Complexity has been studied under the perspective of both physical and functional domains. In the former one, it takes into account aspects of the system structure and configuration, the variety of products it can process, and the system’s resources, such as humans, machines, buffers, and their interdependencies as well as the behavior of the system. In the latter case, complexity is considered as a measure of uncertainty in achieving functional requirements, taking into account aspects of manufacturing system design, its states, and the degree to which it can handle the variety of demand.
Today, manufacturing enterprises are often facing the challenge of having to manufacture highly customized products in small lot sizes. One solution to react to the rapidly varying demands and make the use of resources more flexible lies in the digitalization of the manufacturing systems; this still remains a major challenge in industry. Information and material flow over the production and distribution are becoming increasingly complex. The advent of Industry 4.0 initiative and the modern technologies, such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), open new horizons towards the industrial digitalization by enabling automated procedures and communication by means that were not available in the past. The increased connectivity and interaction among systems, humans, and machines enable the integration of various automated or semiautomated systems, increasing flexibility and productivity. This will also lead to interconnected Industry 4.0-enabled manufacturing systems and networks that will constitute an integrated whole that delivers the needed products to the right place, at the right time, taking into consideration a number of newly created parameters and factors.
Although the adoption of Industry 4.0 and IoT paradigms in manufacturing reveals great potential, the increased complexity that may occur in different areas, including product, information, machine, shop-floor, and enterprise, as well as at a network level, is a main challenge. The technology foreseen in the context of Industry 4.0 paradigm aims to reduce the complexity of the systems; nevertheless managing the amount of generated data, dealing with the increased number of variables, and integrating different tools are all fields of further investigation. In particular, the way the generated data and information from different sources and levels can be integrated into methods and systems for adaptive and effective decision-making needs to be further analyzed, aiming to manage the different and conflicting decision variables. In Industry 4.0 systems and networks, advanced monitoring techniques and novel automation systems are integrated and the overall degree of complexity is highly affected. Existing approaches and methods, including chaos, nonlinear dynamics, and information theories as well as hybrid approaches (Heuristics-Indexes), need to be further exploited, taking into consideration the increased number of parameters and variables of Industry 4.0 systems and networks.
Therefore, the main objective of this special issue is to collect and consolidate innovative and high-quality research contributions, mainly focusing on methods and tools to model, quantify, and control Industry 4.0 complexity. This special issue provides insights on how Industry 4.0 technologies may support effective decision-making, while reducing systems’ and networks’ complexity through the submitted scientific contributions in the form of both research and review papers.
This special issue includes 5 original papers selected by the editors, featuring significant research contributions in the above-mentioned topics. The list of papers is as follows.(1)“Multilayer Network-Based Production Flow Analysis” by T. Ruppert et al.(2)“On the Design Complexity of Cyberphysical Production Systems” by L. Ribeiro and M. Hochwallner.(3)“Smart Scheduling: An Integrated First Mile and Last Mile Supply Approach” by T. Bányai et al.(4)“Topological Structure of Manufacturing Industry Supply Chain Networks” by S. S. Perera et al.(5)“Green Supplier Selection for Process Industries Using Weighted Grey Incidence Decision Model” by J. Quan et al.
Conflicts of Interest
I hereby declare that neither I nor the other guest editors have any possible conflicts of interest or private agreements with companies which are involved in the submissions in this special issue.
Acknowledgments
We would like to thank all authors who submitted their works for this special issue.
Dimitris Mourtzis
Nikolaos Papakostas
Sotiris Makris