• Journal of the Korea Society for Simulation
• /
• v.12 no.4
• /
• pp.1-15
• /
• 2003

To satisfy and respond quickly to customers' demand, many companies are now aggressively focusing on supply chain management in order to strengthen their competitiveness. The modeling and analysis of supply chain environment have been widely studied. This study is concerned with the development of supply chain simulator which deals with stochastic natures existing in the supply chain environment. We proposed the mathematical model for the efficient cost analysis and developed the supply chain simulator based on the proposed mathematical model with object-oriented language C++. The simple experiment which find the best combination of policies considering the whole cost shows the possibility and reasonability of the developed simulator.

• IE interfaces
• /
• v.21 no.3
• /
• pp.343-353
• /
• 2008

Vendor-managed inventory(VMI) is a supply chain strategy to improve the inventory turnover and customer service in supply chain management. Unfortunately, many VMI programs fail because they simply transfer the transactional aspects of placing replenishment orders from customer to vendor. In fact, such VMI programs often degrade supply chain performance because vendors lack capability to plan the VMI operations effectively in an integrated way under the dynamic, complex, and stochastic VMI supply chain environment. This paper presents a decision support system, termed DSSV, for VMI in the retail supply chain. DSSV supports the market forecasting, vendor's production planning, retailer's inventory replenishment planning, vehicle routing, determination of the system operating parameter values, retailer's purchase price decision, and what-if analysis. The potential benefits of DSSV include the provision of guidance, solution, and simulation environment for enterprises to reduce risks for their VMI supply chain operations.

• Journal of the Korea Society for Simulation
• /
• v.10 no.1
• /
• pp.35-50
• /
• 2001

Managing multi-echelon inventory systems has gained importance over the last decade mainly because integrated control of supply chains consisting of several processing and distribution stages has become feasible through modern information technology. Determination of optimal inventory policy for multi-echelon supply chain is made difficult by the complex interaction between the different levels. In this paper, we investigate performance of five inventory policies (fixed quantity order policy, fixed interval order policy, compromised order policy, lead time-fixed quantity order policy, and mixed order policy) in a multi-echelon supply chain by using a simulation model constructed with AweSim simulation language. The results of the simulation study show that the mixed order policy is the best among five inventory policies in the most test problems except the case when the stockout cost per unit is much higher than the inventory holding and transportation costs per unit.

• Journal of Korean Institute of Industrial Engineers
• /
• v.41 no.5
• /
• pp.511-520
• /
• 2015

Inventory, cost, and the level of service are three interrelated key metrics that most supply chain organizations are striving to optimize. One way to achieve this goal is to create a simulation model to conduct sensitivity analysis and optimization on several different supply chain policies that can be implemented in actual operation. In this paper, a case of Xerox global supply chain modeling and analysis to assess several "what if" scenarios for the consumable policy safety stock is presented. The simulation model, combined with analytical cost model and optimization module, is used to optimize the policy safety stock level to achieve the lowest total value chain cost. It was shown quantitatively that the policy safety stock can be reduced, but it is offset by the inbound premium transportation cost to expedite supplies in shortage, and the outbound premium transportation cost to send supplies to customers via express shipment, requiring fine balance.

• Journal of the Korea Society for Simulation
• /
• v.16 no.3
• /
• pp.29-38
• /
• 2007

Recently, the deepening of the environment and accompanying concerns produce various environmental regulations. For example, EPR(Extended Producer Responsibility) is emphasized in many countries such as USA and Europe. According to this, the construction of a reverse supply chain is under compulsion. A diversity of parts can be controlled by a government or enterprises in the construction and management of a reverse supply chain and the effect to the environment is changed according to the policy. Therefore, it is essential to understand the effects of regulations or policies of a government and enterprises on a supply chain. The object of this research is to develop a means to assess the management efficiency and the environmental effect over a whole supply chain and show the effects of various reverse supply chain construction policies on a supply chain. This model can be utilized to establish appropriate policies by understanding the change in the management efficiency and the environmental effect according to the reverse supply chain construction and management method.

• IE interfaces
• /
• v.21 no.4
• /
• pp.444-455
• /
• 2008

In the present manufacturing environment, the appropriate decision making strategy has a significance and it should count on the fast-changing demand of customers. This research derives the optimal levels of the decision variables affecting the inventory related performance in multi-stage supply chain by using simulation and genetic algorithm. Simulation model helps analyze the customer service level of the supply chain computationally and the genetic algorithm searches the optimal solutions by interaction with the simulation model. Our experiments show that the integration approach of the genetic algorithm with a simulation model is effective in finding the solutions that achieve predefined target service levels.

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

• Proceedings of the Korea Society for Simulation Conference
• /
• 2000.04a
• /
• pp.67-72
• /
• 2000

공급망 관리(SCM: Supply Chain Management)는 공급자, 생산자, 고객에 이르는 공급망(Supply Chain) 전체에 대한 합리화된 통합적 관리를 의미한다. 현재까지 최적화와 관련된 분야들에 대한 연구가 많이 진행되어 왔으나 이러한 최적화 값들의 실제 적용에 관한 연구는 미흡하였다. 이에 본 연구에서는 공급망을 모델링하여 원하는 기간 동안의 시뮬레이션을 통해 공급망을 정량화된 데이터로 표현해 주고, 각 구성 요소에서의 운영정책 변화나 공급망 디자인의 변화와 같은 여러 가지 발생 가능한 변인들이 공급망에 어떠한 영향을 미치는지를 나타내 줄 수 있는 Supply Chain Simulator(SCS)에 대한 개략적인 내용을 살펴보고 개발된 SCS들에 대하여 간략하게 알아보고자 한다.

• Korean Chemical Engineering Research
• /
• v.46 no.4
• /
• pp.792-798
• /
• 2008

Supply chain management (SCM) has been drawn increasing attention in industries and academia. The attention is mainly due to a need to integrate the multiple activities in a process network from the overall perspective under the constantly varying economic environment. While many researchers have been addressing various issues of SCM, there is not much research explicitly handling the overall dynamics of supply chain entities from PSE literature. In this two-part series paper, it is investigated how the overall supply chain processing times vary in response to the variation of individual entities using Monte Carlo simulation. Instead of figuring out the operation levels of individual entities, the overall operation time called TAT(Turn-Around-Time) is proposed as a performance indicator. An example of 7 entity-supply chain is presented to illustrate the proposed methodology.

• Journal of the Korea Safety Management & Science
• /
• v.22 no.4
• /
• pp.65-74
• /
• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.