R. Jock MacKay

An Overview of the Shainin System™ for Quality Improvement

ABSTRACT The Shainin System™ (SS) is the name given to a problem solving system, with its associated strategies and tools, developed by Dorian Shainin, and widely used and promoted in the manufacturing sector. Dorian Shainin also called this system Statistical Engineering, reflecting his engineering education and background. The consulting firm, Shainin LLC, offers the system under the trademarked name Red X® Strategy. Much of SS is neither well documented, nor adequately discussed in peer-reviewed journals. The goal of this article is to provide an overview of SS, a critical assessment, and a brief comparison with other industrial problem solving systems. The emphasis is on a discussion of the guiding philosophy and principles. Some specific SS tools are examined and compared with alternative methods. In our assessment, the Shainin System is valuable for many types of problems and many of its elements have been, or should be, incorporated into other process improvement methodologies. However, many of the statistical tools and methods promoted in conjunction with SS are neither novel nor necessarily the best.

Detecting Changes in the Mean from Censored Lifetime Data

In many industrial and medical applications observations are censored either due to inherent limitations or cost/time considerations. For example, with many products their lifetimes are sufficiently long that it is infeasible to test all products until failure even using accelerated testing. As a result, often a limited stress test is performed and only a proportion of the true failure times are observed. In such situations, it may be desirable to monitor the process quality using repeated lifetesting on samples of the process output. However, with highly censored observations a direct application of traditional monitoring procedures is not appropriate. In this article, Shewhart type control charts based on the conditional expected value weight are developed for monitoring processes where the censoring occurs at a fixed level. An example is provided to illustrate the application of this methodology.

Monitoring processes with data censored owing to competing risks by using exponentially weighted moving average control charts

In industry, process monitoring is widely employed to detect process changes rapidly. However, in some industrial applications observations are censored. For example, when testing breaking strengths and failure times often a limited stress test is performed. With censored observations, a direct application of traditional monitoring procedures is not appropriate. When the censoring occurs due to competing risks, we propose a control chart based on conditional expected values to detect changes in the mean strength. To protect against possible confounding caused by changes in the mean of the censoring mechanism we also suggest a similar chart to detect changes in the mean censoring level. We provide an example of monitoring bond strength to illustrate the application of this methodology.

Statistical Engineering — Forming the Foundations

Editors: Christine M. Anderson-Cook, Lu Lu, Panelists: Gordon Clark, Stephanie P. DeHart, Roger Hoerl, Bradley Jones, R. Jock MacKay, Douglas Montgomery, Peter A. Parker, James Simpson, Ronald Snee, Stefan H. Steiner, Jennifer Van Mullekom, G. Geoff Vining, Alyson G. Wilson Los Alamos National Laboratory, Los Alamos, New Mexico Ohio State University, Columbus, Ohio DuPont, Roanoke, Virginia GE Global Research, Schenectady, New York SAS, Cary, North Carolina University of Waterloo, Waterloo, Ontario, Canada Arizona State University, Tempe, Arizona NASA, Langley, Virginia Eglin Air Force Base, Valparaiso, Florida Snee Associates, Newark, Delaware DuPont, Richmond, Virginia Virginia Tech, Blacksburg, Virginia Institute for Defense Analyses, Washington, DC INTRODUCTION

Assessment of a Binary Measurement System in Current Use

Binary measurement systems that classify parts as pass or fail are widely used in industry, especially for systematic inspection in high-volume processes. In this context, we are likely to have available a large number of previously measured passed and failed parts. To support production and quality improvement, it is important to assess the misclassification rates, e.g., the probability of failing a conforming part or passing a nonconforming part. We may also want to estimate the unknown conforming rate. Here we focus on the assessment of a binary measurement system when no gold-standard measurement system is available. The standard assessment plan is to repeatedly measure a sample of parts and use a latent class model. We demonstrate the substantial benefit of supplementing the standard plan with the available data from the previously measured parts. We propose new sampling plans and compare them with the standard plan with respect to the precision of the estimators of the misclassification rates. We also give recommendations for planning an assessment study when we can sample from a population of previously measured parts.

Assessing a Binary Measurement System

Assessing measurement systems is a necessary task in all industrial contexts. While a great deal has been written about assessing measurement systems that yield continuous outputs, little work addresses binary measurement systems despite their widespread use. This article proposes two new plans for assessing a binary measurement system that are applicable when we can assume the pass rate of the system is known. This assumption is often reasonable when we need to assess a system used for 100% inspection in a production process. The plans provide estimates of the misclassification rates as well as the proportion of conforming items produced. The two methods are compared to existing plans.

Strategies for Variability Reduction

An important goal of quality improvement in manufacturing is the reduction of variability in product characteristics. Producing more consistent output improves product performance and may reduce manufacturing costs. This article discusses and contrasts ..

EXTENT OF DOUBLE-BROODING AND SEASONAL MOVEMENT OF NESTING FEMALES IN A NORTHERN POPULATION OF WOOD THRUSHES

Abstract Accurate estimates of the amount of double-brooding within a population are an important parameter for assessing the population sustainability of forest birds. In 1998 and 1999, we color-banded adult female Wood Thrushes (Hylocichla mustelina) at 70 nests early in the breeding season to assess the frequency of double-brooding in a northern population. We found that double-brooding was a common breeding strategy among Wood Thrushes in southern Ontario. Forty-seven females fledged first broods and 74% (n = 35) of them initiated egg-laying in a subsequent nest. It is possible that at least 87% (n = 41) of the females were double-brooded, based on the evidence of 6 later nests built within 50 m of successful first nests that were depredated or fledged young before their owners could be identified. Other second-brood nests were probably missed because they were overlooked and because some females moved considerable distances (100–400 m) between nestings. Most birds that failed in their early nesting attempt were not found again on the site, precluding verification of their renesting efforts.

Monitoring Multiple Stream Processes

ABSTRACT In industry, processes with multiple streams or gauges in parallel are common. We discuss monitoring such processes to detect changes in both the overall process mean and changes in the individual stream or gauge means. We propose two new control chart statistics based on an F test and a likelihood ratio test. One appealing aspect of these approaches is that they can be implemented either with or without process parameter estimates obtained from previous data (i.e., from phase 1 implementation of the control chart). These proposals are shown to compare favorably to available methods. The article is motivated by a truck assembly process in which wheel alignment characteristics are measured on every truck by one of four alignment machines, arranged in parallel within the overall process.

Statistical Engineering—Roles for Statisticians and the Path Forward

Experts from diverse areas of industry, government, and academia are asked about the changing roles for statisticians in the SE workplace and discuss some of the opportunities and challenges for the future.