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Background/Objectives: This study doesn’t introduce the process of manufacturing system in smart factory of company but conducts a study on smart manufacturing’s ultimate goal which is to systematically view and identify the success factors through production system introduction and utilization. Methods/Statistical Analysis: In this study, BSC model was chosen to measure the performance of the enterprise on smart factory and it was modified to fit the purpose of this study. Factors that influences smart factory introduction are user engagement, goal clarification, and capacity of development enterprises. Three specific factors that were found as ERP success factors were drawn from related prior studies. We found out if these influence factors leads to successful introduction and if it would bring positive impact by re-incorporating the study of structural practices and commenting and creating important variables. Findings: We want to empirically identify the results after introducing smart factory through BSC model. First, user participation, introduction motive cost factor, introduction motive relationship factor, development industry’s capacity were four independent variables and using all four at once, PLS analyze result shows all four factors had positive influence on smart factory enterprise introduction. Secondly, when we analyze four categorized factors, user participation, introduction motive relationship factor, development industry’s capacity were important factors to increase learning outcome and process factor. Improvements/Applications: This study’s result provides practical implication of main factors that must be managed to ensure successful introduction to companies who wish to introduce the smart factory.

Background/Objectives: The present study analyzes an improvement plan for bicycle roads by using the public data of Seoul and proposes its implications. Methods/Statistical Analysis: In this study, an analysis was performed by utilizing a big data analysis tool, R. The present study, after defining the deducted problem phenomenon, conducted a process of planning which included the necessary information for finding an alternative for resolution and a data and application analysis technique. The data needed in the analysis was obtained from the Seoul open data square and the public data portal. The study attempted analysis and visualization by using the open source tool of R for analyzing the data, and conducted a process of interpretation. Findings: In this study attempts to find an alternative that can prevent bicycle safety exercises. The study presents the problems of current bicycle roads and their improvement plan by big data analysis which includes the relationship between the installation rate of bicycle roads and the bicycle accident rate, the location of bicycle roads, the location of bicycle amenities, and population data. In this study attempts to find an alternative that can prevent bicycle safety exercises. To achieve this, the study presents the problems of current bicycle roads and their improvement plan by big data analysis which includes the relationship between the installation rate of bicycle roads and the bicycle accident rate, the location of bicycle roads, the location of bicycle amenities, bicycle satisfaction, bicycle use statistics, and population data. The generalization of bicycle roads to regular residences implies a construction by considering the connectivity and stability of bicycle roads, but atypical infrastructure construction has played a part in increased damages resulting from the ignorance of bicycle users to traffic safety regulations. The perception of bicycle users must change and the building of bicycle roads must be based on people centered transportation infrastructure. Application/Improvements: The results of this study can be used as part of improvement of bicycle road accidents by central government and local governments. The results seem to be applied in policy, such as building bike paths centered around pedestrians.