Wong 2010a - Revision history

Scipediacontent at 15:21, 21 January 2021

2021-01-21T15:21:19Z

Scipediacontent: Scipediacontent moved page Draft Content 728675924 to Wong 2010a

2020-10-14T10:56:37Z

Scipediacontent moved page Draft Content 728675924 to Wong 2010a

Scipediacontent: Created page with " == Abstract == Most hi-tech industries owe at least some of their success to being in the right place at the right time. This is especially true for the aircraft parts manuf..."

2020-10-14T10:56:35Z

Created page with " == Abstract == Most hi-tech industries owe at least some of their success to being in the right place at the right time. This is especially true for the aircraft parts manuf..."

New page

== Abstract ==

Most hi-tech industries owe at least some of their success to being in the right place at the right time. This is especially true for the aircraft parts manufacturer approval (PMA) industry. A PMA is both a design approval and a production approval. It is issued for the production of modification or replacement parts for aircraft, which includes materials, parts, processes, and appliances. In the current economic climate, airlines throughout the world are looking for partners with financial stability. The reason is simple, they want partners that will continue to support them with extra savings opportunities in the short and long-term future. As more and more PMA companies are advertising through the Internet, a supplier performance measurement model applying to each of these networked organizations will facilitate the airline selection of long-term PMA partners. In this chapter, the Mahalanobis Taguchi System (MTS) approach, a multivariate data based selection system, will be used to identify the promising PMA suppliers. Suppliers who are known to be promising are called promising groups and their performance data sets are used to create a reference metric for the promising PMA supplier population. In view of the synergetic performance of neural network and data mining technologies, it is expected that this MTS-based PMA partner selection method, implementing through a neural data mining system (NDMS) will provide a practical solution in the identification of the promising PMA suppliers.

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_728675924-beopen246-4256-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://pdfs.semanticscholar.org/84dc/a9cd8e75da8e447e6d9872f638b033c40671.pdf http://pdfs.semanticscholar.org/84dc/a9cd8e75da8e447e6d9872f638b033c40671.pdf]

← Older revision		Revision as of 15:21, 21 January 2021
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	Most hi-tech industries owe at least some of their success to being in the right place at the right time. This is especially true for the aircraft parts manufacturer approval (PMA) industry. A PMA is both a design approval and a production approval. It is issued for the production of modification or replacement parts for aircraft, which includes materials, parts, processes, and appliances. In the current economic climate, airlines throughout the world are looking for partners with financial stability. The reason is simple, they want partners that will continue to support them with extra savings opportunities in the short and long-term future. As more and more PMA companies are advertising through the Internet, a supplier performance measurement model applying to each of these networked organizations will facilitate the airline selection of long-term PMA partners. In this chapter, the Mahalanobis Taguchi System (MTS) approach, a multivariate data based selection system, will be used to identify the promising PMA suppliers. Suppliers who are known to be promising are called promising groups and their performance data sets are used to create a reference metric for the promising PMA supplier population. In view of the synergetic performance of neural network and data mining technologies, it is expected that this MTS-based PMA partner selection method, implementing through a neural data mining system (NDMS) will provide a practical solution in the identification of the promising PMA suppliers.		Most hi-tech industries owe at least some of their success to being in the right place at the right time. This is especially true for the aircraft parts manufacturer approval (PMA) industry. A PMA is both a design approval and a production approval. It is issued for the production of modification or replacement parts for aircraft, which includes materials, parts, processes, and appliances. In the current economic climate, airlines throughout the world are looking for partners with financial stability. The reason is simple, they want partners that will continue to support them with extra savings opportunities in the short and long-term future. As more and more PMA companies are advertising through the Internet, a supplier performance measurement model applying to each of these networked organizations will facilitate the airline selection of long-term PMA partners. In this chapter, the Mahalanobis Taguchi System (MTS) approach, a multivariate data based selection system, will be used to identify the promising PMA suppliers. Suppliers who are known to be promising are called promising groups and their performance data sets are used to create a reference metric for the promising PMA supplier population. In view of the synergetic performance of neural network and data mining technologies, it is expected that this MTS-based PMA partner selection method, implementing through a neural data mining system (NDMS) will provide a practical solution in the identification of the promising PMA suppliers.
−
−	~~Document type: Part of book or chapter of book~~
−
−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_728675924-beopen246-4256-document.pdf</pdf>~~


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	* [http://pdfs.semanticscholar.org/84dc/a9cd8e75da8e447e6d9872f638b033c40671.pdf http://pdfs.semanticscholar.org/84dc/a9cd8e75da8e447e6d9872f638b033c40671.pdf]		* [http://pdfs.semanticscholar.org/84dc/a9cd8e75da8e447e6d9872f638b033c40671.pdf http://pdfs.semanticscholar.org/84dc/a9cd8e75da8e447e6d9872f638b033c40671.pdf]
		+
		+	* [https://www.igi-global.com/chapter/pma-supplier-selection-using-mahalanobis/17748 https://www.igi-global.com/chapter/pma-supplier-selection-using-mahalanobis/17748],
		+	: [https://www.scipedia.com/public/Wong_2010a https://www.scipedia.com/public/Wong_2010a],
		+	: [https://academic.microsoft.com/#/detail/2769704247 https://academic.microsoft.com/#/detail/2769704247]
		+
		+	* [https://www.igi-global.com/viewtitle.aspx?TitleId=17748 https://www.igi-global.com/viewtitle.aspx?TitleId=17748],
		+	: [http://dx.doi.org/10.4018/978-1-59904-885-7.ch162 http://dx.doi.org/10.4018/978-1-59904-885-7.ch162]

← Older revision	Revision as of 10:56, 14 October 2020
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