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A generalization of the Theory of Constraints: Choosing the optimal improvement option with consideration of variability and costs

A generalization of the Theory of Constraints: Choosing the optimal improvement option with... AbstractThe Theory of Constraints (TOC) was proposed in the mid-1980s and has significantly impacted productivity improvement in manufacturing systems. Although it is intuitive and easy to understand, its conclusions are mainly derived from deterministic settings or based on mean values. This article generalizes the concept of TOC to stochastic settings through the performance analysis of queueing systems and simulation studies. We show that, in stochastic settings, the conventional TOC may not be optimal, and a throughput bottleneck should be considered in certain types of machines at the planning stage. Incorporating the system variability and improvement costs, the Generalized Process Of OnGoing Improvement (GPOOGI) is developed in this study. It shows that improving a frontend machine in a production line can be more effective than improving the throughput bottleneck. The findings indicate that we should consider the dependence among stations and the cost of improvement options during productivity improvement and should not simply improve the system bottleneck according to the conventional TOC. According to the GPOOGI, the managers of production systems would be able to make optimal decision during the continuous improvement process. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png IISE Transactions Taylor & Francis

A generalization of the Theory of Constraints: Choosing the optimal improvement option with consideration of variability and costs

IISE Transactions , Volume 52 (3): 12 – Mar 3, 2020

A generalization of the Theory of Constraints: Choosing the optimal improvement option with consideration of variability and costs

IISE Transactions , Volume 52 (3): 12 – Mar 3, 2020

Abstract

AbstractThe Theory of Constraints (TOC) was proposed in the mid-1980s and has significantly impacted productivity improvement in manufacturing systems. Although it is intuitive and easy to understand, its conclusions are mainly derived from deterministic settings or based on mean values. This article generalizes the concept of TOC to stochastic settings through the performance analysis of queueing systems and simulation studies. We show that, in stochastic settings, the conventional TOC may not be optimal, and a throughput bottleneck should be considered in certain types of machines at the planning stage. Incorporating the system variability and improvement costs, the Generalized Process Of OnGoing Improvement (GPOOGI) is developed in this study. It shows that improving a frontend machine in a production line can be more effective than improving the throughput bottleneck. The findings indicate that we should consider the dependence among stations and the cost of improvement options during productivity improvement and should not simply improve the system bottleneck according to the conventional TOC. According to the GPOOGI, the managers of production systems would be able to make optimal decision during the continuous improvement process.

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References (46)

Publisher
Taylor & Francis
Copyright
Copyright © 2020 “IISE”
ISSN
1545-8830
eISSN
0740-817X
DOI
10.1080/24725854.2019.1632503
Publisher site
See Article on Publisher Site

Abstract

AbstractThe Theory of Constraints (TOC) was proposed in the mid-1980s and has significantly impacted productivity improvement in manufacturing systems. Although it is intuitive and easy to understand, its conclusions are mainly derived from deterministic settings or based on mean values. This article generalizes the concept of TOC to stochastic settings through the performance analysis of queueing systems and simulation studies. We show that, in stochastic settings, the conventional TOC may not be optimal, and a throughput bottleneck should be considered in certain types of machines at the planning stage. Incorporating the system variability and improvement costs, the Generalized Process Of OnGoing Improvement (GPOOGI) is developed in this study. It shows that improving a frontend machine in a production line can be more effective than improving the throughput bottleneck. The findings indicate that we should consider the dependence among stations and the cost of improvement options during productivity improvement and should not simply improve the system bottleneck according to the conventional TOC. According to the GPOOGI, the managers of production systems would be able to make optimal decision during the continuous improvement process.

Journal

IISE TransactionsTaylor & Francis

Published: Mar 3, 2020

Keywords: Theory of Constraints; productivity improvement; simulation; queueing theory

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