Forecasting workloads and responding promptly with resource scaling and migration is critical to optimizing operations and enhancing resource management in cloud environments. However, the diverse and dynamic nature of devices within cloud environments complicates workload forecasting. These challenges often lead to service level agreement violations or inefficient resource usage. Hence, this paper proposes an Enhanced Long-Term Cloud Workload Forecasting (E-LCWF) framework designed specifically for efficient resource management in these heterogeneous and dynamic environments. The E-LCWF framework processes individual resource workloads as multivariate time series and enhances model performance through anomaly detection and handling. Additionally, the E-LCWF framework employs an error-based ensemble approach, using transformer-based models and Long-Term Time Series Forecasting (LTSF) linear models, each of which has demonstrated exceptional performance in LTSF. Experimental results obtained using virtual machine data from real-world management information systems and manufacturing execution systems show that the E-LCWF framework outperforms state-of-the-art models in forecasting accuracy.

In Industry 4.0, internet of things (IoT) technologies are expanding and advanced smart factories are currently being developed. To build an automated visual inspection (AVI) and achieve smartization of steel manufacturing, detecting defects in products in real-time and accurately diagnosing the quality of products are essential elements. As in various manufacturing industries, the steel manufacturing process presents a class imbalance problem for products. For example, fewer defect images are available than normal images. This study developed a new image synthesis methodology for the steel manufacturing industry called a latent mapping adversarial network. Inspired by the style-based generative adversarial network (StyleGAN) structure, we constructed a mapping network for the latent space, which made it possible to compose defect images of various sizes. We discovered the most suitable loss function, and optimized the proposed method in terms of convergence and computational cost. The experimental results demonstrate the competitive performance of the proposed model compared to the traditional models in terms of classification accuracy of 92.42% and F-score of 93.15%. Consequently, the problem of data imbalance is solved, and higher productivity in steel products is expected.

In conjunction with Industry 4.0, the manufacturing execution system (MES) has emerged as an essential component for implementing smart factories. The complexity of product specifications and the need to manage production facilities more efficiently has generated a growing demand for digital transformation within MES and enhancement of MES to replace manufacturing operators or support engineers. However, the lack of concrete methods to assess the current utilization of MES poses challenges in efficiently addressing unmanned automation and digital transformation requirements. This study proposes a maturity model (MM) and corresponding index to measure the level of MES within companies. The model quantifies the degree of automation in manufacturing operations and production management processes. Expanding on the existing MM, this method proposes two dimensions (operation and management) to evaluate the MES maturity and balance index across 17 standard system modules. The proposed method aims to provide a tool for companies to prepare for advanced smart factories in the future. Using the proposed production system MM, we conducted a diagnostic assessment of the MES levels of 93 manufacturers, for the first time confirming the maturity index gap and balance index change patterns between operation and management systems across industry groups and revenue scales.

Accurate cycle time (CT) prediction is crucial in semiconductor manufacturing. Hybrid models integrating classification and prediction models can enhance CT prediction accuracy. However, existing methods have limitations, including challenges in capturing the dynamic conditions of the production line and optimizing job classification to ensure high CT prediction performance. In this study, we propose a novel hybrid framework for predicting process step-level CT in semiconductor wafer fabrication, thereby addressing the limitations of previous methods. Moreover, the paper formalizes and introduces dynamically changing manufacturing environment attributes as variables that contribute to CT. The proposed method combines deep embedded clustering (DEC) with a regression ensemble network. First, the DEC extracts cluster-friendly representative features from high-dimensional CT datasets and classifies jobs accordingly. Then, a weighted ensemble approach merges regression networks based on cluster membership probabilities. Unlike existing methods that separately handle feature extraction, job classification, and CT prediction, the proposed unified network synchronizes these processes. Experimental results using real-world operational data from a semiconductor manufacturing system indicate that the proposed prediction method considerably outperforms previous approaches in terms of prediction accuracy. To the best of our knowledge, this is the first study to integrate deep clustering with a regression ensemble network for predicting cycle time at the process step level in semiconductor manufacturing. By synchronizing feature extraction, clustering, and prediction tasks, the proposed framework achieves enhanced accuracy and robustness in dynamically changing manufacturing environments.

Time-series data are generated and collected in manufacturing processes. Time-series data have different distribution characteristics, with the underlying assumption being that all data originate from the same distribution. Time-series anomaly detection methods are trained on stationary data derived from the source domain. However, the performance of the trained model diminishes when confronted with a nonstationary distribution in the target domain. Consequently, there is a need for a model that can adapt to any distribution using only the source domain. In this study, we propose an anomaly detection framework with contrastive learning and multiview augmentation for time-series domain generalization. The proposed method uses multiview augmentation to learn three representations for time-series domain generalization. Moreover, the augmented samples use a temporal convolutional network (TCN) to extract these representations. Subsequently, the extracted representations are trained with a contrastive learning method inspired by simple triplet representation learning (SimTriplet). Therefore, the proposed method can attain the diversity and generalization performance of the source domain. The performance superiority of our proposed method is experimentally validated across various domain shift scenarios. Additionally, the effectiveness of the modules used in the proposed method is confirmed through qualitative analysis.