• Korean Management Science Review
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• v.31 no.3
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• pp.13-26
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• 2014

This paper considers an integrated one-vendor multi-buyer production-inventory model where the vendor manufactures multiple products in lot at their associated finite production rates. In the model, it is allowed for each product to be shipped in lot to the buyers even before the whole product production is not completed yet. Each product lot is dispatched to the associated buyer in a number of shipments. The buyers consume their products at fixed rates. The objective is to the production and shipment schedules in the integrated system, which minimizes the total cost per unit time. The total cost consists of production setup cost, inventory holding cost and shipment cost. For the model, an iterative optimal solution procedure with shipment consolidation policy incorporated. It is then tested through numerical experiments to show how efficient and effective the shipment consolidation policy is.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.5
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• pp.447-452
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• 2015

This paper considers a two-stage make-to-stock production system. The first stage produces a single-component and the second stage produces a make-to-stock product using components. In addition to internal demands, the first stage faces external demands with the option of accepting or rejecting. To ration component inventory, the manufacturer adopts a static rule. This paper analyzes the production controls at both facilities that maximizes the manufacturer's profit. Using the Markov decision process model, we characterize the optimal production policy by two monotonic switching curves.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1999.04a
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• pp.426-426
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• 1999

;There are many sources of uncertainty in a typical production and inventory system. There is uncertainty as to how many items customers will demand during the next day, week, month, or year. There is uncertainty about delivery times of the product. Uncertainty exacts a toll from management in a variety of ways. A spurt in a demand or a delay in production may lead to stockouts, with the potential for lost revenue and customer dissatisfaction. Firms typically hold inventory to provide protection against uncertainty. A cushion of inventory on hand allows management to face unexpected demands or delays in delivery with a reduced chance of incurring a stockout. The proposed strategies are used for the design of a probabilistic inventory system. In the traditional approach to the design of an inventory system, the goal is to find the best setting of various inventory control policy parameters such as the re-order level, review period, order quantity, etc. which would minimize the total inventory cost. The goals of the analysis need to be defined, so that robustness becomes an important design criterion. Moreover, one has to conceptualize and identify appropriate noise variables. There are two main goals for the inventory policy design. One is to minimize the average inventory cost and the stockouts. The other is to the variability for the average inventory cost and the stockouts The total average inventory cost is the sum of three components: the ordering cost, the holding cost, and the shortage costs. The shortage costs include the cost of the lost sales, cost of loss of goodwill, cost of customer dissatisfaction, etc. The noise factors for this design problem are identified to be: the mean demand rate and the mean lead time. Both the demand and the lead time are assumed to be normal random variables. Thus robustness for this inventory system is interpreted as insensitivity of the average inventory cost and the stockout to uncontrollable fluctuations in the mean demand rate and mean lead time. To make this inventory system for robustness, the concept of utility theory will be used. Utility theory is an analytical method for making a decision concerning an action to take, given a set of multiple criteria upon which the decision is to be based. Utility theory is appropriate for design having different scale such as demand rate and lead time since utility theory represents different scale across decision making attributes with zero to one ranks, higher preference modeled with a higher rank. Using utility theory, three design strategies, such as distance strategy, response strategy, and priority-based strategy. for the robust inventory system will be developed.loped.

• Korean Journal of Construction Engineering and Management
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• v.25 no.5
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• pp.41-54
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• 2024

In this study, we compare and analyze the performance of three inventory management policies for raw material inventory management in a Precast Concrete production plant: Fixed Order Quantity Policy (FOQP), Fixed Order Interval Policy (FOIP), and (s, S) Ordering Policy (sSOP). In order to make more realistic conclusion, we developed and utilized the ARENA simulation model, a performance evaluation tool that considers the variance of raw material demand and supply for the entire production process in a PC production plant using multiple raw materials. For the three policies, reorder point, order quantity, target level, and order interval parameters were initialized by using Economic Order Quantity (EOQ) model and then optimized through OptQuest. As a result of optimization, inventory management costs were reduced by an average of 97.28% compared to the EOQ model that does not consider the variance of demand and supply. After setting three influencing factors, Project Occurrence Cycle (POC), Raw Material Lead-time (RML), and Unit Stock-out Cost (USC), a performance evaluation was conducted for the three policies. As a result of evaluation, the inventory management costs of FOQP and sSOP, which determine order intervals by considering inventory levels by real-time or daily, were 30.6% and 27.9% lower than FOIP with fixed order intervals respectively. In addition, inventory management costs were affected by RML and USC factors excluding POC, but the differences were 2.17% and 2.09% respectively, which were not large due to the optimization of parameters for responding the variance of raw material demand and supply.

