Simulation for Industry 4.0

Through history, advancements in technology have revolutionised manufacturing and caused a leap in industrialisation. Industry 4.0, the Fourth Industrial Revolution, comprises of advanced technologies such as robotics, autonomous transportation and production machinery, additive manufacturing, Internet of Things (IoT), 5G mobile communication, sensors, systems integration, Cloud, big data, data analytics, and simulation. Such technologies are used in the production of quality goods, which increased product diversity, and often at lower costs achieved through optimisation and smart production techniques. The goals of Industry 4.0 are to achieve Smart Factories and Cyber-Physical Systems (CPS). The introductory chapter presents concepts from Industry 4.0 and contextualises the role of simulation in bringing about this new industrial age. The history of the industrial revolutions and simulation are discussed. Major concepts in Industry 4.0, such as CPS, vertical and horizontal system integration, Augmented Reality/Virtual Reality (AR/VR), Cloud, big data, data analytics, Internet of Things (IoT), and additive manufacturing are evaluated in the context of simulation. The discussions show that computer simulation is intrinsic to several of these Industry 4.0 concepts and technologies, for example, the application of simulation in hybrid modelling (e.g., digital twins), simulation-based training, data analytics (e.g., prescriptive analytics through the use of computer simulation), designing connectivity (e.g., network simulation), and simulation-based product design. Simulation has a pivotal role in realising the vision of Industry 4.0, and it would not be farfetched to say that simulation is at the heart of Industry 4.0.

Simulation is perhaps the most widely used approach to design and analyze manufacturing systems than to any other application area. Industry 4.0, the latest industrial revolution, also involves the use of simulation and other related technologies in manufacturing. In this study, our main ambition is to provide readers with a comprehensive review of publications which lie within the intersection of Industry 4.0, digitization in manufacturing, and simulation. To achieve this, we follow a two-stage review methodology. Firstly, we review several academic databases and discuss the impact and application domain of a number of selected papers. Secondly, we perform a direct Google Scholar search and present numerical results on global trends for the related technologies between years 2011 and 2018. Our reviews show that simulation is in the heart of most of the technologies Industry 4.0 utilises or provides. Simulation has significant role in Industry 4.0 in terms of supporting development and deployment of its technologies such as Cyber-Physical System (CPS), Augmented Reality (AR), Virtual Reality (VR), Smart Factory, Digital Twin, and Internet of Things (IoT). Additionally in terms of management of these technologies, simulation helps design, operate and optimise processes in factories.

For decades, simulation has been used primarily for facility design improvements. Domains like manufacturing, airports, mining, ports, call centers, supply chains, and military have provided a rich set of case studies describing how simulation has been used to save hundreds of thousands, sometimes even millions, of dollars per project. These are well accepted and documented applications of Discrete Event Simulation (DES). We will first discuss those typical benefits and how those same design-related benefits can be realized in Industry 4.0 applications. But Industry 4.0 introduces many new modeling demands. This chapter also discusses some of those new demands and how mainstream DES technology can be used to help assess the impact of advanced features, identify areas of risk before implementation, evaluate the performance of alternatives, predict performance to custom criteria, standardize data, systems, and processes, establish a knowledgebase, and aid communication. We will illustrate these concepts with four case studies as well as provide a brief tutorial on building a model of such a system using the Simio DES product.

Simulation approaches have long been used in the context of supply-chain management (SCM). Unlike the conventional approach which models the different stages of SC as a single simulation, a distributed supply-chain simulation (DSCS) enables coordinated execution of existing models through use of distributed simulation middleware. The new era of Industry 4.0 has created the “smart factory” of cyber-physical systems which controls the route of products’ assembly line for customised configuration. The collaboration of all supply-chain players in this process is essential for the tracking of a product from suppliers to customers. Therefore, it becomes necessary to examine the role of distributed simulation in designing, experimenting and prototyping the implementation of the large number of highly interconnected components of Industry 4.0 and overcome computational and information disclosure problems amongst supply chain echelons. In this chapter, we present an overview and discuss the motivation for using DSCS, the modelling techniques, the distributed computing technologies and middleware, its advantages, as also limitations and trade-offs. The aim is to inform the organizational stakeholders, simulation researchers, practitioners, distributed systems’ programmers and software vendors, as to the state of the art in DSCS which is fundamental in the complex interconnected and stochastic environment of Industry 4.0.

Industry 4.0 is having machines working connected as a collaborative community, both inside and outside the walls of the manufacturing sites. Manufacturing, sourcing, and delivery supply chains are now connected, making synchronization possible. Physical product delivery has changed significantly. Smart deliveries are now possible by directing end customer location in dynamic conditions. The capabilities of the delivery system can be simulated using discrete event simulation to compromise on-time delivery. Big data analytics are now a fundamental tool for product delivery analysis of optimal vehicle routing conditions and resource allocation. As companies have improved product delivery capabilities, more complex supply chains have been created. Analytic tools can tackle this complexity in estimating delivery time and product delivery windows under different workload scenarios.

