• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.9-21
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• 2004

Semiconductor industry is the one whose supply chain network is distributed all over the world. And it has different characteristics with other manufacturing industries as reentrancy, binning, substitution. In this paper, we suggest supply chain models for the semiconductor industry, consisting of production and distribution chains, where manufacturing characteristics are considered. Three policies for the production chain and two policies for the distribution chain are suggested and formulated mathematically. Six combination policies are tested for the evaluation of performances with example. It is shown that the supply chain is operated, if production and distribution are coordinated and managed based on the demand information, without inventory, as efficiently as the chain with inventory.

• Journal of Korea Society of Industrial Information Systems
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• v.24 no.3
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• pp.31-38
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• 2019

In this paper, we design a green supply chain (gSC) network model. For constructing the gSC network model, environmental and economic factors are taken into consideration in it. Environmental factor is to minimize the $CO_2$ emission amount emitted when transporting products or materials between each stage. For economic factor, the total cost which is composed of total transportation cost, total handling cost and total fixed cost is minimized. To minimize the environmental and economic factors simultaneously, a mathematical formulation is proposed and it is implemented in a genetic algorithm (GA) approach. In numerical experiment, some scales of the gSC network model is presented and its performance is analyzed using the GA approach. Finally, the efficiencies of the gSC network model and the GA approach are proved.

• Management & Information Systems Review
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• v.31 no.3
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• pp.141-160
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• 2012

This study investigate the relationship between embeddedness of supply chain on supply chain performance. The development of research model is based on network embeddedness that the literature of strategic management and sociology. To examine the research model and hypotheses, we have used an empirical method based on field survey in which most of measurements used and verified in previous studies are selected as measurements. The data from survey was analyzed using Partial Least Squares(PLS). The result from empirical model suggest as follow; First, relational embeddedness of supply chain effects on supply chain performance. Especially, reciprocal dependance affects interfirm relation performance. Also trust and tie strength of relational embeddedness affects interfirm relation performance. Second, interfirm relation performance affects financial performance.

• Industrial Engineering and Management Systems
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• v.13 no.1
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• pp.29-42
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• 2014

Extensive research has been conducted on supplier evaluation and selection as a strategic and crucial component of supply chain management in recent years. However, few articles in the previous literature have been dedicated to the use of fuzzy inference systems as an aid in decision-making. Therefore, this essay attempts to demonstrate the application of this method in evaluating suppliers, based on a comprehensive framework of qualitative and quantitative factors besides the effect of gradual coverage distance. The purpose of this study is to investigate the applicability of the numerous measures and metrics in a multi-objective optimization problem of the supply chain network design with the aim of managing the allocation of orders by coordinating the production lines to satisfy customers' demand. This work presents a dynamic non-linear programming model that examines the important aspects of the strategic planning of the manufacturing in supply chain. The effectiveness of the configured network is illustrated using a sample, following which an exact method is used to solve this multi-objective problem and confirm the validity of the model, and finally the results will be discussed and analyzed.

• The Journal of Information Systems
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• v.12 no.2
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• pp.57-72
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• 2003

The purpose of this paper is to propose the modeling of multiagent based SCM and implement the prototype in the Internet environment. SCM process follows the supply chain operations reference (SCOR) model which has been suggested by Supply Chain Counsil. SCOR model has been positioned to become the industry standard for describing and improving operational process in SCM. Five basic processes, plan, source, matte, deliver and return are defined in the SCOR model, through which a company establishes its supply chain competitive objectives. A supply chain is a world wide network of suppliers, factories, warehouses, distribution centers and retailers through which raw materials are acquired, transformed or manufactured and delivered to customers by autonomous or semiautonomous process. With the pressure from the higher standard of customer compliance, a frequent model change, product complexity and globalization, the combination of supply chain process with an advanced infrastructure in terms of multiagent systems have been highly required. Since SCM is fundamentally concerned with coherence among multiple decision makers, a multiagent framework based on explicit communication between constituent agents such as suppliers, manufacturers, and distributors is a natural choice. Multiagent framework is defined to perform different activities within a supply chain. Dynamic and changing functions of supply chain can be dealt with multi-agent by cooperating with other agents. In the areas of inventory management, remote diagnostics, communications with field workers, order fulfillment including tracking and monitoring, stock visibility, real-time shop floor data collection, asset tracking and warehousing, customer-centric supply chain can be applied and implemented utilizing multiagent. In this paper, for the order processing event between the buyer and seller relationship, multiagent were defined corresponding to the SCOR process. A prototype system was developed and implemented on the actual TCP/IP environment for the purchase order processing event. The implementation result assures that multiagent based SCM enhances the speed, visibility, proactiveness and responsiveness of activities in the supply chain.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.2
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• pp.120-125
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• 2020

