• Journal of the Korea Safety Management & Science
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• v.11 no.3
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• pp.139-148
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• 2009

In a real-life supply chain environment, demand forecasting is usually represented by probabilistic distributions due to the uncertainty inherent in customer demands. However, the customer demand used for an actual supply chain planning is a single deterministic value for each of periods. In this paper we study the choice of single demand value among of the given customer demand distribution for a period to be used in the supply chain planning. This paper considers distributed multi-echelon supply chain and the objective function of this paper is to minimize the total costs, that is the sum of holding and backorder costs over the distribution network under the service level constraint, by using demand selection scheme. Some useful findings are derived from various simulation-based experiments.

• IE interfaces
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• v.17 no.spc
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• pp.79-89
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• 2004

Supply Chain (SC) can balance demands with supply activities as executing Supply Chain Planning (SCP). The fluctuated demands, however, will break the balance between demand and supply. It means that the present SCP is useless in responding the changed demands. Thus it is necessary for SCP to be updated with changed demands. We call this procedure as Supply Chain Replanning. However, the existing measures for SC can not deal with the balance between supply and demand so that they can not detect effectively the timing of replanning. For this reason, a new performance measure, Balancing Point, is developed using momentum, a concept of Physics. It can treat the balance between supply and demand. Also, a replanning method based on Balancing Point is proposed. The proposed method is more effective than the existing replanning method, periodic replanning method and net inventory method.

• Journal of the Korea Safety Management & Science
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• v.8 no.3
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• pp.115-123
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• 2006

The main focus of this study is to investigate the performance of a clark-scarf type multi-echelon serial supply chain operating with a base-stock policy and to optimize the inventory levels in the supply chains so as to minimize the systemwide total inventory cost, comprising holding and backorder costs as all the nodes in the supply chain. The source of supply of raw materials to the most upstream node, namely supplier, is assumed to have an infinite raw material availability. Retailer faces random customer demand, which is assumed to be stationary and normally distributed. If the demand exceeds on-hand inventory, the excess demand is backlogged. Using the echelon stock and demand quantile concepts and an efficient simulation technique, we derive near optimal inventory policy. Additionally we discuss the derived results through the extensive experiments for different supply chain settings.

• Management Science and Financial Engineering
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• v.10 no.2
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• pp.29-51
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• 2004

In this paper, the cost impact of incorrect assumptions about the demand process in a supply chain in which there are two participants, a retailer and a manufacturer, is considered. When participants in the supply chain do not notice serial correlation in the demand process, they would turn to a simple inventory model based on an i.i.d. demand assumption. A mathematical model that allows us to quantify the cost incurred by each participant in the supply chain, when they implement inventory policies based on correct or incorrect assumptions about the demand process, is developed. This model enables us to identify how much it differs from the optimal costs.

• Journal of Korean Society for Quality Management
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• v.46 no.4
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• pp.959-972
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• 2018

Purpose: This study developed a simulation model that incorporates the uncertainty of demand and yield to obtain optimized results for supply chain coordination within environmental constraints. The objective of this study is to examine whether yield management for perishable products can achieve the goal of supply chain coordination between a single buyer and a single supplier under a variety of environmental conditions. Methods: We investigated the efficiency of a revenue-sharing contract and a wholesale price contract by considering demand and yield uncertainty, profit maximizing ratio, and success ratio. The implications for environmental variation were derived through a comparative analysis between the wholesale price contract and the revenue-sharing contract. We performed Monte Carlo simulations to give us the results of an optimized supply chain within the environments defined by the experimental factors and parameters. Results: We found that a revised revenue-sharing contracting model was more efficient than the wholesale price contract model and allowed all members of the supply chain to achieve higher profits. First, as the demand variation (${\sigma}$) increased, the profit of the total supply chain increased. Second, as the revenue-sharing ratio (${\Phi}$) increased, the profits of the manufacturer gradually decreased, while the profits of the retailer gradually increased, and this change was linear. Third, as the quality of yield increased, the profits of suppliers appear to increased. At last, success rate was expressed as the profit increased in the revenue-sharing contract compared to the profit increase in the wholesale price contract. Conclusion: The managerial implications of the simulation findings are: (1) a strategic approach to demand and yield uncertainty helps in efficient resource utilization and improved supply chain performance, (2) a revenue-sharing contract amplifies the effect of yield uncertainty, and (3) revised revenue-sharing contracts fetch more profits for both buyers and suppliers in the supply chain.

