Data mining: a tool for detecting cyclical disturbances in supply networks.
Afify, Ashraf A. and Dimov, S. S. and Naim, Mohamed and Valeva, Valentina and Shukla, Vinaya (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. ISSN 0954-4054
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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 clustering and the RULES-6 classification rule-learning algorithms, developed by the present authors’ team, have been applied to identify important patterns in the data set. Results show that the proposed approach has the capability automatically to detect and characterize network-wide cyclical disturbances and generate hypotheses about their root cause.
|Research Areas:||Middlesex University Schools and Centres > Business School > International Management and Innovation|
|Citations on ISI Web of Science:||3|
|Deposited On:||17 Feb 2010 09:55|
|Last Modified:||06 Jan 2015 18:39|
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