title: Multifaceted Modelling of Complex Business Enterprises.
abstract: We formalise and present a new generic multifaceted complex system approach for modelling complex business enterprises. Our method has a strong focus on integrating the various data types available in an enterprise which represent the diverse perspectives of various stakeholders. We explain the challenges faced and define a novel approach to converting diverse data types into usable Bayesian probability forms. The data types that can be integrated include historic data, survey data, and management planning data, expert knowledge and incomplete data. The structural complexities of the complex system modelling process, based on various decision contexts, are also explained along with a solution. This new application of complex system models as a management tool for decision making is demonstrated using a railway transport case study. The case study demonstrates how the new approach can be utilised to develop a customised decision support model for a specific enterprise. Various decision scenarios are also provided to illustrate the versatility of the decision model at different phases of enterprise operations such as planning and control.
authors.affiliation: School of Management and Enterprise, University of Southern Queensland, Springfield Central, Queensland, Australia.
authors.forename: Subrata
authors.initials: S
authors.lastname: Chakraborty
authors.affiliation: School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
authors.forename: Kerrie
authors.initials: K
authors.lastname: Mengersen
authors.affiliation: School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia.
authors.forename: Colin
authors.initials: C
authors.lastname: Fidge
authors.affiliation: School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, Queensland, Australia.
authors.forename: Lin
authors.initials: L
authors.lastname: Ma
authors.affiliation: Queensland Rail, Brisbane, Queensland, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Lassen
mesh_terms: D001499:Bayes Theorem; D003362:Cost-Benefit Analysis; D006801:Humans; D008962:Models, Theoretical