간행물
한국산업경영시스템학회지 KCI 등재 Journal of Society of Korea Industrial and Systems Engineering
- 발행기관 한국산업경영시스템학회
- 자료유형 학술지
- 간기 계간
- ISSN 2005-0461 (Print)2287-7975 (Online)
- 수록기간 1978 ~ 2025
- 주제분류 공학 > 산업공학 공학 분류의 다른 간행물
- 십진분류KDC 530DDC 620
권호리스트/논문검색
Vol.48 No.3 (2025년 9월) 16건
1.
2025.09
구독 인증기관 무료, 개인회원 유료
In order to confirm the optimal conditions for the LED(Light Emitting Diode) wire bonding process, the lead bump ball process optimization was analyzed. In the wire bonding process, it is an important process in which electrical characteristics operate by connecting the Au wire to the LED chip and lead frame. In the wire bonding method, various wire bonding processes, including thermocompression and ultrasonic bonding, were dealt with, and various variables affecting the lead bump ball process of wire bonding were analyzed through process variable analysis. Key variables for wire bonding working conditions were identified and optimized using the Response Surface Method(RSM) of Design of Experiments(DOE), the interaction between variables was confirmed through factor setting experiments, and the process was optimized using the RSM. This paper aims to improve the performance of the lead bump ball process by designing experiments with 5 factors at 3 levels and analyzing 4 response variables to find optimal conditions. It was confirmed that the performance of the lead bump ball process improved under optimized conditions, and as a result, optimal conditions that satisfied the targets for most reaction values, with the exception of ball diameter (BD), were secured.
4,300원
2.
2025.09
구독 인증기관 무료, 개인회원 유료
Anomaly detection is a key technique for ensuring the reliability and stability of systems across various industrial domains. Autoencoder-based reconstruction models are particularly effective in learning normal patterns and detecting deviations. However, conventional loss functions such as Mean Squared Error (MSE) and Mean Absolute Error (MAE) are limited in capturing anomalies that follow heavy-tailed or asymmetric distributions, which are commonly observed in real-world industrial settings. To address this limitation, we propose a Mixture Negative Log-Likelihood (Mixture NLL) loss function based on a combination of Gaussian, Laplace, and Student-t distributions. The loss is constructed using the probability density functions of each distribution, with key parameters such as standard deviation, scale, and degrees of freedom learned during training. The mixture weights representing the contribution of each distribution are also jointly optimized. Experimental results on real-world time-series anomaly detection datasets demonstrate that the proposed MixtureLoss consistently outperforms conventional loss-based Autoencoder models, particularly in detecting tail-end anomalies.
4,000원
3.
2025.09
구독 인증기관 무료, 개인회원 유료
To proceed an efficient acquisition program, a variety of factors such as acquirement of excellent weapon system, research and development of defense technology for independent national defense and efficient obtainments are needed to be considered. But present evaluation system of weapon is not enough to include them all. Therefore this study aims to design weapon evaluation system to overcome the cognitive error of decision makers and to cope with the complexity and uncertainty in national acquisition field. To accomplish the goal, the researchers derive 4 factors (compatibility of hierarchy, extensibility, compromise between cost and non-cost factors and aggregation of evaluation criteria and group) based on AHP. And the research intends to present rational weapon evaluation structure which can include national security environment to analysis and guarantee the objective setting of weights through empirical tests.
4,000원
4.
2025.09
구독 인증기관 무료, 개인회원 유료
The purpose of this study is to empirically analyze the causal relationship between the influence of the differentiation strategy on the management performance of small and medium-sized business successors, who are shaped by the characteristics of the company and its environment. A survey was conducted on 256 business successors in the metropolitan area, and SPSS 29.0 and AMOS 29.0 programs were used to test the hypotheses of the established research model. The results of the empirical analysis showed that environmental characteristics had a greater influence on business successors than the company's characteristics. Second, the influence of the business successors had a positive effect on the company's differentiation strategy. Third, the differentiation strategy was found to have a strong correlation with the company's financial performance, and it was found to have a positive (+) effect on non-financial performance. Fourth, the financial performance of family businesses was found to have a significant influence on their non-financial performance. This study aims to broaden the understanding of why business successors prefer and choose differentiation strategies by combining theories of strategic management and business succession. Existing research on business succession has focused primarily on succession and management performance, with relatively little empirical research on strategy selection. The novelty of this study lies in its unique focus on strategy selection, which will likely aid in designing customized consulting and support policies for future succession companies. This novel approach is sure to intrigue and interest the audience.
