Cost-schedule control in construction projects is inherently a continuous decision-making process conducted under conditions of uncertainty, rather than a purely technical or accounting activity. Conventional approaches, which rely on retrospective performance measurement and fragmented indicators, provide limited support for timely managerial intervention and often lead to delayed or suboptimal decisions. This study develops a decision-centric framework that integrates earned value analytics with organizational decision processes to enable proactive and structured cost–schedule control in small and medium-sized construction projects. The proposed framework conceptualizes cost control as a four-stage decision process—situational awareness, diagnostic analysis, predictive assessment, and intervention execution—and establishes explicit linkages between analytical signals and managerial actions. Within this structure, earned value metrics are reinterpreted as decision triggers rather than passive evaluation tools, while organizational roles are reconfigured to support timely interpretation and coordinated response. The framework is examined through an in-depth case study of a gas station construction project exposed to significant environmental and operational uncertainty. The findings indicate that cost overruns are primarily associated with delayed decision responses, fragmented information flows, and misaligned responsibility structures. By embedding real-time performance evaluation within a coherent decision architecture, the proposed approach enables earlier identification of deviations and more targeted managerial interventions. The study contributes to the literature on intelligent management decision-making by demonstrating how analytical tools can be operationalized within organizational contexts to enhance decision quality under uncertainty. It further provides a transferable framework for structuring data-informed decision processes in resource-constrained project environments.
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