Fine tuning a bayesian network and fairly allocating resources to improve procurement performance

Abolbashari, MH; Hussain, OK; Saberi, M; Chang, E

Fine tuning a bayesian network and fairly allocating resources to improve procurement performance

Abolbashari, MH Hussain, OK Saberi, M

Chang, E

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Networking and Collaborative Systems, 2018, 8, pp. 3-15
Issue Date:: 2018-01-01

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Field	Value	Language
dc.contributor.author	Abolbashari, MH
dc.contributor.author	Hussain, OK
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Chang, E
dc.contributor.editor	Barolli, L
dc.contributor.editor	Woungang, I
dc.contributor.editor	Hussain, OK
dc.date	2017-08-24
dc.date.accessioned	2022-12-23T04:07:30Z
dc.date.available	2022-12-23T04:07:30Z
dc.date.issued	2018-01-01
dc.identifier.citation	Advances in Intelligent Networking and Collaborative Systems, 2018, 8, pp. 3-15
dc.identifier.isbn	978-3-319-65635-9
dc.identifier.issn	2367-4512
dc.identifier.issn	2367-4520
dc.identifier.uri	http://hdl.handle.net/10453/164534
dc.description.abstract	Procurement is one of the most important activities in any organization. Hence it is vital for an organization to track procurement practices. Through performance measurement, the organization will have a clear understanding on how it’s performing as well as the effect of any action that it makes towards improvement. In our previous work, we proposed a Bayesian Network (BN) model to measure the level of procurement performance in an organization. This paper extends that model in two ways. First it uses the Best-Worst Method (BWM) to adjust the impact of each KPI on the procurement performance according to its importance to the overall business strategy. Second is by using the relative importance of the KPIs, it demonstrates how procurement can be improved by re-allocating the available resources among the KPIs in a fair way.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advances in Intelligent Networking and Collaborative Systems
dc.relation.ispartof	International Conference on Intelligent Networking and Collaborative Systems
dc.relation.ispartofseries	Lecture Notes on Data Engineering and Communications Technologies
dc.relation.isbasedon	10.1007/978-3-319-65636-6_1
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Fine tuning a bayesian network and fairly allocating resources to improve procurement performance
dc.type	Conference Proceeding
utslib.citation.volume	8
utslib.location.activity	Toronto, Canada
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-12-23T04:07:28Z
pubs.finish-date	2017-08-26
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2017-08-24
pubs.volume	8
dc.location	Switzerland

Abstract:

Procurement is one of the most important activities in any organization. Hence it is vital for an organization to track procurement practices. Through performance measurement, the organization will have a clear understanding on how it’s performing as well as the effect of any action that it makes towards improvement. In our previous work, we proposed a Bayesian Network (BN) model to measure the level of procurement performance in an organization. This paper extends that model in two ways. First it uses the Best-Worst Method (BWM) to adjust the impact of each KPI on the procurement performance according to its importance to the overall business strategy. Second is by using the relative importance of the KPIs, it demonstrates how procurement can be improved by re-allocating the available resources among the KPIs in a fair way.

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http://hdl.handle.net/10453/164534