• Title/Summary/Keyword: production data

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A Study on major nations and Koea's FTA policy (주요국의 통상정책과 한국의 FTA 정책방향에 관한 연구)

  • Kim Jongkwon
    • Proceedings of the Safety Management and Science Conference
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    • 2004.11a
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    • pp.415-438
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    • 2004
  • This dissertation is assumed to continuously occur adjustment cost on present investment. So, I derived from time-nonseparable production-based CAPM and tested the performance of model through data. I also compared time-nonseparable production-based CAPM with time-separable production-based CAPM and CCAPM, CAPM through testifying the performance of model. At the part of applied application, I estimated time-nonseparable PCAPM-betas. The data of Korea consists of 320 listed companies on Korea Stock Exchange (KOSPI) from first quarter 1987 to first quarter 2002. This data also is categorized by scale and industries. Additionally, I estimated time-nonseparable PCAPM-betas through 500 listed companies of New York Stock Exchange (NYSE) from first quarter 1973 to first quarter 2002. I observed the statistical significance of 230 firms by 320 companies in Korea. After that, I compared time-nonseparable PCAPM-betas by firms with time-separable production-based CAPM-betas and CCAPM-betas, CAPM-betas through individual firms. At empirical test, I found that estimated parameter of adjustment cost on time-nonseparable production-based CAPM by scale and industries in Korea had positive value and statistical significance, Moreover, this approach proved to resolve the underestimation of adjustment cost on time-separable production-based CAPM by scale and industries. I also found that the time-nonseparable PCAPM performed better than time-separable production-based CAPM and CCAPM, CAPM. The result from U.S data proved to have similarity to that of Korea. Specifically, I found that time-nonseparable PCAPM-betas by firms performed better than CAPM-betas on individual firms in Korea.

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Analysis on the Production Efficiency of Private Industrial Enterprises in 31 Provinces of China

  • GAO, Xin;KIM, Hyung-Ho;YANG, Jun-Won
    • The Journal of Economics, Marketing and Management
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    • v.9 no.3
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    • pp.11-21
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    • 2021
  • Purpose: The purpose of this study is to understand the status quo of production efficiency in private industrial enterprises above designated scale in China's 31 provinces (including municipalities directly under the central government, autonomous regions) (hereinafter referred to as China's 31 provinces). Research design, data and methodology: Find out the factors affecting the development of production efficiency in private industrial enterprises, using DEA, Data Envelopment Analysis and Malmquist index analysis, build the evaluation model of production efficiency in private industrial enterprises, and analyze the data of China's 31 provinces private industrial enterprises in 2015-2019. Results: The research results show that the production efficiency of private industrial enterprises in China is improving on the whole. Although the total factor productivity has decreased slightly, the overall efficiency and pure technical efficiency have increased significantly. Conclusions: The conclusion of this study can provide reference for Chinese private industrial enterprises to improve production efficiency and make development plan. The limitation of this paper lies in the fact that the private industrial enterprises in inefficient provinces have not been given specific improvement plans.

A Study on Production Prediction Model using a Energy Big Data based on Machine Learning (에너지 빅데이터를 활용한 머신러닝 기반의 생산 예측 모형 연구)

  • Kang, Mi-Young;Kim, Suk
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.10a
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    • pp.453-456
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    • 2022
  • The role of the power grid is to ensure stable power supply. It is necessary to take various measures to prepare for unstable situations without notice. After identifying the relationship between features through exploratory data analysis using weather data, a machine learning based energy production prediction model is modeled. In this study, the prediction reliability was increased by extracting the features that affect energy production prediction using principal component analysis and then applying it to the machine learning model. By using the proposed model to predict the production energy for a specific period and compare it with the actual production value at that time, the performance of the energy production prediction applying the principal component analysis was confirmed.

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Group Technology Cell Formation Using Production Data-based P-median Model

  • Won Yu Gyeong
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • 2003.05a
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    • pp.375-380
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    • 2003
  • This study is concerned with the machine part grouping m cellular manufacturing. To group machines into the set of machine cells and parts into the set of part families, new p-median model considering the production data such as the operation sequences and production volumes for parts is proposed. Unlike existing p-median models relying on the classical binary part-machine incidence matrix which does not reflect the real production factors which seriously impact on machine-part grouping, the proposed p-median model reflects the production factors by adopting the new similarity coefficient based on the production data-based part-machine incidence matrix of which each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. Computation test compares the proposed p median model favorably.

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Production Line Planning for Functional Sports Wear using Simulation Model (시뮬레이션을 이용한 특수 고기능 의류업체의 생산라인 설계에 관한 연구)

  • 최정욱
    • Journal of the Korean Society of Clothing and Textiles
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    • v.26 no.8
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    • pp.1205-1215
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    • 2002
  • The purpose of this study was to develop a production line using simulation method, which could improve work allocation, labor utility and productivity. Using simulation software AIM, a simulation model of functional sports wear assembly line was developed. A functional sports wear production factory were analysed to gather data necessary for this research. Factory layouts, production facilities, work time of each unit jobs were investigated. The data obtained were used as to build a base simulation model. Then, the base simulation model was verified using the obtained data, such as daily productivity. Using simulation method, low alternative production plans were suggested, which were to enhance productivity, and work efficiency and to reduce queue length and throughput time.

