• 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.

• Journal of the Korean Operations Research and Management Science Society
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• v.29 no.3
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• pp.111-127
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• 2004

We consider a supply chain model with a make-to-order production facility and a single supplier. The model we treat here is a special case of a two-echelon inventory model. Unlike classical two-echelon systems, the demand process at the supplier is affected by production process at the production facility as well as customer order arrival process. In this paper, we address that how the demand variability impacts on the optimal replenishment policy. To this end, we incorporate Erlang and phase-type demand distributions into the model. Formulating the model as a Markov decision problem, we investigate the structure of the optimal replenishment policy. We also implement a sensitivity analysis on the optimal policy and establish its monotonicity with respect to system cost parameters.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.3
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• pp.81-93
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• 2005

This paper models supply chain uncertainties in the dynamic Newsboy Problem context. The system consists of one supplier and one retailer who place an order to the supplier every period. Demand uncertainty is modeled as stochastic period demand, and supply uncertainty as the uncertainty in quantities delivered by the supplier. The supplier delivers exactly the amount ordered by the retailer with probability of $\beta$ and the amount minus K with probability of $(1-\beta)$ We formulate the problem as a dynamic programming problem and derive the first-order optimality condition. Through a numerical study, we measure the extent to which the cost decrease due to the reduction in supply uncertainty depends on the level of demand uncertainty. One of the most important findings In this paper is that this cost decrease is relatively small if demand uncertainty is kept high, and vice versa. We also backup this numerical result by analyzing the distribution of ending Inventory under the supply and demand uncertainties.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.2
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• pp.97-109
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• 2014

This paper considers a reverse supply chain with simultaneous recovery of used products and manufacturing of brand-new ones. Recovered products are downgraded and have to be sold in a market different from that of brand-new products at a different price. In case of a shortage of recovered product inventory, a brand-new item, if available, can be offered at the price of a recovered product. In other words, one-way demand substitution is allowed. We address the joint decision of when to manufacture brand-new product, when to recover returned product, and how to control demand substitution to maximize the hybrid production system's profits. To this end, we propose a Markov decision Process model and investigate the structure of the optimal policy. Performance comparison is numerically implemented between the models with and without downward demand substitution option under different operating conditions of the system parameters.

• Management & Information Systems Review
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• v.27
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• pp.149-172
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• 2008

Since the 9/11 terror attack, the event which caused supply chain disruption, supply chain security has becomes more important than ever before. Furthermore, such company's logistics strategies conflicting supply chain security as increased global sourcing, JIT manufacturing are increasing supply chain vulnerability. It could burden for global companies to strengthen supply chain security because not only it requires additional investment cost but also changes of companiy's global logistics strategy. However, on the other hand, supply chain visibility and resilience can be improved through supply chain security. In addition, it allows companies to stabilize supply chain structure as well as rapid and flexible response to market demand. The key issue is balancing between efficiency and supply chain security. To do this, identifying risk elements under the supply chain and assessing vulnerability of each supply chain components should be performed before developing efficient supply chain security management system without obstructing supply chain efficiency.

• Korean Management Science Review
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• v.33 no.3
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• pp.105-117
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• 2016

In this paper, we present empirical testing result to examine the validity of inbound supply and outbound demand risk factors in the sense of early predicting the firm's bankruptcy risk level. The risk factors are drawn from industry uncertainty attributes categorized as uncertainties of input market (inbound supply), and product market (outbound demand). On the basis of input-output table, industry level inbound and outbound sectors are identified to formalize supply chain structures, relevant inbound and outbound uncertainty attributes and corresponding risk factors. Subsequently, publicly available macro-economic indicators are used to appropriately quantify these risk factors. Total 68 industry level bankruptcy risk forecasting results are presented with the average R-square scores of between 53.4% and 37.1% with varying time lag. The findings offers useful insights to incorporate supply chain risk to the body of firm's bankruptcy risk level prediction literature.

• 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.

• Industrial Engineering and Management Systems
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• v.13 no.3
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• pp.319-329
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• 2014

Supply chain management is a subject that has an increasing importance due to the developments in the global markets and technology. In this paper, a fuzzy multi-objective linear programming model is developed for the supply chain of a company dealing with procurement, storage, filling, and distribution of liquefied petroleum gas (LPG) in Turkey. The model intends to determine the quantities of LPG to be procured, stored, filled to cylinders, and transported between the plants and demand centers for six planning periods. In this model, which aims to minimize both total costs (sum of procurement, storage, filling, and transportation costs) and total transportation distances, demand quantities of the main demand centers and decision maker's aspiration levels about objective functions are fuzzy. After comparing the results obtained from the model with those obtained by using different methods, it is concluded that the proposed method can be applied to real world problems practically and it may be used in this type of problems in order to generate an efficient solution.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.189-209
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• 2004

The collaboration between businesses in a demand chain has three layers: Integration, Exchange, and Synchronize. The latter the layer is, the stronger the collaboration gets. This paper investigated drivers influencing intelligent demand chain integration strategy (supply integration, demand integration, demand chain integration) in Korea manufacturing and services. The drivers are classified into two types: rational efficiency driven and bandwagon driven. We find tile differences in the characteristics of drivers affecting the strategies. Besides, this study suggests the better integration strategy for Korea firms. In conclusion it says that demand integration strategy is chosen to improve efficiency, whereas supply integration strategy is influenced by external pressure.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.11a
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• pp.101-105
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• 2002

In a traditional hierarchical inventory system, direct orders are the only information for inventory management that is exchanged between the firms involved. But due to the rapid development of modern information technology, it becomes possible for the firms to share more information in real time, e.g. demand and inventory status data. And so the term Supply Chain has emerged because it is seen as an important source of competitive advantage. Now it is possible to challenge traditional approaches to inventory management. In the past, one of the de-facto assumptions for inventory management was that the demand pattern follows a specific distribution function. However, it is undesirable to apply this assumption in real situations because the demand information in the supply chain tends to be distorted due to the bullwhip effect in a supply chain. To overcome this weakness, we propose a new solution method using NN (Neural Network). Our method proceeds in three steps. First, we find the patterns of optimal reorder points by analyzing past data. Second. train the NN using these pattern data and finally decide the reorder point. Using simulation experiment, we show that the proposed solution method gives better result than that of traditional research.