<![CDATA[The optimization of Key Performance Indicators (KPIs) in steam turbine power plants is crucial for enhancing operational efficiency in the palm oil processing industry. This study applies the Analytic Hierarchy Process (AHP) to determine the relative weights of KPIs, thereby supporting data-driven decision making for performance improvement. Four critical KPIs were evaluated through pairwise comparisons expertise. A Python based computational model was developed to automate AHP calculations, ensuring accuracy and efficiency in deriving priority weights. This study reveals power output (47.16%) is the most significant KPI, followed by availability factor (38.58%), steam consumption (9.69%), and capacity factor (4.58%). The consistency ratio (CR) for all expert judgments was below 0.10, validating the reliability of the AHP outcomes. This research demonstrates that integrating AHP with Python programming provides a robust framework for KPI prioritization. The findings offer practical insights for industry stakeholders to optimize steam turbine performance and reduce operational inefficiencies.]]>
Copyrights © 2026
