• Journal of the Semiconductor & Display Technology
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• v.10 no.4
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• pp.71-77
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• 2011

This paper presents a production planning methodology for semiconductor manufacturing supply chain network with volatilities caused by uncertainties such as unstable demand and price. In order to take volatilities into account, we develop two approaches; 1) stochastic model with consideration of various cases and 2) deterministic model considering replanning cost, and propose efficient solution methods. Computational experiments show that the performance of the proposed method is superior to that of deterministic approach using various scenarios.

• Journal of the Korean Operations Research and Management Science Society
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• v.31 no.2
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• pp.129-140
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• 2006

Quantity Flexibility contract coordinates individually motivated supplier and buyer to the systemwide optimal outcome by effectively allocating the costs of market demand uncertainty. The main feature of the contract is to couple the buyer's commitment to purchase no less than a certain percentage below the forecast with the supplier's guarantee to deliver up to a certain percentage above. In this paper we refine the previous models by adding some realistic features including the upper and lower limits of the purchase. We also incorporate purchase and canceling costs in a cost function to reflect the real world contracting process more accurately. To obtain the solution of the model, we derive a condition for extreme points using the Leibniz's rule and construct an algorithm for finding the optimal solution of the model. Several examples illustrating the algorithm show that the approach is valid and efficient.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.201-204
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• 1997

This study is focussed on optimization problems which require allocating the restricted inventory to demand points and assignment of vehicles to routes in order to deliver goods for demand sites with optimal decision. This study investigated an integrated model using three step-by-step approach based on relationship that exists between the inventory allocation and vehicle routing with restricted amount of inventory and transportations. we developed several sub-models such as; first, an inventory-allocation model, second a vehicle-routing model based on clustering and a heuristic algorithms, and last a vehicle routing scheduling model, a TSP-solver, based on genetic algorithm. Also, for each sub-models we have developed computer programs and by a sample run it was known that the proposed model to be a very acceptable model for the inventory-allocation and vehicle routing problems.

• Management Science and Financial Engineering
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• v.8 no.2
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• pp.33-45
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• 2002

This study suggests an approach for determining fleet size for LTL (less -than-truckload) transportation with dynamic demand for on-time supply of the parts between the assembly line in an automobile company and its part suppliers in Korea. The vehicles operated by the transportation trucking companies in Korea in general can be classified into three types depending on the ways how their expenses occur; company -owned truck, mandated truck which is owned by outsider who entrusts the company with its operation, and rented truck (outsourcing) . With the forecasted monthly production data a year, a heuristic algorithm is developed to determine the number of company-owned trucks, mandated trucks, and rented trucks in order to minimize the expected annual operating cost, which is based on the solution technologies used in the aggregate production planning and vehicle routing problem. Finally the algorithm is tested for the problem how the trucking company transports parts for the automobile company.

• Proceedings of the Safety Management and Science Conference
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• 2005.05a
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• pp.225-230
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• 2005

A study is for the approach that we could reduce the logistics costs through uprising the efficiency, complying to the demand in time, stock management based on the general production plan in the point of SCM. We will take following steps to get the goal. First, we will overview the physical distribution expenses annually. Second, will find the main cause of optimal load loss by improper container and lack of delivery flexibility to the demand. Third, the goal will be inferred through the fast information share based on organic relationship, the establishment of general production plan, the improvement of stock management plan. By way of this process, it can be accomplished expense reduction as well as flexible establishment of the reduction of worker's load.

• Journal of the Korea Safety Management & Science
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• v.7 no.2
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• pp.41-56
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• 2005

A study is for the approach that we could reduce the logistics costs through uprising the efficiency, complying to the demand in time, stock management based on the general production plan in the point of SCM. We will take following steps to get the goal. First, we will overview the physical distribution expenses annually. Second, will find the main cause of optimal load loss by improper container and lack of delivery flexibility to the demand. Third, the goal will be inferred through the fast information share based on organic relationship, the establishment of general production plan, the improvement of stock management plan. By way of this process, it can be accomplished expense reduction as well as flexible establishment of the reduction of worker's load.

• Industrial Engineering and Management Systems
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• v.1 no.1
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• pp.29-45
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• 2002

In this study, we consider infinite supply of raw materials and backlogged demands as given two boundary conditions. And we need not make any specific assumptions about the inter-arrival of external demand and service time distributions. We propose a numeric model and an algorithm in order to compute the first two moments of inter-departure process. Entropy enables us to examine the convergence of this process and to derive measurable relations of this process. Also, lower bound on the variance of inter-departure process plays an important role in proving the existence and uniqueness of an optimal solution for a numeric model and deriving the convergence order of augmented Lagrange multipliers method applied to a numeric model. Through these works, we confirm some structural properties and numeric examples how the validity and applicability of our study.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.4
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• pp.43-52
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• 1998

The purpose of this study is to establish a method of estimating the daily urban water demend using Backpropagation algorithm is part of ANN(Artificial Neural Network). This method will be used for the development of the efficient management and operations of the water supply facilities. The data used were the daily urban water demend, the population and weather conditions such as treperarture, precipitation, relative humidity, etc. Kwangju city was selected for the case study area. We adjusted the weights of ANN that are iterated the training data patterns. We normalized the non-stationary time series data [-1,+1] to fast converge, and choose the input patterns by statistical methods. We separated the training and checking patterns form input date patterns. The performance of ANN is compared with multiple-regression method. We discussed the representation ability the model building process and the applicability of ANN approach for the daily water demand. ANN provided the reasonable results for time series forecasting.

• 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 Navigation and Port Research
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• v.40 no.5
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• pp.345-351
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• 2016

This study presents a system dynamics methodology to evaluate quantitatively the effect of the Korean government's development policy, such as tax reductions, on the industrial economy. System dynamics is often perceived as an optimized means to identify the dynamic inter-relationships among various factors of development policies, and in particular the industrial characteristics and uncertainties of the coastal shipping industry. The results of simulations used in this study shows that the impact of development policies such as tax reductions would increase shipping demand for about 4 years, and that tax incentives could raise the demand volume for cabotage cargo from 5.26 to 11.11%, through the available freight-down by 90~95% points. The system dynamics approach used in this paper represents an initial attempt to use this methodology in studies of the coastal shipping industry. On the basis of our simulations, the industrial effects of other development policies, such as ship financing support, investment of social overhead, or crew supply, could also be analyzed effectively. Additionally, it should be possible to extend these results by developing a comprehensive model encompassing these various analyses.