Simulation in industry 4.0: A state-of-the-art review

Highlights

• Presents a conceptual framework for modeling and simulation of Industry 4.0 scenarios.

• Describe 10 simulation-based approaches being employed in the context of Industry 4.0.

• Identifies 17 design principles of Industry 4.0.

• Establishes a link between simulation technologies and the design principles of Industry 4.0.

• Provides a comprehensive classification of simulation in the context of Industry 4.0.

Abstract

Simulation is a key technology for developing planning and exploratory models to optimize decision making as well as the design and operations of complex and smart production systems. It could also aid companies to evaluate the risks, costs, implementation barriers, impact on operational performance, and roadmap toward Industry 4.0. Although several advances have been made in this domain, studies that systematically characterize and analyze the development of simulation-based research in Industry 4.0 are scarce. Therefore, this study aims to investigate the state-of-the-art research performed on the intersecting area of simulation and the field of Industry 4.0. Initially, a conceptual framework describing Industry 4.0 in terms of enabling technologies and design principles for modeling and simulation of Industry 4.0 scenarios is proposed. Thereafter, literature on simulation technologies and Industry 4.0 design principles is systematically reviewed using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. This study reveals an increasing trend in the number of publications on simulation in Industry 4.0 within the last four years. In total, 10 simulation-based approaches and 17 Industry 4.0 design principles were identified. A cross-analysis of concepts and evaluation of models’ development suggest that simulation can capture the design principles of Industry 4.0 and support the investigation of the Industry 4.0 phenomenon from different perspectives. Finally, the results of this study indicate hybrid simulation and digital twin as the primary simulation-based approaches in the context of Industry 4.0.

Introduction

The Industry 4.0 (I4.0), i.e., the Fourth Industrial Revolution, is a term conceived at the Hannover Fair in 2011 as part of Germany’s long-term strategy to strengthen the competitiveness of its manufacturing sector (Liao et al., 2017). From Industrie 4.0 working group, it “will lead to the emergence of dynamic, real-time optimized, self-organizing value chains that can be optimized based on criteria such as cost, availability, and resource consumption” (Kagermann et al., 2013 p. 20). After 2013, I4.0 gained worldwide recognition and became a hot topic in scientific literature (Lasi et al., 2014, Liao et al., 2017, Xu et al., 2018). Moreover, it was the main subject of discussion at the 2016 World Economic Forum owing to its high relevance to the manufacturing sector (Schwab, 2017). A global survey with over 2,000 participants proposes that approximately 5% of a companies’ annual revenue will be invested in digitalization projects. In turn, companies expect to reduce their operational costs by 3.6% per year (PwC, 2016). These studies reinforce the argument that the digitalization of production systems will drive innovation over the next decades (Kagermann et al., 2013).

There is no standard definition for the term I4.0 in literature (Liao et al., 2017). Particularly, over a hundred definitions of I4.0 have been developed (Moeuf et al., 2018). I4.0 is often described as a set of design principles and enabling technologies to guide researchers and practitioners to implement I4.0 scenarios in companies (Ghobakhloo, 2018, Hermann et al., 2015). Overall, I4.0 is considered as a new socio-technical paradigm that depends on further development, access, and integration of information and communication technologies (ICT) with automation technologies to promote end-to-end systems integration across the entire value chain (Kagermann et al., 2013). It “is a collective term for technologies and concepts of value chain organization” (Hermann et al., 2015 p. 11), having implications on value creation, business models, services, and work organization (Kagermann et al., 2013, Schwab, 2017, Xu et al., 2018).

A revolutionary aspect of I4.0 is the accessibility to its enabling technologies, made possible by the lowering price and widespread use of sensors throughout value chains (Dalenogare et al., 2018), which aids in removing barriers to effective supply chain integration and management (Cragg and McNamara, 2018, Hofmann et al., 2018, Ralston and Blackhurst, 2020). Nonetheless, from Li et al. (2017), the novelty of I4.0 is classified into three axes: (1) technological advances and integration; (2) scaling of the access and robustness of the internet, and (3) convergence of digital, physical, and biological technologies together with its widespread and influence in the dynamics of business, economy and social development. This is consistent with the definition of I4.0 provided by Schwab (2017). Schneider (2018) also explained that the feasibility of I4.0 differentiates it from previous initiatives because of the increasing number of available technologies, the growth of companies’ digital capabilities, and intra and cross-company integration through a complex value chains network, consistent with Hofmann and Ruesch (2017) and Xu et al. (2018).

Several initiatives related to I4.0 have been launched worldwide to strengthen the competitiveness of the manufacturing sector, predominantly through bi- or tripartite collaboration from a triple helix (university-industry-government) collaboration (Liao et al., 2017). Examples of these initiatives include the manufacturing USA program, also known as national network for manufacturing innovation (NIST, 2019); Canada’s advanced manufacturing supercluster (Elci et al., 2019); the project evolution of networked services through a corridor in Quebec and Ontario for research and innovation — ENCQOR (ISED, 2019); German high-tech strategy 2020 (Kagermann et al., 2013); factories of the future in the European union’s (Liao et al., 2017); and made in China 2025 (Xu et al., 2018).

