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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) · hlm. 2459–2470

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

Abstrak

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.

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  • Universitas Sumatera Utara
  • Universitas Halu Oleo
  • Universitas Muhammadiyah Sumatera Utara
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  • Organisasi berbeda
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Rekan penulisOrganisasiArtikel bersama
Arif Ridho LubisAfiliasi tidak diketahui4
Muharman LubisAfiliasi tidak diketahui4
Romi Fadillah RahmatAfiliasi tidak diketahui2
Arif Ridho LubisAfiliasi tidak diketahui2
Syahril EfendiUniversitas Sumatera Utara2
Michael J. WattsAfiliasi tidak diketahui2
Romi Fadillah RahmatAfiliasi tidak diketahui2
Fitriana FitrianaAfiliasi tidak diketahui1
Baharina BaharinaAfiliasi tidak diketahui1
Ilyas IsmailAfiliasi tidak diketahui1
Rizanizarli RizanizarliAfiliasi tidak diketahui1
Teuku Muttaqin MansurAfiliasi tidak diketahui1
Eryansyah EryansyahAfiliasi tidak diketahui1
Sari NurfadilaAfiliasi tidak diketahui1
Machdalena ViantyAfiliasi tidak diketahui1
Andika Putra PratamaAfiliasi tidak diketahui1
Raihan PutryAfiliasi tidak diketahui1
Mursyid DjawasAfiliasi tidak diketahui1
Fawwaz Bin AdenanAfiliasi tidak diketahui1
Ahmad RizkiAfiliasi tidak diketahui1
Soraya devyAfiliasi tidak diketahui1
Utari ZulfianaAfiliasi tidak diketahui1
Julham JulhamAfiliasi tidak diketahui1
Idham KamilAfiliasi tidak diketahui1
Hariyadi HariyadiAfiliasi tidak diketahui1
Ferry FachrizalAfiliasi tidak diketahui1
Mahyuddin Khairuddin Matyuso NasutionAfiliasi tidak diketahui1
Herman MawengkangAfiliasi tidak diketahui1
Anton Abdulbasah KamilAfiliasi tidak diketahui1
Suherman SuhermanAfiliasi tidak diketahui1
Sarah PurnamawatiAfiliasi tidak diketahui1
Mhd Faris PratamaAfiliasi tidak diketahui1
Sharfina FazaAfiliasi tidak diketahui1
Irvan IrvanUniversitas Halu Oleo1
Habibi Ramdani SafitriAfiliasi tidak diketahui1
Muhammad BasriAfiliasi tidak diketahui1
Akrim AkrimUniversitas Muhammadiyah Sumatera Utara1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>