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Prioritizing Barriers to Industry 4.0 Adoption in Manufacturing MSMEs of Uttarakhand: A Fuzzy AHP and Sensitivity Analysis Approach

Ankur Mittal Karan Oberoi

Economic Sciences2026https://doi.org/10.69889/t4e2rf11article
ABDC C
Weight
0.50

What the paper says

Industry 4.0 technologies offer significant opportunities for improving the competitiveness of manufacturing MSMEs, yet their adoption remains limited due to several persistent challenges. This study identifies and prioritizes the key barriers to Industry 4.0 adoption in manufacturing MSMEs using a fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach. Drawing on an extensive literature review, four main barriers and sixteen sub-barriers were identified and evaluated through expert judgments. Fuzzy AHP was applied to rank these barriers under conditions of uncertainty, and sensitivity analysis was conducted to test the stability of the ranking results. The findings show that a small number of critical barriers play a dominant role in hindering adoption, and their rankings remain largely stable across different scenarios. The study provides practical insights for MSME managers and policymakers by highlighting priority areas for intervention and supports the use of fuzzy AHP as a reliable decision-making tool for analysing complex adoption challenges.

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https://doi.org/https://doi.org/10.69889/t4e2rf11

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@article{ankur2026,
  title        = {{Prioritizing Barriers to Industry 4.0 Adoption in Manufacturing MSMEs of Uttarakhand: A Fuzzy AHP and Sensitivity Analysis Approach}},
  author       = {Ankur Mittal Karan Oberoi},
  journal      = {Economic Sciences},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.69889/t4e2rf11},
}

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Prioritizing Barriers to Industry 4.0 Adoption in Manufacturing MSMEs of Uttarakhand: A Fuzzy AHP and Sensitivity Analysis Approach

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.