4,300원
5.
2025.09
구독 인증기관 무료, 개인회원 유료
This study analyzes the heterogeneous treatment effects of the COVID-19 pandemic on regional tourism demand in South Korea, focusing on the role of geographic distance from the metropolitan area to tourist destinations and the spatial characteristics of tourist destinations. Since a substantial portion of the population resides in the capital region, it can be expected that regional tourism demand is largely driven by residents of the capital region. In addition, the pandemic has particularly discouraged visits to indoor and densely populated areas due to increased perception of infection risk. To estimate these effects, we use a causal machine learning approach using double machine learning, analyzing monthly visitor data from 994 major tourist sites between the years 2019 and 2020. Tourist destinations are classified by spatial characteristics, including indoor, outdoor, and mixed settings as well as by tourism type. The analysis reveals that the impact of COVID-19 was more pronounced for indoor destinations located closer to the metropolitan center, whereas outdoor and mixed destinations showed little variation in treatment effects by distance. These findings highlight the importance of adopting distance-sensitive and space-specific policy measures in tourism planning during pandemics. Our study also demonstrates the practical utility of causal machine learning in tourism analytics, suggesting its potential for enhancing policy precision and resilience against future public health crises.
4,000원
6.
2025.09
구독 인증기관 무료, 개인회원 유료
‘Efficiency,’ a key performance factor of an organization, is affected by various factors in addition to ‘cost-benefit,’ which can be measured. Data Envelopment Analysis (DEA) is a method to evaluate the relative efficiency of an organization by simultaneously considering various factors that are difficult to measure. The significance of this study is that it presents a ‘method for developing an efficiency performance indicator using DEA’ and provides a practical application plan for inefficient organizations (DMUs) to develop and manage appropriate performance indicators to improve efficiency. It presents a methodology for performing research procedures ranging from selection of input and output variables, correlation analysis, DEA execution, calculation of virtual efficiency units (VEUs) through the latent price of the reference group (DMU), and derivation of efficiency performance indicators of the organization.
4,300원
7.
2025.09
구독 인증기관 무료, 개인회원 유료
The Public Procurement Service was established to ensure efficient supply of necessary goods for public institutions and their quality stability. The Public Procurement Service operates the Excellent Product Designation System for quality improvement. Due to the convenience of sole-source contracts and pricing advantages, suppliers prefer to obtain excellent product certification. However, despite these advantages, the designation does not guarantee customer satisfaction, as consumers pay higher prices without assured quality improvements. The goal of this study is to propose a new evaluation system better reflecting customer satisfaction and repurchase intention. Metal window products were selected as the study subject. Candidate factors were derived through literature review, and surveys were conducted to identify significant items for the new evaluation system. Item weights were then calculated using AHP analysis. The proposed system was validated through case analysis comparing two excellent products with two general products.
5,200원
8.
2025.09
구독 인증기관 무료, 개인회원 유료
This study explores strategies for companies to gain competitive advantage amid technological innovation and transformation. By applying the human performance technology model to a global multinational corporation, Company A, we analyzed performance issues and solutions. Key findings identified deficiencies in the compensation system and career development tools, with strategies proposed to address these issues. Success factors include executive support, clear goal setting, and alignment of strategy and execution. This research offers practical insights into building performance-oriented organizations and is expected to contribute to the field of performance consulting.
4,600원
9.
2025.09
구독 인증기관 무료, 개인회원 유료
This study addresses the limitations of traditional Failure Modes and Effects Analysis (FMEA), which heavily relies on expert judgment and lacks the ability to effectively incorporate unstructured failure history data such as warranty claims and maintenance records into the design stage. To overcome these challenges, we propose an automated FMEA framework based on a Retrieval-Augmented Generation (RAG) architecture integrated with a Local Large Language Model (LLM) in a secure, locally managed environment. The proposed system stores claim and test data in a vector database and leverages the LLM to retrieve and analyze relevant information, enabling automatic extraction of new failure modes and dynamic updates to FMEA documents. Additionally, the system recalculates Risk Priority Number (RPN) by adjusting severity, occurrence, and detection ratings when recurring failures are detected. To improve response quality, we applied prompt engineering and optimized chunking parameters during data retrieval. This research demonstrates the feasibility of achieving a life cycle-integrated quality enhancement framework throughout the product lifecycle while ensuring data security.
4,200원
10.
