Spare parts inventory management in a semiconductor fab

Industry 4.0 and Internet of Things (IoT) concepts have been growing in popularity, but there still is a strong resistance to its adaptation in industries. The team at NTU could implement process-oriented forecasts to estimate accurate spare part inventory control for semiconductor fabrication. This...

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Bibliographic Details
Main Author: Chang, Julian Nicholas Zhong-Yang
Other Authors: Wu Kan
Format: Final Year Project (FYP)
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/73092
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author Chang, Julian Nicholas Zhong-Yang
author2 Wu Kan
author_facet Wu Kan
Chang, Julian Nicholas Zhong-Yang
author_sort Chang, Julian Nicholas Zhong-Yang
collection NTU
description Industry 4.0 and Internet of Things (IoT) concepts have been growing in popularity, but there still is a strong resistance to its adaptation in industries. The team at NTU could implement process-oriented forecasts to estimate accurate spare part inventory control for semiconductor fabrication. This is attained through a program tailored to predict spare part demand, using existing data relating to production and on-hand inventories. This paper proposes a conceptual model and installation of a smart spare part inventory computer program to be used by UMC Singapore’s PM Department. This program is set for full adoption since a year of our collaboration in 2016. Current iterations show a 95% accuracy in predicting spare part demand, based on historical production cycles. After discussing effectiveness of proposed implementation, future works for effective adoption and faster computation is suggested. All of which, hints to realistic adoption of Industry 4.0 and smart infrastructure in the manufacturing industry.
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spelling ntu-10356/730922023-03-04T18:19:17Z Spare parts inventory management in a semiconductor fab Chang, Julian Nicholas Zhong-Yang Wu Kan School of Mechanical and Aerospace Engineering United Microelectronics Company DRNTU::Engineering Industry 4.0 and Internet of Things (IoT) concepts have been growing in popularity, but there still is a strong resistance to its adaptation in industries. The team at NTU could implement process-oriented forecasts to estimate accurate spare part inventory control for semiconductor fabrication. This is attained through a program tailored to predict spare part demand, using existing data relating to production and on-hand inventories. This paper proposes a conceptual model and installation of a smart spare part inventory computer program to be used by UMC Singapore’s PM Department. This program is set for full adoption since a year of our collaboration in 2016. Current iterations show a 95% accuracy in predicting spare part demand, based on historical production cycles. After discussing effectiveness of proposed implementation, future works for effective adoption and faster computation is suggested. All of which, hints to realistic adoption of Industry 4.0 and smart infrastructure in the manufacturing industry. Bachelor of Engineering (Aerospace Engineering) 2018-01-02T07:00:48Z 2018-01-02T07:00:48Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/73092 en Nanyang Technological University 65 p. application/pdf
spellingShingle DRNTU::Engineering
Chang, Julian Nicholas Zhong-Yang
Spare parts inventory management in a semiconductor fab
title Spare parts inventory management in a semiconductor fab
title_full Spare parts inventory management in a semiconductor fab
title_fullStr Spare parts inventory management in a semiconductor fab
title_full_unstemmed Spare parts inventory management in a semiconductor fab
title_short Spare parts inventory management in a semiconductor fab
title_sort spare parts inventory management in a semiconductor fab
topic DRNTU::Engineering
url http://hdl.handle.net/10356/73092
work_keys_str_mv AT changjuliannicholaszhongyang sparepartsinventorymanagementinasemiconductorfab