• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.161-166
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• 2003

Information lead time is defined as the time spent by processing orders from some buyers, whereas order lead time is defined as producing and supplying the products. The information lead time significantly serve to magnify the increase in variability due to demand forecasting. This paper models a decentralized supply chain composed of cascade type which has four type phases (or divisions) such as retailer, wholesaler, distributor, and factory. Each phases is managed by different centers individually with their own local inventory information. We investigate whether each phase's Information lead time affects companies networked a value chain. In particular, on several experiments performed with a programmed simulation (like a MIT beer game), we study the following question ; Can information lead times do better than material lead times in cost-benefit perspective\ulcorner Can more much Information lead times in downstream reasonably do worser than in upstream when playing the simulation\ulcorner In the conclusion, we show the importance of information lead time on a SC and, besides, guarantee that improvement of information lead time in upstream do more effective than one in downstream in cost-benefit perspective.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.342-345
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• 2003

Due to the rapid development of manufacturing and information technology, traditional supply chain scheme has been changed dramatically Most companies have been forced to relocate or redesign their logistics network in different countries. A supply chain partnership is a relationship formed between two independent members in supply chain through information sharing to achieve specific objectives and benefits in terms of reductions in total costs and inventories. This study illustrates the benefits of supply chain partnerships based on information sharing and lead-time patterns. We consider three level of information sharing: (1) immediate order information; (2) demand information; (3) inventory information. Given a fixed total lead-time, how lead-time distribution will affect the bullwhip effect and inventory cost under information sharing strategies. The results can help improving supply chain performance and selecting suitable direction for the re-configuration of supply chain network.

• The Journal of Information Systems
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• v.10 no.2
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• pp.129-150
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• 2001

In this study, we model a decentralized supply chain system which is managed by four types of centers, sequentially located: Retailer, Wholesaler, Distributor, and Factory Each center contributes to enhancing the performance of the supply chain system individually with its own local inventory information. Through experiments which are performed with a programmed simulation (like the MIT beer game), we investigate how the information lead time improvement in each center affects the whole system. And we show that the impact of the lead time improvement in the downstream, like retailers, affects more to the system than the one in the upstream, like factories, in a cost-effective way. Moreover, by using information lead time for each center, we analyze how much the extent of the improvement affects the whole system, especially for the total cost and the order level.

• Journal of the Korea Safety Management & Science
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• v.20 no.3
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• pp.11-19
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• 2018

This study suggests a model of production information system that can reduce manufacturing lead time and uniformize quality by using DNC S/W as a part of constructing production information management system in the industrial field of the existing marine engine block manufacturing companies. Under the effect of development of this system, the NC machine interface device can be installed in the control computer to obtain the quality information of the workpiece in real time so that the time to inspect the process quality and verify the product defect information can be reduced by more than 70%. In addition, the reliability of quality information has been improved and the external credibility has been improved. It took 30 minutes for operator to obtain, analyze and manage the quality information when the existing USB memory is used, but the communication between the NC controller computer and the NC controller in real time was completed to analyze the workpiece within 10 seconds.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.428-439
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• 2020

The continuous development of information and communication technologies has resulted in an exponential increase in data. Consequently, technologies related to data analysis are growing in importance. The shipbuilding industry has high production uncertainty and variability, which has created an urgent need for data analysis techniques, such as machine learning. In particular, the industry cannot effectively respond to changes in the production-related standard time information systems, such as the basic cycle time and lead time. Improvement measures are necessary to enable the industry to respond swiftly to changes in the production environment. In this study, the lead times for fabrication, assembly of ship block, spool fabrication and painting were predicted using machine learning technology to propose a new management method for the process lead time using a master data system for the time element in the production data. Data preprocessing was performed in various ways using R and Python, which are open source programming languages, and process variables were selected considering their relationships with the lead time through correlation analysis and analysis of variables. Various machine learning, deep learning, and ensemble learning algorithms were applied to create the lead time prediction models. In addition, the applicability of the proposed machine learning methodology to standard work hour prediction was verified by evaluating the prediction models using the evaluation criteria, such as the Mean Absolute Percentage Error (MAPE) and Root Mean Squared Logarithmic Error (RMSLE).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.19 no.37
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• pp.31-40
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• 1996

