLMIXED procedure to fit concave degradation models, and some SAS macros to estimate the failure distribution.

2000 Papers

• Data Warehousing for Manufacturing Yield Improvement
  Rutledge, Robert A.; IBM Storage Technology Division, SUGI Proceedings, 2000

  This paper will describe the data warehouse/decision support/data mining system that we have developed to improve disk drive manufacturing yields in the IBM Storage Technology Division.

• Motorola’s Engineering Data Analysis System: 10 Years of Analytical Excellence
  Whitney, C. Michael; Fowler, Leslie; Motorola, SUGI Proceedings, 2000

  The SAS system is used as the basis of the EDAS – the Engineering Data Analysis System - an application developed for engineers at Motorola’s Advanced Products Research and Development Laboratory. SAS products discussed in this paper include SAS/AF, QC, GRAPH, STAT, and others. The intended audience is anyone interested in semiconductor data analysis.

1999 and Earlier Papers

• The Quality Data Warehouse: Solving Problems for the Enterprise
  Klenz, Brad; Fulenwider, Donna; SAS Institute, SUGI Proceedings, 1999

  This paper discusses some of the emerging business problems in quality improvement and how the field has evolved. The presentation covers what information is delivered with a data warehouse and how that information is delivered, steps are laid out for building a Quality Data Warehouse, and finally, some important additional points on exploiting the Quality Data Warehouse are covered.

• Reliability Data Analysis in the SAS System
  Johnston, Gordon; SAS Institute, SUGI Proceedings, 1997

  This paper discusses The RELIABILITY procedure, a recent addition to SAS/QC software. It provides tools for reliability and survival data analysis as well as for recurrence data analysis.

• Statistical Methods for Reliability Data Using SAS Software
  Meeker, William Q.; Iowa State University; Escobar, Luis A.; Louisiana State University, SUGI Proceedings, 1997

  In this paper we consider the use of modern methods for analyzing time-to-failure data that can be implemented using SAS software.

• Optimal Designs with Non-exchangeable Runs
  Tobias, Randall; SAS Institute, 1996

  This paper introduces an algorithm for searching for D-optimal experimental designs in the presence of certain kinds of non-exchangeability-in particular, when there are mixed covariates associated with units and/or when the units have a non-trivial covariance structure; this algorithm extends and improves on the one of Cook and Nachtsheim (1989) for finding optimal block designs.

• The Structure of Optimal Design Algorithms
  Tobias, Randall; SAS Institute, 1996

  This paper demonstrates how to solve the problem of finding saturated second-order two-level designs by applying the OPTEX procedure of SAS/QC software.

• Saturated Second-Order Two-Level Designs: An Empirical Approach
  Tobias, Randall; SAS Institute, 1996

  Computer methods are used to explore saturated designs which provide for optimal estimation of main effects and interactions between two-level factors. A series of designs is thus discovered, related to a known series but better for k > 6. Also, a relationship is discovered between two different classes designs which should be fruitful for future research.

• Health Care Applications of Statistical Process Control: Examples Using the SAS System
  Rodriguez, Robert N.; SAS Institute, SUGI Proceedings, 1996

  This paper illustrates the use of SAS software to analyze health care data with u charts, p charts, control charts for individual measurements, analysis of means for rates and proportions, simultaneous confidence intervals for proportions, and basic forecasting methods.

• Recent Issues in Statistical Process Control: SAS Solutions Using Statistical Modeling Procedures
  Rodriguez, Robert N.; SAS Institute, SUGI Proceedings, 1994

  This paper demonstrates the use of SAS procedures for statistical modeling in conjunction with the SHEWHART procedure.

Papers are in Portable Document Format (PDF) and can be viewed with the free Adobe Acrobat Reader.

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Powerpoint presentations and SAS programs can be downloaded as zip files.