• Journal of Korean Society of Industrial and Systems Engineering
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• v.37 no.2
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• pp.55-61
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• 2014

Recently, the manufacturing system is being changed in a mass customization and small quantity batch production. MES is a powerful production management tool supporting production optimization from the process initiation to the final shipment. It is a production management system which plans and executes based on the production data in the shop floor. This study deployed the utilization of production data and web HMI system to process real-time production data through the collection with the shop floor. The developed system was applied to the equipment operating time and other production data could be processed with the real-time. The proposed system and web HMI can be applied for various production systems by using different logic.

• 한국디지털정책학회:학술대회논문집
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• 2004.11a
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• pp.39-53
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• 2004

Data production systems are generally very large, distributed and complex systems used for creating advanced (mainly statistical) reports. Typically, data is gathered periodically and then subsequently aggregated and separated during numerous production steps. These production steps are arranged in a specific sequence (workflow or production chain), and can be located worldwide. Today, a need for improving and automating methods of supervision for data production systems has been recognized. Supervision in this context entails planning, monitoring and controlling data production. Two significant approaches are introduced here for improving this supervision. The first is a 'closely-coupledd' approach (meaning direct communication between production jobs and supervisory tool, informing the supervisory tod immediately about delays in production) - based upon traditional production planning methods typically used for manufacturing (goods) and adopted for working with data production. The second is a 'loosely-coupled' approach (meaning no direct communication between supervisory tool and production jobs is used) - having its origins in proven traditional project management. The supervisory tool just enquires continuously the progress of production. In both cases, dates, costs, resources, and system health information is made available to management. production operators and administrators to support a timely and smooth production of periodic data. Both approaches are theoretically described and compared. The main finding is that, both are useful, but in different cases. The main advantages of the closely coupled approach are the large production optimisation potential and a production overview in form of a job execution plan, whereas the loosely coupled method mainly supports unhindered job execution and offers a sophisticated production overview in form of a milestone schedule. Ideas for further research include investigation of other potential approaches and theoretical and practical comparison.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.615-616
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• 2006

Many manufacturing companies manage their production-related data for quality management and production management. Nevertheless, production related-data should be closely related to each other Stored data is mainly used to monitor their process and products' error. In this paper, we provide an enterprise-wide production data model for decision support system and product automation. Process data, quality-related data, and test data are integrated to identify the process inter or intra dependency, the yield forecasting, and the trend of process status. In addition, it helps the manufacturing decision support system to decide critical manufacturing problems.

• Journal of Information Technology Services
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• v.21 no.2
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• pp.97-107
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• 2022

Recently, interest in smart factories is increasing. Investments to improve intelligence/automation are also being made continuously in manufacturing plants. Facility automation based on sensor data collection is now essential. In addition, we are operating our factories based on data generated in all areas of production, including production management, facility operation, and quality management, and an integrated standard information system. When producing LCD polarizer products, it is most important to link trace information between data generated by individual production processes. All systems involved in production must ensure that there is no data loss and data integrity is ensured. The large-capacity data collected from individual systems is composed of key values linked to each other. A real-time quality analysis processing system based on connected integrated system data is required. In this study, large-capacity data collection, storage, integration and loss prevention methods were presented for optimization of LCD polarizer production. The identification Risk model of inspection products can be added, and the applicable product model is designed to be continuously expanded. A quality inspection and analysis system that maximizes the yield rate was designed by using the final inspection image of the product using big data technology. In the case of products that are predefined as analysable products, it is designed to be verified with the big data knn analysis model, and individual analysis results are continuously applied to the actual production site to operate in a virtuous cycle structure. Production Optimization was performed by applying it to the currently produced LCD polarizer production line.

• Journal of the Korea Safety Management & Science
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• v.8 no.6
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• pp.79-90
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• 2006

Recently the construction of the ERP(Enterprise Resource Planning) system becomes accomplished actively from the many enterprises. But the many problems occur in acquisition of production data which is a fundamental data of system. Also to delay the acquisition of the production result is fatal in the efficient business operation. The construction of the POP(Point of Production) system which acquires production data at real time is become accomplished widely, In the POP system it is most important to acquire the production data which is accurate. But the many enterprises drop the competitive power with acquisition of the data which could not be trusted. In this paper, we analyze these causes and present the method which it can improve the reliability of production data. Also we introduce a real application case.

• Korean Journal of Construction Engineering and Management
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• v.5 no.1 s.17
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• pp.114-123
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• 2004

Contract time determination for highway construction projects has never been easy despite considerable research efforts from academia as well as industry. High variations in crew production rates are considered one of the main barriers to accurate contract time determination. This paper presents a methodology for collecting field information on crew production rates which will help to enhance the accuracy of contract time determination for highway bridge construction. Based on a standard data collection tool developed, data on field crew production rates was collected from 14 on going projects in Texas, USA, over the past two years. The production rates based on the data collected were considered by industry practitioners to be more realistic and practical than those available to the current practices. As more data becomes available, key drivers influencing production rates could be identified and provide site personnel with a means to better plan and control production in a project specific context.

• 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.

• IE interfaces
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• v.15 no.2
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• pp.159-171
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• 2002

Data Warehouses integrate data from multiple heterogeneous information sources and transform them into a multidimensional representation for decision support applications. Data warehousing has emerged as one of the most powerful tools in delivering information to users. Most previous researches have focused on marketing, customer service, financing, and insurance industry. Further, relatively less research has been done on data warehouse systems in the complex manufacturing industry such as ship production, which is characterized complex product structures and production processes. In the ship production, data warehouse systems is a requisite for effective cost analysis because collecting and analysis of diverse and large of cost-related(material/production cost, productivity) data in its operational systems, was becoming increasingly cumbersome and time consuming. This paper proposes architecture of the data warehouse systems to support cost analysis in the ship production. Also, in order to illustrate the usefulness of the proposed architecture, the prototype system is designed and implemented with the object of the enterprise of producing a large-scale ship.

• Information Systems Review
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• v.2 no.1
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• pp.151-160
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• 2000

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.698-701
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• 2004

In many literatures, data mining has been used as a utilization of data warehouse and data collection. The biggest utilizations of data mining are for marketing and researches. This is solely because of the data available for this field is usually in large amount. The usability of the data mining is expandable also to the production process. While the object of research of the data mining in marketing is the customers and products, data mining in the production field is object to the so called 4MlE, man, machine, materials, method (recipe) and environment. All of the elements are important to the production process which determines the quality of the product. Because the final aim of the data mining in production field is the quality of the production, this data mining is commonly recognized as quality data mining. As the variables researched in quality data mining can be hundreds or more, it could take a long time to reveal the information from the data warehouse. Feature selection methodology is proposed to help the research take the best performance in a relatively short time. The usage of available simple statistical tools in this method can help the speed of the mining.