A Decision Support Framework to Integrate Scenario-Based Data-Driven Optimization and Fuzzy Clustering for Blockchain Adoption Readiness in Sustainable Manufacturing

Abstract

Blockchain Technology (BT) is increasingly recognized as a key enabler of transparency, traceability, and trust in sustainable manufacturing supply chains. Despite growing interest, most existing blockchain readiness assessment approaches rely on static evaluation or prioritization methods that only partially capture the complexity of real-world adoption conditions. In particular, previous studies often treat readiness as a fixed state, overlook the distinction between operational efficiency and inefficiency, and provide limited consideration of uncertainty and scenario-dependent variability. To address these limitations, this study proposes a data-driven, uncertainty-aware decision-support framework for evaluating blockchain adoption readiness in sustainable manufacturing. The framework combines scenario-based super-efficiency optimization, slack- and surplus-based inefficiency analysis, DEA-based meta-evaluation, and Fuzzy C-Means (FCM) clustering within an integrated methodology. Scenario-based super-efficiency models are first employed to evaluate firm performance under epistemic uncertainty using boundary stress-test scenarios. Slack- and surplus-based analysis is then used to identify operational inefficiencies that are often hidden in conventional evaluations. Subsequently, efficiency and inefficiency measures are integrated through a DEA-based meta-evaluation into a composite blockchain readiness index that jointly reflects ideal (efficiency-driven) and anti-ideal (inefficiency-driven) perspectives. Finally, FCM clustering is applied to identify performance tiers, benchmark firms, transitional organizations, and atypical readiness profiles, thereby enhancing the interpretability of the evaluation results. Results from a numerical case study indicate that although efficiency rankings vary across uncertainty scenarios, the composite readiness assessment remains stable and robust. Ablation and comparative analyses further demonstrate that each analytical component contributes meaningfully to the overall framework while improving diagnostic capability and robustness. The findings demonstrate that blockchain adoption readiness is a multidimensional construct emerging from the interaction between operational efficiency, inefficiency, and uncertainty, highlighting the value of integrated and uncertainty-aware decision support frameworks for sustainable manufacturing.