• IE interfaces
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• v.13 no.3
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• pp.378-387
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• 2000

As the globalization of manufacturing companies continues, the scope of dependence between these companies and distributors, and other suppliers are growing very rapidly since no one company manufactures or distributes the whole product by themselves. And, the need to increase the efficiency of the whole supply chain is increasing. This paper deals with a multi-plant lot-sizing problem(MPLSP) which happens in a decentralized manufacturing system of a supply chain. In this study, we assume that the whole supply chain is driven by a single source of independent demand and many levels of dependent demands among manufacturing systems in the supply chain. We consider setup cost, transportation cost and time, and inventory holding cost as a decision factor in the MPLSP. The MPLSP is decomposed into two sub-problems: a planning problem of the whole supply chain and a lot-sizing problem of each manufacturing system. The supply chain planning problem becomes a pure linear programming problem and a Generalized Goal Decomposition method is used to solve the problem. Its result is used as a goal of the lot-sizing problem. The lot-sizing problem is solved using the CPLEX package, and then the coefficients of the planning problem are updated reflecting the lot-sizing solution. This procedure is repeated until termination criteria are met. The whole solution process is similar to Lagrangian relaxation method in the sense that the solutions are approaching the optimum in a recursive manner. Through experiments, the proposed closed-loop hierarchical planning and traditional hierarchical planning are compared to optimal solution, and it is shown that the proposed method is a very viable alternative for solving production planning problems of decentralized manufacturing systems and in other areas.

• Journal of the Korea Safety Management & Science
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• v.4 no.4
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• pp.73-85
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• 2002

The supply chain not only includes the manufacturer and suppliers, but also transporters, warehouses, retailers, and customers themselves. Within each organization, such as manufacturer, the supply chain includes all functions involved in filling a customer request. these functions include, but are not limited to, new product development, marketing, operation, distribution, finance, and customer service. Lean Supply chain coordination improves if all supplier of chain take actions that together increase total supply chain profits. To design of Modularity by the grouping supplier, the proposed method is to develop the most appropriate production system models in the Supply Chain Management which is necessity of the times and its importance. The objects of this study is development of model and cost analysis to the modular production system in Lean SCM. Introduction of modular production system in Lean SCM is effective in reducing the cost in processing, manufacturing, inventory holding, ordering, etc.

• 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.

• Journal of the Korean Society of Food Culture
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• v.24 no.6
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• pp.641-648
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• 2009

The principal objective of this study was to analyze Swede's perceptions of Asian food using a means-end chain method, which may contribute to our understanding of new markets for a food industry interested in globalizing Korean food. With the Means-end chain method, one can determine the cognitive structures built in consumer's minds, which are developed by connecting attributes of product, consequences, and values. The attributes of Asian food that are most positively perceived by Swedes are 'fresh vegetables', 'low-fat', 'light meat and seafood', 'exotic ingredients', 'not expensive' and 'unknown food'. The consequences of eating Asian food connected to these attributes are 'healthy', 'tasty', 'good way to save money' and 'curious'. Finally, Swedes expect to enjoy a value of 'achievement' at the end. Based on the result that 'unknown food' attribute is connected to 'achievement' value, Swedes are assumed to be attracted by the fact that Korean food is 'unknown food'. However, the effect of the 'unknown food' attribute will fade away with time; therefore, stressing Korean food's status as a 'healthy' food, which can be attributed to its use of 'fresh vegetable' and 'light meat and seafood' ingredients and 'low fat' cooking method may be effective as a long-term strategy for making Korean food attractive. The 'healthy' consequence is connected to Swedes' perception of the value 'achievement' and simultaneously to their perception of the value 'belonging and love/sense of belonging'.

• Proceedings of the Korean Society for Quality Management Conference
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• 2007.04a
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• pp.498-502
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• 2007

The objective of this study aims at proposing a method of Supply Chain Management using Six Sigma. Six Sigma is at the top of the agenda for many companies that try to reduce cost and improve productivity. This paper explains how Six Sigma can be integrated and processed with Supply Chain Management.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.88-97
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• 1994

We persent a method for kinematic calibration of open chain mechanisms based on the product of exponentials (POE) formula. The POE formula represents the forward kinematics of an open chain as a product of matrix exponentials, and is based on a modern geometric interpretation of classical screw theory. Unlike the kinematic parameters in the POE formula vary smoothly with changes in the joint axes;ad hoc methods designed to address the inherent singularities in the D-H parameters are therefore are therefore unnecessary. After introducing the POE formula, we derive a least-squares kinematic calibration algorithm for general open chain mechanisms. Simulation results with a 6-axis open chain are presented.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2049-2057
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• 2014

In this paper, a numerical method for analyzing coupling between high-altitude electromagnetic pulse (HEMP) as external field and a shielded twisted pair (STP) cable is proposed, which is based on an expanded chain matrix. Load responses of electromagnetic (EM) field excitation in uniform transmission line (TL) are solved by Baum-Liu-Tesche (BLT) equations in frequency domain, however, it is difficult to apply BLT equations to solve load responses of STP cable because the iteratively changing configuration of each twisted pairs are involved in cable. To avoid this problem and decrease memory and CPU time, we proposed the expanded chain matrix modeling method that is calculated using ABCD parameters, and applied multi-conductor transmission line (MTL) theory to consider the EMP coupling effectiveness of each twisted pairs. The results implemented by the proposed method are presented and compared with those obtained by the finite-difference time domain (FDTD) method as a kind of 3D full wave analysis.

• Journal of Navigation and Port Research
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• v.43 no.1
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• pp.23-32
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• 2019

In this paper, we proposed a multi-chain Time of Arrival (ToA) positioning method to estimate positions using all received Loran-C signals from multiple chains without constraining to a single chain. Conventionally, we have to choose only one chain among several available chains for position estimation using Loran-C. Therefore, the number of signals to be used for positioning is limited to three to five. In general, if more signals are used for positioning estimation, its performance tends to be improved in terms of accuracy and availability. To validate the proposed method for multi-chain Loran-C, we firstly carried out a static positioning test in land. By analyzing the test results, we confirmed that the proposed method works well under a multi-chain Loran-C scenario. Subsequently, another mobile positioning test was conducted on board a vessel under a practical application scenario. From this second test, we successfully demonstrated that the multi-chain ToA positioning method even in situations where the conventional single-chain Loran-C approach fails for positioning.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.263-266
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• 2007

This paper discusses the Bayesian statistical inference. This paper discusses the Bayesian inference, MCMC (Markov Chain Monte Carlo) integration, MCMC method, Metropolis-Hastings algorithm, Gibbs sampling, Maximum likelihood estimation, Expectation Maximization algorithm, missing data processing, and BMA (Bayesian Model Averaging). The Bayesian statistical inference is used to process a large amount of data in the areas of biology, medicine, bioengineering, science and engineering, and general data analysis and processing, and provides the important method to draw the optimal inference result. Lastly, this paper discusses the method of principal component analysis. The PCA method is also used for data analysis and inference.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.3
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• pp.34-42
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• 2013

In this study, we develop a method for analyzing business process based on the event-driven process chain (EPC) model. The method consists of five stages such as identifying abnormal events, finding causes for the abnormal events and problems caused by the abnormal events, making cause-and-effect chains, drawing root-cause map, and defining improvement areas. We illustrate how to apply the method with some examples for the domestic registered mail delivery process.