Although some literature report several ongoing projects, I4.0 is nonetheless in its infancy, and most examples are either in the planning stage or are pilot projects (Alcácer and Cruz-Machado, 2019, Liao et al., 2017, Xu et al., 2018). Furthermore, research on risks, costs, revenue potential, and implementation barriers of I4.0 is scarce. Additionally, there is a lack of support to companies desiring to use this new social-technical paradigm (Hofmann & Ruesch, 2017). In this context, simulation techniques play major roles because they offer the possibility to evaluate multiple I4.0 scenarios through the development of planning and exploratory models of complex systems, which can aid addressing partly the aforementioned problems (Kagermann et al., 2013, Lugert et al., 2018).

Modeling and simulation are relevant techniques in the fields of industrial engineering, operations, and supply chain management (Negahban and Smith, 2014, Scheidegger et al., 2018, Shafer and Smunt, 2004). It is an enabling technology of I4.0 for managing complex systems (Alcácer and Cruz-Machado, 2019, Ghobakhloo, 2018, Moeuf et al., 2018). Moreover, an empirical research (Jeong et al., 2018) and patent analysis (Han et al., 2018) proposed modeling and simulation as critical technologies to produce innovations and develop the I4.0.

In manufacturing and logistics systems, which is the primary focus of this study, modeling and simulation denote a set of methods and technological tools that allows the experimentation and validation of products, processes, systems design and to predict system performance. It also supports decision making, education and training, aiding to reduce costs and development cycles (Negahban & Smith, 2014). Moreover, modeling and simulation are robust methods in science and developing theories (Davis et al., 2007), which can be used for different purposes, such as prediction, proof, explanation, prescription, and empirical guidance (Harrison et al., 2007).

Furthermore, the application of simulation technologies is a component of industry leaders’ initiatives and strategy for implementing I4.0, such as General Electric’s (GE) brilliant factory (Thilmany, 2017), and Siemens’ digital factory (Shih, 2016), which addresses manufacturing plant virtualization, visualization, and simulation. Siemens and GE hold different patents related to new simulation techniques (Tao et al., 2019). From Tao et al. (2019), examples of industrial applications include the use of simulation by Siemens for systems planning, operation, and maintenance; the application of simulation by GE for asset management and optimization; and the employment of simulation by Airbus to monitor and optimize production processes. In addition, most leading simulation software vendors (e.g., AnyLogic, MathWorks, Siemens, Arena, Dassault Systèmes, Autodesk, Flexin, Simul8, Aspen Technology, AVEVA, Simio) are investing in the development of commercial solutions for I4.0 (AnyLogic, 2020, Martin, 2019), following the increasing interest from companies in modeling and simulation technologies (Deloitte, 2018).

Nevertheless, advancements in I4.0 and its enabling technologies introduce new challenges to the field of simulation owing to the increasing complexity of systems to be modeled (Martin, 2019, Tao et al., 2018, Uriarte et al., 2019, Vieira et al., 2018, Zhou et al., 2019). Therefore, this study aims to investigate the state-of-the-art of research at the intersection between the emerging field of Industry 4.0 and the field of simulation. The research question (RQ) addressed in this study are the following:

• RQ1 — What are the simulation-based approaches being employed in the context of I4.0?

• RQ2 — What are the purposes, empirical nature, and applications area of studies on simulation in I4.0?

• RQ3 — What are the design principles of I4.0?

• RQ4 — Which I4.0 design principles are captured by each simulation-based approach?

Although there are several reviews on simulation, they either are not in the context of I4.0 (Jahangirian et al., 2010, Negahban and Smith, 2014), focus on a specific simulation technique (Rodič, 2017, Tao et al., 2019, Vieira et al., 2018), or have a different scope/design from this research (Mourtzis, 2019). To the best of our knowledge, this is the first article providing a general overview and comparison between simulation technologies and design principles of I4.0. Furthermore, the time considered in this study extends the dates of coverage of existing reviews, including more recent publications. Additionally, whereas comparing the reference list of this study with the reference lists of existing review articles, through a bibliographic coupling analysis (Van Eck & Waltman, 2014), it overlaps maximum in 6%, indicating that this study introduces new and important insights for those striving to understand the state-of-the-art of research at the intersection of I4.0 domain with the simulation domain.

The main contributions of this study are threefold. First, it presents a broad coverage of the specialized literature using a quantitative and qualitative approach, identifying the simulation approaches used relative to the I4.0. Second, it extends the list of I4.0 design principles provided by Ghobakhloo (2018) and establishes a link between simulation technologies and I4.0 design principles. Third, it provides a comprehensive classification of simulation studies relative to I4.0.

The remainder of this study is organized as follows. Section 2 describes the research methodology used to review the literature. Sections 3 Quantitative analysis, 4 Qualitative analysis present the quantitative and quantitative analyses, respectively. Section 5 presents the discussion. Section 6 introduces the limitations and opportunities for future research. Finally, conclusions are outlined in Section 7.