AI–MCDM INTEGRATED SUPPLIER SELECTION FRAMEWORK FOR INDIAN MSME MANUFACTURING SECTOR

Supplier selection functions as a critical element for supply chain achievement because Indian micro small and medium enterprises (MSMEs) need to compete in limited resource environments. The evaluation process requires assessment of multiple interconnected factors which include expenses and product quality and delivery dependability and manufacturing expertise and environmental sustainability standards. The current evaluation methods which depend on managerial expertise and traditional MCDM tools fail to account for uncertain data and historical information prediction capabilities. The research presents an AI–MCDM system which helps Indian MSMEs select suppliers through evidence-driven decision making. The proposed system combines Fuzzy AHP with supervised learning algorithms and TOPSIS to create an integrated framework. The research team obtained primary data from 120 manufacturers who operated in five different Indian states. The research developed fuzzy weights for vital evaluation criteria before implementing Random Forest (RF) and Support Vector Machine (SVM and Artificial Neural Network (ANN) models to predict supplier outcomes and achieve final rankings through TOPSIS integration. The Random Forest

• Publication year

  2025

• Venue

  Journal of Convergence in Technology and Management: Global Nexus

• Publication date

  Unknown publication date

• Fields of study

  Not labeled

• Identifiers
  DOI 10.70965/jctmgn.vol.1.issue3.11
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

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