• The Journal of Bigdata
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• v.5 no.2
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• pp.111-120
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• 2020

Despite the recent economic contraction caused by the Corona 19 incident, interest in the residential environment is growing as more people live at home due to the increase in telecommuting, thereby increasing demand for remodeling. In addition, the government's real estate policy is also expected to have a visible impact on the sales of the interior and furniture industries as it shifts from regulatory policy to the expansion of housing supply. Accurate demand forecasting is a problem directly related to inventory management, and a good demand forecast can reduce logistics and inventory costs due to overproduction by eliminating the need to have unnecessary inventory. However, it is a difficult problem to predict accurate demand because external factors such as constantly changing economic trends, market trends, and social issues must be taken into account. In this study, LSTM model and 1D-CNN model were compared and analyzed by artificial intelligence-based time series analysis method to produce reliable results for manufacturers producing furniture components.

• Journal of Korean Institute of Industrial Engineers
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• v.11 no.1
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• pp.33-39
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• 1985

A stochastic model for an inventory system in which depletion of stock takes place due to random demand as well as random loss of items is studied under the assumption that the intervals between successive unit demands, as well as those between successive unit losses are independently and identically distributed random variables having negative exponential distribution with respective parameters. We have derived the steady state probability distribution of the stock level assuming instantaneous delivery of order under (s, S) inventory policy. Also we have derived total expected cost expression and the necessary conditions to be satisfied for an optimal solution.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.4
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• pp.125-138
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• 2006

This paper considers a periodic review, two-echelon inventory system with one central warehouse and several re-tailers facing normally distributed demand. The goal is to attain target fill rates, while the systemwide total holding costs are minimized. An important aspect of this problem is material rationing in the case of shortages. If a central warehouse has insufficient inventory to deliver all replenishment orders to retailers, all order quantities are should be adjusted according to some rationing rule. A simple but efficient rationing rule is proposed and compared with the Balanced Stock (BS) rationing as introduced by Heijden which is known to be the best rationing policy in the literature. Numerical results show that the proposed rationing rule is more cost effective than BS rationing, especially for the differences in holding costs between retailers are large.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.441-445
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• 2004

In this paper, we deal with an inventory model of a multi-stage, serial supply chain system where a single product type and nonstationary customer demand pattern are considered. The retailer and suppliers place their orders according to an echelon-stock based replenishment control policy. We assume that the suppliers can access online information on the demand history and use this information when making their replenishment decisions. Using a reinforcement learning technique, the inventory control parameters are designed to adaptively change as the customer demand pattern is altered, in order to maintain a given target service level. Through a simulation based experiment, we verified that our approach is good for maintaining the target service level.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.1
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• pp.51-58
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• 1991

This paper presents a single-echelon, single item, stochastic lead time and static demand inventory model for situations in which, during the stockout period, a fraction ${\beta}$ of the demand is backordered and the remaining fraction $(1-{\beta})$ is lost. In this situations, an objective function representing the average annual cost of inventory system is obtained by defining a time-proportional backorder cost and a fixed penalty cost per unit lost. The optimal operating policy variables minimizing the average annual cost are calculated iteratively. At the extremet ${\beta}=1$, the model presented reduces to the usual backorder case. A numerical example is solved to illustrate the algorithm developed.

• 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 Korea Society for Industrial Systems Conference
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• 2006.05a
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• pp.235-240
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• 2006

The existing inventory managements bear a relation to forecasting or assumptions. So these methods become more complicated and more expensive systems as time goes. This paper developed a practical inventory system which is called DCC(demand control chart). DCC does not 'forecast' but 'control' the trend of demand without assumptions. According to the trend of sales, DCC adjusts an order quantity considering the capacity of shelf in a store. Specially, DCC is a useful method under FRID system. Besides, this paper introduces EPFR(Every Period Full Replenishment) policy for reducing stocks.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.3
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• pp.61-74
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• 2011

The objective of this study is to evaluate the effects of sharing uncertain yield information with a downstream supply chain player. We are interested in understanding how the amount of yield uncertainty affects the supply-side benefits and/or costs, which has not been considered in the literature, in addition to the customer-side benefits. With that purpose, this work evaluates a supplier who provides yield information in comparison with another supplier who shares no information. We simulate an order-up-to type heuristic policy that is adapted from the literature and reasonably modified to represent yield information sharing with error. From the simulation study, we argue that the customer would experience cost reduction, but the cost for supplier's inventory is increasing when sharing yield information. Furthermore, the amount of benefits and costs are situational and affected by level of yield uncertainty and demand variance. Based on the simulation study, we finally make several recommendations for the supply-side approaches to yield information sharing.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.49-58
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• 2008

In this paper, we consider an inventory system where a single supplier purchases and processes raw materials into finished goods in order to deliver finished goods to a single buyer for effective implementation of Just-In-Time Purchasing. An integrated multi-item lot-splitting model of facilitating multiple shipments in small lots between buyer and supplier is developed in a JIT Purchasing environment. Also, an iterative solution procedure is developed to find the order quantity for finished goods and raw materials, and number of shipments between buyer and supplier. We show by numerical example that when the integrated policy is adopted by both buyer and supplier in a cooperative manner, both parties can benefit.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.25-36
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• 1997

The purpose of this paper is to structure a new integrated model that can minimize the total cost for the transportation and inventory systems between m origin points, where origin i has a supply of a commodity, such as distribution centers or warehouses, and n destination points, where destination j requires the commodity. In this case, demands of the destination points are assumed random variables which have a known probability distribution. We will find optimal distribution centers which transport the commodity to the destination points and suggest optimal inventory policy to the selected distribution center which find the optimal pair $$ and safety stock level that minimize total cost with back-ordered case. This new model is formulated as a 0-1 nonlinear integer programming problem. To solve the problem, this paper proposes heuristic computational procedures and program and provides numerical examples.