The amount of safety stock is decided from various information such as the forecasted demand, the lead time, the size of the order quantity and the desired service level. There are two cases to consider the problem of setting safety stock when both the demand in a period and the lead time are characterized as random variables: the first case is the parameters of the demand and lead time distributions are known, the second case is they are unknown and must be estimated. The objective of this study is to present the procedure for setting safety stocks in the case the parameters of the demand and lead time distributions are unknown and must be estimated. In this study, a simple exponential smoothing model is used. to generate the estimates of demand in each period and a discrete distribution of the lead time is developed from historical data, and the optimal service level is used which determined to consider both of a backorder and lost sale.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.508-521
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• 2020

We compared a novel encoding Lempel-Ziv complexity (ELZC) with three common complexity algorithms i.e., approximate entropy (ApEn), sample entropy (SampEn), and classic Lempel-Ziv complexity (CLZC) so as to determine a satisfied complexity and its corresponding quality indices for assessing quality of multi-lead electrocardiogram (ECG). First, we calculated the aforementioned algorithms on six artificial time series in order to compare their performance in terms of discerning randomness and the inherent irregularity within time series. Then, for analyzing sensitivity of the algorithms to content level of different noises within the ECG, we investigated their change trend in five artificial synthetic noisy ECGs containing different noises at several signal noise ratios. Finally, three quality indices based on the ELZC of the multi-lead ECG were proposed to assess the quality of 862 real 12-lead ECGs from the MIT databases. The results showed the ELZC could discern randomness and the inherent irregularity within six artificial time series, and also reflect content level of different noises within five artificial synthetic ECGs. The results indicated the AUCs of three quality indices of the ELZC had statistical significance (>0.500). The ELZC and its corresponding three indices were more suitable for multi-lead ECG quality assessment than the other three algorithms.

• Journal of the Korean Applied Science and Technology
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• v.27 no.2
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• pp.196-201
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• 2010

The influence of red lead($Pb_3O_4$) to curing and formation reaction properties when it was added in positive material of lead acid battery for vehicle use has been investigated. At the results, it was confirmed that the addition of red lead led 4BS crystal size to be smaller and increased the rates of 4BS formation and Pb consumption. Consequently the curing time was shortened to half compared with that of red lead-free one. In addition to this, the lead acid battery prepared by adding red lead showed 14% higher efficiency at the life cycle test than that without red lead.

• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.29-38
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• 2018

Through process quality information, the time required for process quality analysis has been drastically shortened, the process defect rate has been reduced, and the manufacturing lead time has been shortened and the on-time delivery rate has been improved. Therefore, The purpose of this study is to develop a quality information analysis system model that effectively shortens the time required for process quality analysis in automobile safety belt parts manufacturing process. As a result of experiments on communication operation between manufacturing execution system (MES) quality server, injection machine control computer, injection machine programmable logic controller (PLC) and terminal, in analyzing quality information, the conventional handwriting input method took an average of 20 minutes, but the new multi-network method took about 2 minutes on average. In addition, the process defect rate was reduced by 13% and the manufacturing lead time was shortened from 28 hours to 20 hours. The delivery compliance rate improved from 96 to 99%.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1207-1216
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• 2018

Discharge or water level predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. This research aims to predict water level of the tide dominated site, Jamsu bridge in the Han River downstream. Physics-based hydrodynamic approaches are sometimes not applicable for water level prediction in such a tidal river due to uncertainty sources like rainfall forecasting data. In this study, TensorFlow deep learning framework was used to build a deep neural network based LSTM model and its applications. The LSTM model was trained based on 3 data sets having 10-min temporal resolution: Paldang dam release, Jamsu bridge water level, predicted tidal level for 6 years (2011~2016) and then predict the water level time series given the six lead times: 1, 3, 6, 9, 12, 24 hours. The optimal hyper-parameters of LSTM model were set up as follows: 6 hidden layers number, 0.01 learning rate, 3000 iterations. In addition, we changed the key parameter of LSTM model, sequence length, ranging from 1 to 6 hours to test its affect to prediction results. The LSTM model with the 1 hr sequence length led to the best performing prediction results for the all cases. In particular, it resulted in very accurate prediction: RMSE (0.065 cm) and NSE (0.99) for the 1 hr lead time prediction case. However, as the lead time became longer, the RMSE increased from 0.08 m (1 hr lead time) to 0.28 m (24 hrs lead time) and the NSE decreased from 0.99 (1 hr lead time) to 0.74 (24 hrs lead time), respectively.