Abstract :
Optimization of the process of product lifecycle management is an increasingly important objective for
manufacturing enterprises to improve their sustainable competitive advantage. Originally, this approach
was developed to integrate the business processes of an organization and more effectively manage and
utilize the data generated during lifecycle studies. With emerging technologies, product embedded information
devices such as radio frequency identification tags and smart sensors are widely used to
improve the efficiency of enterprisesʹ routine management on an operational level. Manufacturing enterprises
need a more advanced analysis approach to develop a solution on a strategic level from using
such lifecycle Big Data. However, the application of Big Data in lifecycle faces several challenges, such as
the lack of reliable data and valuable knowledge that can be employed to support the optimized
decision-making of product lifecycle management. In this paper, a framework for Big Data driven product
lifecycle management was proposed to address these challenges. Within the proposed framework, the
availability and accessibility of data and knowledge related to lifecycle can be achieved. A case study was
presented to demonstrate the proof-of-concept of the proposed framework. The results showed that the
proposed framework was feasible to be adopted in industry, and can provide an overall solution for
optimizing the decision-making processes in different phases of the whole lifecycle. The key findings and
insights from the case study were summarized as managerial implications, which can guide manufacturers
to ensure improvements in energy saving and fault diagnosis related decisions in the whole
lifecycle.
Author/Authors :
Yingfeng Zhang , Shan Ren , Yang Liu , Tomohiko Sakao , Donald Huisingh
