The performance of machine and equipments is one of the critical factors on pursuing World Class Overall Equipment Effectiveness (OEE) target, especially for large-sclaed manufacturing companies. They have to determine the most appropriate maintenance strategy; thus, the machines achieve high availability. The company might put some sensors, which it functioned to collect realtime data and information. The data will be processed through data analytics algorithm to identify machine’s characteristics, estimate failures, and determine strategic decision related to maintenance schedule in shorter time with higher accuracy. This study proposes a model for implementing data analytics on designing maintenance strategy in a continuous manufacturing through three main stages. Firstly, a model is developed. Then, collect and analyse the data, also model validation as well. At the last stage, finding the right maintenance strategy for its equipments. There are two softwares that are being used, such as MatLab and Phyton. Based on the analysis, it can be concluded that the conceptual model is valid, and the data analytics output has been analysed. This study also finds a condition-based maintenance schedule, which has been expected to be implemented for some other companies with similar characteristics.