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.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.396-407
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• 2020

As fossil fuels are depleted worldwide, alternative resources is required to replace fossil fuels, and biofuels are in the spotlight as alternative resources. Biofuels are produced from biomass, which is a renewable resource to produce biofuels or bio-chemicals. Especially, in order to substitute fossil fuels, the research focusing the biofuel (biodiesel) production based on CO₂ and biomass achieves more attention recently. To produce biomass-based biodiesel, the development of a supply chain network is required considering the amounts of feedstocks (ex, CO₂ and water) required producing biodiesel, potential locations and capacities of bio-refineries, and transportations of biodiesel produced at biorefineries to demand cities. Although many studies of the biomass-based biodiesel supply chain network are performed, there are few types of research handled the uncertainty in CO₂ supply which influences the optimal strategies of microalgae-based biodiesel production. Because CO₂, which is used in the production of microalgae-based biodiesel as one of important resources, is captured from the off-gases emitted in power plants, the uncertainty in CO₂ supply from power plants has big impacts on the optimal configuration of the biodiesel supply chain network. Therefore, in this study, to handle those issues, we develop the two-stage stochastic model to determine the optimal strategies of the biodiesel supply chain network considering the uncertainty in CO₂ supply. The goal of the proposed model is to minimize the expected total cost of the biodiesel supply chain network considering the uncertain CO₂ supply as well as satisfy diesel demands at each city. This model conducted a case study satisfying 10% diesel demand in the Republic of Korea. The overall cost of the stochastic model (US$ 12.9/gallon·y) is slightly higher (23%) than that of the deterministic model (US$ 10.5/gallon·y). Fluctuations in CO₂ supply (stochastic model) had a significant impact on the optimal strategies of the biodiesel supply network.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
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• 2002.05a
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• pp.203-208
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• 2002

In case of the problems with multiple plants, alternative operation sequence, alternative machine, setup time, and transportation time between plants, we need a robust methodology for the integration of process planning and scheduling in supply chain. The objective of this model is to minimize the tardiness and to maximize the resource utilization. So, we propose a multi-objective model with limited-capacity constraint. To solve this model, we develope an efficient and flexible model using adaptive genetic algorithm(AGA), compared to traditional genetic algorithm(TGA)

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.37-50
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• 2022

Modern supply chains include multiple activities from collecting raw materials to transferring final products. These activities involve many parties who share a huge amount of valuable data, which makes managing supply chain systems a challenging task. Current supply chain management (SCM) systems adopt digital technologies such as the Internet of Things (IoT) and blockchain for optimization purposes. Although these technologies can significantly enhance SCM systems, they have their own limitations that directly affect SCM systems. Security, performance, and scalability are essential components of SCM systems. Yet, confidentiality and scalability are one of blockchain's main limitations. Moreover, IoT devices are lightweight and have limited power and storage. These limitations should be considered when developing blockchain-based IoT-SCM systems. In this paper, the requirements of efficient supply chain systems are analyzed and the role of both IoT and blockchain technologies in providing each requirement are discussed. The limitations of blockchain and the challenges of IoT integration are investigated. The limitations of current literature in the same field are identified, and a secure and scalable blockchain-based IoT-SCM system is proposed. The proposed solution employs a Hyperledger fabric blockchain platform and tackles confidentiality by implementing private data collection to achieve confidentiality without decreasing performance. Moreover, the proposed framework integrates IoT data to stream live data without consuming its limited resources and implements a dualstorge model to support supply chain scalability. The proposed framework is evaluated in terms of security, throughput, and latency. The results demonstrate that the proposed framework maintains confidentiality, integrity, and availability of on-chain and off-chain supply chain data. It achieved better performance through 31.2% and 18% increases in read operation throughput and write operation throughput, respectively. Furthermore, it decreased the write operation latency by 83.3%.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.229-238
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• 2020

A model is presented in this paper to provide understanding of the supply chain integration and supply chain information practices' impact on the manufacturing industries. The supply chain information practices play a crucial role in sharing information between the members of SC network. Thus, it is important to develop a comprehensive understanding of the differences and similarities among ISI and information management. It will allow firms to systematically evaluate and carefully choose the information strategy. The empirical findings of this research offer essential and interesting insights about what role SCI, supply chain information and Supply chain ISI play in determining Malaysia's financial performance. The theoretical gaps addressed in this study are of significant importance, since a little empirical evidence is available regarding system infrastructure and supply chain information management's effectiveness. This research provides further paths of exploring system infrastructure and information management, thereby defining the manufacturing industries' next step in SCM struggle i.e. modifying total integrated SC principle in other manufacturing firms. The Resource-based theory discovered organizational resources as an essential organizational success ingredient. Therefore, in order to recognize its potential value, internal resources, for instance, information system and management must be fully utilized.