• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.18-26
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• 2017

Defense industries which require high reliability need an optimized quality management system with well-planned implementation. And the government should examine the overall status of defense industries, then establish practical policies with a proper support plan in required areas to upgrade the quality management level of manufacturers. Thus, DTaQ developed the model for 2 years from 2014, which specialized in quality management level analysis for defense industries. And a survey has been undertaken with that model by DTaQ and Korea Research Center in 2016. The surveyed companies randomly sampled among those which have more than 30 employees and delivery history over past 3 years, and finally 106 defense industries were selected. This paper present survey method and indexes for survey of defense industry quality management level. The survey was conducted in the order of planning, data collection and data processing, and the validity and reliability of the data were verified to increase objectivity of survey results. The survey contents mainly consist of system quality and management quality. System quality includes Product Development Management, Production Operation Management, supply chain quality management, Safety & Environment Management and Reliability Management, on the other hand, management quality includes Strategic Leadership, Human Resource Management, Customer Market Management and Information & Knowledge Management. Thus this proposes the current overall quality management status of the 106 defense industries and shows level differences by company sizes and manufacturing sectors based on the result of survey. Specifically, this paper enables to track the areas which need prompt government support with the policy directions to make quality management level higher. Therefore, it is expected that this can be used as reference data in establishing quality policies for military supplies in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.4
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• pp.641-647
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• 2007

It has been said that Warehouse Management System(WMS) in e-Trade has considerably contributed to improvement of distribution industry. However, in the aspect of management we have many technical problems such as transportation due to tremendous demands of online data. Therefore, for Korea to be a hub of distribution and information, this is the best time to prepare the foundation of Supply Chain Management(SCM) under Enterprise Resource Planning (ERP) the and make a good use of WMS. The purposes of this study are presented as follows: First, it is to examine the general flow process of WMS in each field of our e-Trade Second, it is to present problems and plans of improvements involved in WMS in e-Trade. In short, this study aims at presenting interpretative foundation of WMS in a practical viewpoint, not presenting technical directions of WMS.

• The Journal of Information Systems
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• v.27 no.4
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• pp.1-33
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• 2018

Purpose The purpose of this study is to review topic and survey methodological trends in 'The Journal of Information Systems' in order to present the practical guidelines for the future IS research. By attempting to conduct a meta-analysis on both topic and survey methodological trends, this study could provide researchers wishing to pursue this line of work further with what can be done to improve IS disciplines. Design/methodology/approach In this study, we have reviewed 185 papers that were published in 'The Journal of Information Systems' from 2010 to 2018 and classified them based on topics studied and survey methodologies used. The classification guidelines, which was developed by Palvia et al.(2015), has been used to capture the topic trends. We have also employed Struab et al.(2004)s' guidelines for securing rigor of validation issues. By using two guidelines, this study could also present topic and rigor trends in 'The Journal of Information Systems' and compare them to those trends in International Journals. Findings Our findings have identified dominant research topics in 'The Journal of Information Systems'; 1) social media and social computing, 2) IS usage and adoption, 3) mobile computing, 4) electronic commerce/business, 5) security and privacy, 6) supply chain management, 7) innovation, 8) knowledge management, and 9) IS management and planning. This study also could offer researchers who pursue this line of work further practical guidelines on mandatory (convergent and discriminant validity, reliability, and statistical conclusion validity), highly recommended (common method bias testing), and optional validations (measurement invariance testing for subgroup analysis, bootstrapping methods for testing mediating effects).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.2
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• pp.24-35
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• 2021

Due to increasing awareness on the treatment of end-of-use/life products, disassembly has been a fast-growing research area of interest for many researchers over recent decades. This paper introduces a novel lot-sizing problem that has not been studied in the literature, which is the service-parts lot-sizing with disassembly option. The disassembly option implies that the demands of service parts can be fulfilled by newly manufactured parts, but also by disassembled parts. The disassembled parts are the ones recovered after the disassembly of end-of-use/life products. The objective of the considered problem is to maximize the total profit, i.e., the revenue of selling the service parts minus the total cost of the fixed setup, production, disassembly, inventory holding, and disposal over a planning horizon. This paper proves that the single-period version of the considered problem is NP-hard and suggests a heuristic by combining a simulated annealing algorithm and a linear-programming relaxation. Computational experiment results show that the heuristic generates near-optimal solutions within reasonable computation time, which implies that the heuristic is a viable optimization tool for the service parts inventory management. In addition, sensitivity analyses indicate that deciding an appropriate price of disassembled parts and an appropriate collection amount of EOLs are very important for sustainable service parts systems.

