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 ~ 2024
- 주제분류 공학 > 산업공학 공학 분류의 다른 간행물
- 십진분류KDC 530DDC 620
권호리스트/논문검색
Vol. 46 No. 2 (2023년 6월) 17건
1.
2023.06
구독 인증기관 무료, 개인회원 유료
To make semiconductor chips, a number of complex semiconductor manufacturing processes are required. Semiconductor chips that have undergone complex processes are subjected to EDS(Electrical Die Sorting) tests to check product quality, and a wafer bin map reflecting the information about the normal and defective chips is created. Defective chips found in the wafer bin map form various patterns, which are called defective patterns, and the defective patterns are a very important clue in determining the cause of defects in the process and design of semiconductors. Therefore, it is desired to automatically and quickly detect defective patterns in the field, and various methods have been proposed to detect defective patterns. Existing methods have considered simple, complex, and new defect patterns, but they had the disadvantage of being unable to provide field engineers the evidence of classification results through deep learning. It is necessary to supplement this and provide detailed information on the size, location, and patterns of the defects. In this paper, we propose an anomaly detection framework that can be explained through FCDD(Fully Convolutional Data Description) trained only with normal data to provide field engineers with details such as detection results of abnormal defect patterns, defect size, and location of defect patterns on wafer bin map. The results are analyzed using open dataset, providing prominent results of the proposed anomaly detection framework.
4,300원
2.
2023.06
구독 인증기관 무료, 개인회원 유료
In the recent era of the fourth industrial revolution, many industries aim to maximize the efficiency of products and services by introducing cutting-edge technologies such as artificial intelligence and big data. In this situation, organizational culture is changing a lot due to the influx of the MZ generation with strong individualistic tendencies and the decreased face-to-face communication between members. However, active communication with colleagues is still essential to maximize performance, and the margins created by simplifying work processes and automating processes must be used for creating work performance. This requires cooperation and commitment through the job immersion of members who have an active attitude. This study analyzed how the organization's autonomous work environment and trust among members, which are creative work performance conditions, affect job immersion using raw data from the Occupational Safety and Health Research Institute. As a result, it was found that both the organization's autonomous working environment and trust among members significantly effected the members' job immersion. in order to achieve productivity and value improvement in companies, efforts are needed to increase workers' job immersion by building an autonomous working environment and trust among members. The results of this study are expected to contribute significantly to the search for ways to increase workers’ job commitment to improve organizational productivity.
4,000원
3.
2023.06
구독 인증기관 무료, 개인회원 유료
Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.
4,000원
4.
2023.06
구독 인증기관 무료, 개인회원 유료
Recently, small and medium-sized manufacturing companies have shown increased interest in and participation in smart factories in order to survive in market competition. However, many SMEs build smart factories without a systematic review or preparation, which leads to them not being able to use them properly. This study considers the main reason for the low utilization rate of smart factories to be a lack of sufficient reflection of user requirements. Therefore, a method and procedure for deriving the priority of smart factory requirement functions and operating performance indicators based on QFD is proposed as a solution to this issue.
4,800원
5.
2023.06
구독 인증기관 무료, 개인회원 유료
This study identified the core competencies of aircraft maintenance quality engineers and compared the importance and retention of core competencies. Through literature research, 21 core competencies were derived in three areas of management technology, elemenal technology and collaboration technology, and a survey was conducted on the importance and retention of core competencies for 42 aircraft maintenance quality engineers. As a result of the survey, the importance of all core competencies of aircraft maintenance quality engineers is 3.95/5 on average, and the retention of all core competencies is 3.99/5 on average. 'Risk Management’, ‘Creating Document’, ‘Honesty/Moral’ were identified as the most important competencies in each area, and ‘Quality Management’, ‘Language’, ‘Honesty/Moral’ were identified as the most possessed competencies in each area. An IPA (Importance-Performance Analysis) was performed to analyze the details. Through IPA, ‘Risk Management’ and ‘Safety Management’ were evaluated as having a low degree of retention compared to a high level of importance. Therefore, they were identified as a core competencies that need to be improved first. In addition, the characteristics of each core competency and the recognition level in the field were also identified. This study will be helpful in defining the roles and functions of aircraft maintenance quality engineers to improve flight quality and prevent aviation accidents.
4,000원
6.
