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.

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.

This paper presents a comparative analysis of doctoral study programs across the nine partner universities within the European University of Technology (EUt+) alliance. The study examines structural elements such as program duration, modes of study, financing mechanisms, supervision frameworks, and evaluation procedures. The findings highlight both common features, such as structured research training, annual evaluations, and thesis defense procedures, and institutional specificities regarding admission requirements, available funding, and program flexibility. By mapping these similarities and differences, the paper aims to identify opportunities for harmonizing doctoral education practices and facilitating greater collaboration among EUt+ institutions. The study also contributes to ongoing discussions about building a unified European doctoral education model.

The transition towards the circular economy and improved environmental management is a major challenge for manufacturing SMEs under growing pressures for resource efficiency, waste reduction and industrial sustainability. Although circular economy, environmental management, digitalization and artificial intelligence are widely discussed, they are often treated separately and rarely integrated into maturity-assessment frameworks. This article proposes CEEMMI—Circular Economy and Environmental Management Maturity Index—a digital- and AI-oriented diagnostic framework with a multi-criteria structure for manufacturing SMEs. CEEMMI integrates eight dimensions covering circular strategy, eco-design, resource efficiency, life cycle management, circular supply chains, digitalization and AI, organizational capabilities and sustainable performance. In its current version, the model is operationalized as an equal-weighted additive index for preliminary self-assessment, pending future content-validity testing and expert-derived weighting. The framework supports five-level maturity classification, profile-based interpretation, compensability safeguards and illustrative sensitivity analysis, offering a reproducible basis for diagnosis, decision support and future empirical validation.

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.

Manual assembly of multi-pin cable harnesses remains vulnerable to miswiring issues when conductors are visually indistinguishable. This paper presents an industrial case study of a quick-connect harness composed of two connectors (receptacle-type and pin-type) linked by 16 black conductors (2.5 mm² and 200 mm length), where the dominant failure mode is a two-wire swap that breaks correct pin-to-pin mapping and may cause downstream equipment damage. In the baseline state, end-of-line verification relies on visual inspection only (1 min/unit), resulting in an internal nonconformity rate of 4% (repairable). To achieve the operational goal of zero defects (zero escapes), we proposed and integrated an electronic pin-to-pin continuity and mapping fixture as a deterministic End-of-Line (EOL) quality-gate implementing poka-yoke logic (“no PASS—no shipment”) and enabling structured traceability records. Using a before–after workload model that includes a mandatory retest after rework, the fixture reduces test time to 0.33 min/unit. For a monthly volume of 1500 units, total quality workload (test + rework + retest) decreases from 31 h/month to 13.58 h/month, releasing 17.42 h/month. Global quality productivity increases from 48.39 units/h to 110.46 units/h (+128%). The proposed architecture couples deterministic electrical verification with data logging aligned to digital-thread and data-driven quality-management concepts to sustain continuous improvement and prevent customer escapes.