University of Tehran Press Advances in Industrial Engineering 2783-1744 60 1 2026 06 01 A Data-Driven Industrial Engineering Framework for Hospital Performance Evaluation Using the Balanced Scorecard 125 141 106335 10.22059/aie.2026.409978.1967 EN Zahra Mojaradi Ph.D. Candidate, Department of Industrial Engineering, Alborz Campus, University of Tehran, Tehran, Iran. 0000-0002-1180-9572 Ali Bozorgi-Amiri Associate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran. 0000-0002-1180-9572 Zeinab Sazvar Associate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran. 0000-0002-0059-8781 Journal Article 2026 01 27 Performance evaluation in complex service systems is a central concern in industrial engineering, particularly in regulated environments such as healthcare. This study develops a data-driven industrial engineering framework for hospital performance evaluation structured through the Balanced Scorecard (BSC) and supported by exploratory predictive modelling. A structured evidence-based screening process was conducted to identify operationally measurable performance indicators, which were subsequently organised within the four BSC perspectives and aggregated into composite performance dimensions using standardised equal-weight scoring. Quarterly organisational data (2018–2024) from a tertiary hospital were used to examine structural relationships among indicators through a multilayer perceptron neural network. The predictive component is intended as exploratory validation rather than universal forecasting. Results indicate strong alignment between predicted and observed composite performance scores (R = 0.84), suggesting that the selected indicators collectively explain substantial variation within the studied organisational context. Importance and sensitivity analyses further identify cost-efficiency and operational-process variables as influential drivers, while safety, workforce, and patient-related indicators demonstrate meaningful associations. The proposed framework enhances transparency in indicator selection, clarifies multidimensional performance structuring, and provides analytically informed decision support for complex service systems. https://aie.ut.ac.ir/article_106335_fb9d64b4666a7ae6f663481331860895.pdf