By Murat M. Gunal
Murat M. Gunal
Industry 4.0, Digitisation in Manufacturing, and Simulation:
A Review of the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Murat M. Gunal and Mumtaz Karatas
Traditional Simulation Applications in Industry 4.0. . . . . . . . . . . . . . . . 39
David T. Sturrock
Distributed Simulation of Supply Chains in the Industry 4.0 Era:
A State of the Art Field Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Korina Katsaliaki and Navonil Mustafee
Product Delivery and Simulation for Industry 4.0 . . . . . . . . . . . . . . . . . 81
Oliverio Cruz-Mejía, Alberto Márquez and Mario M. Monsreal-Berrera
Sustainability Analysis in Industry 4.0 Using Computer Modelling
and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
Masoud Fakhimi and Navonil Mustafee
Interactive Virtual Reality-Based Simulation Model Equipped
with Collision-Preventive Feature in Automated Robotic Sites . . . . . . . . 111
Hadi Alasti, Behin Elahi and Atefeh Mohammadpour
IoT Integration in Manufacturing Processes . . . . . . . . . . . . . . . . . . . . . 129
Data Collection Inside Industrial Facilities
with Autonomous Drones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Murat M. Gunal
Symbiotic Simulation System (S3) for Industry 4.0 . . . . . . . . . . . . . . . . 153
Bhakti Stephan Onggo
High Speed Simulation Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
Simon J. E. Taylor, Anastasia Anagnostou and Tamas Kiss
Using Commercial Software to Create a Digital Twin . . . . . . . . . . . . . . 191
David T. Sturrock
Virtual Simulation Model of the New Boeing Sheffield Facility . . . . . . . 211
Ruby Wai Chung Hughes
Use of a Simulation Environment and Metaheuristic Algorithm
for Human Resource Management in a Cyber-Physical System . . . . . . . 219
Hankun Zhang, Borut Buchmeister, Shifeng Liu and Robert Ojstersek
Smart Combat Simulations in Terms of Industry 4.0 . . . . . . . . . . . . . . . 247
M. Fatih Hocaoğlu and İbrahim Genç
Simulation for the Better: The Future in Industry 4.0 . . . . . . . . . . . . . . 275
Murat M. Gunal
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 285
About This Book
The book shows how simulation’s long history and close ties to industry since the Third Industrial Revolution have led to its growing importance in Industry 4.0. It also emphasizes the role of simulation in the New Industrial Revolution, and its application as a key aspect of making Industry 4.0 a reality—and thus achieving the complete digitization of manufacturing and business. It presents various perspectives on simulation and demonstrates its applications, from augmented or virtual reality to process engineering, and from quantum computing to intelligent management.
Simulation for Industry 4.0 is a guide and milestone for the simulation community, as well as for readers working to achieve the goals of Industry 4.0. The connections between simulation and Industry 4.0 drawn here will be of interest not only to beginners, but also to practitioners and researchers as a point of departure in the subject, and as a guide for new lines of study.
Chapter “Simulation and the Fourth Industrial Revolution” is the introductory chapter which sets up the scene for the book and gives a background information including a historical review of the industrial revolutions and historical perspective of simulation. Concepts within Industry 4.0 are introduced, and their interaction with simulation is evaluated. This chapter reveals that simulation has a significant role in Industry 4.0 concepts such as cyber-physical systems (CPSs), augmented reality/virtual reality (AR/VR), and data analytics. Its role will continue in analysis for supply chains, lean manufacturing and for training people.
Chapter “Industry 4.0, Digitisation in Manufacturing, and Simulation: A Review of the Literature” is a review of the literature written by Gunal and Karatas (2019). Their review is conducted in two parts; first, selected publications between 2011 and 2019 are critically evaluated, and second, Google Scholar is used to count studies with selected keywords. Their review revealed that the number of papers on Industry 4.0 increased exponentially in recent years and these papers are not only from Europe but also from other countries in the world. This suggests that “Industry 4.0” is adopted by the whole world.
