Predictive models of management accounting for digital transformation costs under conditions of high market volatility

Abstract

The relevance of the study is determined by the intensification of enterprise digital transformation, the growing volume of investments in digital technologies, and the necessity to improve cost management efficiency under conditions of high market volatility. It has been established that the instability of the market environment, fluctuations in the cost of digital resources, rapid technological changes, and increasing financial risks reduce the effectiveness of traditional approaches to management cost accounting and require the use of adaptive forecasting and analytical instruments. The purpose of the study is to provide scientific substantiation of theoretical and methodological approaches to the integration of predictive models into the system of management accounting for digital transformation costs of enterprises under conditions of an unstable market environment. Methods. The study employed methods of analysis and synthesis to generalize theoretical approaches to the application of predictive analytics in management accounting, system analysis to investigate the interrelation between digital transformation and cost management, comparative analysis to assess the influence of market volatility on the effectiveness of cost forecasting, as well as logical generalization to substantiate practical recommendations for improving the management accounting system. Results. The economic essence of predictive models in management cost accounting and their functional role in enterprise digital transformation processes have been investigated. It has been revealed that the use of predictive analytics ensures increased accuracy of cost forecasting, operational efficiency of financial control, and adaptability of managerial decisions to changes in market conditions. It has been proven that the integration of Enterprise Resource Planning (ERP) systems, Business Intelligence (BI) platforms, and Big Data technologies creates the prerequisites for forming a unified digital environment for cost management. At the same time, it has been established that the effectiveness of predictive models is constrained by the fragmentation of information flows, insufficient integration of digital systems, difficulties in interpreting analytical results, cybersecurity risks, and the lack of digital competencies among personnel. Conclusions. The expediency of applying adaptive forecasting algorithms, scenario modeling, and continuous cost monitoring systems in enterprise digital transformation processes has been substantiated. It has been concluded that predictive models contribute to improving the efficiency of management accounting, financial stability, and the quality of strategic managerial decisions under conditions of high market volatility. Prospects for further research are associated with the application of artificial intelligence technologies, machine learning, and automated risk analysis systems in enterprise cost management systems.