AI Smart Factory Model for Integrated Management of Packaging Container Production Process

  • Kim, Chigon;Park, Deawoo
    • International Journal of Internet, Broadcasting and Communication
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    • v.13 no.3
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    • pp.148-154
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    • 2021
  • We propose the AI Smart Factory Model for integrated management of production processes in this paper .It is an integrated platform system for the production of food packaging containers, consisting of a platform system for the main producer, one or more production partner platform systems, and one or more raw material partner platform systems while each subsystem of the three systems consists of an integrated storage server platform that can be expanded infinitely with flexible systems that can extend client PCs and main servers according to size and integrated management of overall raw materials and production-related information. The hardware collects production site information in real time by using various equipment such as PLCs, on-site PCs, barcode printers, and wireless APs at the production site. MES and e-SCM data are stored in the cloud database server to ensure security and high availability of data, and accumulated as big data. It was built based on the project focused on dissemination and diffusion of the smart factory construction, advancement, and easy maintenance system promoted by the Ministry of SMEs and Startups to enhance the competitiveness of small and medium-sized enterprises (SMEs) manufacturing sites while we plan to propose this model in the paper to state funding projects for SMEs.

A study for production simulation model generation system based on data model at a shipyard

  • Back, Myung-Gi;Lee, Dong-Kun;Shin, Jong-Gye;Woo, Jong-Hoon
    • International Journal of Naval Architecture and Ocean Engineering
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    • v.8 no.5
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    • pp.496-510
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    • 2016
  • Simulation technology is a type of shipbuilding product lifecycle management solution used to support production planning or decision-making. Normally, most shipbuilding processes are consisted of job shop production, and the modeling and simulation require professional skills and experience on shipbuilding. For these reasons, many shipbuilding companies have difficulties adapting simulation systems, regardless of the necessity for the technology. In this paper, the data model for shipyard production simulation model generation was defined by analyzing the iterative simulation modeling procedure. The shipyard production simulation data model defined in this study contains the information necessary for the conventional simulation modeling procedure and can serve as a basis for simulation model generation. The efficacy of the developed system was validated by applying it to the simulation model generation of the panel block production line. By implementing the initial simulation model generation process, which was performed in the past with a simulation modeler, the proposed system substantially reduced the modeling time. In addition, by reducing the difficulties posed by different modeler-dependent generation methods, the proposed system makes the standardization of the simulation model quality possible.

Suggestion of OSMU Content New Business Market through Development of Integrated Platforms for Software-oriented Tailored Costume Production (소프트웨어 중심의 주문 형 의상제작 융합플랫폼 개발을 통한 OSMU콘텐츠 뉴비즈니스 시장 창출 제안)

  • Jung, Minsoo
    • Journal of Korea Multimedia Society
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    • v.21 no.8
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    • pp.1021-1026
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    • 2018
  • 3D SCAN enables easy human body measurement via a digital method in the process of film costume production which used to be done manually. Software-oriented computer graphic, which integrates 3D SCAN data in the process of manual film costume production, can induce quick and diverse design outcomes. While, 3D PRINT, which integrates computer graphic data in the process of manual film costume production, can automate the process of special costume production using a digital method. Integration of 3D Scan + Computer Graphic + 3D Print using integrated platforms for tailored costume production as developed in this study allows significant reduction of costume production period and costs. It also allows efficient integration of costume production outcomes in various industries related with OSMU contents in particular. In other words, using it, we can create a new business market that integrates multiple areas of film content, drama content and game content.

Database design and production in ETLARS system (ETLARS 시스템에서의 데이터베이스 설계 및 생성에 관한 연구)

  • 김상도;박계숙;김희섭;홍기채;우동진;기민호
    • Journal of the Korean Society for information Management
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    • v.11 no.2
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    • pp.165-184
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    • 1994
  • DB production process includes a series of data analysis, DB design, source data input, data conversion and load, and DB operation and maintenance. Software development and operation technique for data input and DR production is similar to each other, even though each computer environment or the function of information retrieval system is a little different. The purpose of this paper is to introduce software for data input and DR production in ETLARS system, which is providing services to 6.000 users ( a s of Oct. 10, 1994) by Electronics and Telecommunications Research Institute, and to open know-how information in the production and operation of Hanguel DR.

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An Observation Supporting System for Predicting Citrus Fruit Production

  • Kang, Hee Joo;Yoo, Seung Tae;Yang, Young Jin
    • Agribusiness and Information Management
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    • v.7 no.1
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    • pp.1-9
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    • 2015
  • The purpose of this study is to develop a growth prediction model that can predict growth and development information influencing the production of citrus fruits: the growth model algorithm that can predict floral leaf ratio, number of fruit sets, fruit width, and overweight depending on the main period of growth and development with consideration of the applied weather factors. Every year, large scale of manpower was mobilized to investigate the production of outdoor-grown citrus fruits, but it was limited to recycling the data without an observation supporting system to systemize the database. This study intends to create a systematical database based on the basic data obtained through the observation supporting system in application of an algorithm according to the accumulated long term data and prepare a base for its continuous improvement and development. The importance of the observed data is increasingly recognized every year, and the citrus fruit observation supporting system is important for utilizing an effective policy and decision making according to various applications and analysis results through an interconnection and an integration of the investigated statistical data. The citrus fruit is a representative crop having a great ripple effect in Jeju agriculture. An early prediction of the growth and development information influencing the production of citrus fruits may be helpful for decision making in supply and demand control of agricultural products.