미·중 기술패권 경쟁 가운데, 첨단 디지털 기술의 공급망과 데이터 수 집, 유출, 조작, 유포 등의 우려가 제기되면서 공급망안보와 데이터안보 가 교차하는 양상이 두드러지기 시작했다. 데이터안보나 공급망안보 각 각에 대한 기존 연구는 있었으나, 이 둘의 교차점에 초점을 맞춘 연구는 드물다. 이 연구에서 다루는 데이터안보 사안들은 정부뿐 아니라 개인이 나 민간 행위자들이 취급하는 데이터들이 국가안보적 사안으로 전환되는 모습을 보인다. 이러한 신흥위협이 공급망위협과 상호작용하며, 국가안보 적 우려로 비화한 사례로 화웨이의 5G, ZPMC 항만 대형 크레인, 미래 자동차의 핵심 기술 라이다, 중국산 DJI 드론 등이 있다. 본 연구는 이러 한 사례들에 대해 국가 차원에서 어떤 대응이 이루어졌는지 탐구하였다. 특히, 관련 이슈들을 안보화하고 대응책을 앞서 제시하고 있는 미국에 초점을 맞추었다. 또한 기존의 안보화 이론을 보완하여 정치경제적 측면 을 추가하였다. 행정명령, 전략서 발간, 연방 예산안 그리고 새로운 전문 기구들의 창설을 통해 신흥 위협에 대처하고 있으며, 그 과정에서 공급 망 재편, 표준, 규범, 규제의 설립을 통해 변화하는 국가의 안보 제공자 로서의 역할을 조명하였다.

The green supply chain has become a central concern for global businesses, particularly in maritime industries, where sustainable development is pursued as both an economic growth strategy and a means of environmental preservation. This study seeks to identify the key challenges to implementing green supply chain in Vietnam. The Analytical Hierarchy Process (AHP) is employed to assess the significance of various factors, while Fuzzy Structural Modeling (FSM) is used to explore their interrelationships. Five major factors - economic, technological, organizational, governmental, and social - are identified as critical to the implementation of green supply chain. The study highlights that the organizational factor is the most crucial, with customer pressure, particularly regarding environmental standards from export countries, being the most influential sub-factor. The findings provide important insights for developing government policies, offering support to businesses, and guiding investment decisions in green supply chain.

In this paper, an alternative inventory policy that trades off the bullwhip effect at an upstream facility with cost minimization at a current facility, with the goal of reducing system wide total expected inventory costs, when external demand distributjon is autocorrelated, is considered. The alternative inventory policy has a form that is somewhere between one that completely neglects the autocorrleation and one that actively utilizes the autocorrelation. For this purpose, a mathematical model that allows us to evaluate system wide total expected inventory costs for a periodic review system is developed. This model enables us to identify an optimal inventory policy at a current facility that minimizes system wide total expected inventory costs by the best tradeoff of the bullwhip effect at an upstream facility with cost minimization at a current facility. From numerical experiments, it has been found that (i) when the autocorrelation is negative, the optimal policy is one that actively utilizes the autocorrelation, (ii) when the autocorrelation is small and positive, the optimal policy is one that neglects the autocorrelation, and (iii) when the autocorrelation is large and positive, the optimal policy is somewhere between one that actively utilizes the autocorrelation and one that neglect the autocorrelation.

Food upcycling has emerged as an effective approach to sustainably utilize the food waste generated within the food supply chain. This review article examines upcycled food with respect to its definition, consumers’ knowledge and perception on it, and the process by which by-products from the food supply chain are utilized for the creation of upcycled food products. The definition of upcycled food varied among manufacturers, research institutions, and the Upcycled Food Association, depending on the specific values and objectives of each sector. This has resulted in the use of different keywords to highlight the distinctive characteristics of their respective interpretations of upcycled food. This review also summarizes the various consumer traits that can influence the awareness and acceptance of upcycled food, encompassing functional, empirical and emotional, symbolic and self-expressive, and economic benefits. Additionally, the review presents strategies to utilize by-products produced in large quantities in Korea, while also addressing the control of hazardous components to ensure biological or chemical safety and the changes in nutritional value that may occur during the utilization of these byproducts.

Recently, the luxury sector has witnessed a significant rise in luxury consumption, reaching £233 Billion in 2022 (Statista, 2022). This rise demonstrates the growing popularity of the luxury consumption phenomenon globally. However, the climate crisis may impact future trends in luxury consumption (Gardetti and Muthu, 2019). The luxury sector has endorsed a considerable growing demand for sustainability from environmental and ethical luxury consumers. In recent years, concerns have grown around the ethicality of supply chains, where consumers develop contradictory feelings and beliefs, veering between conscious and hedonistic decision-making (Kleinhaus, 2011; Helm, 2020; Wang et al. 2021). Moreover, consumers face a conflict between choosing what they believe is ethically right and indulgence (Hennigs et al. 2013). The supply chain plays an important role in achieving sustainability goals, and yet some researchers argue that the luxury supply chain can involve ethical and environmental breaches in terms of labour and raw materials, such as use of leather and fur (Klerk et al. 2018). However, some luxury brands such as Stella McCartney and Vivienne Westwood are focused on sustainability and the use of vegan raw materials (YNAP, 2021).

Local consumption is considered to have a positive environmental and social impact. A new supply chain strategy has been devised to provide small and medium-sized local farms with enhanced efficiency and accessibility: a bidirectional distribution. Bidirectional distribution is a practice of backhauling local produce on emptied wholesale trucks for redistribution through wholesale markets while employing the existing network of rural stores and wholesale suppliers. Building on the cue utilization theory, this study investigated the effect of product information about bidirectional distribution on consumers’ perceived environmental value, personal well-being value, quality beliefs, and community social and economic value.

