Integrating Agentic AI with Six Sigma: A Hybrid Fuzzy DEMATEL-ANP-TOPSIS Framework for Socio-Technical Readiness

Integrating Agentic Artificial Intelligence (AI) into Lean Six Sigma (LSS) environments offers a transformative path toward self-regulating manufacturing, yet it remains a complex socio-technical challenge. Moving beyond simple defect reduction requires a deep understanding of how technical, organizational, and governance factors interact. This study proposes a comprehensive framework to guide this transition, identifying thirteen critical enablers ranging from high-fidelity data infrastructure to human-in-the-loop oversight. To navigate the uncertainties inherent in expert judgment, a hybrid methodology is introduced that combines Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to map causal relationships, the Analytic Network Process (ANP) to assign interdependency-aware weights, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final prioritization. Unlike traditional linear models, the framework in this work reveals that readiness is a dynamic configuration where foundational capabilities — such as leadership commitment and process stability — must mature alongside advanced algorithmic autonomy. This research contributes a reproducible, scientifically grounded roadmap for practitioners seeking to elevate LSS toward prognostic maturity. By linking operational discipline with autonomous adaptation, the study offers a novel strategic lens for decision-makers aiming to deploy responsible and resilient agentic systems in modern production landscapes.