Industry 4.0 proposes the use of digital and connected manufacturing technologies for enhanced value creation. The measures that are traditionally associated with value creation include the reduction in waste, increased productivity and efficiency improved profitability, etc. With a growing interest in sustainability, it is important to supplement the conventional definition of value-creation with factors related to the environment and the society. This inclusive definition could help the realisation of sustainable development. Computer simulation and modelling (M&S) could be valuable in providing the understandings and insights necessary for coping with such all-inclusive systems which have high levels of complexity. In addition, M&S could also provide immense opportunities for stakeholders to understand the underlying dynamics of industry 4.0’s contribution to sustainable development targets. Although, the researchers have recently been applying M&S to plan and test industry 4.0 approaches but our findings show that using M&S for analysing the contribution of industry 4.0 on sustainable development are scarce. The outcome of this chapter provides insights toward future research directions and needs. Finally, this research argues for a shift from normal to post-normal M&S paradigms for sustainability analysis this is achieved through a discussion on normal and post-normal science concepts and assumptions.

Technological changes have made historic moves in the industry trajectory towards industry 4.0. Simulation of the work environment is one of the effective tools in an automated robotic site. It contributes to a better work environment’s awareness toward the machines’ and robots’ behavior, enhancement of the monitoring and troubleshooting of processes, and selection of the optimum adaptable design for the system. This book chapter mainly focuses on proposing an innovative interactive VR-based simulation model for automated robotics sites. Consolidating all features of an effective VR tool, a system design simulation software (SIMIO) and a robot programming simulation software (Epson RC+) results in an effective VR-based simulation for the entire manufacturing system. Such a proposed model, interacts with workforce and decision makers effectively. Decision makers will be able to test and evaluate various design scenarios and potential states in the whole response space. In this way, the optimum alternative, which optimizes the performance measures’ values, will be captured in a timely manner. Such a model, proactively recognizes the potential collisions via simulation. Utilizing such a tool will improve the scheduling process, reduce down-time and delays, enhance the system productivity and reliability, and detect maintenance time of robots and machines in a faster way, which are among the main goals of systems’ automation.

In manufacturing processes, simulation parameters such as arrival times have traditionally been drawn from statistical distributions or from empirical datasets. Although this approach may lead to relatively accurate parameters, there may be applications in which a more precise methodology is required. IoT is a technology that enables for the processing of real time data through microcontrollers and servers. A simulation may ingest this real-time data to modify downstream simulation parameters towards values that will produce higher yield. This chapter will introduce two techniques that are made possible by the availability of real-time data in simulation. First, the chapter will discuss possible optimizations that may be made by selectively choosing parameters that lead to higher production based on real-time data input. Then, the chapter will focus on the ability of IoT-based simulations to dispatch real-time instructions to robots placed in the manufacturing process. The chapter introduces these concepts by the model construction of a drug manufacturing process using a discrete-event simulation software called Tao.

Advancements in drone and image processing technologies opened a new era for data collection. Comprehension by visual sensors is an emerging area which created a completely new view point to many sectors including the production industry. New dimensions are added to abilities of visual human sensors with these technologies. Image processing provide fast, reliable, and integrated information that the industrial facilities require for improving efficiency. On top of this, drones can extend these properties by providing multi-dimensional and continuous view. In this chapter, we propose a new approach for data collection in industrial facilities. Our approach utilises autonomous drones that can fly over the production lines, collect indoor aerial image and video, processes the visual data, and converts it to useful managerial information. Although developing such a system for different manufacturing domains is a challenge, especially Small and Medium-Sized Enterprises (SMEs) can utilise this approach to help achieve Industry 4.0 goals in their manufacturing facilities.

This chapter discusses symbiotic simulation system, a simulation system that is designed to support online short-term operations management decision. The prevalence of real-time data and the advances in Industry 4.0 technologies have made the real-world implementation of the vision of using simulation to support real-time decision making a reality. The main contributions of this chapter are to provide a review of similar concepts in simulation, to provide the architecture of symbiotic simulation system at the conceptual level, to classify the types of symbiotic simulation applications, and to highlights research challenges in symbiotic simulation.