본 연구는 미-중 전략경쟁, 코로나19의 심화와 러시아-우크라이나 전 쟁을 통해 국제 공급망 재편에 대한 요구가 강제적 공급망 재편 현상으 로 변화하고 있는 시점에서, 핵심 영역이라고 할 수 있는 반도체 분야에 대한 독일의 대응 전략 분석을 목적으로 한다. 미-중 전략경쟁 속에서 미국과는 전통적 안보 관계를, 중국과는 중국의 경제적 부상과 함께 상 당한 수준의 경제적 상호의존 관계를 형성하고 있는 한국과 독일은 구조 적 유사성으로 인해 독일 사례 분석은 정책적으로 유의미하다. 독일은 한국과 달리 경제안보 개념은 인간안보 개념 속에서 이해할 수 있으며 한국적 맥락의 경제안보는 공급망 안보로 이해할 수 있다. 따라서 본 연 구는 독일의 경제안보 개념에 대한 이해를 추적하고 유럽의 개방형 전략 적 자율성 개념을 통해 안보와 방위를 넘어서는 무역과 산업, 디지털화, 기후변화, 보건 등의 의제를 포괄적으로 접근하고 있을 뿐만 아니라 독 일의 기술 주권과 디지털 주권 수호를 위한 핵심 사안임을 인정하여, 독 일의 반도체 전략을 분석하고 공급망 안보 영역에서 공동 대응을 통해 협력의 가능성을 모색하고자 한다.

The guidelines for cyber security regulations at domestic and foreign nuclear facilities, such as KINAC/RS-015, NRC’s RG5.71 and NEI 13-10, require the establishment of security measures to maintain the integrity of critical digital assets (CDAs) and protect them as threats to the supply process. According to the requirements, cyber security requirements shall be reflected in purchase requirements from the time of introduction of CDAs, and it shall also be verified whether cyber security security measures were properly applied before introduction. Domestic licensees apply measures to control the supply chain in the nuclear safety sector to cyber security policies. The safety sector supply chain control policy has areas that functionally overlap with the requirements of cyber security regulations, so regulatory guidelines in the safety sector can be applied. However, since most of the emergency preparedness and physical protection functions introduce digital commercial products, there is a limit to applying the control of the supply chain in the safety field as it is. It is necessary to apply supply chain control operator policies, procedures, and purchase requirements for each SSEP function, or to establish cyber security integrated supply chain control requirements. In this paper, based on the licensee’s current supply chain control policy, the cyber security regulation plan for supply chain control according to the SSEP (Safety-Security-Emergency Preparedness) function of CDAs is considered.

The global cod market is supposed to have weak structure with a high dependence on the supply of Russia, the United States, Norway, and China. The COVID-19 pandemic has significantly disrupted the cod supply chain for the worse. Fish processing facilities in China stopped their operation, and cod demand declined due to shrinking consumption in Europe. The position of South Korea as an intermediary trade country between Russia and China strengthened due to the U.S.-China trade war and the Atlantic cod decrease in 2019. However, this global cod supply chain collapse has caused South Korea to export accumulated cod to Indonesia and Vietnam at a bargain price, showing that South Korea was unable to cope with this supply chain crisis. The primary purpose of this study is to investigate changes in the global cod supply chain and their impacts on the intermediary trade of South Korea caused by the COVID-19 pandemic. It also aims to provide implications by analyzing advanced cases in Denmark. As the cod supply chain crisis countermeasures, this study suggests that South Korea develop high value-added marine products, gain competitive advantages by solidifying the value chains of related countries, and activate export by discovering alternative markets in terms of the supply chain of the cod industry.

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

The purpose of this study was to analyze structural relationships with regard to the effect of customer integration, which is a type of integration in the supply chain, and market orientation of supply chain on the resulting change in the supply chain and management performance. The results of analysis in this study are as follows: First, customer integration and market orientation had a positive effect on reducing the flexibility and uncertainty of SCM. The decreased flexibility and uncertainty of SCM had a positive effect on non-financial performance, which also had a positive effect on financial performance. Second, customer integration and market orientation had a positive effect on financial and non-financial performance indirectly by decreasing the flexibility and uncertainty of SCM. Third, the effect of customer integration and uncertainty of SCM on the flexibility of SCM changed depending on the position in the supply chain; the effect was larger in the distribution group. The implications based on the analysis results are as follows: It is expected that the ability to deal with market changes in the overall supply chain is improved by laying the foundation for cooperation through establishing information infrastructure, including sharing information with trade partners and integrating systems, and implementing customer integration based on these achievements. It is also necessary to consider the business types and characteristics of individual companies in establishing information infrastructure.

Supply chain managers seek to achieve global optimization by solving problems in the supply chain's business process. However, companies in the supply chain hide the adverse information and inform only the beneficial information, so the information is distorted and cannot be the information that describes the entire supply chain. In this case, supply chain managers can directly collect and analyze supply chain activity data to find and manage the companies described by the data. Therefore, this study proposes a method to collect the order-inventory information from each company in the supply chain and detect the companies whose data characteristics are explained through deep learning. The supply chain consists of Manufacturer, Distributor, Wholesaler, Retailer, and training and testing data uses 600 weeks of time series inventory information. The purpose of the experiment is to improve the detection accuracy by adjusting the parameter values of the deep learning network, and the parameters for comparison are set by learning rate (lr = 0.001, 0.01, 0.1) and batch size (bs = 1, 5). Experimental results show that the detection accuracy is improved by adjusting the values of the parameters, but the values of the parameters depend on data and model characteristics.