Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/52293
Title: Multifaceted Modelling of Complex Business Enterprises
Contributor(s): Chakraborty, Subrata  (author)orcid ; Mengersen, Kerrie (author); Fidge, Colin (author); Ma, Lin (author); Lassen, David (author)
Publication Date: 2015-08-06
Open Access: Yes
DOI: 10.1371/journal.pone.0134052
Handle Link: https://hdl.handle.net/1959.11/52293
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.

Publication Type: Journal Article
Source of Publication: PLoS One, 10(8), p. 1-30
Publisher: Public Library of Science
Place of Publication: United States of America
ISSN: 1932-6203
Fields of Research (FoR) 2020: 460199 Applied computing not elsewhere classified
460902 Decision support and group support systems
460209 Planning and decision making
Socio-Economic Objective (SEO) 2020: 280115 Expanding knowledge in the information and computing sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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