• Journal of Digital Convergence
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• v.18 no.6
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• pp.109-120
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• 2020

The supply chain without digitization is just a series of discrete, siloed steps taken through marketing, product development, manufacturing, and logistics, and finally into the hands of the customer. Digitization brings down those walls, and the chain becomes a completely integrated network fully transparent to all the parties involved. The ulitimate goals of digitizatized supply chain management are velocity and visibility. This network will depend on a number of key technologies including integrated planning and execution systems, supply chain analytics, autonomous logistics, smart warehousing and factory, etc, enabling companies to react to disruptions in the supply chain, and even anticipate them, by fully modeling the network, creating "what-if" scenarios, and adjusting the supply chain in real time as conditions change. This paper presents a number of studies on digitalization of supply chains and provides a discussion on issues raised in the process of technology adoption. Implications of the study findings are also provided.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.262-269
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• 2003

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involved reducing both production and distribution costs. The purpose of this study is to determine the optimum quantity of production and transportation with minimum cost in the supply chain network. We have presented a mathematical model that deals with real world factors and constructs. Considering the complexity of solving such model, we have applied the genetic algorithm approach for solving this model computational experiments using a commercial genetic algorithm based optimizer. The results show that the real size problems we encountered can be solved In reasonable time

• The Journal of Information Systems
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• v.14 no.3
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• pp.31-36
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• 2005

Firms seldom survive and prosper solely through their individual efforts. Each firm's performance depends upon the activities and performance of others and hence upon the nature and quality of the direct and indirect relationships a firm develops with its counterparts. Textile companies have tried to improve their organizational competitiveness in order to survive in the digital age global market. The challenge in textile supply chain management is the development of collaboration network which accommodates diverse concerns of various participants while explicitly recognizing interdependencies and promoting effective relationship management. Major contents of the study are as follows. First, ideal collaboration network model from the supply chain of the textile industry is suggested. Second, utilizing the collaboration model, A framework for textile e-marketplaces supply chians is designed to improve customer services and delivery time, to promote information sharing, and shorten product life cycle time. The framework suggested is expected to promote corporate innovation and information sharing, generate infrastructure which provides appropriate communication and operations capabilities for the textile companies.

• Korean Management Science Review
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• v.25 no.2
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• pp.89-109
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• 2008

As the importance of logistics is increasing, Enterprises try to design the supply chain(SC) network to minimize the total costs considering inventory holding cost, transportation cost and apply the efficient strategies of supply chain based on SC network. Despite of this efforts, Calculating the logistics costs without reflecting the real components of logistics like the packaging cost, transportation related cost, storage cost, loading & unloading cost, and distribution costs, the companies has many limitation to calculate the logistics cost of real enterprise. For overcoming such problem, this paper is aimed at establishing SC strategies which can be an efficient alternative for a decision making on supply chain, based on existing reference and current logistics networks of 'L' company in Korea. Also, we analyze the interaction effects between strategies as well as install the optimal SC network reflecting concrete logistics components from the viewpoint of total logistics costs using the simulation and statistic methods. we expect that analysis results of this paper would be applied various industries and be utilized to the efficient tools for the decision making by planing and execution of the logistics budget from enterprises.

• IE interfaces
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• v.13 no.3
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• pp.348-357
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• 2000

SCM(Supply Chain Management) is a new approach to satisfy customers via an integrated management for the whole business processes of the manufacturing from the raw material procurement to the product or service delivery to customers. Typically the semiconductor industry is the one whose supply chain network is distributed all over the world, and its manufacturing process has the particular characteristics which has to be considered in the modeling of supply chain. In this paper we suggest the push and pull type supply chain models based on the manufacturing characteristics and their mathematical formulation for the semiconductor industry. Push supply chain model pursuits the high throughput and the balance of the WIP flow, and pull supply chain model does to minimize the total cost of order-based manufacturing, distribution and transportation process in order to meet customer's request appropriately.

• 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.

• Industrial Engineering and Management Systems
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• v.15 no.4
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• pp.374-384
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• 2016

The efficiency of a supply chain can be extremely affected by its design which includes determining the flow pattern of material from suppliers to costumers, selecting the suppliers, and defining the opened facilities in network. In this paper, a multi-objective multi-echelon multi-product supply chain design model is proposed in which several suppliers, several manufacturers, several distribution centers as different stages of supply chain cooperate with each other to satisfy various costumers' demands. The multi-objectives of this model which considered simultaneously are 1-minimize the total cost of supply chain including production cost, transportation cost, shortage cost, and costs of opening a facility, 2-minimize the transportation time from suppliers to costumers, and 3-maximize the service level of the system by minimizing the maximum level of shortages. To configure this model a graph theoretic approach is used by considering channels among each two facilities as links and each facility as the nodes in this configuration. Based on complexity of the proposed model a multi-objective Pareto-based vibration damping optimization (VDO) algorithm is applied to solve the model and finally non-dominated sorting genetic algorithm (NSGA-II) is also applied to evaluate the performance of MOVDO. The results indicated the effectiveness of the proposed MOVDO to solve the model.

• 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%.

• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.227-241
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• 2010

A memory module industry's supply chain usually consists of multiple manufacturing sites and multiple distribution centers. In order to fulfill the variety of demands from downstream customers, production planners need not only to decide the order allocation among multiple manufacturing sites but also to consider memory module industrial characteristics and supply chain constraints, such as multiple material substitution relationships, capacity, and transportation lead time, fluctuation of component purchasing prices and available supply quantities of critical materials (e.g., DRAM, chip), based on human experience. In this research, a directed graph-based supply network planning (DGSNP) model is developed for memory module industry. In addition to multi-site order allocation, the DGSNP model explicitly considers production planning for each manufacturing site, and purchasing planning from each supplier. First, the research formulates the supply network's structure and constraints in a directed-graph form. Then, a proposed genetic algorithm (GA) solves the matrix form which is transformed from the directed-graph model. Finally, the final matrix, with a calculated maximum profit, can be transformed back to a directed-graph based supply network plan as a reference for planners. The results of the illustrative experiments show that the DGSNP model, compared to current memory module industry practices, determines a convincing supply network planning solution, as measured by total profit.

• 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.