Sergey Evgenievich Barykin
The increasing complexity of business process management arises from globalization, digital transformation, and economic disruptions such as pandemics and sanctions. Addressing these challenges requires a neutrosophic-driven, holistic approach that integrates uncertainty modeling, multi-criteria decision-making (MCDM), and sustainability constraints into supply chain design. This study proposes a multidimensional, systematic framework that incorporates neutrosophic logic and fuzzy decision-making methods to enhance the resilience and adaptability of modern supply chains. Organizations must re-engineer supply chain structures to optimize strategic objectives, such as cost efficiency and customer retention, while ensuring environmental and financial sustainability. The research introduces an adjusted neutrosophic tabular framework, supported by advanced analytical models and uncertainty-aware logistics methods, to enhance supply chain optimization. Key findings include a generalized uncertainty-driven algorithm for structuring supply chains, supplemented by real-world simulation-based validation. While the study acknowledges limitations such as data generalization, its reliance on aggregated enterprise data covering an entire manufacturing cycle enhances applicability. This research provides a decision-support tool for managers and policymakers, enabling them to navigate uncertainty, optimize investments, and implement sustainable business practices. By incorporating neutrosophic reasoning and ecological strategies, businesses can improve sustainability performance while maintaining operational efficiency. The proposed framework offers a scalable, intelligent model for supply chain design, serving as a strategic instrument for economic policy formulation in logistics and business resilience planning.