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:	T Technology
Online Access:	http://eprints.usm.my/46785/1/Extended%20Nearest%20Centroid%20Neighbor%20Method%20With%20Training%20Set%20Reduction%20For%20Classification.pdf
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id	my-usm-ep.46785
record_format	uketd_dc
spelling	my-usm-ep.467852021-11-17T03:42:09Z Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification 2020-06-01 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 Thesis http://eprints.usm.my/46785/ http://eprints.usm.my/46785/1/Extended%20Nearest%20Centroid%20Neighbor%20Method%20With%20Training%20Set%20Reduction%20For%20Classification.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology T Technology
spellingShingle	T Technology T Technology 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
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
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
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2020
url	http://eprints.usm.my/46785/1/Extended%20Nearest%20Centroid%20Neighbor%20Method%20With%20Training%20Set%20Reduction%20For%20Classification.pdf
_version_	1747821728249151488

Extended Nearest Centroid Neighbor Method With Training Set Reduction For Classification

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