Doctoral research monitoring in technical universities can be conceptualized as a socio-technical system in which research trajectories, supervisory practices, institutional rules, digital infrastructures, and assessment norms interact within a broader socioeconomic environment. This article develops a conceptual framework and a Provisional Diagnostic Model (PDM) for Engineering and Management through an integrative conceptual synthesis. The framework specifies system boundaries and hypothesized reinforcing, balancing, and metric-gaming feedback loops. The model separates institutional opportunity conditions from evidence of individual progress and engagement, and reports effective weights and evidence coverage alongside its aggregate score. It distinguishes research novelty from applied relevance through profile-specific contribution rules and introduces a separate, non-compensatory gate for novelty, research integrity, and methodological rigor. Fully specified synthetic examples demonstrate calculation, missing-data treatment, and parameter sensitivity. The framework is neither an empirically validated instrument nor a calibrated system-dynamics simulation. It provides an explicit conceptual specification for future testing and formative doctoral monitoring without treating resource access or output counts as sufficient evidence of research quality.

Doctoral research monitoring in technical universities can be conceptualized as a socio-technical system in which research trajectories, supervisory practices, institutional rules, digital infrastructures, and assessment norms interact within a broader socioeconomic environment. This article develops a conceptual framework and a provisional diagnostic model (PDM) for Engineering and Management through an integrative conceptual synthesis. The framework specifies system boundaries and hypothesized reinforcing, balancing, and metric-gaming feedback loops. The model separates institutional opportunity conditions from evidence of individual progress and engagement and reports effective weights and evidence coverage alongside its aggregate score. It distinguishes research novelty from applied relevance through profile-specific contribution rules and introduces a separate, non-compensatory gate for novelty, research integrity, and methodological rigor. Fully specified synthetic examples demonstrate calculation, missing-data treatment, and parameter sensitivity. The framework is neither an empirically validated instrument nor a calibrated system-dynamics simulation. It provides an explicit conceptual specification for future testing and formative doctoral monitoring without treating resource access or output counts as sufficient evidence of research quality.

Background: Engineer-to-order (ETO) manufacturing faces material shortages arising from changing requirements and uncertain supply. This study develops Artificial Intelligence–Supply Chain Decision Intelligence (AI-SCDI), linking shortage prediction to feasible interventions under cost and capacity constraints. Methods: Six specialists assessed framework content and parameter plausibility. A locked experiment used 12 synthetic cohorts and 6000 paired Monte Carlo replications per policy, supplemented by model-recovery diagnostics, matched policy ablations, calibration assessment, and joint sensitivity analysis. Results: Histogram gradient boosting achieved a mean precision–recall area under the curve of 0.7587, compared with 0.5399 for logistic regression and 0.7415 for random forest. Under baseline assumptions, AI-SCDI reduced disruption time by 30.0% and synthetic operating cost by 19.1% relative to no proactive intervention. At baseline capacity, estimated-net-benefit selection and abstention contributed more to cost reduction than impact weighting alone. Learner rankings varied across generating mechanisms, horizon-specific calibration showed risk overestimation, and mean net benefit was negative in 19 of 64 informed-parameter scenarios. Conclusions: AI-SCDI provides a reproducible, capacity-constrained decision heuristic. Its benefits remain conditional on synthetic assumptions; external validation and empirical cost calibration are required before industrial effectiveness can be claimed.