Using Bayesian network for analyzing cycle time to find key influenced factors and Constructing cycle time evolution table to predict cycle time in PCB industry with case studies.

• Main Authors: Chuang, Yin Yin, 莊茵茵
• Other Authors: Chien, Chen-Fu
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/qj262q

碩士 === 國立清華大學 === 工業工程與工程管理學系 === 105 === Competition in high tech industry forces the field to consider the ways to monitor the duration of cycle time and to keep produce efficiency within a budget. Particularly, Printed Circuit Board (PCB) industry is sensitive to this issue since their product characteristic is about small-volume and large-variety production. The product complexity of PCB is high, and its manufacturing processes of PCB go through thirty-six processes so how to monitor each station and to estimate the total cycle time are the issues we concerned. In this paper, we use data mining framework to build up a model for factors extraction and proposes a cycle time evolution table for estimation the cycle time. The Bayesian network extracts the main factors that significant influence on total cycle time and the cycle time evolution table estimate the total cycle time per piece of the board. This study cooperates with PCB company in Taiwan for empirical research. Proposed framework extracts critical stations which influence the total cycle time from huge data to validate the results. Furthermore, the engineers follow the results to find the indirect impact factor. On the other hand, the study also uses the cycle time evolution table on estimating cycle time. The results give decision makers a criterion on estimating cycle time and committing delivery day.