Chapter “Traditional Simulation Applications in Industry 4.0” is presenting traditional simulation applications in Industry 4.0, written by Sturrock (2019). He emphasizes that DES products are routinely used for purposes supply chain logistics, transportation, staffing, capital investment, and productivity. He presents case studies in health care, iron foundry, logistics, and manufacturing. He discusses that a smart factory can benefit from simulation to assess the impact of any specific advanced features. Furthermore, with DES, decision-makers can identify areas of risks before implementation and evaluate the performance of alternatives. He also gives a tutorial for building a simple model using Simio simulation software. In this model, a simple production system is built. A Gantt chart is generated and optimized for scheduling which is an important feature desired in smart factories of the future.
Chapter “Distributed Simulation of Supply Chains in the Industry 4.0 Era: A State of the Art Field Overview” is discussing distributed simulation of supply chains in Industry 4.0 context and written by Katsaliaki and Mustafee (2019). They highlight the significance of distributed simulation for supply chain analysis and review simulation techniques including parallel simulation, DES, ABS, and SD. They present distributed simulation around two scenarios, first as an enabler of large and complex supply chain models, and second, as an enabler of inter-organizational supply chain models. Although they point out that parallel DES is dominant in most of the studies, potential of ABS and hybrid modelling is great in terms of modelling autonomy, complexity, and scalability in the problem domain.
Chapter “Product Delivery and Simulation for Industry 4.0” is debating on product delivery and simulation issues in Industry 4.0 context, written by Cruz-Mejia, Marquez, and Monsreal-Berrera (2019). They propose “Smart Coordinated Delivery” (SCD) within supply chain players to re-balance the workload and increase the efficiency. Simulation can be used to assess the performance of SCD and to help design “standard interfaces” to enable coordination. They put forward “merge in transit” operations are needed to consolidate multi-item shipments, and this could be implemented using technology such as IoT. The role of simulation here is to help design such systems since simulation is a powerful tool when data availability is limited or problematic. For improving the “last mile delivery” performance, the authors highlight the potential of “what3words.com” concept and using VR/AR. Furthermore, ABS is mentioned as an excellent option for business modelling since it is about autonomous decision-making entities as in the real-life examples. They point out that simulation software vendors should adapt the software to Industry 4.0 to answer the needs emerged by the new concepts. For example, a new dynamic and intelligent queueing objects must exist in the software to mimic smart factory operations such as picking the next part to process on a machine from a que of jobs with some prespecified rule.
Chapter “Sustainability Analysis in Industry 4.0 Using Computer Modelling and Simulation” is written by Fakhimi and Mustafee (2019) and is discussing sustainability in manufacturing and supply chain systems from Industry 4.0 and modelling and simulation point of views. They point out that modelling and simulation techniques could provide significant insights in coping with the uncertainty associated with triple-bottom-line (TBL) management and highlight that there are opportunities for the realization of sustainable development in using simulation in Industry 4.0.
Chapter “Interactive Virtual Reality-Based Simulation Model Equipped with Collision-Preventive Feature in Automated Robotic Sites” is written by Alasti, Elahi, and Mohammadpour (2019) and demonstrates how a DES model of a manufacturing facility with robot arms can work with a robot arm simulation software. The VR created can help design robot operations in a facility. Their approach is a template for modelling manufacturing with robots. This chapter also summarizes the use of VR in manufacturing including in design and prototyping phase, planning phase, simulation, workforce training, machining process, assembly, inspection, and maintenance phases.
Chapter “IoT Integration in Manufacturing Processes” presents an implementation Event Graphs methodology called TAO, written by Adduri (2019). A novel feature is the “pending edge” which is an entry to Future Event List (FEL). TAO allows editing FEL in simulation. An event can be scheduled when an earlier event is scheduled. This feature can be useful in cases such as an IoT device is to be fed to a simulation model. Real-time data, for example provided from IoT devices, could be used in models. Simulation is suggested as a production management software rather than being a tool to design the production system. This way of use is a novel approach.