2025.09
구독 인증기관 무료, 개인회원 유료
In the era of big data, where massive volumes of information are collected at high velocity from various sources, data mining has become a crucial tool for organizations seeking competitive advantage. Among its core tasks, clustering plays a key role in uncovering hidden patterns within unlabeled data by grouping similar objects into distinct clusters. Widely used methods such as k-means and its robust counterpart PAM (Partitioning Around Medoids) require the number of clusters, k, to be predefined—a task that remains a major challenge despite extensive research. This study addresses the problem of selecting the optimal number of clusters by proposing three novel enhancements to the widely-used gap statistic method: the 1stDaccSEmax heuristic rule, the recursive gap strategy, and the two-way bootstrapping technique. Collectively termed the new gap, this approach aims to overcome the limitations of the original gap statistic, particularly in datasets with overlapping clusters, hierarchical structures, or large volumes. Extensive experiments on both synthetic and real-world datasets—including Iris, Breast Cancer, Seeds, and Khan gene expression datasets—demonstrate that the new gap method outperforms traditional techniques such as the elbow method, silhouette analysis, and the original gap statistic in both accuracy and computational efficiency. Although PAM was used throughout the experiments for its robustness, the proposed approach is algorithm-agnostic and can be integrated with other clustering methods that require the selection of k. The results suggest that the new gap method provides a more reliable and scalable solution for determining the number of clusters, thereby enhancing the effectiveness of clustering-based data analysis in real-world applications.
4,000원
11.
2025.09
구독 인증기관 무료, 개인회원 유료
In the production sites of small and medium sized manufacturing enterprises, the increasing proportion of foreign workers has led to frequent difficulties in responding promptly to process defects and equipment setting errors during night and weekend shifts due to the absence of Korean supervisors. If such issues are not addressed in a timely manner, they can lead to large scale defects and reduced production efficiency. In this study, we developed an AI-based defect prediction and prevention system for the bearing machining process to overcome these on site management limitations. Real time machining data, equipment information, and quality inspection results were collected from the production lines of the target company, and the prediction accuracy of three models, RNN(Recurrent Neural Network), LSTM(Long Short-Term Memory), and GRU(Gated Recurrent Unit), was compared. As a result, the LSTM model demonstrated the best performance. The developed system visualizes real time defect prediction results in the form of a dashboard, enabling workers to immediately detect anomalies and adjust the process accordingly. Particularly in bearing machining processes where mass production occurs in short periods, the risk of lot level defects is high, while this system can contribute to improved production quality and efficiency by enabling early defect prediction and immediate response.
4,000원
12.
2025.09
구독 인증기관 무료, 개인회원 유료
Smart factory technology, a core component of the Fourth Industrial Revolution, demonstrates significant disparities in technological development across countries. To quantitatively assess these international technology gaps, this study proposes an integrated analytical framework that combines text mining-based topic modeling and social network analysis (SNA), using global smart factory-related patent data from 2017 to 2023. Approximately 4,300 patent documents (titles and abstracts) were collected through the GPASS system and preprocessed. Through Latent Dirichlet Allocation (LDA) modeling with optimized hyperparameters, major technology topics were identified. Semantic interpretation using ChatGPT and expert review enabled the assignment of precise topic labels, which were further mapped to CPC (Cooperative Patent Classification) codes to construct a standardized technology taxonomy. Subsequently, the network structures of topic and classification nodes were analyzed by country (China, the United States, and South Korea), and the relative importance of key technology areas was evaluated using centrality metrics such as degree, closeness, betweenness, and eigenvector centrality. The analysis revealed that, globally, the most central technology areas include manufacturing process management and control, IoT and data-driven decision making, and facility-based process optimization. At the national level, China showed a strategic focus on technologies related to product quality improvement and cost reduction, South Korea emphasized IoT-enabled technologies and equipment-level optimization, while the United States prioritized control systems and data-driven project management. By utilizing patent-based textual data, this study offers a novel methodology for quantitatively diagnosing structural differences in national technological capabilities. The proposed framework provides valuable insights for country-specific R&D planning and strategic decision-making in the field of smart manufacturing.
4,800원
13.
