Transformation of Economic Process Management for Sustainable Data-Driven Decision-Making in Manufacturing Enterprises: An Integrated Approach

Manufacturing enterprises operate in increasingly complex and data-intensive environments, where transforming operational and economic data into actionable managerial knowledge is essential for improving managerial decision-making, maintaining competitiveness, and achieving sustainable organizational performance. Despite substantial investments in enterprise information systems and digital technologies, organizations continue to face challenges related to fragmented data, disconnected information systems, and limited analytical integration. Existing research has primarily focused on the implementation of individual digital technologies, while less attention has been devoted to their integration within economic process management. The aim of this study is to propose an integrated approach to the transformation of economic process management for sustainable data-driven decision-making in manufacturing enterprises. The research is based on a qualitative case study employing process analysis, enterprise information system analysis, gap analysis, and enterprise architecture design to examine the current state of economic process management, identify key limitations, and develop a target decision-support environment. The resulting transformation framework establishes a structured transition from fragmented economic process management toward an integrated decision-support environment by combining process analysis, enterprise information system integration, centralized data management, Business Intelligence, and performance measurement. Within the scope of this study, sustainability is understood primarily as the long-term organizational and economic sustainability of data-driven decision-making processes, supported by integrated data management, standardized performance measurement, and reduced dependence on fragmented manual data processing; environmental and social sustainability outcomes were not directly measured. The industrial case study provides empirical grounding for the proposed approach, while expert-based evaluation assesses the logical consistency, organizational applicability, and perceived potential of the IEPTF to support the transformation of economic process management toward sustainable data-driven decision-making. Unlike approaches that address enterprise architecture, Business Intelligence, or digital transformation as separate domains, the proposed IEPTF integrates organizational process assessment, information-system analysis, enterprise architecture design, centralized data management, analytical capabilities, and managerial decision support into a unified transformation methodology specifically focused on economic process management.