Article Preview
TopIntroduction
Since the 2010s, many innovative and advanced information and communication technologies (ICTs) have emerged in our society, such as cyber-physical systems (CPS), advanced sensor technology, and big data. These technologies are increasingly integrated into the manufacturing industry, shifting the traditional mode of production toward smart manufacturing, called Industry 4.0 (Culot et al., 2020; Hwang et al., 2017). Industry 4.0 is an important concept for both developed and developing countries to enhance the core competitiveness and maintain manufacturing enterprise’s sustainable development. The smart factory is a key concept concerned with the vision of Industry 4.0. Based on a set of advanced technologies, a smart factory integrates personnel, products, and machines to enable real-time management, coordination, and control among multiple systems, as well as dealing with constantly growing amounts of data generated in production processes (Kovacova & Lewis, 2021). Furthermore, a smart factory addresses the vertical integration of different components for the flexible production of different products to realize a more refined and dynamic production pattern (Shi et al., 2020; Strozzi et al., 2017).
Since the concept of smart factories was developed, academic researchers and industrial practitioners have investigated it. One of the most urgent problems in smart factory development is how to use advanced tools and algorithm models to process huge amounts of industrial data generated in product research and design, manufacturing, marketing, supply chain, and the after-sales service that aims to support intelligent production and decision-making (Jwo et al., 2021; Wang et al., 2018). In this situation, ‘industrial big data’ solutions become the key component to ensuring the success of smart factory development by providing the mechanism of data planning, data governance, and data application (Sultana et al., 2021). If we regard CPS and smart devices as the ‘trunk’ of smart factory initiatives, technology applications based on industrial big data are the ‘brain,’ which directly affects the successful construction of smart factories (Xing et al., 2019). In a real application, several industrial big data solutions, such as MindSphere developed by Siemens and Predix developed by GE, are leveraged by smart factory pioneers for improving the flexibility of manufacturing product lines and the utilization efficiency of the resources (Pauli et al., 2021).
Despite the strong demand and enormous opportunities that industrial big data solutions bring, there seems to be a scarcity of research to study the phenomenon of embedding industrial big data solutions in smart factories. Particularly, current literature and studies showed that most studies in this field focus on the issues of industrial big data solutions or smart factories separately (Kovacova & Lewis, 2021; Won & Park, 2020). There is little empirical research studying the combination of industrial big data solutions in a smart factory context. Meanwhile, technical perspectives, such as smart sensors and operators, primarily drive current studies in the industrial big data solutions and smart factory field (Zolotová et al., 2020), data security (Javaid et al., 2021), data model and matching algorithm (Yan et al., 2018), application of CPS in Industry 4.0 from an engineering perspective (Tucker, 2021). Indeed, the development of industrial big data analytics and smart factories highly depends on advanced technologies. However, its success still relies on how to use and manage the industrial big data solutions in smart factories more efficiently and effectively from an information system’s (IS) point of view. In other words, barriers and problems cannot be addressed by simply focusing on the technical perspective. Considering this discussion, studies on utilizing industrial big data in the smart factory context are important and currently neglected, especially from an IS perspective. Therefore, there is a lack of studies that explore the barriers to industrial big data implementation in smart factories from a socio-technical view.