2025.09
구독 인증기관 무료, 개인회원 유료
This study investigates the structural mechanisms underlying user acceptance of generative AI services by integrating cognitive and affective dimensions of user experience. Based on the Technology Acceptance Model, Expectation–Confirmation Theory, and flow theory, a research model was developed and tested through an online survey of 387 Korean users with more than three months of experience. Structural equation modeling confirmed that cognitive and affective responses significantly influence satisfaction and trust, which in turn predict loyalty, with trust showing the strongest direct effect. Satisfaction and trust also mediated these relationships, while flow strengthened the satisfaction–loyalty path and resistance to technology was not significant. These findings highlight the importance of incorporating emotional and experiential factors alongside functional aspects. Practical implications suggest that fostering trust, engagement, and perceived value is essential for sustaining loyalty in generative AI services.
4,200원
14.
2025.09
구독 인증기관 무료, 개인회원 유료
The semiconductor equipment industry is characterized by high technological intensity and a strong demand for customized functionality from client firms. This necessitates the collection and implementation of diverse client requirements, which in turn demands close cross-departmental collaboration and leads to highly complex project structures. To manage this complexity, many semiconductor-related companies operate global matrix organizations based on multiple reporting lines by region, function, and product. These structures entail intricate communication process and pose significant collaboration challenges. This study empirically investigates key factors influencing collaboration and project performance within global matrix organizations in the high-tech semiconductor equipment industry, applying the Technology-Organization-Environment (TOE) framework. The analysis identified significant effects of all examined factors on inter-organizational collaboration performance: three technological sub-factors (usability of communication tools, process standardization, and change management systems), two organizational sub-factors (clarity of roles and responsibilities, and robustness of information-sharing systems), and three environmental sub-factors (alignment of requirements, cultural understanding, and responsiveness to industry standards and regulations). Furthermore, the level of agile implementation was tested as a moderating variable in the relationships between TOE factors and collaboration performance. The results revealed significant moderating effects of agility in specific areas: the usability of communication tools and systematic change management (technological factors); information-sharing structure (organizational factor); and requirement alignment (environmental factor). These findings suggest that agile approaches do not operate as a single-factor solution but interact dynamically with various organizational conditions. By focusing on the underexplored dynamics of global matrix organizations in collaboration performance, this study provides a structured empirical analysis of their operational characteristics. Its theoretical contribution lies in offering an integrative perspective on technological, organizational, and environmental drivers of collaboration and project success, extending current research in high-tech project and organizational performance.
4,200원
15.
2025.09
구독 인증기관 무료, 개인회원 유료
The global e-waste problem is becoming increasingly serious. China, as one of the largest producers and consumers of electronic products, still has a low formal recycling rate. Consumers, as the owners of waste electronics, are the key to successful reverse logistics. However, many choose to store or dispose of e-waste at home rather than use official recycling channels. While many previous studies focus on factors that encourage recycling, fewer examine what stops people from taking part. This study applies Valence Theory to identify the factors that increase consumers’ psychological resistance to recycling small e-waste in China’s first-tier cities. It also examines how these factors influence social value and resistance behavior. The research model includes perceived price unfairness, perceived inconvenience, perceived benefits, and information publicity, with social value as a mediator. Data were collected through an online survey of 303 residents in Beijing, Shanghai, Guangzhou, and Shenzhen. Structural equation modeling (SEM) was used for analysis. The results show that perceived inconvenience and perceived benefits significantly influence social value. Perceived price unfairness, perceived inconvenience, and social value significantly affect consumer resistance. These findings expand the application of Valence Theory in e-waste research and address gaps in the Theory of Planned Behavior by considering both perceived risks and benefits. Practically, this study suggests that manufacturers, recyclers, and policymakers should improve recycling facilities, make the process more convenient, ensure fair and transparent pricing, and create targeted measures to reduce consumer resistance and encourage participation in formal recycling systems.
4,600원
16.
2025.09
구독 인증기관 무료, 개인회원 유료
In today’s project environment, significant changes are taking place, prompting a shift in project management knowledge from a traditional process oriented approach to one that is principle and performance based, allowing for more flexible application across diverse industry contexts. This study empirically examines the impact of four key knowledge areas from the PMBOK (Project Management Body of Knowledge) stakeholder management, planning management, project work management, and delivery management on both project performance and corporate performance. The research focuses on the construction industry as the target sector and additionally analyzes whether project size and company size function as moderating variables. The results indicate that a higher level of maturity in the key knowledge areas leads to improved project and corporate performance, with such positive effects being particularly pronounced in large scale projects and large enterprises. In the construction industry, in particular, the four knowledge areas were found to be closely associated not only with project performance but also with non-financial corporate performance indicators such as customer satisfaction, customer loyalty, and corporate image.
5,200원
