• Title/Summary/Keyword: Quality Characteristics of Smart Factory

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Effects of Smart Factory Quality Characteristics & Innovative Activities on Business Performance : Mediating Effect of Using Smart Factory

  • CHO, Ik-Jun;KIM, Jin-Kwon;AHN, Tony-DongHui;YANG, Hoe-Chang
    • The Journal of Economics, Marketing and Management
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    • v.8 no.3
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    • pp.23-36
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    • 2020
  • Purpose: The purpose of this study is to identify the strategic direction of organizations and their employees to efficiently utilize smart factories and enhance business performance among Korean manufacturing companies. Research design, data, and methodology: We derived a structured research model to check the mediated effect of utilization of smart factory between the characteristics of smart factory and the innovation activities. Results: Quality characteristics of smart factory and Innovation activities were all found to have a statistically significant effect on utilization of smart factory, utilization of smart factory was found to have a statistically significant effect on the business performance. And it has been shown that the utilization of smart factory is partially mediated relative to the quality characteristics of smart factory and business performance and relative to innovation activities and business performance. Conclusions: Smart factory builders can reflect the areas that affect utilization of the smart factory in their strategies by considering the quality characteristics of the smart factory and innovation Activities. Therefore, smart factory builders can identify the quality characteristics of smart factory and reflect them in the process and analyze active utilize measures through the innovative activities of the employees of the organization, thereby influencing business performance.

Effects of Smart Factory Quality Characteristics and Dynamic Capabilities on Business Performance: Mediating Effect of Recognition Response

  • CHO, Ik-Jun;KIM, Jin-Kwon;YANG, Hoe-Chang;AHN, Tony-DongHui
    • The Journal of Industrial Distribution & Business
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    • v.11 no.12
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    • pp.17-28
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    • 2020
  • Purpose: The purpose of this study is to confirm the strategic direction of the firm regarding the capabilities of the organization and its employees in order to increase the utilization and business performance of employees by that introduce smart factories in the domestic manufacturing industry. Research design, data, and methodology: This study derived a structured research model to confirm the mediating effect of recognition responses between the quality characteristics of smart factories and dynamic capabilities. For the analysis, a total of 143 valid questionnaires were used for 200 companies that introduced smart factories from domestic SME's. Results: Quality Characteristics of Smart Factory and Dynamic Capabilities had a statistically significant effect on Usefulness. Recognition Response had a statistically mediating on the relationship between quality characteristics of smart factory and business performance. Recognition Response had a statistically significant effect on business performance. Conclusions: It suggests that firms introducing smart factory reflect them in their empowerment strategic because the recognition responses of its employees differ according to the quality characteristics and dynamic capabilities of smart factories. It also means that the information derived from the smart factory system is useful and effective to business performance and employees.

Development of Smart Factory Diagnostic Model Reflecting Manufacturing Characteristics and Customized Application of Small and Medium Enterprises (제조업 특성을 반영한 스마트공장 진단모델 개발 및 중소기업 맞춤형 적용사례)

  • Kim, Hyun-Deuk;Kim, Dong-Min;Lee, Kyung-Geun;Yoon, Je-Whan;Youm, Sekyoung
    • Journal of Korean Society of Industrial and Systems Engineering
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    • v.42 no.3
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    • pp.25-38
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    • 2019
  • This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.

Quality Strategy for Building a Smart Factory in the Fourth Industrial Revolution (4차 산업혁명시대의 스마트 팩토리 구축을 위한 품질전략)

