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An exploratory study to identify rogue seasonality in a steel company's supply network using spectral principal component analysis

Thornhill, Nina F. and Naim, Mohamed Mohamed 2006. An exploratory study to identify rogue seasonality in a steel company's supply network using spectral principal component analysis. European Journal of Operational Research 172 (1) , pp. 146-162. 10.1016/j.ejor.2004.09.044

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Abstract

Variability in the information flows within asupplynetwork requires production companies to either track the variations, hence leading to increased production on-costs, or to buffer themselves via the use of inventory which leads to stock holding costs. Customer demands generate variability, often in the form of seasonal patterns, but must be satisfied. In contrast, “rogueseasonality”, i.e. unintended variability, may be generated by acompany’s own internal processes such as inventory and production control systems. Importantly, rogueseasonality may propagate through asupplynetwork. Thus there is a motivation for automated detection of network-wide rogueseasonality and for the diagnosis of its root cause. In this article, a data-driven technique known as spectral principal component analysis is used to detect and characterise cyclical disturbances in a supply network that indicate seasonality. All the information and material flows participating in each disturbance are detected, and the distribution of each disturbance enables a hypothesis to be reached about its root cause. The technique is applied to a supply network consisting of four autonomous business units in the steel industry. Two main cyclical disturbances were detected and diagnosed. One was found to be rogue seasonality and the other was externally induced by the pattern of customer orders.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Business (Including Economics)
Centre for Advanced Manufacturing Systems At Cardiff (CAMSAC)
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HD Industries. Land use. Labor
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
H Social Sciences > HE Transportation and Communications
T Technology > TN Mining engineering. Metallurgy
Uncontrolled Keywords: Uncertainty; Variability; Time series; Frequency spectrum; Multivariate analysis; Spectral principal component analysis
Publisher: Elsevier
ISSN: 0377-2217
Related URLs:
Last Modified: 04 Jun 2017 04:25
URI: http://orca-mwe.cf.ac.uk/id/eprint/38608

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