• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.4
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• pp.71-81
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• 2005

This Paper focuses on the effects of outside competition on an optimal echelon base stock level in a two stage supply chain. This is new in that we have been studying the effects of inside competition within a supply chain up to now. It is known that the optimal echelon base stock level with inside competition within a supply chain is less than the global optimal echelon base stock level without inside competition. This is due to the ' public goods ' nature of inventory. That is, more inventory is better, but one wants the other to invest more, thus resulting in under-investment. However, this phenomenon becomes weaker as outside competition increases. We show that as outside competition becomes stronger, the ' public goods ' effects decrease and the optimal echelon base stock level increases. If the level of competition is sufficiently high, the optimal echelon base stock level goes even higher than the global optimal echelon base stock level. We develop a theoretical model for the analysis and conduct a numerical analysis.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.636-647
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• 2004

The objective of this paper is to provide an improved reorder decision policy for general multi-echelon distribution systems utilizing the shared stock information. It has been known that traditional reorder policies sometimes show poor performance in distribution systems. Thus, in our previous research we introduced the order risk policy which utilizes the shared stock information more accurately for the 2-echelon distribution system and proved the optimality. However, since the real world supply chain is generally composed with more than 2 echelons, we extend the order risk policy for the general multi-echelon systems. Since the calculation of the exact order risk value for general multi-echelon systems is very complex, we provide two approximation methods for the real-time calculation. Through the computational experiment comparing the order risk policy with the existing policies under various conditions, we show the performance of the order risk policy and analyze the value of the shared stock information varying with the characteristics of the supply chain.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.159-165
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• 2003

The objective of this paper is to provide an improved reorder decision policy for general multi-echelon distribution systems utilizing the shared stock information. Since it has been known that traditional reorder policies sometimes show poor performance in distribution systems, in our previous research we introduced the order risk policy which utilizes the shared stock information more accurately f3r the 2-echelon distribution system and proved the optimality. However, since the real world supply chain is generally composed with more than 2 echelons, we extend the order risk policy for the general multi-echelon systems. Since the calculation of the exact order risk value fur general multi-echelon systems is very complex, we provide two approximation methods. Through the computational experiment comparing the order risk policy with the existing policies under various conditions, we show the performance of the order risk policy and analyze the value of the shared stock information varying with the characteristics of the supply chain.

• IE interfaces
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• v.17 no.3
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• pp.359-374
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• 2004

The objective of this paper is to provide an improved reorder decision policy for general multi-echelon distribution systems utilizing the shared stock information. It has been known that traditional reorder policies sometimes show poor performance in distribution systems. Thus, in our previous research we introduced the order risk policy which utilizes the shared stock information more accurately for the 2- echelon distribution system and proved the optimality. However, since the real world supply chain is generally composed with more than 2 echelons, we extend the order risk policy for the general multi-echelon systems. Since the calculation of the exact order risk value for general multi-echelon systems is very complex, we provide two approximation methods for the real-time calculation. Through the computational experiment comparing the order risk policy with the existing policies under various conditions, we show the performance of the order risk policy and analyze the value of the shared stock information varying with the characteristics of the supply chain.

• Journal of the Korean Operations Research and Management Science Society
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• v.15 no.1
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• pp.63-72
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• 1990

This problem in this paper concerns the determination of safety stock for multi-echelon invenetory system. In this model the criterion is to minimize system safety stock subject to a service level constraint and expected annual total cost. Then, safety stock is determined by minimizing expected annual total cost and satisfying given service level. This expected annual total cost is obtained by expected total inventory holding cost plus the expected total stockout cost. Numerical example is given in a three-echelon inventory system. The results obtained by the use of the Hill Algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.136-148
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• 2015

We analyzed the effect of three different types of inventory systems for saving the total cost using simulation on the system where multiple depots and many retailers disperse on the limited area. Three types of inventory systems are single echelon system with inventory exchange and two-echelon system and the variant two-echelon system. Variant two echelon system is the two-echelon system where the inventory transshipmentsare allowed on every two stage inventory echelons. Inventories kept on every retailer are commonly used for all retailers when certain retailer has stock-out. And when all retailers are stock-out, inventories kept on every depot are commonly used for the retailers whose assigned depots are stock-out. These all three systems are simulated with the constraint of service level on wide range of parameter settings. Simulation results show that cost saving effect appear clear for single echelon system and two-echelon system when shortage cost portion and transportation cost portion becomes large respectively irrespective of depot number. Variant two echelon system seems to be superior to two other systems when transportationcost portion becomes very small. But this superiority is not proved in terms of statistics. So we may conclude that the variant two echelon system may be useless with the higher administrative efforts due to frequent inventory exchange. Also we note that the traditional two echelon system becomes inferior to two other systems in terms of statistics when service level becomes high or when demand variance becomes very large. And inventory integration effect that cost becomes saved when depot number decrease, diminishes when transportation cost or stock-out cost increases irrespective of inventory systems.

• 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.

• IE interfaces
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• v.13 no.3
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• pp.444-454
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• 2000

Due to the improvement of modern information technologies, sharing stock information among the supply chain members is a common practice nowadays. Many companies are planning to adopt the information systems to possess the real-time shared stock information. Thus, it is needed to quantify the value of shared stock information. The purpose of this paper is to evaluate the value of the shared stock information for two-echelon distribution systems. Existing reorder policies can be classified into installation stock policies and echelon stock policies. Since installation stock policies do not utilize the shared stock information, and both classes of policies may show poor performances for distribution systems, we cannot evaluate the value of the shared stock information with the existing policies. Thus, we provide a new type of reorder policy, named order risk policy. We define the order risk using marginal analysis, and prove the optimality. Through computational experiment that compares the order risk policy with the existing policies, it is shown that a significant cost reduction is achieved with the effective utilization of the shared stock information. We also show the effect of the system characteristics on the value of the shared stock information.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.39-47
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• 2006

In most of supply chain planning practices, the estimated demands, which are forecasted for each individual period in a forecasting window, are regarded as deterministic. But, in reality, the forecasted demands for the periods of a given horizon are stochastically distributed. Instead of using a safety stock, this study considers a direct control of service level by choosing the demand used in planning from the distributed forecasted demand values for the corresponding period. Using the demand quantile and echelon stock concept, we propose a simple but efficient heuristic algorithm for multi-echelon serial systems under service level constraints. Through a comprehensive simulation study, the proposed algorithm was shown to be very accurate compared with the optimal solutions.

• Journal of the Korea Safety Management & Science
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• v.16 no.4
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• pp.323-330
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• 2014

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.