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This paper aims to investigate bank credit policies and uncover yacht building finance assessment factors from bank credit policies toward the yacht industry.

This study’s questionnaire attempts to identify survey respondents’ degrees of awareness through difference analysis, and then uses entropy weighting and gray relational analysis to discover priority ranking order of bank credit assessment considerations from the perspective of Taiwan’s banking sector.

The research findings show that yacht builders have to review their ship financing application methods and improve shortcomings to meet banks’ credit granting requirements.

Banks emphasize yacht builders’ repayment ability to protect their depositors and shareholders.

Financial price forecast issues are always a concern of investors. However, the financial applications based on machine learning methods mainly focus on stock market predictions. Few studies have explored credit risk predictions. Understanding credit risk trends can help investors avoid market risks. The purpose of this study is to investigate the prediction model that can effectively predict credit default swaps (CDS).

A novel generative adversarial network (GAN) for CDS prediction is proposed. The authors take three features into account that are highly relevant to the future trends of CDS: historical CDS price, news and financial leverage. The main goal of this model is to improve the existing GAN-based regression model by adding finance and news feature extraction approaches. The proposed model adopts an attentional long short-term memory network and convolution network to process historical CDS data and news information, respectively. In addition to enhancing the effectiveness of the GAN model, the authors also design a data sampling strategy to alleviate the overfitting issue.

The authors conduct an experiment with a real dataset and evaluate the performance of the proposed model. The components and selected features of the model are evaluated for their ability to improve the prediction performance. The experimental results show that the proposed model performs better than other machine learning algorithms and traditional regression GAN.

There are very few studies on prediction models for CDS. With the proposed novel approach, the authors can improve the performance of CDS predictions. The proposed work can thereby increase the commercial value of CDS predictions to support trading decisions.

Business Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.

The BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.

The findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.

The single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.

The experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.