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Determining process variability using fuzzy triangular distribution in dynamic value stream mapping

One of the lean tools is value stream mapping (VSM), which used to visually map and analyze the flow of materials, information and processes required to deliver a product or service to customer. VSM is widely used to streamline processes, reduce lead time and enhance overall operational performance....

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Main Authors: Manoharan, Thulasi, Abdul Aziz, Faieza, Leman, Zulkiffle, Ahmad, Siti Azfanizam
Format: Article
Language:English
Published: Universiti Putra Malaysia Press 2023
Online Access:http://psasir.upm.edu.my/id/eprint/107270/1/17%20JST-4362-2023.pdf
http://psasir.upm.edu.my/id/eprint/107270/
http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-4362-2023
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spelling my.upm.eprints.1072702024-10-17T01:55:50Z http://psasir.upm.edu.my/id/eprint/107270/ Determining process variability using fuzzy triangular distribution in dynamic value stream mapping Manoharan, Thulasi Abdul Aziz, Faieza Leman, Zulkiffle Ahmad, Siti Azfanizam One of the lean tools is value stream mapping (VSM), which used to visually map and analyze the flow of materials, information and processes required to deliver a product or service to customer. VSM is widely used to streamline processes, reduce lead time and enhance overall operational performance. While VSM is a powerful tool, there are some challenges associated with its conventional application in manufacturing industry. Conventional VSM typically represents a snapshot of the value stream at a specific point in time. This static representation might not capture the dynamic and evolving nature of modern manufacturing environments. Hence this research is conducted to address the problem of static representation of conventional VSM by applying Fuzzy Triangular Distribution (TFN) in the manufacturing industry by introducing a more flexible and dynamic approach. A conveyor manufacturing company was selected as a case study based on the high variety and low volume type of manufacturing process. TFN approach was used to analyze variabilities in process parameters to identify their mean, minimum, and maximum values and remove all the outliers. Integrating TFN with VSM gives dynamic behavior to the conventional VSM. Based on the identifications, appropriate lean improvement tools were applied to develop an optimized future VSM. As a result, the future state map shows a 71.74 and 19.45 of improvement ratio in terms of production lead time and value-added time respectively compared to the current VSM. This study can be further extended by investigating how the reduction of lead time and WIP inventory can be helpful in the cost reduction of a company. Universiti Putra Malaysia Press 2023 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/107270/1/17%20JST-4362-2023.pdf Manoharan, Thulasi and Abdul Aziz, Faieza and Leman, Zulkiffle and Ahmad, Siti Azfanizam (2023) Determining process variability using fuzzy triangular distribution in dynamic value stream mapping. Pertanika Journal of Science and Technology, 32 (4). pp. 1765-1788. ISSN 0128-7680; ESSN: 2231-8526 http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-4362-2023 10.47836/pjst.32.4.17
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description One of the lean tools is value stream mapping (VSM), which used to visually map and analyze the flow of materials, information and processes required to deliver a product or service to customer. VSM is widely used to streamline processes, reduce lead time and enhance overall operational performance. While VSM is a powerful tool, there are some challenges associated with its conventional application in manufacturing industry. Conventional VSM typically represents a snapshot of the value stream at a specific point in time. This static representation might not capture the dynamic and evolving nature of modern manufacturing environments. Hence this research is conducted to address the problem of static representation of conventional VSM by applying Fuzzy Triangular Distribution (TFN) in the manufacturing industry by introducing a more flexible and dynamic approach. A conveyor manufacturing company was selected as a case study based on the high variety and low volume type of manufacturing process. TFN approach was used to analyze variabilities in process parameters to identify their mean, minimum, and maximum values and remove all the outliers. Integrating TFN with VSM gives dynamic behavior to the conventional VSM. Based on the identifications, appropriate lean improvement tools were applied to develop an optimized future VSM. As a result, the future state map shows a 71.74 and 19.45 of improvement ratio in terms of production lead time and value-added time respectively compared to the current VSM. This study can be further extended by investigating how the reduction of lead time and WIP inventory can be helpful in the cost reduction of a company.
format Article
author Manoharan, Thulasi
Abdul Aziz, Faieza
Leman, Zulkiffle
Ahmad, Siti Azfanizam
spellingShingle Manoharan, Thulasi
Abdul Aziz, Faieza
Leman, Zulkiffle
Ahmad, Siti Azfanizam
Determining process variability using fuzzy triangular distribution in dynamic value stream mapping
author_facet Manoharan, Thulasi
Abdul Aziz, Faieza
Leman, Zulkiffle
Ahmad, Siti Azfanizam
author_sort Manoharan, Thulasi
title Determining process variability using fuzzy triangular distribution in dynamic value stream mapping
title_short Determining process variability using fuzzy triangular distribution in dynamic value stream mapping
title_full Determining process variability using fuzzy triangular distribution in dynamic value stream mapping
title_fullStr Determining process variability using fuzzy triangular distribution in dynamic value stream mapping
title_full_unstemmed Determining process variability using fuzzy triangular distribution in dynamic value stream mapping
title_sort determining process variability using fuzzy triangular distribution in dynamic value stream mapping
publisher Universiti Putra Malaysia Press
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/107270/1/17%20JST-4362-2023.pdf
http://psasir.upm.edu.my/id/eprint/107270/
http://www.pertanika.upm.edu.my/pjst/browse/regular-issue?article=JST-4362-2023
_version_ 1814054627013820416
score 13.211869

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