• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.111-116
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• 2005

This paper considers the problem of optimally choosing the sub-process means of a mixture production process where two important ingredients are mixed. The quantity of each ingredient is controlled through each corresponding sub-process. The values of the sub-process mean directly affect the defective rate, production, scrap and reprocessing costs for the mixture production process. After inspecting every incoming item, each conforming item is sold in a regular market for a fixed price and any nonconforming item is scraped. A model is constructed on the basis of the selling price, production, inspection, and scrap and reprocessing costs. The goal is to determine the optimum sub-process mean values based on maximizing expected profit function relating selling price and cost components. A method of finding the optimum sub-process means is presented when the quantities of the two ingredients are assumed to be normally distributed with known variances. A numerical example is given and numerical studies are performed.

• Journal of the Korea Safety Management & Science
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• v.9 no.6
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• pp.163-169
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• 2007

This study deals with the improvement of production process on a flow production system with the consideration of six sigma. We analyze the production process and survey the important factors of improvement of productivity. Using a six sigma, we find strategic point and suggest a reformation of production process. We applied a simulation technique to simulate the production line proposed by the result of the Six sigma. With the result of the simulation, this study analyzes the propriety of production line and proposes the alternatives of new production process.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.20-37
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• 2020

In this paper, we propose a simulation method based on backward simulation and process-oriented simulation to take into account the characteristics of shipbuilding production, which is an order-based industry with a job shop production environment. The shipyard production planning process was investigated to analyze the detailed process, variables and constraints of mid-term production planning. Backward and process-centric simulation methods were applied to the mid-term production planning process and an improved planning process, which considers the shipbuilding characteristics, was proposed. Based on the problem defined by applying backward process-centric simulation, a system which can conduct Discrete Event Simulation (DES) was developed. The developed mid-term planning system can be linked with the existing shipyard Advanced Planning System (APS). Verification of the system was performed with the actual shipyard mid-term production data for the four ships corresponding to a one-year period.

• Ocean Systems Engineering
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• v.2 no.1
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• pp.49-68
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• 2012

In this paper, an offshore process front end engineering design (FEED) method is systematically introduced and reviewed to enable efficient offshore oil and gas production plant engineering. An integrated process engineering environment is also presented for the topside systems of a liquefied natural gas floating production, storage, and offloading (LNG FPSO) unit, based on the concepts and procedures for the process FEED of general offshore production plants. Various activities of the general process FEED scheme are first summarized, and then the offshore process FEED method, which is applicable to all types of offshore oil and gas production plants, is presented. The integrated process engineering environment is presented according to the aforementioned FEED method. Finally, the offshore process FEED method is applied to the topside systems of an LNG FPSO in order to verify the validity and applicability of the FEED method.

• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.111-122
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• 2012

Capacity planning plays an important role not only for master production plan but also for facility or layout design in shipbuilding. Product work breakdown structure, attributes of production resources, and production method or process data are associated in order to make the discrete event simulation model of shipyard layout plan. The production amount of each process and the process time is assumed to be stochastic. Based on the stochastic discrete event simulation model, the production capacity of each facility in shipyard is estimated. The stochastic model of product arrival time, process time and transferring time is introduced for each process. Also, the production capacity is estimated for the assumed master production schedule.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.329-347
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• 2019

The shipbuilding industry is characterized by order production, and various processes are performed simultaneously in the construction of ships. Therefore, effective management of the production process and productivity improvement form important key factors in the industry. For decades, researchers and process managers have attempted to improve processes by using business process analysis (BPA). However, conventional BPA is time-consuming, expensive, and mainly based on subjective results generated by employees, which may not always correspond to the actual conditions. This paper proposes a method to improve the production process of offshore plant modules by analysing the process mining data obtained from the shipbuilding industry. Process mining uses information accumulated from the system-provided event logs to generate a process model and determine the values hidden within the process. The discovered process is visualized as a process model. Subsequently, alternatives are proposed by brainstorming problems (such as bottlenecks or idle time) in the process. The results of this study can aid in productivity improvement (idle time or bottleneck reduction in the production process) in conjunction with a six-sigma technique or ERP system. In future, it is necessary to study the standardization of the module production processes and development of the process monitoring system.

• Proceedings of the Safety Management and Science Conference
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• 2007.11a
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• pp.9-19
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• 2007

This study deals with the improvement of production process on a flow production system with the consideration of six sigma. We analyze the production process and survey the important factors of improvement of productivity. Using a six sigma, we find strategic point and suggest a reformation of production process. We applied a simulation technique to simulate the production line proposed by the result of the Six sigma. With the result of the simulation, this study analyzes the propriety of production line and proposes the alternatives of new production process.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1993.10a
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• pp.175-184
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• 1993

Modeling the production process is a necessary and essential aspect of the production planning. This paper introduces a theoretical model of the multi-stage production process. A multi-stage production process is regarded as a network of interrelated production activities which use system exogenous inputs of goods in production and the intermediate products transfers between activities to produce final products. Our model is characterized by (1) a few of the production-related assumptions and (2) two types of elements "goods and activities" that are represented in terms of the network terminology. This model is different from the another multi-stage production models, so-called production network models in relation to the production-theoretical concept. It is not based on the concept of the production correspondence and the activity production functions, but the technology model of Koopmans. Koopmans.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.20-32
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• 1995

This paper is concerned with the economic selection of both the lower limit and the process mean for a continuous production process. Consider a production process where items are produced continuously. All of the items are subject to acceptance inspection. The items for which the measured values of the quality characteristic are larger than the lower limit are accepted, and those smaller than the lower limit are rejected and excluded from shipment. The process mean may be set higher to reduce the costs incurred by imperfect quality. Using a higher process mean, however, results in a higher production cost when production cost is an increasing function of the quality characteristic. Assuming that the quality characteristic is normally distributed with known variability, cost models are constructed which involve production cost, cost incurred by imperfect quality, rejection cost, and inspection cost. Methods of finding optimal values of the lower limit and the process mean are presented and numerical examples are given.

• IE interfaces
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• v.21 no.4
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• pp.418-434
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• 2008

This paper studied issues in decision making on the production control policy of a cathode plate manufacturing process in zinc refining plant. The present production system has a long lead time from raw materials (aluminum plate) to products (cathode plate) due to many WIP inventories. Because WIP inventories are stocked at each process and moved from one place to another frequently, they are the main cause of inefficiency in the process. In this paper, to solve this problem, several production control policies have been identified and studied. Several simulation models are used to compare the performances of these production control policies. The output lead time and WIP (Work In Process) of real production system are compared with those of simulation models. PUSH, CONWIP, DBR, KANBAN and CONWIP-DBR models have been used to simulate and review the optimized production control policy that achieves the target output quantities with decreased lead time and WIP. The simulation results of each production control policy show that CONWIP and CONWIP-DBR models are the good production control policy under the present production system. Especially in present production system, CONWIP with one parameter is easier control policy than CONWIP-DBR with two parameters. Therefore CONWIP has been selected as the best optimum production control policy. With CONWIP, lead time has been reduced by 97% (from 6,653 to 187 minute) and WIP has been reduced from 1,488 to 53, compared to the present system.