• Journal of the Korean Institute of Gas
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• v.26 no.1
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• pp.7-19
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• 2022

Hydrogen purification is generally performed through chemical and physical methods. Among various types of purification method PSA(Pressure Swing Adsorption) is widely used with its purification capacity and economic efficiency. In Korea, most of the hydrogen used in automobiles and power generation fuel cells is purified using PSA. Hydrogen produced in petrochemical complexes has difficulties in transportation. The government is planning to install hydrogen extractors that produce hydrogen directly from consumers in connection with the city gas supply chain, and companies are also installing related research and demonstration facilities one after another. Europe and others have recently established safety standards related to PSA and are making efforts for systematic safety management at the construction and operation stage, but domestic safety standards related to PSA are still insufficient. This study aims to identify problems of existing facilities through surveys and risk assessment by companies operating existing PSA, and to prepare domestic technical standards including them in overseas technical standards to promote the safety of new and existing PSA systems.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.1-7
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.210-216
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• 2023

Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.343-352
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• 2020

Modular integrated construction (MiC) is an innovative construction method where components of a building are manufactured in an offsite factory, trucked to the job site in sections, set in place with cranes, and assembled together to form a whole building. Where circumstances merit, favorable conditions exist and implemented effectively; MiC improves project performance. However, several key factors need to converge during implementation to realize the full benefits of MiC. Thus, a thorough understanding of the factors which are critical to the success of MiC projects is imperative. Drawing on a systematic review of 47 empirical studies, this research identified 25 key success factors (KSFs) for MiC projects. Of these, the five topmost cited KSFs for MiC projects include effective working collaboration and communication among project participants; standardization, optimization, automation and benchmarking of best practices; effective supply chain management; early design freeze and completion; and efficient procurement method and contracting. The study further proposed a conceptual model of the KSFs, highlighting the interdependences of people, processes, and technology-related KSFs for the effective accomplishment of MiC projects. The set of KSFs is practically relevant as they constitute a checklist of items for management to address and deal with during the planning and execution of MiC projects. They also provide a useful basis for future empirical studies tailored towards measuring the performance and success of MiC projects. MiC project participants and stakeholders will find this research useful in reducing failure risks and achieving more desired performance outcomes. One potential impact of the study is that it may inform, guide, and improve the successful implementation of MiC projects in the construction industry. However, the rigor of the analysis and relative importance ranking of the KSFs were limited due to the absence of data.

• Fashion & Textile Research Journal
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• v.25 no.6
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• pp.673-689
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• 2023

To realize the traceability of sustainable textile products, this study presents a low-carbon process through energy savings in the textile material manufacturing process. Traceability is becoming an important element of Life Cycle Assessment (LCA), which confirms the eco-friendliness of textile products as well as supply chain information. Textile products with complex manufacturing processes require traceability of each step of the process to calculate carbon emissions and power usage. Additionally, an understanding of the characteristics of the product planning-manufacturing-distribution process and an overall understanding of carbon emissions sources are required. Energy use in the textile material manufacturing stage produces the largest amount of carbon dioxide, and the amount of carbon emitted from processes such as dyeing, weaving and knitting can be calculated. Energy saving methods include efficiency improvement and energy recycling, and carbon dioxide emissions can be reduced through waste heat recovery, sensor-based smart systems, and replacement of old facilities. In the dyeing process, which uses a considerable amount of heat energy, LNG, steam can be saved by using "heat exchangers," "condensate management traps," and "tenter exhaust fan controllers." In weaving and knitting processes, which use a considerable amount of electrical energy, about 10- 20% of energy can be saved by using old compressors and motors.

• Journal of Korea Water Resources Association
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• v.41 no.1
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• pp.35-47
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• 2008

The low flow analysis is an important part in water resources engineering. Also, the results of low flow frequency analysis can be used for design of reservoir storage, water supply planning and design, waste-load allocation, and maintenance of quantity and quality of water for irrigation and wild life conservation. Especially, for identification of the uncertainty in frequency analysis, the Bayesian approach is applied and compared with conventional methodologies in at-site low flow frequency analysis. In the first manuscript, the theoretical background for the Bayesian MCMC (Bayesian Markov Chain Monte Carlo) method and Metropolis-Hasting algorithm are studied. Two types of the prior distribution, a non-data- based and a data-based prior distributions are developed and compared to perform the Bayesian MCMC method. It can be suggested that the results of a data-based prior distribution is more effective than those of a non-data-based prior distribution. The acceptance rate of the algorithm is computed to assess the effectiveness of the developed algorithm. In the second manuscript, the Bayesian MCMC method using a data-based prior distribution and MLE(Maximum Likelihood Estimation) using a quadratic approximation are performed for the at-site low flow frequency analysis.