• Journal of the Korea Society for Simulation
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• v.12 no.1
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• pp.35-48
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• 2003

This paper suggests the long-term strategy of the production distribution planning considering the capacity of factory production and the uncertain demand in a supply chain. This paper determines the near optimal capacity of factory production by using the advantages of mathematical and simulation models. Also, the relationship between the capacity from the suggested model and the strategy of production and distribution in a supply chain is studied. Arena is used for modeling and analysis.

• Journal of the Korea Safety Management & Science
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• v.8 no.5
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• pp.139-150
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• 2006

The aim of this study is to establish an efficient distribution planning for a capacitated multi-stage supply chain. We assume that the demand information during planning horizon is given a deterministic form using a certain forecasting method. Under such a condition, we present a cost effective heuristic method for minimizing chain-wide supply chain inventory cost that is the sum of holding and backorder costs by using look-ahead technique. We cope with the capacity restriction constraints through look-ahead technique that considers not only the current demand information but also future demand information. To evaluate performance of the proposed heuristic method, we compared it with the extant research that utilizes echelon stock concept, under various supply chain settings.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.4
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• pp.91-112
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• 2009

The bullwhip effect means the phenomenon of increasing demand variation as moving UP to the upstream in the supply chain. Therefore, it is recognized that the bullwhip effect is problematic for effective supply chain operations. In this paper, we exactly quantifies the bullwhip effect for the case of stochastic lead time and seasonal demand in two-echelon supply chain where retailer employs a base-stock policy considering SARMA demand processes and stochastic lead time. We also investigate the behavior of the proposed measurement for the bullwhip effect with autoregressive and moving average coefficient, stochastic lead time, and seasonal factor.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.10a
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• pp.371-374
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• 2004

The ultimate purpose of Supply Chain Management (SCM) is maximizing the profits of the overall Supply Chain (SC) through increasing customer satisfaction and decreasing operating cost. It can be successfully accomplished only when SC system balances demands with supply activities coordinated by aggregate planning, mid-term level of Supply Chain Planning(SCP). However, the existing measures to mainly estimate the specific function of SCM are not enough to evaluate the state of SC with respect to the balance between supply and demand in operating. To solve this problem, we develop a new SC performance measure, Balancing Point, using momentum concept. a fundamental knowledge of physics. Momentum concept can explain the relation among objects so that it can consider the balance between supply and demand in SC operating. The developed measure can not only consider the current state of the SC system but also take planned but not executed supply activities and upcoming demands into account. Therefore, using Balancing point, we can be aware of the unbalanced state of SC in advance.

• Industrial Engineering and Management Systems
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• v.14 no.2
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• pp.129-158
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• 2015

It is necessary for retailers to determine the optimal ordering policy of products considering supply disruptions due to a natural disaster and a production process failure as quality and machine breakdowns. Under the situation, a dualsourcing supply chain (DSSC) is one of effective SC for retailers to order products reliably. This paper proposes the optimal ordering policy of a product in a DSSC with a retailer and two manufacturers. Two manufacturers may face supply disruptions due to a natural disater and a production process failure after they received the retailer's order of products. Here, two scenarios of demand information of products are assumed: (i) the demand distribution is known (ii) mean and variance of the demand are known. Under above situations, two types of DSSC are discussed. Under a decentralized DSSC (DSC), a retailer determines the optimal ordering policy to maximize his/her total expected profit. Under the integrated DSSC (ISC), the optimal ordering policy is determined to maximize the whole system's total expected profit. Numerical analysis investigates how demand information and supply disruptions affect the optimal decisions under DSC and ISC. Besides, profitability of supply chain coordination adjusting the wholesale price is evaluated to encourage the optimal decision under ISC.