Towards I4.0: A comprehensive analysis of evolution from I3.0
Graphical abstract
Introduction
It is a consensual understanding that Manufacturing Automation Systems (MAS) are under a disrupting paradigm shift. This technological paradigm shift is considered the fourth industrial revolution and coined with the term Industrie 4.0 (Kagermann, Helbig, Hellinger, & Wahlster, 2013) in Germany. The main technology leading this change is the Internet of Things (IoT), enabling pervasive use of embedded computing and standardization of digital network communication, and the inclusion of Cyber-Physical Systems (CPS). This initiative envisions a fully connected collaborative manufacturing environment with automated data collection and processing. The same idea was shared by other countries, such as the United States, and denoted by Industrial Internet (Evans & Annunziata, 2012), or in China, under the term Intelligent Manufacturing (Zhou, 2018). Many other countries also envision the future scenario, depicting potential economic and social changes and opportunities (Ferreira and Serpa, 2018, Pathfinder Consortium, 2014) to innovate production. I4.0 is in the hype, and many researchers made attempts to define it (Hermann et al., 2015, Drath and Horch, 2014, Geissbauer et al., 2014 apud Plattform Industrie 4.0 (Plattform Industrie 4.0), 2014; Anderl, 2014, Schulz, 2015), but still, there is not a consensual understanding about a composed technology toward I4.0. Therefore, it does not exist a consensus on how to make a transition from the current stage to this new production model. Furthermore, Bidet-Mayer and Ciet (2016) states that there are different approaches to I4.0 outside Germany not universally accepted, and with slightly different definitions. German telecommunications association BITKOM reveals that there are more than 100 different definitions of I4.0 (Bidet-Mayer & Ciet, 2016). Piccarozzi, Aquilani, and Gatti (2018) also fail to identify a consensual definition of I4.0 and cites several different attempts to characterize and formulate definitions of I4.0 from a management perspective. The lack of consensus covers definitions and enabling technologies to support the transition I3.0-I4.0 (Xu, Xu, & Li, 2018).
The transition phase between the legacy I3.0 – concerned with the integrated production controlled by computers – to the I4.0 is embedded by great expectations, both from industry and from the academy. On the other hand, the full understanding of this transition is crucial to the evolution of concepts toward a full integration between society, production, and life – Society 5.0 (Ferreira and Serpa, 2018, Shiroishi et al., 2018) – as well as to the evolution of the market, which faces a demand for sustainability and service-orientation (Nof & Silva, 2018). Fig. 1 depicts our proposal to structure the transition from I3.0 to I4.0.
Fig. 1 divides the transition from I3.0 to I4.0 in two parallel axes: in the bottom, a basis supported by Computer Integrated Manufacturing (CIM), integrated with the supply chain. On top, the target I4.0 with the proper composition of new features. Each of these axis embrace activities that go from the foundation to vertical integration: from manufacturing plant to product marketing and design; and horizontal integration, from the manufacturing environment to supply chain and to market. Notice that in each parallel axis, different technological concepts are associated, as well as different technologies – which eventually have a match or evolutionary counterpart in the other axis. For example, the first evolution from CIM and manufacturing cell goes vertically to the concept of CPS and Service-Oriented Architecture (SOA) (Jammes and Smit, 2005, Nof and Silva, 2018, Zuehlke, 2010). In horizontal integration, concepts match a formal and distributed connection with Supply Chain sources and different market niches. In this construct, IIoT (Industrial Internet of Things) is a subset of IoT and is shown separated from IoT, once only IIoT is present in the Manufacturing infrastructure. However, other non-industrial IoTs – such as cellphones and other smart products – may also be part of I4.0 Manufacturing Infrastructure.
Efficiency, Flexibility, and Agility are the goals for the whole process as its benefits are claimed in the literature to fit in one or more of these three categories. For instance, Kagermann et al. (2013) list eight potential sub-goals for I4.0 platform which matches these main goals:
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Meeting individual customer requirements (Flexibility and Agility).
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Flexibility (Flexibility).
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Optimized decision-making (Efficiency).
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Resource productivity and efficiency (Efficiency and Agility).
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Creating value opportunities through new services (Flexibility).
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Responding to demographic change in the workplace (Agility and Flexibility).
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Work-Life-Balance (Flexibility and Efficiency).
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A high-wage economy that is still competitive (Efficiency).
We will return to the structure Fig. 1 later, but it is important to highlight the loose coupling between concepts in different axis, to suggest that a smooth transition should give some attention to these couplings, looking for a strategy to achieve concepts such as Big Data (Efficiency), Evolvable Production Systems (EPS) (Flexibility) and Virtualization (Agility).
Therefore, the structure in Fig. 1 is a guide toward a strategy of transition, which should be adjusted to a production system according to its position in the market and to its technological level and insertion in the global market. It is not a “guideline”, as will be shown. In the next section, we will present methods and data used to support the evolutionary analysis extracted from bibliometric data as well as expectations extracted from the market.
Section snippets
Extracting data from academics and the market
There is a controversy about enabling technologies to pave the transition from I3.0 to I4.0, which are inflated by overambitious marketing expectations. Therefore, it is not so simple to identify the essential technologies required to make this transition.
The methodology used in this work to find required technologies were based on academic literature review and bibliometric analysis, to spotlight the most focused articles (and corresponding technologies) as key references in the subject. These
Results
The research evolution of PSIA and CN can be analyzed through Fig. 3 and Fig. 5. They show publications ranked according to Sigma metric (scientific novelty), based on different time windows, always taking 2005 as the baseline period. The vertical axis corresponds to the position in Sigma ranking while the horizontal axis represents the time slice considered.
Discussion
The results from the academic evolution of concepts and from the industry (which came from interviews and also from what was exposed in Hannover Fair) lead to a match between the two viewpoints and was synthesized in the schema in Fig. 7. It confirmed that manufacturing systems are shifting from a centralized, monolithic and complex structure of I3.0 factory to distributed, modular and service-oriented structure in I4.0. That would be the main characteristic of the transition I3.0-I4.0 and
Conclusion
According to the Gartner Technology Hype Cycle Graph (Gartner, 2018), the basic elements of I4.0 and consequently the I4.0 platform has not reached the production plateau yet. Basic technologies, IoT and IoS, which comprise I4.0 infrastructure, are not ready and fully integrated yet, being still in development. Therefore, there is not enough maturity for many of the companies involved, to accelerate in the transition from I3.0 to I4.0. However, managers and practitioners mention partial
CRediT authorship contribution statement
Ruy Somei Nakayama: Conceptualization, Methodology, Software, Investigation. Mauro Mesquita Spínola: Supervision, Validation, Methodology. José Reinaldo Silva: Supervision, Conceptualization, Methodology, Writing - review & editing.
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