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<p>The rapid evolution of communication and information technologies—such as cloud computing, the Internet of Things (IoT), big data analytics, and machine learning—has revolutionized traditional manufacturing, giving rise to intelligent and interconnected production ecosystems. These technological advancements not only streamline production processes but also reshape supplier selection strategies by incorporating both conventional and sustainability-oriented evaluation criteria. In light of these developments, this study proposes a novel multi-criteria group decision-making (MCGDM) framework for supplier selection under the N-Cubic Fuzzy Set (NCFS) environment. NCFSs offer a robust mathematical structure for capturing uncertain, vague, and imprecise information, particularly within the interval <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40747_2025_2057_Article_IEq1.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="47" /> </InlineMediaObject> <EquationSource Format="TEX">\([-1 ,0]\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">[</mo> <mo>-</mo> <mn>1</mn> <mo>,</mo> <mn>0</mn> <mo stretchy="false">]</mo> </mrow> </math></EquationSource> </InlineEquation>, making them highly suitable for complex, real-world decision-making scenarios. To facilitate effective aggregation of expert judgments, three advanced aggregation operators are introduced: the N-Cubic Generalized Fuzzy Weighted Average (NCGFWA), the N-Cubic Generalized Fuzzy Ordered Weighted Average (NCGFOWA), and the N-Cubic Generalized Fuzzy Hybrid Weighted Average (NCGFHWA). These operators are designed to systematically consolidate the preferences of multiple decision-makers and produce a reliable ranking of potential suppliers. The proposed methodology is validated through a real-world case study involving a manufacturer of agricultural machinery and implements. The results demonstrate the practical effectiveness, flexibility, and robustness of the NCFS-based framework in supporting supplier selection within the paradigm of smart manufacturing.</p>

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Smart supplier selection using N-cubic fuzzy aggregation: a case study in agricultural manufacturing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月1日

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  • Xi Chen,
  • Adnan Khurshid,
  • Tahir Abbas,
  • Muhammad Gulistan,
  • Mohammed M. Alshamiri,
  • Muhammad Azhar

摘要

The rapid evolution of communication and information technologies—such as cloud computing, the Internet of Things (IoT), big data analytics, and machine learning—has revolutionized traditional manufacturing, giving rise to intelligent and interconnected production ecosystems. These technological advancements not only streamline production processes but also reshape supplier selection strategies by incorporating both conventional and sustainability-oriented evaluation criteria. In light of these developments, this study proposes a novel multi-criteria group decision-making (MCGDM) framework for supplier selection under the N-Cubic Fuzzy Set (NCFS) environment. NCFSs offer a robust mathematical structure for capturing uncertain, vague, and imprecise information, particularly within the interval \([-1 ,0]\) [ - 1 , 0 ] , making them highly suitable for complex, real-world decision-making scenarios. To facilitate effective aggregation of expert judgments, three advanced aggregation operators are introduced: the N-Cubic Generalized Fuzzy Weighted Average (NCGFWA), the N-Cubic Generalized Fuzzy Ordered Weighted Average (NCGFOWA), and the N-Cubic Generalized Fuzzy Hybrid Weighted Average (NCGFHWA). These operators are designed to systematically consolidate the preferences of multiple decision-makers and produce a reliable ranking of potential suppliers. The proposed methodology is validated through a real-world case study involving a manufacturer of agricultural machinery and implements. The results demonstrate the practical effectiveness, flexibility, and robustness of the NCFS-based framework in supporting supplier selection within the paradigm of smart manufacturing.