On Applicability of Big Data Analytics in the Closed Loop Product Lifecycle: Integration of CRISP DM Standard
The product use data can have an important role in closed loop product lifecycle management (CL PLM) where information feedbacks from the use data can contribute to improve the product design and performance. The product usage data can nowadays be collected easier than before, with the aid of sensors and technologies embedded in products. However, the collected data can have complex characteristics. They come from various sources, have different formats and high volume. In order to improve the product lifecycle processes with these data, discussing the use of data analysis in the product lifecycle is necessary. Analyzing the data with such characteristics has been also considered in the context of big data analytics. In this paper an approach for standardization of the process of usage data analysis based on a standard called Cross Industry Standard Process for Data Mining (CRISP DM) is introduced and its potential integration in CL PLM is investigated. The reference steps of analyzing usage data are identified. They cover the processes between data generation until feeding back the knowledge of use to the product design phase.
PLM