Manufacturing big data ecosystem: A systematic literature review

Abstract

Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business.Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware.Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security.Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.

Current landscape and influence of big data on finance

“…At present, most of the areas of business are linked to big data. It has significant influence on various perspectives of business such as business process management, human resources management, R&D management [8,63], business analytics [19,26,42,59,63], B2B business process, marketing, and sales [30,39,53,58], industrial manufacturing process [7,15,40], enterprise's operational performance measurement [20,69,81], policy making [2], supply chain management, decision, and performance [4,38,64], and so other business arenas.…”

“…This study also identified an Overall framework of BDA capabilities in manufacturing process, and mentioned some values of Big Data Analytics for manufacturing process, such as enhancing transparency, improving performance, supporting decision-making and increasing knowledge. Also, Cui et al [15] mentioned four most frequently big data applications (Monitoring, prediction, ICT framework, and data analytics) used in manufacturing. These are essential to realize the smart manufacturing process.…”

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

“…In fact, as recognized by several authors, innovation and sustainability are two crucial issues for the present and future generations of smart manufacturing systems [4,5]. Indeed, the extraction of natural resources, excessive waste production, and global warming are problems known to all [6]. In this regard, emerging searches have shown how a circular model to reuse waste has a positive influence on improving the entire supply chain to manufacture products [7][8][9], while others have focused on the impacts of digital technologies in the domain of manufacturing, optimization processes, and scheduling problems [10,11] to solve the problem of industrial pollution and a waste of resources.…”

A Product/Service System Design Schema: Application to Big Data Analytics

“…BDA (big data analytics) can be defined as a holistic process that involves the collection, analysis, use, and interpretation of data for various functional divisions with a view to gaining actionable insights, creating business value, and establishing competitive advantage [39]. In relation to manufacturing in a broad sense, a variety of subjects are relevant to BDA, as reported by a recent review [16], and all the lifecycle phases, in principle, have the potential to benefit from BDA, such as marketing, design, production, logistics, use, maintenance, and end-of-use treatment. The manufacturing industry has increased interest in BDA, and earlier literature reported industry-relevant applications in different areas, such as factory operations (e.g., [40]) and operations management (e.g., [41]).…”

“…Today, thanks to industry's increasing interest in big data (big data can be recorded), IoT (Internet of Things; things can be connected, and thereby the big data can be collected efficiently), and big data analytics (the collected data can be analyzed to produce useful information; BDA, hereafter) as reviewed by, e.g., [16], we see an enormous opportunity to further advance the practice of PSS design and delivery in industry. This opportunity is evident in industrial reports: e.g., a report in the UK [17] listed these technologies of importance to further enhance PSS, while another in Germany [18] expects different types of services enabled by various technologies of Industry 4.0.…”