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Supply chain efficiency transformation: analysis of raw material staff selection based on preference selection index

IAES International Journal of Artificial Intelligence · Vol. 14 No. 3 (2025) · pp. 2459–2470

DOI
10.11591/ijai.v14.i3.pp2459-2470
ISSN
2252-8938, 2089-4872
Published
2025-06-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

In the era of intense business globalization, supply chain management is becoming a vital key to improving the efficiency and competitiveness of enterprises. The selection of raw material supply staff is an important aspect of supply chain management, affecting smooth supply, efficiency and cost control. This research focuses on using the preference selection index (PSI) method in the selection of raw material supply staff. PSI is a tool that integrates data from multiple criteria in the selection process. The results show that PSI provides an effective evaluation in staff selection, identifies key variables that affect selection success and analyzes the impact of using PSI on supply chain efficiency and company productivity. This research fills the knowledge gap in the application of PSI in the context of raw material supply staff selection and contributes to the understanding of efficient and sustainable supply chain management. The results provide valuable insights for industries and organizations that depend on reliable raw material supply and demonstrate the potential to improve the overall staff selection process. The outcome of this study found that Muliyono received a PSI score of 0.9643 and was ranked first, while Ramli received a PSI score of 0.9548 and was ranked second.

Keywords

Collaboration network of the authors

40 authors · 98 co-authorship links · showing the 40 most connected

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  • Universitas Sumatera Utara
  • Universitas Halu Oleo
  • Universitas Muhammadiyah Sumatera Utara
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Arif Ridho LubisAffiliation unknown4
Muharman LubisAffiliation unknown4
Romi Fadillah RahmatAffiliation unknown2
Arif Ridho LubisAffiliation unknown2
Syahril EfendiUniversitas Sumatera Utara2
Michael J. WattsAffiliation unknown2
Romi Fadillah RahmatAffiliation unknown2
Fitriana FitrianaAffiliation unknown1
Baharina BaharinaAffiliation unknown1
Ilyas IsmailAffiliation unknown1
Rizanizarli RizanizarliAffiliation unknown1
Teuku Muttaqin MansurAffiliation unknown1
Eryansyah EryansyahAffiliation unknown1
Sari NurfadilaAffiliation unknown1
Machdalena ViantyAffiliation unknown1
Andika Putra PratamaAffiliation unknown1
Raihan PutryAffiliation unknown1
Mursyid DjawasAffiliation unknown1
Fawwaz Bin AdenanAffiliation unknown1
Ahmad RizkiAffiliation unknown1
Soraya devyAffiliation unknown1
Utari ZulfianaAffiliation unknown1
Julham JulhamAffiliation unknown1
Idham KamilAffiliation unknown1
Hariyadi HariyadiAffiliation unknown1
Ferry FachrizalAffiliation unknown1
Mahyuddin Khairuddin Matyuso NasutionAffiliation unknown1
Herman MawengkangAffiliation unknown1
Anton Abdulbasah KamilAffiliation unknown1
Suherman SuhermanAffiliation unknown1
Sarah PurnamawatiAffiliation unknown1
Mhd Faris PratamaAffiliation unknown1
Sharfina FazaAffiliation unknown1
Irvan IrvanUniversitas Halu Oleo1
Habibi Ramdani SafitriAffiliation unknown1
Muhammad BasriAffiliation unknown1
Akrim AkrimUniversitas Muhammadiyah Sumatera Utara1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Supply chain efficiency transformation: analysis of raw material staff selection based on preference selection index</dc:title>
	<dc:creator>Amrullah, Amrullah</dc:creator>
	<dc:creator>Idaman, Akbar</dc:creator>
	<dc:creator>Al-Khowarizmi, Al-Khowarizmi</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Accuracy analysis; Decision support system; Multi-criteria decision making; Preference selection index; Raw material staff selection; Supply chain efficiency transformation</dc:subject>
	<dc:description xml:lang="en-US">In the era of intense business globalization, supply chain management is becoming a vital key to improving the efficiency and competitiveness of enterprises. The selection of raw material supply staff is an important aspect of supply chain management, affecting smooth supply, efficiency and cost control. This research focuses on using the preference selection index (PSI) method in the selection of raw material supply staff. PSI is a tool that integrates data from multiple criteria in the selection process. The results show that PSI provides an effective evaluation in staff selection, identifies key variables that affect selection success and analyzes the impact of using PSI on supply chain efficiency and company productivity. This research fills the knowledge gap in the application of PSI in the context of raw material supply staff selection and contributes to the understanding of efficient and sustainable supply chain management. The results provide valuable insights for industries and organizations that depend on reliable raw material supply and demonstrate the potential to improve the overall staff selection process. The outcome of this study found that Muliyono received a PSI score of 0.9643 and was ranked first, while Ramli received a PSI score of 0.9548 and was ranked second.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">Universitas Muhammadiyah Sumatera Utara and Universitas Satya Terra Bhinneka</dc:contributor>
	<dc:date>2025-06-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23254</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i3.pp2459-2470</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 3: June 2025; 2459-2470</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23254/14588</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>