FORMATION OF AN INDUSTRIAL ENTERPRISES’ CHANGE MANAGEMENT SYSTEM USING MARKETING AND ACCOUNTING INFORMATION

Abstract

The article substantiates the conceptual foundations for the formation and functioning of a comprehensive change management system at industrial enterprises, based on the synergy of marketing and accounting information. It is determined that under conditions of global instability and a BANI environment, change management is transforming from a situational response to crises into a continuous intellectual function of adaptation. Within the proposed approach, marketing tools (customer experience monitoring and market trend analysis) act as a strategic «sensor» for identifying early signals of transformation, while the management accounting system serves as a rigid «economic filter» for verifying the financial feasibility and resource efficiency of planned projects. The mechanism of convergence of these data is examined as a key driver of organizational flexibility and strategic resilience. The author details the stages of implementing a change management system, ranging from preventive diagnostics of external signals to post-audit of the economic effectiveness of implemented transformational decisions. The article classifies and systematizes key indicators of marketing and accounting monitoring (in particular, Customer Lifetime Value (CLV), Return on Marketing Investment (ROMI), and the dynamics of marginal profit), which makes it possible to transform the change management process into a proactive, data-centric model. Special attention is paid to the technological aspect: it is proven that only through the integration of digital automation tools (ERP, CRM, and BI platforms) is it possible to create a unified information space of the enterprise. Such a space ensures high data processing speed and well-grounded managerial decision-making, which directly contributes to enhancing the competitiveness of industrial enterprises in a highly competitive and nonlinear environment. The practical significance of the obtained results lies in the possibility of applying the proposed algorithm to minimize risks during large-scale organizational and technological transformations at domestic industrial enterprises.