Data mining: a tool for detecting cyclical disturbances in supply networks
Article
Afify, A., Dimov, S., Naim, M., Valeva, V. and Shukla, V. 2007. Data mining: a tool for detecting cyclical disturbances in supply networks. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture. 221 (12), pp. 1771-1785. https://doi.org/10.1243/09544054JEM879
Type | Article |
---|---|
Title | Data mining: a tool for detecting cyclical disturbances in supply networks |
Authors | Afify, A., Dimov, S., Naim, M., Valeva, V. and Shukla, V. |
Abstract | Disturbances in supply chains may be either exogenous or endogenous. The ability automatically to detect, diagnose, and distinguish between the causes of disturbances is of prime importance to decision makers in order to avoid uncertainty. The spectral principal component analysis (SPCA) technique has been utilized to distinguish between real and rogue disturbances in a steel supply network. The data set used was collected from four different business units in the network and consists of 43 variables; each is described by 72 data points. The present paper will utilize the same data set to test an alternative approach to SPCA in detecting the disturbances. The new approach employs statistical data pre-processing, clustering, and classification learning techniques to analyse the supply network data. In particular, the incremental k-means |
Publisher | SAGE Publications |
Journal | Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture |
ISSN | 0954-4054 |
Publication dates | |
2007 | |
Publication process dates | |
Deposited | 17 Feb 2010 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1243/09544054JEM879 |
Language | English |
File |
https://repository.mdx.ac.uk/item/823w1
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