Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework

Wa-Muzemba Anselm Tshibangu

Abstract

From the performance view point, manufacturing strategy relates to the decision about where to focus concentration among quality, speed, dependability, flexibility and cost. This study analyzes a hypothetical flexible manufacturing system (FMS) and aims to illustrate an optimization procedure based on a variance reduction applied on two strategic performance measures, namely the Throughput Rate (TR) and the Mean Flow Time (MFT). The study uses a Taguchi robust design of experiments (DOE) methodology to model and simulate the hypothetical FMS, analyzes the output of the simulations, then proposes a unique and hybrid (empirical-analytical) methodology to quickly uncover the optimal setting of operating parameters. The robust design is used to guarantee the system stability necessary to improve the system and validate the outcomes. Using the key principle of the Six Sigma methodology that advocates a reduction of variability to improve quality and processes the proposed methodology quickly reaches a near optimum by considering both the main and interaction effects of the control factors that will minimize the variability of the performances. Fine-tuned follow-up runs may be necessary to compromise and uncover the true optimum.

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Paper Citation


in Harvard Style

Tshibangu W. (2017). Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework . In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 295-303. DOI: 10.5220/0006436702950303


in Bibtex Style

@conference{icinco17,
author={Wa-Muzemba Anselm Tshibangu},
title={Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={295-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006436702950303},
isbn={978-989-758-263-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Flexible Manufacturing System Optimization by Variance Minimization: A Six Sigma Approach Framework
SN - 978-989-758-263-9
AU - Tshibangu W.
PY - 2017
SP - 295
EP - 303
DO - 10.5220/0006436702950303