A Hybrid Fuzzy DEMATEL–ELECTRE Framework for Evaluating the Impact of Industry 4.0 Technologies on Warehouse Management Strategy Outcomes

Abstract

Integrating Industry 4.0 (I4.0) technologies into warehouse management critically enhances strategic performance. However, existing studies often overlook the causal relationships between strategic outcomes and the transparency of technology prioritization. This study proposes a hybrid multi-criteria decision-making (MCDM) framework integrating Fuzzy DEMATEL to determine the relative weights of strategic outcomes, Fuzzy ELECTRE II to rank technologies, and SHAP-based Explainable Artificial Intelligence (XAI) to enhance model transparency and interpretability. The analysis relies on Delphi-based expert evaluations from 12 senior industrial engineers across three manufacturing firms. The results reveal that Cost Reduction (weight = 0.225), Operational Efficiency (0.097), and Inventory Management (0.115) are the most critical strategic outcomes. Artificial Intelligence, Internet of Things, and Big Data Analytics emerged as the top-ranked technologies based on ELECTRE II scores. SHAP analysis further identified Cost Reduction (SHAP value: +1.62), Customer Satisfaction (SHAP value: +0.50), and Real-time Data Processing (SHAP value: +0.40) as the primary drivers behind the technology rankings. The proposed framework offers a transparent, interpretable, and causally grounded decision-support model for aligning digital transformation investments with strategic warehouse performance objectives.

Keywords

• Industry 4.0
• Warehouse management
• Fuzzy DEMATEL
• Fuzzy ELECTRE
• Explainable artificial intelligence

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