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Decision Support Tools: Machine Learning Application in Smart Planner

Immaculate Project Planning and Execution (PPE) is capital to edge over competitors, decrease costs and honour delivery dates.Project Management Information System (PMIS) is necessary towards an improved and efficient quality of any project.Machine Learning (ML) Algorithms enabled learned the date o...

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Main Authors: Baharom, M.A.A., Rahman, M.S.A., Sabudin, A.R., Nor, M.F.M.
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2023
Online Access:http://scholars.utp.edu.my/id/eprint/34221/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140731150&doi=10.1007%2f978-981-19-1939-8_58&partnerID=40&md5=650b4e28177f8514c68ce15153573c51
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spelling oai:scholars.utp.edu.my:342212023-01-04T02:54:05Z http://scholars.utp.edu.my/id/eprint/34221/ Decision Support Tools: Machine Learning Application in Smart Planner Baharom, M.A.A. Rahman, M.S.A. Sabudin, A.R. Nor, M.F.M. Immaculate Project Planning and Execution (PPE) is capital to edge over competitors, decrease costs and honour delivery dates.Project Management Information System (PMIS) is necessary towards an improved and efficient quality of any project.Machine Learning (ML) Algorithms enabled learned the date of experience to develop insights into various associations between data and outcomes.A defined set of rules prescribed by the analysts makes the probability of the fault possible.In this paper, Regression Model compute across all viable sectors expending the tool for Downstream Business and other Facilities Upstream, including Resource Estimation Schedule Generation.Extending structured information into a reliable database allows super users to define the data structures and completely configurable the settingâ��s dynamics.The model used to decrease the approximation error and measure the closest possible outcome.This subset of artificial intelligence has tremendous potential in improving schedule generation configuration to develop Project Planning timely and financially smartly.This paper aims to share standard protocols and methods applied in ML-aided as a tool in PPE decision making.Additionally, the abundant used data resources devoted to implementing ML are outlined.Finally, ML success as a Decision Support tool in project management by having a Smart Planner in supporting project recommendation accelerates the decision process, increases stakeholder confidence, and minimizes uncertainty; results are reviewed and analyzed where gaps and potential improvement for future projects are being noted and highlighted. © 2023, Institute of Technology PETRONAS Sdn Bhd. Springer Science and Business Media Deutschland GmbH 2023 Article NonPeerReviewed Baharom, M.A.A. and Rahman, M.S.A. and Sabudin, A.R. and Nor, M.F.M. (2023) Decision Support Tools: Machine Learning Application in Smart Planner. Lecture Notes in Mechanical Engineering. pp. 753-760. ISSN 21954356 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140731150&doi=10.1007%2f978-981-19-1939-8_58&partnerID=40&md5=650b4e28177f8514c68ce15153573c51 10.1007/978-981-19-1939-8₅₈ 10.1007/978-981-19-1939-8₅₈
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Immaculate Project Planning and Execution (PPE) is capital to edge over competitors, decrease costs and honour delivery dates.Project Management Information System (PMIS) is necessary towards an improved and efficient quality of any project.Machine Learning (ML) Algorithms enabled learned the date of experience to develop insights into various associations between data and outcomes.A defined set of rules prescribed by the analysts makes the probability of the fault possible.In this paper, Regression Model compute across all viable sectors expending the tool for Downstream Business and other Facilities Upstream, including Resource Estimation Schedule Generation.Extending structured information into a reliable database allows super users to define the data structures and completely configurable the setting�s dynamics.The model used to decrease the approximation error and measure the closest possible outcome.This subset of artificial intelligence has tremendous potential in improving schedule generation configuration to develop Project Planning timely and financially smartly.This paper aims to share standard protocols and methods applied in ML-aided as a tool in PPE decision making.Additionally, the abundant used data resources devoted to implementing ML are outlined.Finally, ML success as a Decision Support tool in project management by having a Smart Planner in supporting project recommendation accelerates the decision process, increases stakeholder confidence, and minimizes uncertainty; results are reviewed and analyzed where gaps and potential improvement for future projects are being noted and highlighted. © 2023, Institute of Technology PETRONAS Sdn Bhd.
format Article
author Baharom, M.A.A.
Rahman, M.S.A.
Sabudin, A.R.
Nor, M.F.M.
spellingShingle Baharom, M.A.A.
Rahman, M.S.A.
Sabudin, A.R.
Nor, M.F.M.
Decision Support Tools: Machine Learning Application in Smart Planner
author_facet Baharom, M.A.A.
Rahman, M.S.A.
Sabudin, A.R.
Nor, M.F.M.
author_sort Baharom, M.A.A.
title Decision Support Tools: Machine Learning Application in Smart Planner
title_short Decision Support Tools: Machine Learning Application in Smart Planner
title_full Decision Support Tools: Machine Learning Application in Smart Planner
title_fullStr Decision Support Tools: Machine Learning Application in Smart Planner
title_full_unstemmed Decision Support Tools: Machine Learning Application in Smart Planner
title_sort decision support tools: machine learning application in smart planner
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/34221/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140731150&doi=10.1007%2f978-981-19-1939-8_58&partnerID=40&md5=650b4e28177f8514c68ce15153573c51
_version_ 1754532143776661504
score 13.223943

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