Closing the Gap between Smart Manufacturing Applications and Data Management

Abstract

Smart manufacturing refers to the intensified collaboration of  machines, products, and people throughout the manufacturing and the supply  chain. This facilitates innovative products, services, business models, and  processes. Smart manufacturing is premised on emerging technologies such as cloud computing, mobile computing, the Internet of Things, data analytics, and  artificial intelligence. A plethora of companies struggles with the  implementation of corresponding applications. In research and practice, we see  general data management approaches with primary attention on building  architectures that are not tailored to fit a particular domain/ application  scenario. However, a robust data management concept is vital, as smart  manufacturing 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  manufacturers. The result is a catalog of requirements and a framework for data  management that fosters the implementation of smart manufacturing  applications.

Introduction

The manufacturing industry is undergoing a paradigm shift, in which machines,  products, and people tightly collaborate and are self-organized, enabling innovative  products, services, business models, and processes [1–3]. New technologies such as cloud computing, mobile computing, internet of things, data analytics, and  increasingly artificial intelligence (AI) facilitate this transition [4]. Aside from smart manufacturing, various synonymous terms such as smart factory, Industry 4.0, or  additive manufacturing are popular [5]. Smart manufacturing applications, e.g.  collaborative robots for safe human-machine interaction [6] combine increasingly  available data spawned by manufacturing systems to create new value-added  potentials [7]. As data (analytics) are vital resources for smart manufacturing, a robust  data management concept is critical to foster smart manufacturing applications [8]. Researchers have developed various concepts for data management, mainly under the  term of data warehouse [9] or data lakes [10]. Current publications also consider the  ascending influence of big data [10] and propose specially designed data architectures  [11].

However, practical applications of actual smart manufacturing have been observed  to be rather rare [12, 13], which can be traced back to inadequate data management  concepts. A sustainable data management concept depends on the specific use and  setting [14] and requires a practical evaluation [11]. In general, scientific publications  either prioritize the elaboration of a particular smart manufacturing application  scenario [15] or the development of a reference architecture for data management  [16]. Bridging both worlds has been mainly neglected by research [17, 18]. Smart  manufacturing brings along specific challenges, which must be considered by a  sufficient data management concept. A tremendous number of IT systems and  software [2, 19], a lack of central integration across various databases [20], a diverse  composition and use of systems [21], and a shifting customer demand towards a  highly flexible and customizable product, affect the manufacturing process [22].  Isolated, these properties are not exceptional, but in combination, the unique  complexity hinders the theory transfer between application domains [23].

Against this background, the research question of this paper is: “how to develop a data management concept for smart manufacturing applications?”. To answer this  question, we develop a catalog of requirements and a framework for data  management. 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  the domain of manufacturing. In line with the identified requirements, we designed a  data management framework which we then evaluated by conducting interviews with  a new set of information systems and data management experts. Workshop and  interviews were conducted with one of the leading German automotive  manufacturers, allowing us to extract valuable knowledge and feedback on our  artifact [27].

The contribution of this work is a catalog of requirements and a framework, (1)  ensuring the usability of all relevant data for smart manufacturing application  scenarios and (2) creating awareness about necessary management tasks. With  convenient access to all relevant data and the resulting analytics findings, decision  support for employees or independent decision-making systems are possible. This enhancement should lead to greater efficiency and productivity in manufacturing  processes as employees are supported in executing their tasks, and transparency along  the value chain is enhanced.

This paper is structured as follows: Section 2 presents theoretical foundations  regarding data management concepts. Section 3 describes the applied research  method. In sections 4, we present our artifacts: a list of potential smart manufacturing  applications, a catalog of requirements, and a framework. In section 5, we evaluate  the artifacts. Finally, we discuss our finding in section 6 and conclude with a summary.