Learning curves for quality and productivity

Learning curves for quality and productivity Purpose – The purpose of this paper is to build a curve that can portray quality level, with standard deviation, as a function of the production process related to elements such as operating time and cumulative units produced. Design/methodology/approach – The Cobb-Douglas multiplicative power model will be introduced to represent the proposed function in simultaneously describing the learning process for productivity and quality. The experimental devices consisted of reflective mirror, path paper, iPod Touch and pen. They were arranged as shown in Plate 1. The students were instructed to draw a line with a pen along the middle of the rail line on the path paper through the mirror indirectly. The iPod Touch acted as a stopwatch to monitor the time taken to complete each experiment. The path paper is shown in Figure 1. This statistical analysis is completed by computer programs, SAS. Findings – This study presented an experiment in which subjects drew a line on a path while looking through a mirror. This study uses the Cobb-Douglas model to regress the S as a function of 0.3366× x 1 −0.347× x 2 −0.011. Research limitations/implications – All units produced are acceptable in quality, disregarding the magnitude of standard deviation in the produced quality level. Like Porteus (1986) with the fixed probabilistic distribution is assumed. The fatigues are ignored in presented curve. In fact, operators are easy to get tired for attending quality and productivity simultaneously. The initial value of operating time or standard deviation for the first unit is estimated from a subject having been trained for a sufficient period of time; however, this consideration does exist in the present experiment. Practical implications – The economic order (production) quantity model with learning effects in a production system could be considered. The other implication could be in a wider framework, such as multistage and multivariate of production development production systems and supply chains. Social implications – For a life cycle application, the criteria considered in resolving the production problem should not only be limited to the costs involved in the production process, but also the quality-related costs incurred after the goods are delivered to customers. Originality/value – Previous works regarding the learning process never mention the quality-related learning process. However, this study aims to achieve the above goals in finding the relationship of quality vs production volume and production time simultaneously. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Quality & Reliability Management Emerald Publishing

Learning curves for quality and productivity

, Volume 32 (8): 15 – Sep 7, 2015
15 pages

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References (36)

Publisher
Emerald Publishing
ISSN
0265-671X
DOI
10.1108/IJQRM-04-2013-0073
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to build a curve that can portray quality level, with standard deviation, as a function of the production process related to elements such as operating time and cumulative units produced. Design/methodology/approach – The Cobb-Douglas multiplicative power model will be introduced to represent the proposed function in simultaneously describing the learning process for productivity and quality. The experimental devices consisted of reflective mirror, path paper, iPod Touch and pen. They were arranged as shown in Plate 1. The students were instructed to draw a line with a pen along the middle of the rail line on the path paper through the mirror indirectly. The iPod Touch acted as a stopwatch to monitor the time taken to complete each experiment. The path paper is shown in Figure 1. This statistical analysis is completed by computer programs, SAS. Findings – This study presented an experiment in which subjects drew a line on a path while looking through a mirror. This study uses the Cobb-Douglas model to regress the S as a function of 0.3366× x 1 −0.347× x 2 −0.011. Research limitations/implications – All units produced are acceptable in quality, disregarding the magnitude of standard deviation in the produced quality level. Like Porteus (1986) with the fixed probabilistic distribution is assumed. The fatigues are ignored in presented curve. In fact, operators are easy to get tired for attending quality and productivity simultaneously. The initial value of operating time or standard deviation for the first unit is estimated from a subject having been trained for a sufficient period of time; however, this consideration does exist in the present experiment. Practical implications – The economic order (production) quantity model with learning effects in a production system could be considered. The other implication could be in a wider framework, such as multistage and multivariate of production development production systems and supply chains. Social implications – For a life cycle application, the criteria considered in resolving the production problem should not only be limited to the costs involved in the production process, but also the quality-related costs incurred after the goods are delivered to customers. Originality/value – Previous works regarding the learning process never mention the quality-related learning process. However, this study aims to achieve the above goals in finding the relationship of quality vs production volume and production time simultaneously.

Journal

International Journal of Quality & Reliability ManagementEmerald Publishing

Published: Sep 7, 2015