This study examines maintenance capacity adjustment strategies under split acquisition, a common practice in defense procurement driven by budgetary and production constraints. Using the Republic of Korea Air Force (ROKAF) F-35A as a case, the study investigates how time gaps between acquisition phases, combined with bathtub-shaped failure patterns, generate non-linear shocks in maintenance workload that threaten operational availability. A System Dynamics (SD) model is developed to simulate a 40:20 split acquisition scenario and to compare three capacity adjustment strategies: Level (fixed), Chase (reactive), and Lead (proactive). The strategies are evaluated under review intervals of 3, 5, and 10 years to reflect realistic defense planning environments. The simulation results show that the reactive Chase strategy consistently fails to achieve the availability target of 75 % due to workforce training delays. In contrast, the proactive Lead strategy successfully defends availability by anticipating future workload changes. A 3-year review interval achieves the highest cost-effectiveness, while a 5-year interval, aligned with the Mid-term Defense Plan cycle, is identified as a realistic threshold for sustaining availability. However, under a 10-year review interval, even proactive strategies lose effectiveness. The findings highlight the importance of proactive, forecast-based decision-making and suggest that synchronizing maintenance review cycles with acquisition schedules within a 5-year horizon is essential for robust maintenance policy design.
The rapid growth of artificial intelligence (AI) has shifted the development of wearable health monitoring devices toward intelligent design. However, conventional approaches still rely heavily on experience, involve inefficient user requirement acquisition, and lack systematic support for innovation. To address these limitations, this study proposes a data-driven framework for intelligent product development that integrates deep learning, Kansei Engineering, and TRIZ theory. The framework establishes an AI-assisted work-flow comprising online data acquisition, requirement identification, knowledge mapping, concept generation, and engineering optimization. First, web crawlers collect user reviews and product images. A convolutional neural network (CNN) identifies form features, while a generative adversarial network (GAN) generates diverse design concepts. Kansei Engineering then maps user requirements to design elements, and TRIZ theory resolves engineering contradictions to support systematic optimization. A case study of smart health watches, using a dataset of nearly 30,000 images and more than 12,000 reviews, demonstrates that the framework can effectively automate requirement extraction, concept generation, and design optimization. Compared with traditional experience-driven methods, the proposed framework significantly improves decision-making efficiency and supports product innovation. This study extends the application of deep learning to engineering design support and provides a transferable methodology for data-driven intelligent product development.
Raw material ordering under demand uncertainty is often hindered by inaccurate forecasts, resulting in excess inventory, stockouts, and increased operating costs. Traditional Newsvendor models typically assume static demand distributions and therefore have limited ability to capture dynamic changes in demand. To address this limitation, this study proposes a Markov chain–based Newsvendor decision framework incorporating an [L, U] inventory boundary policy. The main contribution of the proposed framework is the integration of state-transition-based probabilistic demand forecasting with inventory optimization. Specifically, raw material demand is classified into a finite set of states, and the transition probability matrix is estimated from historical demand-state observations. The resulting predicted demand distribution is then incorporated into the Newsvendor model to determine the optimal order-up-to level and the corresponding replenishment quantity. A simulation-based case study of an electronics manufacturing firm is conducted using a generated 52-week demand dataset. The results show that the proposed MC-NM model reduces the expected total ordering cost from 4887.68 yuan to 4269.92 yuan, corresponding to a cost reduction of 12.6 %. These findings indicate that the proposed framework can reduce inventory-related costs while maintaining responsiveness to demand fluctuations, thereby providing practical decision support for raw material procurement under uncertainty.
The mixing stage of automotive battery production requires reliable monitoring of raw material types, personnel actions, and correct tool use. However, accurate multi-scale object detection in complex scenes remains challenging because of background interference and the requirements of embedded deployment and real-time operation. This study proposes an enhanced YOLOv5 framework for industrial multi-object detection. The method adopts a dual-stage feature-enhancement strategy designed to improve robustness while limiting parameter count and computational complexity. First, the Convolutional Block Attention Module (CBAM) is embedded in the Backbone and Neck of YOLOv5 to provide multi-granularity feature enhancement and improve the detection of small objects, such as tools. Second, the conventional CIOU loss is replaced with the Focal-EIOU loss function to optimize bounding-box regression, reduce false detections across multiple target scales, and accelerate model convergence. Finally, K-means clustering is applied to target geometric features to generate specialized anchor-box parameters better suited to industrial scenarios. Experimental results on an automotive battery production-site dataset show that the improved model's mAP@0.5 increased by 2.4 % compared with the original model, reaching 95.2 %, while mAP@0.5:0.95 improved by 3.6 %, reaching 78.5 %. The proposed framework provides a lightweight and reliable solution for multi-object detection in complex industrial environments and supports the development of intelligent visual monitoring systems for smart manufacturing.
Equipment reliability is critical to maintaining production efficiency and controlling manufacturing costs. Predictive maintenance (PdM), based on machine condition prediction, can effectively reduce the risk of machine failure; consequently, machine degradation assessment and the prediction of remaining maintenance life (RML) are crucial for maintenance decision-making. Moreover, because equipment condition affects production planning, PdM should be integrated into the traditional economic production quantity (EPQ) model. The main contribution of this study is the introduction of RML as a decision-support metric linking equipment degradation prediction with production lot-sizing decisions, thereby enabling the joint optimization of EPQ and PdM policies. To minimize expected average cost, maintenance decisions are integrated into the production lot-sizing model to determine the optimal production lot size and maintenance policy. This study considers a single-machine production process in which the ARMA method is used to forecast the machine degradation index. Cox’s proportional hazard model (PHM) is then employed to estimate machine reliability based on the predicted degradation index. Based on this reliability assessment, remaining maintenance life (RML), rather than the traditional remaining useful life (RUL), is employed to link degradation prediction with the EPQ model and to characterize the machine deterioration process. An integrated EPQ–PdM model is developed to jointly determine the optimal production lot size and maintenance policy while minimizing the expected average cost (EAC) over the production cycle. Finally, a case study of an automotive bumper factory demonstrates the effectiveness of the proposed framework. The results show that the framework reduces EAC by 19.6 % relative to the current production strategy and identifies an optimal maintenance threshold of Rsafe = 0.4 with six maintenance cycles.
