Despite the extensive use of Value Engineering (VE) in industrial projects to optimize cost, quality, and functional performance, the absence of a robust quantitative measurement system has constrained the objective evaluation of its effectiveness and limited evidence-based managerial decision-making. In practice, VE outcomes are predominantly reported in qualitative or case-specific terms, with weak empirical linkage to overall project performance indicators. This study addresses this gap by developing an operational, multi-dimensional Key Performance Indicator (KPI) framework specifically designed to assess VE effectiveness in industrial projects. The methodological novelty of the research lies in grounding the framework in Parmenter’s KPI hierarchy, enabling a clear distinction between Key Result Indicators (KRIs), Performance Indicators (PIs), and true KPIs that support proactive management. The research adopts a mixed-methods approach comprising a systematic literature review, semi-structured interviews with 15 industry experts, and empirical validation through a petrochemical project case study. The proposed framework consists of 28 indicators structured across five dimensions: financial, technical, quality, schedule, and stakeholder-related performance. Statistical analyses reveal that KPIs such as the number of design errors identified through VE and improved monthly cash flow rate exhibit a statistically significant positive correlation with final project budget control. Moreover, paired t-test results demonstrate a significant increase in the VE proposal acceptance rate, from 55% to 75%, following implementation of the framework. Overall, the findings demonstrate that the proposed KPI framework shifts Value Engineering from a periodic, experience-driven practice toward a data-driven continuous improvement mechanism, enhancing transparency, strengthening strategic alignment, and supporting more effective managerial decision-making in complex industrial projects