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A Three R Approach for Supply Chain Business Intelligence

Denton, Paul, Tan, Kim H., Little, David and Bonner, John V.H. (2007) A Three R Approach for Supply Chain Business Intelligence. In: Proceedings of 19th International Conference on Production Research ICPR19, Valparaiso, Chile, 29th July - 2nd August 2007. ICPR. ISBN 978 956 310 7517

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Abstract

Today, manufacturing enterprises are required to improve their competitive performance by establishing effective controls over the complex processes and datasets, both internal and external, which drive their business. At the internal level, mastery of this performance measurement can be assisted through the implementation of Enterprise Resource Planning (ERP) systems, Data Warehouses (DW) and appropriate report writing tools. These can integrate with other best-of-breed Executive Information Systems (EIS) components to create software platforms capable of delivering effective Business Intelligence (BI) that can support superior decision-making. However, at the external level, where there exists a larger degree of uncertainty, a plethora of isolated systems and commercial issues, such as data sensitivity and collaboration, can present formidable challenges. Within this paper we define what Small to Medium sized Enterprise (SME) management requires from supply chain performance and consider whether or not the desired corporate goals can be achieved through the use of effective use of IT-based BI frameworks alone. Through a range of case study implementations a process-orientated approach to effective supply chain BI execution, comprising of 3Rs: Right Quality, Right Time and Right Cost, is evaluated.

Item Type: Book Chapter
Subjects: H Social Sciences > HE Transportation and Communications
Schools: School of Computing and Engineering
School of Computing and Engineering > Systems Engineering Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Cherry Edmunds
Date Deposited: 13 May 2009 15:36
Last Modified: 19 Aug 2015 22:23
URI: http://eprints.hud.ac.uk/id/eprint/4320

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