  • Chong, Hye Ran;Bae, Kyoung Han;Lee, Min Koo;Kwon, Hyuck Moo;Hong, Sung Hoon
    • Journal of Korean Society for Quality Management
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    • v.48 no.1
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    • pp.87-105
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    • 2020
  • Purpose: This paper aims to propose a practical strategy for smart factories and a step-by-step quality strategy according to the maturity of smart factory construction. Methods: The characteristics, compositional requirements, and diagnosis system are examined for smart factories through theoretical considerations. Several cases of implementing smart factory are studied considering the company maturity level from the aspect of the smartness concept. And specific quality techniques and innovation activities are carefully reviewed. Results: The maturity level of smart factory was classified into five phases: 1) ICT non-application, 2) basic, 3) intermediate 1, 4) intermediate 2, 5) advanced level. A five-step quality strategy was established on the basis of case studies; identify, measure, analyze, optimize, and customize. Some quality techniques are introduced for step-by-step implementation of quality strategies. Conclusion: To build a successful smart factory, it is necessary to establish a quality strategy that suits the culture and size of the company. The quality management strategy proposed in this paper is expected to contribute to the establishment of appropriate strategies for the size and purpose of the company.

A Study on the Limits of Manufacturing Innovation and Policy Direction of SMEs in the 4th Industrial Revolution : Focusing on the Limitations and Examples of Pohang SME's Smart Factory Introduction (4차 산업혁명시대 지역 중소기업의 제조혁신 한계와 스마트공장 정책 방향성 연구: 포항지역 중소기업의 스마트공장 조사를 중심으로)

  • Kim, Eunyoung;Park, Munsu
    • Journal of Science and Technology Studies
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    • v.18 no.2
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    • pp.269-306
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    • 2018
  • Through this study, it is aimed to derive the policy direction considering the characteristics of the present Smart Factory, the industrial condition of Pohang area, and the promotion field. Secondly, the questionnaire data of the regional enterprises will prepare for the improvement of the industrial structure and the implications for efficiency, and preparation for regional preparation and industrial changes in preparation for the next generation of production revolution. The construction of Smart Factory in Pohang can be divided into two major directions. First, it is analyzed that smart factory pilot projects are highly needed, focusing on competitive medical precision manufacturing field among the SMEs in the region, primary metal and nonmetal manufacturing industries, and other machinery fields. In addition, local SMEs are willing to introduce smart factories for reasons of quality improvement and cost reduction, and it is confirmed that they will actively promote employee training and expertise if they can upgrade continuously.

A Study on Improving the Quality of Clothing Companies: Focusing on Kutesmart using Quality 4.0 Matrix (의류기업의 품질 개선 방안 연구: Quality 4.0 매트릭스를 활용한 쿠트스마트 사례)

  • Jang, Jin Myeong;Seo, Seung Ju;Lee, Yuna;Kim, Youn Sung
    • Journal of Korean Society for Quality Management
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    • v.47 no.1
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    • pp.199-211
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    • 2019
  • Purpose: The concept of quality is changing in the quality 4.0 era with the fourth industrial revolution in the world. This research aims to understand the characteristics of well-adapted companies against the quality 4.0 era and to improve the quality of clothing companies. Methods: We analyzed companies that responded well to the quality 4.0 era, especially Kutesmart using Quality 4.0 Matrix. We focused on the service process of Kutesmart and we suggested modified service process to improve quality. We also interviewed an expert to verify this process is valid. Results: We found that two types are classified of well-adapted companies against the quality 4.0 era. Especially, Kutesmart has built a smart factory and introduced new technologies like 3D scanner and big data analysis. However, Kutesmart has a weakness in post-purchase process like other clothing companies. Kutesmart could solve this problem with modular production method for damaged part of customer. Conclusion: This research can be used for better understanding of the characteristics of well-adapted companies against the quality 4.0 era and service process of Kutesmart that is custom clothing company for providing information for benchmarking in this industry. This study suggests that further empirical researches on the costs and the efficiencies of applying the new technologies are necessary.

