• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.4
• /
• pp.240-246
• /
• 2022

This paper introduces a container loading problem and proposes a theoretical approach that efficiently solves it. The problem is to determine a proper weight of products loaded on a container that is delivered by third party logistics (3PL) providers. When the company pre-loads products into a container, typically one or two days in advance of its delivery date, various truck weights of 3PL providers and unpredictability of the randomness make it difficult for the company to meet the total weight regulation. Such a randomness is mainly due to physical difference of trucks, fuel level, and personalized equipment/belongings, etc. This paper provides a theoretical methodology that uses historical shipping data to deal with the randomness. The problem is formulated as a stochastic optimization where the truck randomness is reflected by a theoretical distribution. The data analytics solution of the problem is derived, which can be easily applied in practice. Experiments using practical data reveal that the suggested approach results in a significant cost reduction, compared to a simple average heuristic method. This study provides new aspects of the container loading problem and the efficient solving approach, which can be widely applied in diverse industries using 3PL providers.

• Fourth Industrial Review
• /
• v.3 no.2
• /
• pp.27-35
• /
• 2023

Purpose - In this study, the current author explores how Human Resource Management (HRM) is changing in the context of the Fourth Industrial Revolution (4IR). Understanding the distinctive features of HRM in this day is crucial, given how rapidly industries are changing due to technology. Research design, data, and methodology - This study adopts a thorough literature review methodology to pinpoint and clarify these distinctive characteristics, advancing our understanding of the role of HRM in the modern world. Regarding methodology, this study uses the PRISMA approach to systematically gather pertinent publications from various sources that have undergone peer review. Result - By carefully choosing and examining these studies, the present author was able to identify four crucial HRM traits that are representative of the Fourth Industrial Revolution. The findings emphasizes how common flexible work schedules are. Using data analytics to influence HRM decisions is increasingly important for maximizing hiring, reviewing performance, and fostering organizational growth. Conclusion - By recalibrating their HRM practices in the 4IR, businesses may encourage flexibility, innovation, and employee well-being. This work makes a substantial contribution to both HRM theory and practice and our comprehension of the transformative effects of the 4IR by filling a gap in the existing literature.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.162-169
• /
• 2022

In an attempt to disseminate modernized Korean traditional housing (Hanok), a ten-year research project was initiated in 2010 by the Korean Government to reduce the construction cost, improve the facility performance, and automate the Hanok construction industry. To meet these objectives, various research areas, including public policies, planning methods, design standards, new building materials, construction standards, maintenance procedures, advanced project management tools, and integrated IT applications have been developed. In addition, comprehensive technologies developed were applied to the ten pilot Hanok buildings to validate the real-world performance as part of the research project. To further facilitate the digital transformation of the Hanok industry by using the research results, it is required to disseminate the developed technologies in an automated and standardized manner. In particular, it is crucial to systematize and manage the interoperability of various technical data and accumulated historical data for different business functions, especially within the highly fragmented industry. In this context, this paper proposes an ontology-based Hanok information dissemination platform to enable industry-wide automated knowledge and information sharing. The system architecture, standardized historical database, and advanced analytics based on ontology web language (OWL) for the Hanok industrialization platform are introduced.

• Nuclear Engineering and Technology
• /
• v.55 no.2
• /
• pp.603-622
• /
• 2023

Human error (HE) is an important concern in safety-critical systems such as nuclear power plants (NPPs). HE has played a role in many accidents and outage incidents in NPPs. Despite the increased automation in NPPs, HE remains unavoidable. Hence, the need for HE detection is as important as HE prevention efforts. In NPPs, HE is rather rare. Hence, anomaly detection, a widely used machine learning technique for detecting rare anomalous instances, can be repurposed to detect potential HE. In this study, we develop an unsupervised anomaly detection technique based on generative adversarial networks (GANs) to detect anomalies in manually collected surveillance data in NPPs. More specifically, our GAN is trained to detect mismatches between automatically recorded sensor data and manually collected surveillance data, and hence, identify anomalous instances that can be attributed to HE. We test our GAN on both a real-world dataset and an external dataset obtained from a testbed, and we benchmark our results against state-of-the-art unsupervised anomaly detection algorithms, including one-class support vector machine and isolation forest. Our results show that the proposed GAN provides improved anomaly detection performance. Our study is promising for the future development of artificial intelligence based HE detection systems.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.273-276
• /
• 2023

