Equipment failure in paper mills represents a critical barrier to operational efficiency and the adoption of Industry 4.0 principles. To address this, a systematic literature review was conducted to identify the multifactorial determinants of such failures. A novel hybrid methodology was proposed, integrating the Functional Analysis Systems Technique (FAST), enhanced by Lean 5S (Sort “Seiri”, Set in Order “Seiton”, Shine “Seiso”, Standardize “Seiketsu”, Sustain “Shitsuke”) principles, to structure the qualitative data collection. The analysis was performed using a Pugh matrix, followed by a Principal Component Analysis (PCA) to extract knowledge systematically. This approach facilitated the development of a conceptual model for downtime causation. The PCA results indicate that two principal components collectively explain 58.5% of the observed variance in failure data. The f irst component was strongly correlated with maintenance practices and operational errors, while the second was associated with intrinsic equipment characteristics and their operating conditions. This data-driven modeling elucidates underlying correlations between disparate factors, providing a robust foundation for prioritizing targeted maintenance optimization actions. This research contributes to the field of industrial intelligence by demonstrating an original methodology for transforming qualitative systematic review data into a quantifiable analytical framework. The application of PCA to this corpus enables the identification of multidimensional interactions that are frequently overlooked in conventional analyses, thereby enriching root-cause failure analysis and informing strategic decision making for predictive maintenance. The identified factors underscore the imperative of a balanced integration between technical data and human factors for the successful digital transformation of production systems.

In the highly competitive landscape of modern manufacturing, the efficient and timely operation of production processes is paramount for sustaining productivity and ensuring customer satisfaction. Delays and latent bottlenecks, however, often hinder optimal performance. A data-driven methodology for identifying these inefficiencies is presented, employing process mining techniques. By analyzing event logs from Manufacturing Execution Systems (MES) and Enterprise Resource Planning (ERP) systems, the actual execution flow of production processes is reconstructed and compared against the designed process models. Through process discovery, conformance checking, and performance analysis, the underlying causes of delays and capacity bottlenecks are pinpointed. A case study from a manufacturing facility is used to demonstrate the effectiveness of process mining in uncovering critical areas for process improvement. The findings indicate that process mining not only enhances transparency but also provides actionable insights for optimizing resource planning, reducing cycle times, and maximizing overall operational effectiveness. The approach is demonstrated to facilitate the identification of inefficiencies, leading to targeted interventions that significantly improve process performance and business outcomes.

The early stages of engineering innovation are typically characterized by high levels of uncertainty, strong dependence on expert experience, and complex coupling among design objectives, manufacturing constraints, and solution maturity. These characteristics make the associated decision-making processes difficult to formalize and reproduce. To address this challenge, an industrial intelligence framework integrating Extenics and the Theory of Inventive Problem Solving (TRIZ) was proposed to support structured reasoning and consistent decision-making in the early phase of engineering innovation. Within the proposed framework, engineering objectives, constraint conditions, and solution maturity are represented as structured industrial knowledge elements, enabling unified processes of conflict identification, rule-based reasoning, and multi-criteria evaluation. Extenics is employed to construct formal representations of problem elements and their interrelationships, while TRIZ is utilized to support systematic principle-based resolution of contradictions. Through this integration, engineering decision-making is shifted from reliance on implicit experiential knowledge toward an explicit, knowledge-driven paradigm. The applicability and effectiveness of the framework were demonstrated through a conceptual design case study of a household product. The results indicate that the proposed approach enhances the transparency and consistency of early-stage engineering decisions, reduces dependence on individual expertise, and provides an interpretable industrial intelligence solution for supporting knowledge-intensive engineering innovation.

In critical supply chains like pharmaceuticals, balancing operational cost with service resilience is paramount. While complex adaptive models dominate academic literature on inventory routing, the potential of simpler, managerially intuitive heuristics remains underexplored, creating a gap between theory and practice. This study investigates whether a rigorously optimized, simple time-based heuristic can achieve superior performance and robustness compared to a state-of-the-art, multi-parameter adaptive policy within a stochastic Vendor-Managed Inventory (VMI) system. We formalize a time-to-stockout rule into a novel, single-parameter metaheuristic called the Optimized Urgency Threshold (OUT) policy. Using a simulation-optimization framework powered by a Genetic Algorithm, we benchmarked the OUT policy against a non-optimized heuristic and a complex Dynamic Inertial policy across five problem instances subjected to environmental shocks. The OUT policy demonstrated superior performance, achieving the lowest average total cost (€ 58,595.46) and reducing stockouts by 66.3% compared to the Dynamic Inertial model. Sensitivity analysis confirmed the OUT policy's balanced robustness to demand and capacity shocks, whereas the complex policy exhibited service failures under demand surges. Our findings show that a parsimonious, optimized heuristic can outperform a complex adaptive model, challenging the assumption that parametric complexity is necessary for high performance in stochastic IRPs. The OUT policy provides a transparent, effective, and easily implementable solution for enhancing supply chain resilience and mitigating stockouts.

Reliable lane perception is a core enabling function in industrial intelligent driving systems, providing essential structural constraints for downstream tasks such as lane keeping assistance, trajectory planning, and vehicle control. In real-world deployments, lane detection remains challenging due to complex road geometries, illumination variations, occlusions, and the limited computational resources of on-board platforms. This study presents Attention-Guided Cross-Layer Refinement Network (AG-CLRNet), a real-time lane perception framework designed for industrial intelligent driving applications. Built upon an anchor-based detection paradigm, the framework integrates adaptive multi-scale contextual fusion, channel–spatial attention refinement, and long-range dependency modeling to improve feature discrimination and structural continuity while maintaining computational efficiency. The proposed design strengthens the representation of distant and slender lane markings, suppresses background interference caused by shadows and pavement textures, and enhances global geometric consistency in curved and fragmented scenarios. Extensive experiments conducted on the CULane benchmark demonstrate that AG-CLRNet achieves consistent improvements in precision, recall, and F1 score over representative state-of-the-art methods, while sustaining real-time inference performance suitable for practical deployment. Ablation studies further confirm the complementary contributions of the proposed modules to robustness and structural stability under challenging conditions. Overall, AG-CLRNet provides a practical and deployable lane perception solution for industrial intelligent driving systems, offering a balanced trade-off between accuracy, robustness, and real-time performance in complex road environments.