Smart manufacturing refers to the intensified collaboration of \u00a0machines, products, and people throughout the manufacturing and the supply \u00a0chain. This facilitates innovative products, services, business models, and \u00a0processes. Smart manufacturing is premised on emerging technologies such as cloud computing, mobile computing, the Internet of Things, data analytics, and \u00a0artificial intelligence. A plethora of companies struggles with the \u00a0implementation of corresponding applications. In research and practice, we see \u00a0general data management approaches with primary attention on building \u00a0architectures that are not tailored to fit a particular domain\/ application \u00a0scenario. However, a robust data management concept is vital, as smart \u00a0manufacturing decisively depends on data. To address this substantial deficit, we conduct a comprehensive literature review, an expert workshop, and semi-structured expert interviews with one of the leading German automotive \u00a0manufacturers. The result is a catalog of requirements and a framework for data \u00a0management that fosters the implementation of smart manufacturing \u00a0applications.<\/p>\n\n\n\n
The manufacturing industry is undergoing a paradigm shift, in which machines, \u00a0products, and people tightly collaborate and are self-organized, enabling innovative \u00a0products, services, business models, and processes [1\u20133]. New technologies such as cloud computing, mobile computing, internet of things, data analytics, and \u00a0increasingly artificial intelligence (AI) facilitate this transition [4]. Aside from smart manufacturing, various synonymous terms such as smart factory, Industry 4.0, or \u00a0additive manufacturing are popular [5]. Smart manufacturing applications, e.g. \u00a0collaborative robots for safe human-machine interaction [6] combine increasingly \u00a0available data spawned by manufacturing systems to create new value-added \u00a0potentials [7]. As data (analytics) are vital resources for smart manufacturing, a robust \u00a0data management concept is critical to foster smart manufacturing applications [8]. Researchers have developed various concepts for data management, mainly under the \u00a0term of data warehouse [9] or data lakes [10]. Current publications also consider the \u00a0ascending influence of big data [10] and propose specially designed data architectures \u00a0[11].<\/p>\n\n\n\n
However, practical applications of actual smart manufacturing have been observed \u00a0to be rather rare [12, 13], which can be traced back to inadequate data management \u00a0concepts. A sustainable data management concept depends on the specific use and \u00a0setting [14] and requires a practical evaluation [11]. In general, scientific publications \u00a0either prioritize the elaboration of a particular smart manufacturing application \u00a0scenario [15] or the development of a reference architecture for data management \u00a0[16]. Bridging both worlds has been mainly neglected by research [17, 18]. Smart \u00a0manufacturing brings along specific challenges, which must be considered by a \u00a0sufficient data management concept. A tremendous number of IT systems and \u00a0software [2, 19], a lack of central integration across various databases [20], a diverse \u00a0composition and use of systems [21], and a shifting customer demand towards a \u00a0highly flexible and customizable product, affect the manufacturing process [22]. \u00a0Isolated, these properties are not exceptional, but in combination, the unique \u00a0complexity hinders the theory transfer between application domains [23]. <\/p>\n\n\n\n
Against this background, the research question of this paper is: \u201chow to develop a<\/em> data management concept for smart manufacturing applications?<\/em>\u201d. To answer this \u00a0question, we develop a catalog of requirements and a framework for data \u00a0management. Accordingly, we adopt the design science research process (DSR) idea of iteratively building and evaluating an artifact [26]. We first developed a catalog of requirements based on a systematic literature review and a workshop with experts in \u00a0the domain of manufacturing. In line with the identified requirements, we designed a \u00a0data management framework which we then evaluated by conducting interviews with \u00a0a new set of information systems and data management experts. Workshop and \u00a0interviews were conducted with one of the leading German automotive \u00a0manufacturers, allowing us to extract valuable knowledge and feedback on our \u00a0artifact [27].<\/p>\n\n\n\n The contribution of this work is a catalog of requirements and a framework, (1) \u00a0ensuring the usability of all relevant data for smart manufacturing application \u00a0scenarios and (2) creating awareness about necessary management tasks. With \u00a0convenient access to all relevant data and the resulting analytics findings, decision \u00a0support for employees or independent decision-making systems are possible. This enhancement should lead to greater efficiency and productivity in manufacturing \u00a0processes as employees are supported in executing their tasks, and transparency along \u00a0the value chain is enhanced.<\/p>\n\n\n\n This paper is structured as follows: Section 2 presents theoretical foundations \u00a0regarding data management concepts. Section 3 describes the applied research \u00a0method. In sections 4, we present our artifacts: a list of potential smart manufacturing \u00a0applications, a catalog of requirements, and a framework. In section 5, we evaluate \u00a0the artifacts. Finally, we discuss our finding in section 6 and conclude with a summary.<\/p>\nYour downloads<\/h2>