• 산업경영시스템학회지
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• 제40권3호
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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.

• 한국정보통신학회논문지
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• 제11권4호
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• pp.641-647
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• 2007

전자무역에서 WMS는 물류산업을 보다 편리하고 효율적으로 개선시키는데 많은 부문에 기여했다고 할 수 있다. 그러나 운영 측면에서 볼 때 전자데이터 등의 폭발적인 수요 폭주로 인하여 운송방법 등의 기술적인 문제점들이 적잖게 노출되어 있는 것이 현실이다. 따라서 우리나라가 물류정보 중심지로 성장하기 위해서는 전자무역 ERP 관리하의 SCM 기반구축과 WMS를 충분히 활용하여야 할 시점이다. 본 연구의 목적은 전자무역 WMS의 효율적인 운영을 위하여 첫째, 우리나라 전자무역의 부분별 종합 WMS의 플로우 과정을 고찰하고 둘째, 전자무역 WMS의 문제점과 개선방안을 제시 하고자 한다. 본 논문은 WMS의 기술적인 방향을 제시하기보다는 실무적 관점에서 해석적 기초를 제시하는데 그 목적이 있다.

• 한국정보시스템학회지:정보시스템연구
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• 제27권4호
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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).

• 산업경영시스템학회지
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• 제44권2호
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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.

• 한국가스학회지
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• 제26권1호
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• pp.7-19
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• 2022

일반적으로 수소의 정제는 화학적, 물리적인 방법을 통해 수행한다. 여러 종류의 정제방법 중 현재는 정제 용량 및 경제성이 가장 우수한 PSA(Pressure Swing Adsorption)를 이용한 정제방법이 가장 널리 사용되고 있다. 국내도 대부분 PSA를 이용하여 자동차 및 발전용 수소 연료전지 등에 사용하는 수소를 정제하고 있다. 기존 석유화학 단지중심의 부생수소는 운송 등의 어려움이 있다. 정부는 도시가스 공급망과 연계하여 소비지에서 직접 수소를 생산하는 수소추출기 설치 계획하고 있으며, 기업들도 이와 관련된 연구 및 실증 설비를 속속 설치하고 있는 실정이다. 유럽 등은 최근 PSA와 관련된 안전기준을 마련하여 시공 및 운영단계에서 체계적인 안전관리를 위한 노력을 기울이고 있으나, 국내는 PSA와 관련된 안전기준 마련이 아직까지는 미흡하다. 본 연구에서는 기존 PSA를 운영하고 있는 회사의 설문 및 위험성평가를 통해 기존설비의 문제점을 파악하고, 국외 기술기준에 이를 포함한 국내 기술기준을 작성하여 신규설치 및 기존 운영되고 있는 PSA시스템의 안전을 도모하고자 한다.

• International Journal of Computer Science & Network Security
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• 제23권9호
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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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• 제23권8호
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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.

• 국제학술발표논문집
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• The 8th International Conference on Construction Engineering and Project Management
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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.

• 한국의류산업학회지
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• 제25권6호
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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.

• 한국수자원학회논문집
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• 제41권1호
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• pp.35-47
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• 2008

저수분석(low flow analysis)은 수자원공학에서 중요한 분야 중 하나이며, 특히 저수량 빈도분석(low flow frequency analysis)의 결과는 저수(貯水)용량의 설계, 물 수급계획, 오염원의 배치 및 관개와 생태계의 보존을 위한 수량과 수질의 관리에 중요하게 사용된다. 그러므로 본 연구에서는 저수량 빈도분석을 위한 점 빈도분석을 수행하였으며, 특히 빈도분석에 있어서의 불확실성을 탐색하기 위하여 Bayesian 방법을 적용하고 그 결과를 기존에 사용되던 불확실성 탐색방법과 비교하였다. 본 논문의Ⅰ편에서는 Bayesian 방법 중 사전분포(prior distribution)와 우도함수(likelihood function)의 복잡성에 상관없이 계산이 가능한 Bayesian MCMC(Bayesian Markov Chain Monte Carlo) 방법과 Metropolis-Hastings 알고리즘을 사용하기 위한 여러 과정의 이론적 배경과 Bayesian 방법에서 가장 중요한 요소인 사전분포를 구축하고 이를 비교 및 평가하였다. 고려된 사전분포는 자료에 기반하지 않은 사전분포와 자료에 기반한 사전분포로써 두 사전분포를 이용하여 Metropolis-Hastings 알고리즘을 수행하고 그 결과를 비교하여 저수량 빈도분석에 합리적인 사전분포를 선정하였다. 또한 알고리즘의 수행과정에서 필요한 제안분포(proposal distribution)를 적용하여 그에 따른 알고리즘의 효율성을 채택률(acceptance rate)을 산정하여 검증해 보았다. 사전분포의 분석 결과, 자료에 기반한 사전분포가 자료에 기반하지 않은 사전분포보다 정확성 및 불확실성의 표현에 있어서 우수한 결과를 제시하는 것을 확인할 수 있었고, 채택률을 이용한 알고리즘의 효용성 역시 기존 연구자들이 제시하였던 만족스러운 범위를 가지는 것을 알 수 있었다. 최종적으로 선정된 사전분포는 본 연구의 II편에서 Bayesian MCMC방법의 사전분포로 이용되었으며, 그 결과를 기존 불확실성의 추정방법의 하나인 2차 근사식을 이용한 최우추정(maximum likelihood estimation)방법의 결과와 비교하였다.