Big data in manufacturing: a systematic mapping study

Abstract

The manufacturing industry is currently in the midst of a data-driven revolution, which promises to transform traditional manufacturing facilities in to highly optimised smart manufacturing facilities. These smart facilities are focused on creating manufacturing intelligence from real-time data to support accurate and timely decision-making that can have a positive impact across the entire organisation. To realise these efficiencies emerging technologies such as Internet of Things (IoT) and Cyber Physical Systems (CPS) will be embedded in physical processes to measure and monitor real-time data from across the factory, which will ultimately give rise to unprecedented levels of data production. Therefore, manufacturing facilities must be able to manage the demands of exponential increase in data production, as well as possessing the analytical techniques needed to extract meaning from these large datasets. More specifically, organisations must be able to work with big data technologies to meet the demands of smart manufacturing. However, as big data is a relatively new phenomenon and potential applications to manufacturing activities are wide-reaching and diverse, there has been an obvious lack of secondary research undertaken in the area. Without secondary research, it is difficult for researchers to identify gaps in the field, as well as aligning their work with other researchers to develop strong research themes. In this study, we use the formal research methodology of systematic mapping to provide a breadth-first review of big data technologies in manufacturing.

Internet of Things: A Scientometric Review

“…Publications of this topic related to IoT have a sigmoid growth in exponential phase, with 182 publications in 2016, noting United States as the leading country with 34 documents. The most important related topics are: Industry 4.0 [109][110][111][112], big data [113][114][115], and security [116][117][118]. Next, Healthcare and E-Health related to IoT exhibited a sigmoid growth in a transitional phase, with a total of 180 documents, and India as the leading country with 20 publications.…”

Big data analytics in supply chain management: A state-of-the-art literature review

“…For instance, O'Donovan et al (2015), Dutta and Bose (2015), and Babiceanu and Seker (2016) conducted literature reviews on material flow in manufacturing operations while focused on logistics applications. A literature review that takes a broad perspective of SC as a whole and cross-maps with BDA techniques in SCM is yet scarce (Olson, 2015;Addo-Tenkorang and Helo, 2016;Hazen et al, 2016;Mishra et al, 2016).…”

Cyber risk prediction through social media big data analytics and statistical machine learning

“…The most common tools used include Factor models, the Bayesian model and neural networks [5]. Besides, forecasting with big data that includes Time series methodology [6] will be beneficial for manufacturing [7], health care [8,9], and the retail sector [10]. Meanwhile, in the Tourism sector, big data forecasting plays a significant role [11][12][13].…”