• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.313-320
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• 2014

We consider the problem to find economic inventory quantity of a single commodity under stochastic demands and order cancellation. In contrast to the traditional economic production quantity (EPQ) model, we assume that once the amount of inventory reaches to a predetermined level of quantity then the production is not halted but its production speed decreases until the inventory level drops to zero. We establish two probabilistic models representing the behaviors of both the high-production period and low-production period, respectively, and derive the relationship between the level of inventory and costs of production, cancellation, and holding, from which the quantity of economic inventory is obtained.

• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.61-67
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• 2020

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

• Journal of Korean Institute of Industrial Engineers
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• v.5 no.1
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• pp.1-6
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• 1979

This paper discusses an application of discrete variable Servo Theory to the analysis of the effectiveness of production-inventory control system which uses exponential smoothing as a specific forecasting technique by establishing a new model which consists of such three departments as production planning, production, and inventory. The objective of the new production-inventory model is to keep the production to the optimal level of minimum production cost in production planning problem for obtaining, the stability of inventory subject to demand variation. On this basis, the dynamic characteristic of the system with the change of the parameters is clarified by the numerical analysis. The results of the numerical analysis show the effect that is obtained by the simultaneous stability of production and inventory as soon as possible.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.2
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• pp.89-97
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• 2012

This paper considers a manufacturer with a two-station make-to-stock and make-to-order serial production system. The MTS facility produces a single type of component and provides components for the MTO facility that produces customized products. In addition to the internal demand from the MTO facility, the MTS facility faces demands from the spot market with the option of to accept or reject each incoming demand. This paper addresses a joint component inventory rationing and batch production control which maximizes the manufacturer's profit. Using the Markov decision process model, we investigate the structural properties of the optimal inventory rationing and batch production policy, and present two types of heuristics. We implement a numerical experiment to compare the performance of the optimal and heuristic policies and a simulation study to examine the impact of the stochastic process variability on the inventory rationing and batch production control.

• Journal of the military operations research society of Korea
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• v.18 no.2
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• pp.167-180
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• 1992

This paper considers a two-location production and inventory model for a single product which can be produced and demanded at each of two locations. Demands during a finite number of discrete time periods are known and must be satisfied by production, inventory or transshipment. We consider the change of production capacity. The costs to be incurred are restricted to production, inventory and transshipment costs, and all cost functions we assumed to be concave. The objective is to minimize the total cost of production, inventory and transshipment. The model is formulated as a shortest path problem for an acyclic network from which properties associated with optimal solutions are derived. Using these properties. we develop a dynamic programming algorithm that finds optimal solutions for problems.

• Journal of the military operations research society of Korea
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• v.17 no.1
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• pp.84-104
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• 1991

Up to the present, the evaluation measures in the production and inventory management have been studied under the pre-condition that the costs for major factors(e.g,. cost of carrying inventory, cost of demand shortage) are given easily, although in practice, it is difficult. The case in which multiple participants have a different viewpoints in production and inventory management has not been studied, in spite of its frequent occurrence. This study suggests a production and inventory model with multiple objectives corresponding to major factors and the related interactive algorithm based on the preference structures of participants. The problem can be solved through a weighting vector generated by an interaction with participants. The concept of equity is also used in order to guarantee the reasonable distribution of group utility in determining the individual relative weights of participants. This study includes the reality of the model and the decision process in the production and inventory management.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.11a
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• pp.162-163
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• 2011

This paper presents the system dynamics methodology for modeling and control the production/inventory system. Under system dynamics point of view, we can apply some production/inventory policies as if we use the control laws for dynamics systems, then the behavior of system is analyzed and evaluated to improve the performance of production/inventory system. We also utilize the hybrid modeling method for the dynamic of production system with the combination of Matlab/Simulink and Matlab/Sateflow. Finally, the numerical simulation results are carried out in Matlab/Simulink environment and compare with the results from other works. It is shown that our approach can obtain some good performances (such as operational cost, stability of inventory, customer service level).

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.1
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• pp.13-34
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• 1991

A production/inventory system is considered in which a production facility produces one type of product. The demand for the product is given by a compound Poison process and is supplied directly from inventory when inventory is available and is lost when inventory is out of stock. The processing time to produce one item is assumes to follow a general distribution. An (s, S) policy is considered in which production stops at the instant the stock on hand reachs S and the setup of the production facility begins at an inspection point when the stock on hand drops to or below s for the first time. The time interval between two successive inspection points during a non-production period is a random variable which follows a general distribution.

• Management Science and Financial Engineering
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• v.22 no.1
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• pp.21-26
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• 2016

We analyze how firms' global production activities affect their inventory supply and financial performance in regards to its production location. For the analysis, we use information on global production quantities of 3,076 Korean multinational firms that operate business in Europe and Asia through foreign direct investment (FDI) from 2006 to 2013. Our estimation results show that an increase in global production ratio, measured by global production/total production, decreases inventory supply and financial performance of firms that produce in European countries, while it decreases financial performance of firms that produce in Asian countries. Although our results indicate that global production decreases financial performance of firms that produce in Europe and Asia, we find that its negative effects on financial performance are different based on the market demand uncertainty.