Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification

Lean manufacturing seeks Kaizen in terms of quality, cost and cycle time. A robust problem-solving often extends to external parties such as vendors, to draw in their unique technology resources and knowledge. The perusal of contemporary peer-reviewed literature reveals limited academic investigatio...

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Main Author: Mukahar, Nordiana
Format: Thesis
Language:English
Published: 2020
Subjects:
Online Access:http://eprints.usm.my/46785/1/Extended%20Nearest%20Centroid%20Neighbor%20Method%20With%20Training%20Set%20Reduction%20For%20Classification.pdf
http://eprints.usm.my/46785/
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Institution: Universiti Sains Malaysia
Language: English
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spelling my.usm.eprints.46785 http://eprints.usm.my/46785/ Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification Mukahar, Nordiana T Technology TK1-9971 Electrical engineering. Electronics. Nuclear engineering Lean manufacturing seeks Kaizen in terms of quality, cost and cycle time. A robust problem-solving often extends to external parties such as vendors, to draw in their unique technology resources and knowledge. The perusal of contemporary peer-reviewed literature reveals limited academic investigation onto such form of partnership; particularly vendor engagement having elements of properly defined risk and reward sharing. In this premise, Vendor Risk and Reward Sharing – Kaizen (VRRS-Kaizen) framework was proposed as a generic and holistic prescriptive system to guide personnel to duly deal with vendors. The objective of the framework is to ensure systematic and effective practice. Plan-Do-Check-Act underpins the framework and dichotomises the relevant stages of Kaizen. VRRS-Kaizen commences with the identification by Kaizen Team for the need of calling in vendors for countermeasure development. Lean tools, proof of concept and multi-criteria scoring methods were used for assessments in the framework. Framework verification was performed through three case studies at an electronic measurement system company in Penang. Their scopes involve 100% elimination of device under test high internal temperature failures (Case Study One), reduction of high workstation electricity by 60.9% and maintenance charges by 55.6% (Case Study Two) and mitigation of high freight charges of Packaging Assembly 64A by 24% (Case Study Three). Evidently different in nature, these three cases have been successfully deployed following the framework. In total, these were translated into RM 204,105.86 in return (between 2017 to 2018), of which 45.52% was shared with vendors as financial reward sharing. The research objectives have been achieved. 2020-06-01 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/46785/1/Extended%20Nearest%20Centroid%20Neighbor%20Method%20With%20Training%20Set%20Reduction%20For%20Classification.pdf Mukahar, Nordiana (2020) Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle T Technology
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Mukahar, Nordiana
Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification
description Lean manufacturing seeks Kaizen in terms of quality, cost and cycle time. A robust problem-solving often extends to external parties such as vendors, to draw in their unique technology resources and knowledge. The perusal of contemporary peer-reviewed literature reveals limited academic investigation onto such form of partnership; particularly vendor engagement having elements of properly defined risk and reward sharing. In this premise, Vendor Risk and Reward Sharing – Kaizen (VRRS-Kaizen) framework was proposed as a generic and holistic prescriptive system to guide personnel to duly deal with vendors. The objective of the framework is to ensure systematic and effective practice. Plan-Do-Check-Act underpins the framework and dichotomises the relevant stages of Kaizen. VRRS-Kaizen commences with the identification by Kaizen Team for the need of calling in vendors for countermeasure development. Lean tools, proof of concept and multi-criteria scoring methods were used for assessments in the framework. Framework verification was performed through three case studies at an electronic measurement system company in Penang. Their scopes involve 100% elimination of device under test high internal temperature failures (Case Study One), reduction of high workstation electricity by 60.9% and maintenance charges by 55.6% (Case Study Two) and mitigation of high freight charges of Packaging Assembly 64A by 24% (Case Study Three). Evidently different in nature, these three cases have been successfully deployed following the framework. In total, these were translated into RM 204,105.86 in return (between 2017 to 2018), of which 45.52% was shared with vendors as financial reward sharing. The research objectives have been achieved.
format Thesis
author Mukahar, Nordiana
author_facet Mukahar, Nordiana
author_sort Mukahar, Nordiana
title Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification
title_short Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification
title_full Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification
title_fullStr Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification
title_full_unstemmed Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification
title_sort extended nearest centroid neighbor method with training set reduction for classification
publishDate 2020
url http://eprints.usm.my/46785/1/Extended%20Nearest%20Centroid%20Neighbor%20Method%20With%20Training%20Set%20Reduction%20For%20Classification.pdf
http://eprints.usm.my/46785/
_version_ 1717094474365009920