2023.06
구독 인증기관 무료, 개인회원 유료
In this study, we analyze a finite-buffer M/G/1 queueing model with randomized pushout space priority and nonpreemptive time priority. Space and time priority queueing models have been extensively studied to analyze the performance of communication systems serving different types of traffic simultaneously: one type is sensitive to packet delay, and the other is sensitive to packet loss. However, these models have limitations. Some models assume that packet transmission times follow exponential distributions, which is not always realistic. Other models use general distributions for packet transmission times, but their space priority rules are too rigid, making it difficult to fine-tune service performance for different types of traffic. Our proposed model addresses these limitations and is more suitable for analyzing communication systems that handle different types of traffic with general packet length distributions. For the proposed queueing model, we first derive the distribution of the number of packets in the system when the transmission of each packet is completed, and we then obtain packet loss probabilities and the expected number of packets for each type of traffic. We also present a numerical example to explore the effect of a system parameter, the pushout probability, on system performance for different packet transmission time distributions.
4,800원
7.
2023.06
구독 인증기관 무료, 개인회원 유료
This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.
4,000원
8.
2023.06
구독 인증기관 무료, 개인회원 유료
이 논문은 한국산업경영시스템학회 연구윤리위원회 심의(2024.7.3.)결과, 중복게재가 확인되어 게재가 철회된 논문임.
This study is to identify the maintenance service quality of eco-friendly cars, which are rapidly increasing recently. Research is conducted by synthesizing research from the perspectives of internal employees and external customers by using the service profit chain model. Specifically, it is to study the overall structural relationship between internal customer satisfaction, physical quality, interaction quality, outcome quality, external customer satisfaction and long-term orientation. For the study, 202 questionnaires were collected from internal employees and 204 questionnaires from external customers. The results of testing the research hypotheses targeting the internal employee group are as follows. As a result of testing hypothesis 1, internal customer satisfaction has a significant positive (+) effect on physical quality and interaction quality. As a result of testing hypothesis 2, the service quality of eco-friendly car maintenance has a significant positive (+) effect on each other. As a result of testing hypothesis 3, physical quality and outcome quality have a significant positive (+) effect on external customer satisfaction. The results of testing the research hypotheses targeting an external customer group are as follows. As a result of testing hypothesis 2, in the relationship between eco-friendly car maintenance service quality, physical quality has a significant positive (+) effect on interaction quality, and interaction quality has a significant positive (+) effect on outcome quality. As a result of testing hypothesis 3, interaction quality and outcome quality have a significant positive (+) effect on external customer satisfaction. As a result of testing Hypothesis 4, external customer satisfaction has a significant positive (+) effect only on intention to reuse. Finally, as a result of examining the difference in perception between the internal employee group and the external customer group in eco-friendly car maintenance service quality and external customer satisfaction, it was verified that there was a significant difference only in outcome quality and external customer satisfaction.
5,500원
9.
2023.06
구독 인증기관 무료, 개인회원 유료
Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.
4,000원
10.
2023.06
구독 인증기관 무료, 개인회원 유료
This paper studied the problem of determining the optimal inventory level to meet the customer service target level in a situation where the customer demand for each branch of a nationwide retailer is uncertain. To this end, ISR (In-Stock Ratio) was defined as a key management indicator (KPI) that can be used from the perspective of a nationwide retailer such as Samsung, LG, or Apple that sells goods at branches nationwide. An optimization model was established to allow the retailer to minimize the total amount of inventory held at each branch while meeting the customer service target level defined as the average ISR. This paper proves that there is always an optimal solution in the model and expresses the optimal solution in a generalized form using the Karush-Kuhn-Tucker condition regardless of the shape of the probability distribution of customer demand. In addition, this paper studied the case where customer demand follows a specific probability distribution such as a normal distribution, and an expression representing the optimal inventory level for this case was derived.
4,000원
11.
2023.06
구독 인증기관 무료, 개인회원 유료
Technology innovation companies are focusing on contributing to business performance by R&D project as a strategic tool. Successful R&D leads to corporate competitiveness enhancement, national industrial development, but there are high uncertainty and risks in R&D. Public and private R&D projects are carried out to achieve various purposes. It was verified how the risk management and benefit management of the R&D project affect the detailed R&D project performance between the Public and private domain. The impact of Project Leadership on R&D performance was also analyzed. Those who have participated in the Public and Private R&D projects at companies or research institutes were surveyed. First, it was found that project risk and benefit management have partially an effect on R&D project performance. Second, Public and private R&D Project Leadership showed partially a interaction effect between project management and project performance.
5,100원
12.
