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....
Saved in:
| Main Authors: | , , , |
|---|---|
| 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 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Universiti Putra Malaysia |
| Language: | English |
| Summary: | 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. |
|---|