• Journal of the Society of Naval Architects of Korea
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• v.46 no.1
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• pp.78-86
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• 2009

A capacity calculation and process analysis is a very important part for the entire ship production planning. Ship's production plan is set up with a concept that the product is produced based on the capacity achievable by the processes while general manufacturing sets up the production plan based on product lead-time. Therefore, in case the calculation of capacity for each process of shipbuilding yard is different from actual conditions, a series of production plan - ship table composition, dual schedule plan and execution schedule plan, etc - may accumulate errors, lose reliability of planning information and cause heavy cost deficit in this course. In particular, in case of new shipbuilding yard, stocks between processes are built up and half blocks are not supplied in timely manner, and that is sometimes due to the clumsiness of the operator but it is more often because of the capacity to execute each process is not logically calculated. Therefore, this paper presents the process to calculate the assembly leadtime and assembly process capacity for shipbuilding yard assembly factory. This paper calculated the block type for calculation of assembly lead time based on block DAP(detailed assembly procedure), and introduced cases that calculate production capacities by assembly surface plate by considering the surface plate occupied area of the blocks that change depending on assembly field area and assembly processes through assembly simulation.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.135-144
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• 2014

In devising technological innovation strategies and implementing successful technological innovation, some of the most important factors may be to determine whether to protect technological innovation and to choose how to protect it. Traditionally, technological innovation has been emphasized to obtain compensation as much as possible for innovation in terms of economics and strategy. However, it can be regarded as a very complicated problem to determine such a protection and its level. Generally speaking, enterprises have some common mechanisms to secure compensation for technological innovation, which are known to be patents, secrecy and lead time advantage. From the standpoint of enterprises, it is very important what strategies should be devised to secure profits for technological innovation. According to some domestic and oversea research results revealed that specific patents are not the best way to Appropriability for technological innovation, while also implying that there exist several different kinds of mechanisms to Appropriability for technological innovation in each industry. Nevertheless, since it shouldn't be ignored that most of the researches have overlooked the characteristics of Korean enterprises and industrial differences, this study intends to clarify the effectiveness of technological innovation Appropriability mechanisms reflecting actual circumstances and industrial characteristics in Korea while classifying them. Also The questionnaires and delphi method used in this study. As the result of analysis, in the entire industries, the priorities turned out to be in the order of Superior sales and service efforts, Leadtime advantage, Complementary manufacturing.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1131-1141
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• 2021

In this study, the artificial neural network model is applied for real-time dam inflow prediction and then evaluated for the prediction lead times (1, 3, 6 hr) in dam basins in Korea. For the training and testing the model, hourly precipitation and inflow are used as input data according to average annual inflow. The results show that the model performance for up to 6 hour is acceptable because the NSE is 0.57 to 0.79 or higher. Totally, the predictive performance of the model in dry seasons is weaker than the performance in wet seasons, and this difference in performance increases in the larger basin. For the 6 hour prediction lead time, the model performance changes as the sequence length increases. These changes are significant for the dry season with increasing sequence length compared to the wet season. Also, with increasing the sequence length, the prediction performance of the model improved during the dry season. Comparison of observed and predicted hydrographs for flood events showed that although the shape of the prediction hydrograph is similar to the observed hydrograph, the peak flow tends to be underestimated and the peak time is delayed depending on the prediction lead time.

• The Journal of Bigdata
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• v.6 no.2
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• pp.17-28
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• 2021

Current development of technologies related to 4^th industrial revolution and the pandemic of COVID-19 lead the rapid expansion of e-marketplace. The level of competition among several companies gets increased by introducing different strategies. To cope with the current change in the market and satisfy the customers who request the better delivery service, the new concept, fulfillment, has been introduced. It makes the leadtime of process from order picking to delivery reduced and the efficiency improved. Still, the efficiency of operation in fulfillment centers constrains the service level of the entire delivery process. In order to solve this problem, several different approaches for demand forecasting and coordinating supplies using Bigdata, IoT and AI, which there exists the trivial limitations. Because it requires the most lead time for operation and leads the inefficiency the process from picking to packing the ordered items, the logistics service providers should try to automate this procedure. In this research, it has been proposed to develop the efficient plans to automate the process to move the ordered items from the location where it stores to stage for packing using AGV and AMR. The efficiency of automated devices depends on the number of items and total number of devices based on the demand. Therefore, the result of simulation based on several different scenarios has been analyzed. From the result of simulation, it is possible to identify the several factors which should be concerned for introducing the automated devices in the fulfillment centers. Also, it can be referred to make the optimal decisions based on the efficiency metrics.