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손숙영(SookYoung Son),Bernardo Nugroho Yahya,송민석(Minseok Song),최상수(SangSu Choi),현정호(JeongHo Hyeon),이범기(BumGee Lee),장용(Yong Jang),성낙윤(NakYun Sung),신연식(YeonSik Sin) (사)한국CDE학회 2014 한국 CAD/CAM 학회 학술발표회 논문집 Vol.2014 No.2
As ‘big data’ becomes a key issue of the IT industry, there has been a lot of research work conducted for the unstructured data analysis in the service or IT industry. However, one of the main industries of Korea is manufacturing, and it usually produces structured rather than unstructured data. Therefore, developing a systematic analysis methodology for the structured data analysis is needed, especially for a manufacturing data analysis. Accurate manufacturing data analysis provides useful information for a competitive production capacity, which will influence on the productivity and competitiveness of a company. A Manufacturing Execution System (MES) is a computerized system used in the manufacturing industry. It extracts manufacturing related information automatically, which is useful for manufacturing manager to make better decisions. Manufacturing data analysis is able to be performed by using MES. Process Mining aims at extracting useful knowledge by analyzing event logs which are gathered from information systems (e.g. ERP, MES, and CRM). Manufacturing data analysis with process mining techniques can derive not only manufacturing process models, but also several performance measures related to processes, resources, and equipment. In this paper, we propose a framework for analyzing manufacturing processes using process mining techniques with the aim of deriving useful information from manufacturing transaction logs. Furthermore, we conducted a case study of the Samsung Electro-Mechanic (SEM) manufacturing process to prove the proposed framework.
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