Manufacturing networks in the era of digital production and operations: A socio-cyber-physical perspective
Introduction
Distributed production systems favour economies of scale and scope, allowing for improved competitiveness. The performance of manufacturing networks relies on interactions among suppliers and customers, as well as transport and logistic operations. Industry 4.0 technologies make data for the characterization of the system state available, which can support the informational integration among operations. Indeed, suitable data exchange, in terms of frequency and scope, is made possible by the increasing use of information and communication technology, which connects physical and information flows in cyber-physical systems (CPS) (Frazzon et al., 2018a; Frazzon, Kück & Freitag, 2018b). This cyber-physical vision enables the acquisition of real-time system state data that can be used to support better decisions along manufacturing networks. Cyber-physical connection is paramount to cooperative processes, where people at different levels, autonomous devices and the environment interact to reach operational decision-making (Isasi, Frazzon & Uriona, 2015). Therefore, decision-making supportive tools need to consider the idiosyncrasies of the agents involved (Leusin et al., 2018).
Digitalization can support an evidence-based understanding of partners’ individual characteristics, thereby potentially contributing to systems efficiency, accountability, sustainability and scalability (Buer, Strandhagen & Chan, 2018; Frazzon, Hartmann, Makuschewitz & Scholz-Reiter, 2013). In fact, dealing with uncertain situations and flexible problem solving require frequent human participation within cyber-physical systems (Frazzon et al., 2013). Both the design and operationalization of decision-making processes are bounded by human capabilities, relationships and interconnected activities within organizations (Karltun, Karltun, Berglund & Eklund, 2017). The human-machine interaction within CPS will (i) occur more frequently, (ii) involve a variety of individuals and (iii) take place in different contexts and environments. Thus, the outcomes of the implementation of these systems will depend on behavioural aspects of the participants involved and context- related variables affecting this interaction (Frazzon et al., 2013). Recent publications have discussed the relevance of taking a systemic perspective to deal with the emerging challenges of the modelling, planning and control of manufacturing (Morel, Pereira & Nof, 2019).
In this context, the integration of manufacturing and logistics along supply chains leads to increasing complexity, so that operational planning and control needs to be supported by proper decision-making models, methods, and software tools (Scholz-Reiter, Makuschewitz, Novaes, Frazzon & Lima, 2011). Such systems can be based on simulation, optimization, data analytics and combinations of these. At the same time, these systems are connected to sensors for data acquisition, as well as communication systems to exchange data and information in both the vertical and horizontal directions. The proper exchange of data between the physical process and intelligent systems allows the emergence of adaptive, agile and resilient manufacturing systems and supply chains. The integration of different manufacturing systems and supply chains tasks needs to consider: production planning, scheduling and control, transportation and logistics planning, scheduling and control, inventory planning and warehouse management and operations, manufacturing systems operations, as well as coupled services and technologies which can lead to improved performance.
This vision paper discusses the development of decision-support systems for digital manufacturing systems in the era of Industry 4.0 based on a shared understanding of different disciplines: Manufacturing management, Control, Operations Research (OR) and Data Science. More specifically, this research aims to advance scientific knowledge regarding distributed decision-making on digital and integrated manufacturing networks, considered under a socio-cyber-physical systems (SCPS) perspective. The remainder of this manuscript is organized as follows: the next section presents a narrative literature review, which is followed by the proposition of a research framework. Next, a discussion regarding research avenues and a proposed agenda is performed, followed by conclusions.
Section snippets
Narrative literature review
This section discusses theoretical aspects of management decision-support systems for digital manufacturing networks in the era of Industry 4.0. Recent advances in the area are also discussed, addressing cyber-physical systems, manufacturing networks, and digital twin models considered under an SCPS perspective.
Framework proposition
There is a need for an integrated and convergent vision addressing the questions on how manufacturing networks can be planned and controlled in the future, as well as on which are the most relevant decision-making methods and technology drivers fostering new research opportunities with potential practical impact. New concepts, decision-making methods and smart technology-based approaches will fundamentally change the understanding of planning and control in manufacturing networks.
In the
Discussion
Production technology, advanced manufacturing and Industry 4.0 methods and approaches have the potential to ensure manufacturing competitiveness by means of enhanced efficiency and resilience. In the manufacturing arena, mid-size companies are especially under pressure due to unbalanced global competition and the need for continuous innovation in products and processes.
Among several other day-to-day real-world industrial challenges, the scheduling and control of production resources have a
Conclusions
This vision paper presented a narrative review of the literature on advanced manufacturing, and a framework that aims to guide future research on manufacturing networks in the era of digital production and operations. Social aspects of CPS towards SCPS are highly relevant for the present and forthcoming challenges of manufacturing networks. New concepts, decision-making methods and smart technology-based approaches will fundamentally change the understanding and the relationship between
Declaration of Competing Interests
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work is funded by the German Research Foundation (DFG) under reference number FR 3658/1-2 and also by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil) under reference number 99999.006033/2015-06, in the scope of the BRAGECRIM program.
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2022, Computers and Industrial EngineeringCitation Excerpt :I40 comprises a heterogeneity of ideas and technologies to increase the overall performance of the system, allowing and facilitating the efficiency of the work, bringing benefits perceived by the various stakeholders (Pinzone et al., 2020). In terms of information, I40 technologies provide data to characterize the status of systems, which can support the integration of information between suppliers and customers, as well as transport and logistical operations (Frazzon et al., 2020). The principle of the I40 is to use technologies to collect as much information as possible from the different processes in the value chain to be analyzed with computerized machines that result in an increase in quality, a reduction in the costs of production, and a high performance of the overall system (Fernandez-Carames and Fraga Lamas, 2018; Sandengen et al., 2016).