Comparison of Characteristics for Establishing Quality Standards of Modular Buildings for Temporary Classrooms (임시교실용 모듈러 건축물의 품질기준 마련을 위한 특성비교)

  • Lee, Jong Sung;Park, Jae-Woong;Lim, Gun-Su;Kim, Jong;Han, Min-Cheol;Han, Cheon-Goo
    • Proceedings of the Korean Institute of Building Construction Conference
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    • 2023.05a
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    • pp.83-84
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    • 2023
  • Wall structure smart modular is a building construction method where modules are manufactured in a factory and assembled on-site. This method is gaining popularity in the construction industry as it reduces construction time and mitigates risks such as material supply and labor costs. Wall structure smart modular is necessary as it provides comfortable temporary classroom space during renovation and remodeling of aging school buildings. The structure and characteristics of each type of temporary classroom modular were compared, and wall structure modular showed superior performance in terms of height and weight competitiveness compared to mixed structures. With these advantages, wall structure modular can ensure economic efficiency and recyclability as a temporary classroom. In the future, we aim to compare and analyze the standards such as inter-floor noise and heat transfer coefficient for wall structure and mixed structures.

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Development of Controlled Gas Nitriding Furnace : Controlled Gas Nitriding Technology and Present Situation in Korea (질화포텐셜 제어 가스질화로 개발(I) : 제어질화 및 국내 기술 현황)

  • Won-Beom Lee;Sukwon Son
    • Journal of the Korean Society for Heat Treatment
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    • v.36 no.1
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    • pp.40-46
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    • 2023
  • Controlled nitriding is a technology that controls the nitriding potential based on the gas partial pressure received through an IOT-based sensor. Controlled nitriding is characterized by easy control of the phase of the nitride compound and excellent reproducibility of quality. In particular, it is possible to form a compound layer of excellent quality with fewer pores on the surface. However, despite these advantages, the application of controlled nitriding still needs to be improved in Korea. This paper explains the characteristics of controlled nitriding and describes the future direction and the problems of controlled nitriding in Korea.

Connected-IPs: A Novel Connected Industrial Parks Architecture for Building Smart Factory in Korea (연결형 산업단지(CIPs): 한국의 스마트공장 구축을 위한 연결형 산업단지 아키텍처)

  • Yang, Young-Chuel;Jeong, Jongpil
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.18 no.4
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    • pp.131-142
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    • 2018
  • In Korea, for the past 50 years, industrial parks have played an important role in economic growth as a cluster of national key industries. However, due to various problems of these old industrial parks, they are weakening competitiveness. It is necessary to be converted into a model for the management and fostering of high-tech industrial complex park by classifying them into development plans, management plans, and support plans according to types and characteristics of industrial parks. For this purpose, we propose CIPs (Connected-Industrial parks) using new technologies such as Cloud Computing, RFID, WSN, CPS, and Big Data analysis based on IoT. It is a hub that supports various services in transportation, warehousing and manufacturing fields while possessing and operating physical assets as concept. each CIP (Connected-Industrial park) is connected and expanded Through such CIPs, network-type collaborative manufacturing and intelligent logistics innovation enables cost reduction, delivery shortening, quality improvement.

Deep Learning-based Rheometer Quality Inspection Model Using Temporal and Spatial Characteristics

  • Jaehyun Park;Yonghun Jang;Bok-Dong Lee;Myung-Sub Lee
    • Journal of the Korea Society of Computer and Information
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    • v.28 no.11
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    • pp.43-52
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    • 2023
  • Rubber produced by rubber companies is subjected to quality suitability inspection through rheometer test, followed by secondary processing for automobile parts. However, rheometer test is being conducted by humans and has the disadvantage of being very dependent on experts. In order to solve this problem, this paper proposes a deep learning-based rheometer quality inspection system. The proposed system combines LSTM(Long Short-Term Memory) and CNN(Convolutional Neural Network) to take advantage of temporal and spatial characteristics from the rheometer. Next, combination materials of each rubber was used as an auxiliary input to enable quality conformity inspection of various rubber products in one model. The proposed method examined its performance with 30,000 validation datasets. As a result, an F1-score of 0.9940 was achieved on average, and its excellence was proved.