Conceptual Framework for Measuring Project Benefits Using Belief—Plausibility and Type 1 Fuzzy Inference System

Ghildyal, A; Ru, D; Chang, E; Joiner, K; Saberi, M; Despande, A

Conceptual Framework for Measuring Project Benefits Using Belief—Plausibility and Type 1 Fuzzy Inference System

Ghildyal, A Ru, D Chang, E Joiner, K Saberi, M

Despande, A

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Publisher:: Springer, Cham
Publication Type:: Chapter
Citation:: Recent Developments and the New Direction in Soft-Computing Foundations and Applications, 2021, 393, pp. 243-256
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Ghildyal, A
dc.contributor.author	Ru, D
dc.contributor.author	Chang, E
dc.contributor.author	Joiner, K
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Despande, A
dc.date.accessioned	2022-06-22T07:12:33Z
dc.date.available	2022-06-22T07:12:33Z
dc.date.issued	2021-01-01
dc.identifier.citation	Recent Developments and the New Direction in Soft-Computing Foundations and Applications, 2021, 393, pp. 243-256
dc.identifier.isbn	978-3-030-47123-1
dc.identifier.uri	http://hdl.handle.net/10453/158319
dc.description.abstract	We propose a combination of belief and plausibility (Bl–Pl) and Type 1 fuzzy inference system (FIS) methods to measure benefits realization in this reserach. This approach can help line managers trace the project outcomes and validate the benefits and return on investment. BI–Pl computations are centered around an expert’s belief as a focal element or a power set of a classical set. This is part of Type 1 FIS, which is embraced by concepts of partial belief and fuzzy sets due to the approximate reasoning of the experts and the fuzzy rule base system. The project’s output can be ranked based on the difference between Bl-Pl, while Type 1 FIS allows us to transform expert knowledge or experience and then trace the project benefits automatically. A commentary on various governing parameters in enterprise benefit management, expert classification and an illustrative example using belief and plausibility form an integral part of this Chapter.
dc.format.extent	46
dc.language	en
dc.publisher	Springer, Cham
dc.relation.ispartof	Recent Developments and the New Direction in Soft-Computing Foundations and Applications
dc.relation.ispartofseries	Studies in Fuzziness and Soft Computing
dc.relation.isbasedon	10.1007/978-3-030-47124-8_20
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Conceptual Framework for Measuring Project Benefits Using Belief—Plausibility and Type 1 Fuzzy Inference System
dc.type	Chapter
utslib.citation.volume	393
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	2022-06-22T07:12:32Z
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.volume	393
dc.location	Switzerland

Abstract:

We propose a combination of belief and plausibility (Bl–Pl) and Type 1 fuzzy inference system (FIS) methods to measure benefits realization in this reserach. This approach can help line managers trace the project outcomes and validate the benefits and return on investment. BI–Pl computations are centered around an expert’s belief as a focal element or a power set of a classical set. This is part of Type 1 FIS, which is embraced by concepts of partial belief and fuzzy sets due to the approximate reasoning of the experts and the fuzzy rule base system. The project’s output can be ranked based on the difference between Bl-Pl, while Type 1 FIS allows us to transform expert knowledge or experience and then trace the project benefits automatically. A commentary on various governing parameters in enterprise benefit management, expert classification and an illustrative example using belief and plausibility form an integral part of this Chapter.

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http://hdl.handle.net/10453/158319