Chapter “Data Collection Inside Industrial Facilities with Autonomous Drones” is a conceptual study of a drone-based data acquisition and processing system, written by Gunal (2019). To achieve Industry 4.0 targets, a manufacturing facility can benefit from such system in sensing and collecting data at the shop floor. In the proposed system, there is an autonomous drone which can fly over predefined path inside a facility and collect visual data. The data is processed on the return, and useful managerial information is obtained by processing vision data. The system can be a solution for SMEs to increase their Industry 4.0 maturity levels.
Chapter “Symbiotic Simulation System (S3) for Industry 4.0” is presenting symbiotic simulation system (S3) and written by Onggo (2019). S3 is a tool designed to support decision-making at the operational management level by making use of real-time or near-real-time data which is fed into the simulation at run-time. Symbiotic simulation is very relevant to Industry 4.0 as it makes use of real-time data, and can be a significant part in CPS. This chapter includes the architecture of S3, three types of S3 applications for Industry 4.0, and challenges for adoption.
Chapter “High Speed Simulation Analytics” is written by Taylor, Anagnostou, and Kiss (2019) and presents high-speed simulation analytics from an Industry 4.0 perspective. They see that distributed simulation and high-speed experimentation with cloud computing are the keys to achieve high-speed analytics. A novel commercial system has been presented that demonstrates how cloud computing can be used to speed up simulation experimentation. This chapter highlights the role of simulation in data analytics as one of the comprising technologies of Industry 4.0.
Chapter “Using Commercial Software to Create a Digital Twin” is presenting how a digital twin using a commercial simulation software can be constructed, and written by Sturrock (2019). First, he discusses the digital twin concepts and how it addresses the challenges of Industry 4.0. Secondly, he evaluates how modern simulation software can be used to create a digital twin of the entire factory. Finally, Risk-based Planning and Scheduling (RPS) system which provides a unique solution to achieve smart factory is presented.
Chapter “Virtual Simulation Model of the New Boeing Sheffield Facility” is presenting a virtual simulation model of Boeing Company’s facility in Sheffield, UK, and written by Hughes (2019). The factory is expected to become an Industry 4.0 flagship facility for Boeing, with robust IT infrastructure and a fully connected virtual simulation model working between its digital and physical systems—a “digital twin” factory. The digital twin is built using commercial simulation software. This chapter presents the key elements in the simulation model and discusses the approach of linking the model to physical systems.
Chapter “Use of a Simulation Environment and Metaheuristic Algorithm for Human Resource Management in a Cyber-Physical System” is a study conducted on workforce planning problems in Industry 4.0 and written by Hankun, Borut, Shifeng, and Robert (2019). They presented 5C CPS architectural model and applied five-level architecture implemented with simulation. Heuristic Kalman algorithm (HKA) and improved HKA are presented as evolutionary methods for determining the number of workers in a virtual factory. They demonstrated the benefits of these algorithms with a simulation model. Their algorithms can help determine an optimum number of workers in a CPS.
Chapter “Smart Combat Simulations in Terms of Industry 4.0” is presenting the concepts in military and their links with Industry 4.0, from Command, Control, Computer, Communication, Intelligence, Surveillance, and Reconnaissance (C4ISR) point of view, and written by Hocaoglu and Genc (2019). Their study shows that data sharing, fusing data received from different sources, distributed decision, automated decision-making, integration of systems, and handling big amount of data are common points for both C4ISR and Industry 4.0. They also discussed agent-based simulation technologies and demonstrated an application of C4ISR concepts in a simulation environment.
Chapter “Simulation for the Better: The Future in Industry 4.0” is the final chapter and a conclusion of the book, written by Gunal (2019). This chapter states the role of simulation in Industry 4.0 era and links the concepts of Industry 4.0 with simulation. A discussion is included on how simulation can contribute to designing, developing, and improving manufacturing systems of the future.