• Journal of Korean Institute of Industrial Engineers
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• v.35 no.3
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• pp.203-212
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• 2009

The bullwhip effect refers to the phenomenon where demand variability is amplified when one moves upward a supply chain. In this paper, we exactly quantify the bullwhip effect for cases of seasonal demand processes in a two-echelon supply chain with a single retailer and a single supplier. In most of the previous research, some measures of performance for the bullwhip effect are developed for cases of non-seasonal demand processes. The retailer performs demand forecast with a multiplicative seasonal mixed model by using the minimum mean square error forecasting technique and employs a base stock policy. With the developed bullwhip effect measure, we investigate the impact of seasonal factor on the bullwhip effect. Then, we prove that seasonal factor plays an important role on the occurrence of the bullwhip effect.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.17-26
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• 2005

There is the uncertainty of demands at each retailer in the supply chain. To satisfy customers' demand, retailer must have enough inventory. Nevertheless, stockout is occurred for some retailers. A lateral transshipment policy can be effectively used to deal with stockout. The new lateral transshipment policy, referred to service level adjustment (SLA), is suggested. The difference between SLA and previous policies is the integration of an emergency lateral 'transshipment with a preventive lateral transshipment to efficiently respond customers' demand in the proposed policy. Additionally, the service level to decide the quantity of products is considered. Simulation experiment is executed to treat stochastic factors in the two-echelon supply chain. The proposed policy can reduce total cost and is more effective to the change of demand, penalty cost, and ordering cost than the currently used policies.

• Korean Management Science Review
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• v.25 no.1
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• pp.107-121
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• 2008

As to more efficiently manage the inventory in the retail supply chain and to meet the customer demand in a timely manner, vendor-managed inventory (VMI) has been widely accepted, which manages inventory in the retail supply chain via sharing information and collaborating with the retailers. Applying VMI generates vendor-managed inventory/distribution problem (VMIDP), which involves inventory management for both the vendor and the retailers, and the design of vehicle routes for delivery, to minimize the total operating cost in the supply chain. In this paper, we suggest a mixed integer programming (MIP) model to obtain the optimal solution for VMIDP in a two-echelon retail supply chain, and develop an efficient heuristic based on the operating principles of the MIP model. To evaluate the performance of the heuristic, its solution was compared with the one of the MIP model on a total of twenty seven test problems. As a result, the heuristic found optimal solutions on seven problems in a significantly reduced time, and generated a 4.3% error rate of total cost in average for all problems. The heuristic is applied to the case problem of the local famous franchise company together with GIS, showing that it is capable of providing a solution efficiently in a relatively short time even in the real world situation.

• 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.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2002.05a
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• pp.742-749
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• 2002

This paper deals with a continuous-review two-echelon inventory model with one-for-one replenishment and Poisson demand where transshipments among retailers are allowed. Two classes of inventory systems are considered by the number of distribution centers(DCs) which provide each retailor with inventory items. 1:N class inventory system and M:N class inventory system respectively. Two-phase model is constructed to find out the optimal inventory positions which minimize supply chain costs. Approximations for customer service levels of the system are evaluated in the first phase, and the optimal inventory positions are found subject to the constraints for service level in the second phase. Simulation tests are performed to assure the effectiveness of the proposed model. The effect of transshipment is evaluated.

• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.2
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• pp.101-127
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• 2016

Irrational ordering decisions of supply chain members have been gaining growing importance in the area of supply chain management. Irrational ordering behaviors that deviate from the profit maximizing decisions in the newsvendor settings have observed with human experiments in recent research. These behaviors can be modeled with several typical decision bias elements. This bias in ordering decisions affects the performance of supply chain contracts designed based on the assumption that the supply chain members make optimal decisions, making it necessary to design supply chain contracts by considering the irrationality. The purpose of this research is to derive a method to design the revenue sharing contract that considers human irrationality in ordering decisions. This research considers a simple two-echelon supply chain consisting of one supplier and one retailer, where the supplier is assumed to be perfectly rational while the retailer making newsvendor type ordering decisions displays irrational ordering behaviors. Under this environment, this research analytically models the revenue sharing contract to maximize the total supply chain profit or the supplier's own profits while considering the three decision bias patterns of the retailer, which include the pull-to-center effect, the prospect theory, and the increased subjective sensitivity to the revenue sharing ratio. Irrationality parameters are measured through human experiments based on which and through numerical simulations, we showed that significant improvements in the supply chain performance can be achieved.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.3
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• pp.27-40
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• 2006

Today's customer demands in supply chains tend to change quickly, variously even in a short time Interval. The uncertainties of customer demands make it difficult for supply chains to achieve efficient inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. Un this paper, we propose an adaptive vendor managed inventory (VMI) model for a two-echelon supply chain with non-stationary customer demands using the action-reward learning method. The Purpose of this model is to decrease the inventory cost adaptively. The control Parameter, a compensation factor, is designed to adaptively change as customer demand pattern changes. A simulation-based experiment was performed to compare the performance of the adaptive VMI model.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.1
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• pp.38-49
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• 2016

This paper proposes a mathematical model-based solution for sourcing decisions with an objective of minimizing the manufacturer's total cost in the two-echelon supply chain with supply capacity risk. The risk impact is represented by uniform, beta, and triangular distributions. For the mathematical model, the probability vector of normal, risk, and recovery statuses are developed by using the status transition probability matrix and the equations for estimating the supply capacity under risk and recovery statuses are derived for each of the three probability distributions. Those formulas derived are validated using the sampling method. The results of the simulation study on the test problem show that the sourcing decisions using the proposed solution reduce the total cost by 1.6~3.7%, compared with the ones without a consideration of supply capacity risk. The total cost reduction increases approximately in a linear fashion as the probability of risk occurrence or reduction rate of supply capacity due to risk events is increased.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.63-78
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• 2004

This paper deals with a supplier-managed inventory(SMI) control for a two-echelon supply chain model with a service facility and a single supplier. The service facility is allocated to customers and provides a service using items of inventory that are purchased from the supplier, Assuming that the supplier knows the information of customer queue length as well as inventory position in the service facility at the time when it makes a replenishment decision, we identify an optimal replenishment policy which minimizes the total supply chain costs by reflecting these information into the replenishment decision. Numerical analysis demonstrates that the SMI strategy can be more cost-effective when the information of both customer queue length and inventory position is shared than when the information of inventory position only is shared.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.3
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• pp.362-372
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• 2008

Manufacturers, or vendors, and their customers continue to adopt vendor-managed inventory(VMI) program to improve supply chain performance through collaboration achieved by consolidating replenishment responsibility upstream with vendors. In this paper, we construct a mixed integer linear programming model and propose a genetic algorithm for the integrated inventory and routing problems with lost sales maximizing the total profit in the VMI supply chains which comprise of a single manufacturer and multi-retailer. The proposed GA is compared with the mathematical model on the various sized test problems with respect to the solution quality and computation time. As a result, the GA demonstrates the capability of reaching solutions that are very close to those obtained by the mathematical model for small problems and stay within 3.2% from those obtained by the mathematical model for larger problems, with a much shorter computation time. Finally, we investigate the effects of the cost and operation variables on the total profit of the problem as well as the GA performance through the sensitivity analyses.