A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. (10th February 2019) - Ren, Shan; Zhang, Yingfeng; Liu, Yang; Sakao, Tomohiko; Huisingh, Donald; Almeida, Cecilia M.V.B.
Abstract: Smart manufacturing has received increased attention from academia and industry in recent years, as it provides competitive advantage for manufacturing companies making industry more efficient and sustainable. As one of the most important technologies for smart manufacturing, big data analytics can uncover hidden knowledge and other useful information like relations between lifecycle decisions and process parameters helping industrial leaders to make more-informed business decisions in complex management environments. However, according to the literature, big data analytics and smart manufacturing were individually researched in academia and industry. To provide theoretical foundations for the research community to further develop scientific insights in applying big data analytics to smart manufacturing, it is necessary to summarize the existing research progress and weakness. In this paper, through combining the key technologies of smart manufacturing and the idea of ubiquitous servitization in the whole lifecycle, the term of sustainable smart manufacturing was coined. A comprehensive overview of big data in smart manufacturing was conducted, and a conceptual framework was proposed from the perspective of product lifecycle. The proposed framework allows analyzing potential applications and key advantages, and the discussion of current challenges and future research directions provides valuable insights for academia and industry. Highlights: The implications and purposes of sustainable smart manufacturing were defined. A comprehensive review of big data in smart manufacturing was conducted. A framework of big data analytics in sustainable smart manufacturing was proposed. The potential applications and advantages of the proposed framework were evaluated. The challenges and directions for further research were identified.