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Article
Publication date: 1 August 2016

George N Kenyon, R. Samual Sale, Kurt Hozak and Paul Chiou

The purpose of this paper is to develop an yield-based process capability index (PCI), C py , to overcome the shortcomings…

Abstract

Purpose

The purpose of this paper is to develop an yield-based process capability index (PCI), C py , to overcome the shortcomings of existing PCIs that limit their use and lead to inaccurate measures of quality conformance under a variety of common conditions.

Design/methodology/approach

C py is developed conceptually to flexibly and accurately reflect conformance and then used to numerically measure inaccuracies of C pk .

Findings

C py overcomes many of the problems associated with existing PCIs, including C pk . The degree of process distribution non-normality, level of quality (the sigma level), and whether the process is centered or shifted left or right affect the direction and size of process capability error produced by C pk . The accuracy of C pk can be greatly affected by process data that deviate even slightly from normality.

Practical implications

C py offers numerous advantages compared to existing PCIs. It accurately reflects process conformance regardless of the process distribution. It is applicable even if the process has multiple characteristics and with both variable and attribute data. Its calculation is relatively simple and the necessary data for it are likely already captured by most organizations.

Originality/value

The main contributions are the development of a new PCI, C py ; a conceptual analysis of its advantages; and a numerical analysis of the improved accuracy of C py as compared to C pk for shifted and non-shifted process means for normal, nearly normal, and highly non-normal distributions over a range of process variability levels.

Details

International Journal of Quality & Reliability Management, vol. 33 no. 7
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 3 August 2015

Kurt Hozak and Eric O. Olsen

– The purpose of this paper is to develop insights about the psychological factors that contribute to lean’s success as a holistic and adaptive system.

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Abstract

Purpose

The purpose of this paper is to develop insights about the psychological factors that contribute to lean’s success as a holistic and adaptive system.

Design/methodology/approach

The authors use the best-selling book “Thinking, Fast and Slow” (TFAS) (2011) by Nobel-prize winner Daniel Kahneman to provide a familiar lens for readers who might not otherwise be familiar with the psychology theories that are used in this paper to study lean conceptually. With this approach, the paper sheds light on psychological factors that tie together many of the philosophies, principles and practices of lean.

Findings

The paper shows how lean’s philosophies, principles and practices provide a synergistic and self-reinforcing system that drives employee thinking and actions. TFAS characterizes thought processes as “fast System 1 thinking” that relies on intuition and “slow System 2 thinking” that is more rational and logical. Lean psychology eliminates waste and adds customer value by supporting, enhancing and taking advantage of beneficial fast thinking and motivating and imposing appropriate slow thinking.

Originality/value

The authors develop the concept of lean psychology to describe the relationship between psychology theories and lean. By applying lean psychology, organizations can go beyond superficially adopting a checklist of tools and techniques to more fully take advantage of lean and improve their operations performance.

Details

International Journal of Lean Six Sigma, vol. 6 no. 3
Type: Research Article
ISSN: 2040-4166

Keywords

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