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
TypeArticle
TitleData mining: a tool for detecting cyclical disturbances in supply networks.
AuthorsAfify, 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
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.

LanguageEnglish
PublisherSAGE Publications
JournalProceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture
ISSN0954-4054
Publication dates
Print2007
Publication process dates
Deposited17 Feb 2010
Output statusPublished
Digital Object Identifier (DOI)https://doi.org/10.1243/09544054JEM879
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