2023.06
구독 인증기관 무료, 개인회원 유료
The large process plant is currently implementing predictive maintenance technology to transition from the traditional Time-Based Maintenance (TBM) approach to the Condition-Based Maintenance (CBM) approach in order to improve equipment maintenance and productivity. The traditional techniques for predictive maintenance involved managing upper/lower thresholds (Set-Point) of equipment signals or identifying anomalies through control charts. Recently, with the development of techniques for big analysis, machine learning-based AAKR (Auto-Associative Kernel Regression) and deep learning-based VAE (Variation Auto-Encoder) techniques are being actively applied for predictive maintenance. However, this predictive maintenance techniques is only effective during steady-state operation of plant equipment, and it is difficult to apply them during start-up and shutdown periods when rises or falls. In addition, unlike processes such as nuclear and thermal power plants, which operate for hundreds of days after a single start-up, because the pumped power plant involves repeated start-ups and shutdowns 4-5 times a day, it is needed the prediction and alarm algorithm suitable for its characteristics. In this study, we aim to propose an approach to apply the optimal predictive alarm algorithm that is suitable for the characteristics of Pumped Storage Power Plant(PSPP) facilities to the system by analyzing the predictive maintenance techniques used in existing nuclear and coal power plants.
4,000원
13.
2023.06
구독 인증기관 무료, 개인회원 유료
Expanding exports of small and medium-sized companies is crucial for the continuous growth of the Korean economy. Therefore, the government operates various support systems to enhance the export capabilities of these companies. This study aims to analyze the impact of the Korean government's flagship export support system, known as the export initiation support system, on the performance of participating domestic companies. A fixed effect model using panel data was applied to examine the characteristics of 11,099 companies that participated in the export initiation support system from 2016 to 2019. The analysis revealed that the number of exporting countries, employees, and previous export volume had a significant impact on the export amount of participating companies. However, contrary to expectations, the number of overseas marketing participation and the GCL (global competence level) test did not show a significant impact. This study is significant as it provides implications for the development of support projects tailored to the specific needs of small and medium-sized companies, with the goal of improving the export support system.
4,000원
14.
2023.06
구독 인증기관 무료, 개인회원 유료
Due to COVID-19, changes in consumption trends are taking place in the distribution sector, such as an increase in non-face-to-face consumption and a rapid growth in the online shopping market. However, it is difficult for small and medium-sized export sellers to obtain forecast information on the export market by country, compared to large distributors who can easily build a global sales network. This study is about the prediction of export amount and export volume by country and item for market information analysis of small and medium export sellers. A prediction model was developed using Lasso, XGBoost, and MLP models based on supervised learning and deep learning, and export trends for clothing, cosmetics, and household electronic devices were predicted for Korea's major export countries, the United States, China, and Vietnam. As a result of the prediction, the performance of MAE and RMSE for the Lasso model was excellent, and based on the development results, a market analysis system for small and medium sellers was developed.
4,000원
15.
2023.06
구독 인증기관 무료, 개인회원 유료
This study focuses on the development of a Last-Mile delivery service using unmanned vehicles to deliver goods directly to the end consumer utilizing drones to perform autonomous delivery missions and an image-based precision landing algorithm for handoff to a robot in an intermediate facility. As the logistics market continues to grow rapidly, parcel volumes increase exponentially each year. However, due to low delivery fees, the workload of delivery personnel is increasing, resulting in a decrease in the quality of delivery services. To address this issue, the research team conducted a study on a Last-Mile delivery service using unmanned vehicles and conducted research on the necessary technologies for drone-based goods transportation in this paper. The flight scenario begins with the drone carrying the goods from a pickup location to the rooftop of a building where the final delivery destination is located. There is a handoff facility on the rooftop of the building, and a marker on the roof must be accurately landed upon. The mission is complete once the goods are delivered and the drone returns to its original location. The research team developed a mission planning algorithm to perform the above scenario automatically and constructed an algorithm to recognize the marker through a camera sensor and achieve a precision landing. The performance of the developed system has been verified through multiple trial operations within ETRI.
4,000원
16.
2023.06
구독 인증기관 무료, 개인회원 유료
In the era of the 4th Industrial Revolution, Logistic 4.0 using data-based technologies such as IoT, Bigdata, and AI is a keystone to logistics intelligence. In particular, the AI technology such as prognostics and health management for the maintenance of logistics facilities is being in the spotlight. In order to ensure the reliability of the facilities, Time-Based Maintenance (TBM) can be performed in every certain period of time, but this causes excessive maintenance costs and has limitations in preventing sudden failures and accidents. On the other hand, the predictive maintenance using AI fault diagnosis model can do not only overcome the limitation of TBM by automatically detecting abnormalities in logistics facilities, but also offer more advantages by predicting future failures and allowing proactive measures to ensure stable and reliable system management. In order to train and predict with AI machine learning model, data needs to be collected, processed, and analyzed. In this study, we have develop a system that utilizes an AI detection model that can detect abnormalities of logistics rotational equipment and diagnose their fault types. In the discussion, we will explain the entire experimental processes : experimental design, data collection procedure, signal processing methods, feature analysis methods, and the model development.
4,000원
17.
2023.06
구독 인증기관 무료, 개인회원 유료
With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.
4,000원
