Comparing Platform Integration Maturity and Decision Speed in Energy Utility Operations

Abstract

The modern energy utility sector is undergoing a profound digital transformation driven by the integration of distributed renewable energy sources, smart grid technologies, and real-time sensor networks. Managing these complex systems requires rapid operational decision-making, which is heavily constrained by the integration maturity of disparate enterprise platforms. This paper presents a comprehensive process modeling framework that correlates platform integration maturity with decision speed in energy utility operations. By analyzing data flows across Supervisory Control and Data Acquisition systems, Advanced Metering Infrastructure, Geographic Information Systems, and Enterprise Resource Planning platforms, we construct a multi-level maturity model ranging from localized silos to fully cognitive, self-orchestrating integration. We evaluate the impact of maturity on decision-making latency across typical operational scenarios, including grid balancing and outage response. Our simulation and empirical analysis indicate that advancing from low-level point-to-point integration to semantic integration reduces average decision latency by over sixty percent. These findings offer utility executives a structured roadmap to align IT investment with grid reliability and operational resilience.