STRATEGIES FOR ANTICIPATIVE PRODUCTION MANAGEMENT IN CONDITIONS OF UNCERTAINTY

Abstract

The article examines strategies for anticipatory production management amid multi-vector uncertainty, focusing on the integration of cognitive-analytical approaches and dynamic adaptation mechanisms in management systems. In modern industrial environments, where external conditions fluctuate unpredictably, the ability to forecast, adjust, and optimize production processes is a crucial factor in ensuring operational stability and competitiveness. The research methodology is based on the use of a systemic approach to the analysis of mechanisms of anticipatory production management with the involvement of scenario modeling tools, structural-functional analysis, and elements of the theory of complex dynamic systems. Methods of decomposition of management processes, logical-semantic generalization and conceptual structural synthesis were applied, which allowed identifying key determinants of the anticipatory capacity of the enterprise in conditions of uncertainty. The formalization of models is executed through cognitive representation of management decisions and parameterization of scenario variants, enabling flexible adaptation to shifting environmental conditions. This methodological framework allows for multi-layered analysis of management systems, facilitating proactive identification of risks and strategic optimization of production processes. The article elaborates on the formation and implementation of anticipatory management strategies, emphasizing the need for enterprise-wide transition from reactive models to innovative approaches. The synthesis of scenario modeling, cognitive adaptability, and multifactor risk analysis serves as the foundation for enhanced strategic sensitivity and decision-making agility. The study underscores the importance of integrating strategic sensitivity and parametric elasticity into management mechanisms, ensuring that enterprises remain adaptable to external dynamism and exogenous uncertainty. To address growing challenges in industrial management, the article advocates for the implementation of virtualized systems capable of actively identifying destabilizing factors and undergoing flexible readaptation. The research concludes that anticipatory production management represents a paradigm shift from traditional reactive frameworks to innovative, foresight-driven models. The integration of digitalized decision-making, data-driven risk assessment, and adaptive strategic planning enables enterprises to enhance long-term sustainability, minimize disruptions, and optimize resource allocation in volatile market environments.