Table of Contents
Advances in Decision Sciences
Volume 2013, Article ID 234939, 23 pages
Research Article

Modelling Framework to Support Decision-Making in Manufacturing Enterprises

1Manufacturing and Materials Department, School of Applied Sciences, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK
2Rolls-Royce plc, P.O. Box 31, Derby DE24 8BJ, UK

Received 29 January 2012; Revised 10 October 2012; Accepted 15 October 2012

Academic Editor: Albert Jones

Copyright © 2013 Tariq Masood and Richard H. Weston. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Systematic model-driven decision-making is crucial to design, engineer, and transform manufacturing enterprises (MEs). Choosing and applying the best philosophies and techniques is challenging as most MEs deploy complex and unique configurations of process-resource systems and seek economies of scope and scale in respect of changing and distinctive product flows. This paper presents a novel systematic enhanced integrated modelling framework to facilitate transformation of MEs, which is centred on CIMOSA. Application of the new framework in an automotive industrial case study is also presented. The following new contributions to knowledge are made: (1) an innovative structured framework that can support various decisions in design, optimisation, and control to reconfigure MEs; (2) an enriched and generic process modelling approach with capability to represent both static and dynamic aspects of MEs; and (3) an automotive industrial case application showing benefits in terms of reduced lead time and cost with improved responsiveness of process-resource system with a special focus on PPC. It is anticipated that the new framework is not limited to only automotive industry but has a wider scope of application. Therefore, it would be interesting to extend its testing with different configurations and decision-making levels.