• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.83-89
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• 2015

Selection of efficient supplier is a very important process as risk or uncertainty of a supply chain and its environment are increasing. Previous deterministic DEA and probabilistic DEAs are very limited to handle various types of risk and uncertainty. In this paper, I propose an improved probabilistic DEA which consists of two steps; Monte Carlo simulation and statistical decision making. The simulation results show that the proposed method is proper to distinguish supplier's performance and provide statistical decision background.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.32 no.1
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• pp.117-129
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• 2009

The bullwhip effect is known as the significant factor which causes unnecessary inventory, lost sales or cost increase in supply chains. Therefore, the causes of the bullwhip effect must be examined and removed. In this paper, we develop two analytical tools for the bullwhip effect control in supply chains. First, we develop the quantitative models for computing the bullwhip effect in a three-stage supply chain consisted of a single retailer, a single distributor and a single manufacturer when the fixed-interval replenishment policy is applied at each stage. The quantitative models are developed under the different conditions for the demand forecasting and share of customer demand information. They are validated through the computational experiments. Second, we develop a simulation-based decision support system for the bullwhip effect control in a more diverse dynamic supply chain environment. The system includes a what-if analysis function to examine the effects of varying input parameters such as operating policies and costs on the bullwhip effect.

• Industrial Engineering and Management Systems
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• v.16 no.1
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• pp.44-51
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• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• The Journal of Information Systems
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• v.10 no.2
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• pp.129-150
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• 2001

In this study, we model a decentralized supply chain system which is managed by four types of centers, sequentially located: Retailer, Wholesaler, Distributor, and Factory Each center contributes to enhancing the performance of the supply chain system individually with its own local inventory information. Through experiments which are performed with a programmed simulation (like the MIT beer game), we investigate how the information lead time improvement in each center affects the whole system. And we show that the impact of the lead time improvement in the downstream, like retailers, affects more to the system than the one in the upstream, like factories, in a cost-effective way. Moreover, by using information lead time for each center, we analyze how much the extent of the improvement affects the whole system, especially for the total cost and the order level.

• Proceedings of the Safety Management and Science Conference
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• 2013.04a
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• pp.355-365
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• 2013

Many manufacturing industries need more efficient organization since the customer expects a greater response to orders Due to this increased expectation, a supply chain management (SCM) has become one of the most important methods of competitive advantage in business today. This study has developed a simulator for the supply chain management problem. The simulator designed to help determine considering to the capacities and the costs of production and distribution facilities. The simulator developed using commercial simulation tool ARENA and the results of computational experiments for a simple example were given and discussed to validate the developed simulator. The simulator can be used to decide an realistic production-distribution planning in the area.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.2
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• pp.106-119
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• 2005

Uncertainties inherent in customer demand patterns make it difficult for supply chains to achieve just-in-time inventory replenishment, resulting in loosing sales opportunity or keeping excessive chain wide inventories. In this paper, we propose two intelligent adaptive inventory control models for a supply chain consisting of one supplier and multiple retailers, with the assumption of information sharing. The inventory control parameters of the supplier and retailers are order placement time to an outside source and reorder points in terms of inventory position, respectively. Unlike most extant inventory control approaches, modeling the uncertainty of customer demand as a stationary statistical distribution is not necessary in these models. Instead, using a reinforcement learning technique, the control parameters are designed to adaptively change as customer demand patterns change. A simulation based experiment was performed to compare the performance of the inventory control models.

• Journal of Korea Society of Industrial Information Systems
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• v.19 no.5
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• pp.81-90
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• 2014

It is an element certainly required for the cost reduction of a company that forward and reverse logistics chain are unified and constitutes a resource closed-loop supply chain (CLSC). In this study, the inventory control which unifies inventory of distribution centers (DCs) of forward logistics and processing center of reverse logistics in the CLSC environment is proposed. The inventory system model for newly-constructed CLSC considers the JIT(Just-In-Time) delivery from the processing center to the manufacturer, including the making of decisions on whether to wait for the arrival of end-of-life products or to back-order necessary products for manufacturer when the supply of end-of-life products at the processing center via the returning center is insufficient for the demands of the manufacturers. The validity of the proposed model was verified using the genetic algorithm (GA). In order that a parameter might investigate the effect which it has on a solution, the simulation was carried out for priGA(priority-based GA) on three kinds of parameter conditions. Moreover, mhGA(modified hybrid GA) to which a parameter is adjusted for every Study on Reducing Logistics Costs and Inventory Control System according to facilities integration in the Closed-Loop Supply Chain Environment generation, the simulation was carried out to a four-kind numerical example.

• Journal of the military operations research society of Korea
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• v.34 no.3
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• pp.1-12
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• 2008

Design of supply chain network (SCN) is required to optimize every factor in SCN and to provide a long-term and strategic decision-making. A mathematical model can not reflect the real world because design of SCN contains variables and stochastic factors according to status of its system. This paper presents the designing methodology of military SCN using the mathematical model and the simulation model. It constructs SCN to minimize its total costs using the Mixed Integer Programming (MIP) model. And we solve problems of a vehicle assignment and routing through adaptation of experiment parameters repeatedly in the simulation model based on the results from the MIP model. We implement each model with CPLEX and AutoMod, and experiment to reconstruct SCN when the Logistic Support Unit is restricted to support military units. The results from these experiments show that the proposed method can be used for a design of military SCN.

• International Commerce and Information Review
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• v.15 no.2
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• pp.71-82
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• 2013

The automotive supply chain is increasingly complex as automakers seek more profitable solutions with global out-sourcing and manufacturing strategies. In the automotive industry, using returnable plastic containers (RPCs) is very common for domestic operations, but for internationally, it has not been considered by many companies because of issues such as overall distance and difficulty of control. The results of this simulation can help to analyze the interactive and coherent behavior of packaging and supply chain systems. The data obtained from the model can be applied to make substantial decisions for choosing the most profitable packaging types, at the same time as it can lead to designing an optimum supply chain for RPCs used in international supply chains.

• 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.