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Sustainable Development· 2026Q1

An Integrated MCDM Approach for Establishing Suppliers' Capability Differences and Selection Based on Sustainable Industry 4.0 Initiatives

Nidhi Ahuja, Anshu Gupta, P. C. Jha

Short summary

A novel hybrid framework integrating fuzzy Delphi, FAHP-FTOPSIS, and MANOVA robustly assesses and ranks suppliers based on sustainability and Industry 4.0 criteria, demonstrating high ranking stability (Spearman's ρ = 0.9273–0.9758).

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Key points

  • A four-step hybrid framework integrates fuzzy Delphi, FAHP-FTOPSIS, and MANOVA for supplier assessment.
  • Quality, price, and delivery performance received the highest weights in criteria significance.
  • MANOVA identified employee welfare, price, R&D, agility, delivery, green/smart technology, and social responsibility as key inter-group performance differentiators.
  • Sensitivity analysis showed strong ranking stability (Spearman's ρ = 0.9273–0.9758), validating the framework's robustness.

AI-generated from the title and abstract; the full text is not read.

Abstract

ABSTRACT The growing emphasis on sustainability, driven by increased customer awareness and shifting consumer needs, has fundamentally impacted corporate buying methods. Concurrently, the rapid expansion of Industry 4.0 technologies such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) altered production systems by increasing their cognitive ability and adaptability, influencing production operations and strategic supplier selection decisions. Motivated by these imperatives, the current study proposes a comprehensive hybrid framework for evaluating supplier performance and developing supplier skills within an integrated sustainability and Industry 4.0 environment. The proposed framework is made up of four sequential methodological steps. The fuzzy Delphi method (FDM) first identifies and validates critical supplier assessment criteria through structured expert elicitation. Second, a hybrid fuzzy analytic hierarchy process and fuzzy technique for order of preference by similarity to ideal solution (FAHP‐FTOPSIS) framework computes criteria significance weights, with quality, price and delivery performance receiving the most weight; subsequently, it assesses and prioritises supplier performance. Third, multivariate analysis of variance (MANOVA) categorises suppliers into high‐ and low‐performing groups, with employee welfare, price, R&D, agility, delivery performance, green and smart technology and social responsibility being the most significant inter‐group performance distinguishers. Fourth, a sensitivity study with nine weight perturbation scenarios shows strong ranking stability (Spearman's ρ = 0.9273–0.9758; mean ρ = 0.9502), highlighting the framework's robustness and data‐driven nature. The findings establish an empirical foundation for targeted supplier development programs and benchmarking approaches. The framework is empirically validated through a real‐world case study within an Indian electronic retail supply chain.

The authors' abstract, as published at the source. Sustainable Development, 2026 · DOI ↗

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Field: Management Science and Operations Research

Management Science and Operations ResearchDecision Sciences