Strategic Management of Industrial Digital Twin Implementation at Oil and Gas, Oil Refining, and Chemical Enterprises

Industrial digital twins are becoming an important instrument of digital transformation in capital-intensive production systems. Their potential value lies in the ability to integrate operational data, engineering models, monitoring, forecasting, scenario analysis, and optimization within a unified framework for supporting managerial decision-making. At the same time, the technical viability of a digital model alone does not guarantee the achievement of sustainable operational or economic outcomes. The purpose of this study is to develop a strategic approach to the implementation of an industrial digital twin that links technological architecture with the business objective, the decision-making system, organizational responsibility, and the validation of the value created. The methodological framework of the study is based on systems analysis, structural decomposition of the digital twin lifecycle, and conceptual synthesis of the managerial conditions required for its implementation. Particular attention is given to enterprises in the oil and gas, oil refining, and chemical industries, which are characterized by high capital intensity, strong technological interdependencies among processes, long asset lifecycles, and high costs associated with erroneous decisions. The maturity of an industrial digital twin is proposed to be assessed according to the extent to which it is integrated into the actual managerial decision-making cycle: from observation and diagnostics to forecasting, scenario analysis, optimization, and decision support. An implementation lifecycle model is developed that encompasses the definition of the business problem, identification of the use case, formulation of expected value, establishment of the governance system, preparation of data and technologies, piloting, organizational integration, operation, outcome assessment, and scaling. The study demonstrates that sustainable implementation requires the coordinated development of technological, informational, and organizational components. Furthermore, scaling involves the replication not only of the software solution itself, but also of the associated system of data, models, processes, responsibilities, and management practices.