Simulation, especially Discrete-event simulation (DES) and Agent-based simulation (ABS), is widely used in industry to support decision making. It is used to create predictive models or Digital Twins of systems used to analyse what-if scenarios, perform sensitivity analytics on data and decisions and even to optimise the impact of decisions. Simulation-based Analytics, or just Simulation Analytics, therefore has a major role to play in Industry 4.0. However, a major issue in Simulation Analytics is speed. Extensive, continuous experimentation demanded by Industry 4.0 can take a significant time, especially if many replications are required. This is compounded by detailed models as these can take a long time to simulate. Distributed Simulation (DS) techniques use multiple computers to either speed up the simulation of a single model by splitting it across the computers and/or to speed up experimentation by running experiments across multiple computers in parallel. This chapter discusses how DS and Simulation Analytics, as well as concepts from contemporary e-Science, can be combined to contribute to the speed problem by creating a new approach called High Speed Simulation Analytics. We present a vision of High Speed Simulation Analytics to show how this might be integrated with the future of Industry 4.0.

In the manufacturing environment, the Industrial Internet of Things (IIoT) allows machines, products, and processes to communicate with each other to achieve more efficient production. With the growing move to Industry 4.0, increased digitalization is bringing its own unique challenges and concerns to manufacturing. An important component of meeting those challenges is with the use of a Digital Twin. A digital twin provides a virtual representation of a product, part, system or process that allows you to see how it will perform, sometimes even before it exists. A digital twin of the entire manufacturing facility performs in a virtual world very similar to how the entire manufacturing facility performs in the physical world. This broad definition of a digital twin may seem unattainable, but it is not—advanced discrete event simulation products and modeling techniques now make it possible. This chapter will describe the importance of a digital twin and how data-driven and data-generated models, real-time communication, and integral risk-analysis based on an advanced DES product can solve many of the challenges and help realize the benefits offered by Industry 4.0. We will illustrate by providing a brief tutorial on building a data-generated model using the Simio DES product.

In October 2018, The Boeing Company opened their first production facility in Europe. Located in Sheffield in the United Kingdom, the factory will 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. With the vision of developing a digital twin factory, the Boeing Information Technology and Data Analytics team collaborated with the University of Sheffield Advanced Management Research Centre’s (AMRC) Manufacturing Intelligence (MI) team led by Dr Ruby Hughes to set out a strategic plan to simulate the current factory concept, de-risk the introduction of new technologies, monitor factory performance in real-time, and feedback optimal decisions back to the physical environment based on the latest factory situation data. This chapter presents the key elements within the first stage of the strategy plan—simulate—and discusses the approach of linking the simulation model to physical systems to achieve the creation of a digital twin factory.

At the time of Industry 4.0 and the emergence of collaborative workplaces based on the cooperation of robots (machines) and humans, the number of human workplaces in the Industry 4.0 production system is crucial. In this chapter, we present the use of the evolutionary computation methods that use the input data of a real production system and transfer it through the five-stage Cyber-Physical System architecture into the simulation environment in order to determine the optimal number of workers. By using these methods, we confirm the hypothesis of the importance of correctly determining the number of workers in the manufacturing process in Industry 4.0. Number of workers’ determination has a key influence on the product flow time, machine utilization and cost-effectiveness of a production system. Research results show the importance and effectiveness of combining evolutionary computation methods and simulation modelling for the purpose of implementing the advanced approaches of Industry 4.0. The demonstrated approach of combining evolutionary computing, simulation environments and methods of Industry 4.0 can be used from mass customization to mass production systems for the purpose of single-criteria or multi-criteria optimization.

The military Command, Control, Computer, Communication, Intelligence, Surveillance, and Reconnaissance (C4ISR) concepts and those of Industry 4.0 (I4.0) have lots in common. The analysis of defense systems is described by showing the corresponds of the three basic concepts of I4.0 in defense systems. These are connections between cyber-physical systems and automated weapon systems, between Internet of the things and shared tactical picture and sensory data, and between smart factories and computer in the C4ISR concept. The main motivation of this study is to make a conceptual association between C4ISR and I4.0 technologies and an intelligent analysis and run-time decision making mechanism as an intersection of both technologies is exemplified with a smart war effectiveness analysis system which is designed as an intelligent agent for a land-based air defense system.

Simulation help achieve the better in the industry in many ways. It reduces the waste in time and resources and increase efficiency in manufacturing. It also helps increase productivity and the revenue. Simulation has also significant role in the design of products. Furthermore, as the complexity in technology increase, skilled workers required by the industry can be trained by using simulation. Additionally, work safety issues are more important than it was in the past with the emergence of autonomous machines in manufacturing. The data will help create smartness and intelligence in manufacturing and simulation help data analytics in comprehension and knowledge extraction. This chapter is the concluding chapter of this book and summarizes the role of simulation in Industry 4.0. There are explicit and implicit imposed roles of simulation which are summarized in terms of technologies composed of Cyber-Physical Systems (CPS) and smart factory. In conclusion, as this book makes it clear with evidences, simulation is at the heart of Industry 4.0 and the main driver of the new industrial revolution.