Modelling and simulation of a virtual manufacturing enterprise

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. A manufacturing enterprise is a complex hierarchical entity with multiple components that are essential to the success of the firm. Such an enterprise must implement successful strategic and tactical policies at the various levels of the organization. High-level executive strategies determine long-term investment policies, while lower level decisions affect short-term operations. This research models a manufacturing enterprise and implements the model in a competitive simulation game. The simulation links various decisions made at different levels of the enterprise to its advancement. The simulation is designed to operate in a competitive environment that includes multiple enterprises, depending on the number of users participating in a given exercise. Accurately representing a manufacturing enterprise in a concise simulation model has many challenges that must be overcome. This paper discusses relationship models used to link enterprise factors, as well as methods for aggregating various factor influences. Furthermore, two types of simulation methods and their application to modelling such an enterprise are discussed. These methods include analytical based simulation and discrete-event simulation. A methodology for creating a model that includes both types is presented and a technique for linking the two methods is provided. The methodology is demonstrated using numerical results.