This paper presents the integration of Digital twin and Blockchain-based Supply Chain Management (DB-SCM) in a smart warehouse to create a more efficient, secure, and transparent facility. The process involves creating a digital twin of the warehouse using sensors and IoT devices and then integrating it with a blockchain-based supply chain management system to connect all stakeholders. All data are collected and tracked in real-time as goods move through the warehouse, and smart contracts are automatically executed to ensure accountability for all parties involved. The study also highlights the critical role of effective supply chain management in modern business operations and the significance of smart warehouses, which leverage advanced technologies such as robotics, AI, and data analytics to optimize warehouse operations. Later, we discuss the importance of digital twins, which allow for creating a virtual representation of a physical object or system, and their potential to revolutionize a wide range of industries. Therefore, DB-SCM offers numerous benefits, including enhanced efficiency, improved customer satisfaction, and increased sustainability, and provides a valuable case study for organizations seeking to optimize their supply chain operations.

• Smart Media Journal
• /
• v.12 no.10
• /
• pp.38-46
• /
• 2023

Farming activities are transforming from traditional skill-based agriculture into knowledge-based and technology-driven digital agriculture. The use of intelligent information and communication technology introduces the idea of smart farming that enables farmers to collect weather data, monitor crop growth remotely and detect crop diseases easily. The introduction of Plantix, a pest and disease management tool in the form of a mobile application has allowed farmers to identify pests and diseases of the crop using their mobile devices. Hence, this study collected the reviews of Plantix to explore the response of the users on the Google Play Store towards the application through Latent Dirichlet Allocation (LDA) topic modeling. Results indicate four latent topics in the reviews: two positive evaluations (compliments, appreciation) and two suggestions (plant options, recommendations). We found the users suggested the application to additional plant options and additional features that might help the farmers with their difficulties. In addition, the application is expected to benefit the farmer more by having an early alert of diseases to farmers and providing various substitutes and a list of components for the remedial measures.

• The Journal of Industrial Distribution & Business
• /
• v.15 no.4
• /
• pp.27-33
• /
• 2024

Purpose: Arts and cultural management are a field with unique meaning and significance. This study is uniquely based on the focus of arts and cultural management on social and cultural sustainability sets it apart from other related study fields. Through delving into arts and cultural management, one can quickly gain skills vis-à-vis creativity and innovation in traditional and emerging media platforms. Research design, data and methodology: The current researcher relied on the descriptive research design, arriving at and evaluating the findings. The descriptive research design was the most ideal because of the need to evaluate the various literature sources systematically and later describe them without undue influence. Results: This research's core finding of art and cultural management in the current literature may be split up four findings, such as (1) Art and Cultural Management is Fast Embracing Digital Innovations and Related Elements, (2) Data and Analytics in Art and Cultural Management, (3) Interdisciplinary Nature of Arts and Cultural Management Elements, and (4) Arts and Cultural Management Face Numerous Challenges that Define it and its Future. Conclusions: All in all, based on the literature findings, the present research concludes that It is incumbent upon the various stakeholders, such as the government, to prioritize the arts and cultural management field through adequate budgeting and allocation of money.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.44-55
• /
• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• Management & Information Systems Review
• /
• v.36 no.1
• /
• pp.273-290
• /
• 2017

Internet of Things (IoT) is considered to be the next wave of Information Technology transformation after the Internet has changed the process of doing business. Since the domain of IoT ranging from the sensor technology to service to the users is wide, the structure of the research domain is not delineated clearly. To do that we suggest to use the Technology Stack Model proposed by Porter et al.(2014) to measure the maturity level of IoT in organizations. Based on the Stack Model, for the general understandings of IoT, we do keyword analyses on the academic papers whose major research issue is IoT. It is found that the current status of IoT application from the perspectives of cloud and big data analytics is not active, meaning that the real value of IoT has not been realized. We also examine the cases which deal with the part of cloud process which is crucial for value accrual. Based on these findings, we suggest the future direction of IoT research. We also propose that IT is to value chain what IoT is to the Stack Model to derive value in organizations.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.4
• /
• pp.154-163
• /
• 2017

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The 'cold-start' problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.