• Journal of the Korea Institute of Building Construction
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• v.5 no.4 s.18
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• pp.153-164
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• 2005

This study aims at establishing JIT production management system to enable manage the resources input into from procurement through construction based on correct identification of the process, an analysis on the amount of input materials and information sharing. This study has focused on the process control and working process of rebar work in domestic apartment house construction where the overall scope of Process from the planning phase to the construction phase has been analyzed in this study. Also construction phase was selected for the application of a sample case. A basic model for JIT production was generated with these processes. Furthermore A questionnaire and the on-site survey with process, checklist and control data were prepared and performed for the application of JIT production management model into rebar work. The governing scopes of JIT production management system include process management, material management, yard loading and moving management and inventory control, and the operation of each control item

• Journal of Korean Institute of Industrial Engineers
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• v.24 no.1
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• pp.105-113
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• 1998

This paper deals with a production line which consists of two production stages that are separated by a finite storage buffer. The inventory level in the storage buffer controls the production rate of the preceding stage. That is, the production rate becomes high (low) when the buffer inventory is low (high). We analyze the system characteristics utilizing the Markov process theory and then find an optimal control policy which maximizes a given system profit function. Also, a sensitivity analysis is made to examine the effects of various system parameters on the system performances.

• Korean Management Science Review
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• v.32 no.4
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• pp.19-28
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• 2015

This study presents a mathematical programming model to develop production planning in the manufacturing processes for photovoltaic silicon ingots and wafers. The model is formulated as a linear programming model that maximizes total growth margin, which is composed of production cost, inventory cost, shortage cost, and sales profit while considering the constraints associated with the production environments of photovoltaic materials. In order to demonstrate the utility of the model for production planning, we run operations for a planning horizon of a year for a case study. When the primary results of this mathematical programming are compared with the historical records, the model could have resulted in the considerable increase of the total growth margin by effectively reducing inventory cost if a decision maker had employed the model as a decision support system with perfect information for sales demand.

• Journal of the Korea Safety Management & Science
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• v.5 no.4
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• pp.209-217
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• 2003

The traditional Kanban needs a lot of preconditions for fitting conditions of dynamic production processing environment. The traditional Kanban isn't suitable conditions of dynamic production processing environment. Therefore conditions of dynamic production processing environment is needed more stable system. This study is describe CONWIP system such as suitable in dynamic production processing environment. Most Pull system is a Kanban system than use Kanban cards or signal for production management and inventory control. The object of Kanban system is reducing inventory between shop-floor that can reduce inventiry cost. If the system reduce the number of Kanban cards would be reduce the working process WIP, can be reduced and can be found all potential problem of production between shop-floors. This study apply to CONWIP system model for Korean industrial companies.

• Industrial Engineering and Management Systems
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• v.8 no.2
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• pp.72-79
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• 2009

The ultimate goal of inventory management is to decide the timing and the quantity of ordering in response to uncertain demands. Recently, some researchers have focused upon an impact of distortions in the information, e.g., customer order cancellation, on an economical inventory policy. The customer order cancellation is considered a kind of distortions in demands, because a demand that is eventually cancelled is equivalent to a phony demand. Also, there are some additional distortions in the inventory information. For instance, the procurement of suppliers may include some nonconforming items as a result of imperfect production and inspection by the suppliers, and/or damage in transit. The nonconforming item should be considered a kind of distortions in the inventory information, because the nonconforming item is equivalent to a phony stock. In this article, we consider an inventory model under the situation that customers can cancel their orders and the procurement of suppliers may include some nonconforming items. Then, we introduce the customer order reservation into the inventory model for the purpose of avoiding the costly backlogs, because the customer order reservation gives retailers a period to fulfill customer's requests. We formulate a periodic review (s, S) inventory model and investigate the economical operation under the situation mentioned above. Further, through the sensitivity analysis, we show the impact of these distortions and the effect of the customer order reservation on the inventory policy.

• Journal of the Korea Society of Computer and Information
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• v.12 no.5
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• pp.273-284
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• 2007

This paper studies a periodic review inventory model with an e-MarketPlace transaction in reconfigurable manufacturing system(RMS). A decision maker can expand/reduce production capacity/quantities and/or replenish/dispose inventories from/to e-MarketPlace urgently to satisfy the stochastic demands. If inventories are replenished or disposed through e-MarketPlace, this leadtime is shorter than the production leadtime, but unit purchasing or selling cost is more expensive than that of expanding capacity or reducing production quantities respectively. Henceforth, trade-off on these alternatives is considered. In addition to this, in order to consider the economy of scale, our model includes the fixed cost for purchasing from e-MarketPlace and capacity expansion. We use dynamic programming and K convexity methods to characterize the nature of the optimal policy. Finally, We present the optimal inventory control policy which is composed by the combinations of a base stock and (s,S) type policy.