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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sg-ntu-dr.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 |
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DRNTU::Engineering Chang, Julian Nicholas Zhong-Yang Spare parts inventory management in a semiconductor fab |
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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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Wu Kan |
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Wu Kan Chang, Julian Nicholas Zhong-Yang |
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Final Year Project |
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Chang, Julian Nicholas Zhong-Yang |
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Chang, Julian Nicholas Zhong-Yang |
title |
Spare parts inventory management in a semiconductor fab |
title_short |
Spare parts inventory management in a semiconductor fab |
title_full |
Spare parts inventory management in a semiconductor fab |
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Spare parts inventory management in a semiconductor fab |
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Spare parts inventory management in a semiconductor fab |
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spare parts inventory management in a semiconductor fab |
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2018 |
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http://hdl.handle.net/10356/73092 |
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1759856536848433152 |