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Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model
The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual busi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4752506/ https://www.ncbi.nlm.nih.gov/pubmed/26871694 http://dx.doi.org/10.1371/journal.pone.0148391 |
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author | Damij, Nadja Boškoski, Pavle Bohanec, Marko Mileva Boshkoska, Biljana |
author_facet | Damij, Nadja Boškoski, Pavle Bohanec, Marko Mileva Boshkoska, Biljana |
author_sort | Damij, Nadja |
collection | PubMed |
description | The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results. |
format | Online Article Text |
id | pubmed-4752506 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-47525062016-02-26 Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model Damij, Nadja Boškoski, Pavle Bohanec, Marko Mileva Boshkoska, Biljana PLoS One Research Article The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results. Public Library of Science 2016-02-12 /pmc/articles/PMC4752506/ /pubmed/26871694 http://dx.doi.org/10.1371/journal.pone.0148391 Text en © 2016 Damij et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Damij, Nadja Boškoski, Pavle Bohanec, Marko Mileva Boshkoska, Biljana Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model |
title | Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model |
title_full | Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model |
title_fullStr | Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model |
title_full_unstemmed | Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model |
title_short | Ranking of Business Process Simulation Software Tools with DEX/QQ Hierarchical Decision Model |
title_sort | ranking of business process simulation software tools with dex/qq hierarchical decision model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4752506/ https://www.ncbi.nlm.nih.gov/pubmed/26871694 http://dx.doi.org/10.1371/journal.pone.0148391 |
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