Browse > Article
http://dx.doi.org/10.15207/JKCS.2018.9.5.179

An Analysis of the Characteristics of Companies introducing Smart Factory System Using Data Mining Technique  

Oh, Jeong-yoon (Dept. Management Information Systems, Chungbuk National University)
Choi, Sang-hyun (Dept. Management Information Systems, Chungbuk National University)
Publication Information
Journal of the Korea Convergence Society / v.9, no.5, 2018 , pp. 179-189 More about this Journal
Abstract
Currently, research on smart factories is steadily being carried out in terms of implementation strategies and considerations in construction. Various studies have not been conducted on companies that introduced smart factories. This study conducted a questionnaire survey for SMEs applying the basic stage of smart factory. And the cluster analysis was conducted to examine the characteristics of the company. In addition, we conducted Decision Tree and Naive Bay to examine how the characteristics of a company are derived and compare the results. As a result of the cluster analysis, it was confirmed that the group was divided into the high satisfaction group and the low satisfaction group. The decision tree and the Naive Bay analysis showed that the higher satisfaction group has high productivity.
Keywords
Smart Factory; Cluster; Decision Tree; Naive Bayes; Questionnaire;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
1 Y. J. JO. (2015). Possibility of smart factory as a plan for advanced domestic manufacturing. KDB Bank.2015.8.21 https://rd.kdb.co.kr/er/wcms.do?actionId=ADERERERWCE03&contentPage=/er/er/er/ERER27I00012_01RS.jsp& menuId=ERERER0013&cid=19772
2 C. W. Lee & Y. B. Jang. (2017). Leading the Fourth Industrial Revolution by Building 30,000 Smart Factories by 2025. KOSF(Korea Smart Factory Foundation). https://www.smart-factory.kr/datum/popup/datumDetail .do?dboardNo=121
3 M. K. Jung. (2016). Smart factory, check performance and revisit the past two years. KOSF(Korea Smart Factory Foundation). http://www.smart-factory.kr/Service/Notice/appl/Report View.asp
4 H. S. Lee. (2017). 4th Industrial Revolution Leading Smart Factory, 5,000 Spreads by Year. KOSF(Korea Smart Factory Foundation). http://www.smart-factory.kr/ServiceNotice/appl/ReportView.asp
5 J. S. Park & K. S. Kang. (2017). Strategies of smart factory building and Application of small & medium-sized manufacturing enterprises. Korea Safety Management & Science, 19(1), 227-236.   DOI
6 J. Hoh & C. Y. Jung (2017). Convergence-based Smart Factory Security Threats and Response Trends, Journal of the Korea Convergence Society, 8(11), 29-35.   DOI
7 J. P. Park (2017). Analysis on Success Cases of Smart Factory in Korea: Leveraging from Large, Medium, and Small Size Enterprises, The Korea Society of Digital Policy and Management, 15(5), 107-115.
8 M. S. Lim (2016). (The)Convergence between Manufacturing and ICT : The Exploring Strategies for Manufacturing version 3.0 in Korea, The Korea Society of Digital Policy and Management, 14(3), 219-226.
9 T. S. Jeong. (2016). The Suggestion for Successful Factory Converging Automation by Reviewing Smart Factories in German, Journal of the Korea Convergence Society, 7(1), 189-196.   DOI
10 C. S. Seo (2016). Study on Small Business Increased Productivity via Smart Factory. Master dissertation. Busan National University, Busan.
11 C. Louis. (2015). Data Analytics, Mobile Technologies And Robotics Defining The Future Of Digital Factories, Forbes. https://www.forbes.com/sites/louiscolumbus/2015/02/15/big-data-analytics-mobile-technologies-and-robotics-defining-the-future-of-digital-factories/#137e08fe7e9d
12 S. R. Jo. (2015). SmartFactory. June, Industry Soulution http://www.google.co.kr/url?sa=t&rct=j&q=&esrc=s&so urce=web&cd=2&ved=0ahUKEwjz3eit1sjZAhVHlZQKHa pKBOoQFggsMAE&url=http%3A%2F%2Fcfile10.uf.tisto ry.com%2Fattach%2F27488035557A86CB2C79A7&usg= AOvVaw3vNkBlgv3Mff51GivZs_4K
13 Nguyen, T. D., T. B. Ho & H. Shimodaira. (2001). A Scalable Algorithm for Rule Post- pruning of Large Decision Trees. 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 467-476.
14 Deloitte. (2015). The US recovers the top position in the global manufacturing competitiveness in 2020 ,Deloitte Korea. https://www2.deloitte.com/kr/ko/footerlinks/pressrelease spage/2015/press-release-20151211.html
15 Shmueli, G., R. P. Nitin & C. B. Peter. (2012). Data Mining for Business Intelligence. Seoul : E&B Plus.
16 J. G. Jo & S. H. Choi (2016). Firm's Market Value Trends after Information Security Management System(ISMS) Certification acquisition, Journal of the Korea Convergence Society, 7(6), 237-247.   DOI
17 He, Q. (1999). A Review of Clustering Algorithms as Applied in IR. Technical Report UIUCLIS-1999/6+IRG, University of Illinois at Urbana-Champaing.
18 J. S. Bae. (2014). A Study on Priority of Determinants of Career Decision Level in High School Students Based on Decision Tree Analysis. The Korean Society for the Study fo Career Education, 28(4), 79-105.
19 N. Y. Park, J. I. Kim & Y. G. Jung. (2013). Breast Cancer Diagnosis using Naive Bayes Analysis Techniques. The society of Service Science, 3(1), 87-93.
20 Kass, G. (1980). An exploratory technique for investigation large quantities of categorical data. Applied Statistics, 29, 119-129.   DOI
21 Breiman, L., J. H. Friedman, R. A. Olshen, & C. J. Stone. (1984). Classification and regression tress, Wadsworth.
22 Loh, W. & Y. Shih.(1997). Split selection methods for classification trees. Statistica Sinica, 7, 815-840.
23 Quinlan, J. R.(1993). C4.5 Programs for machine learning, Morgan Kaufmann, San Mateo.
24 K. Larsen. (2005). Generalized Naive Bayes Classifiers. SIGKDD Explorations, 7(1), 76-81.
25 H. R. Jeong, H. H. Kim, S. M. Park, K. H. Kim & I. S. Yun. (2017) Prediction of Severities of Rental Car Traffic Accidents using Naive Bayes Big Data Classifier. Korea Inst. Intelligenct Transportation System, 2017(4), 411-414.