Simulation for collaborative processes in industry 4.0.

Abstract

The processes in manufacturing industry are becoming more integrated and complex as the manu- facturing industry is going through a paradigm shift towards industry 4.0. The process simulation in this context becomes increasingly significant to test the design and implementation of such complex systems before making any financial commitments. Simulating different variations of processes can provide valuable insights for decision making stakeholders related to design, per- formance and maintenance of complex manufacturing systems and all the processes involved in these systems. Traditional simulation approaches either focus on a specific process, use case or a part of a pro- cess which are mostly monolithic in nature. Complex processes involved in industry 4.0 based systems, where multiple organisations can form a collaboration to achieve a common objective, cannot be simulated using monolithic simulation approaches. The simulation approaches designed for industry 4.0 also have limited scope for example, focus is on optimising a particular part of the process like scheduling systems. Therefore, this research aims to bridge this gap in literature by proposing a comprehensive and generic simulation framework for simulating complex, collabora- tive processes particularly focusing on industry 4.0 based systems. This research theoretically un- derpins important concepts of simulation and formally describes the working of each component of simulation framework. The use of federated simulation approach is justified for the require- ments of such complex systems(multiple collaborators with varying simulation requirements) in consideration because of the ability to use multiple simulators in a collaborative environment. After formally describing the working of simulation framework for completeness, comprehen- sive implementation of the core elements of framework is developed to show how the proposed framework is expected to work in a real-world use case. The proposed simulation framework is evaluated using 3 real-world case studies. First one is manufacturing of different types of fabrics in which we simulate different types of products with different simulators to show the working of collaborative simulation along with core concepts of the framework and refinements. The second case study we use to evaluate the proposed framework is Machine Shop in which products are manufactured in a sequential way and interruptions occur in the form of parts mal- function which are simulated by using a repairman and disruptions are simulated by forwarding the simulate time by the time it takes to repair a machine. This case study again evaluates federated simulation concept of the proposed framework with low complexity. A more complex case study of supply chain is then implemented to test multiple, existing simulators being used in a federated way to show the working of the framework in a collaborative process which closely depicts how collaborative processes behave in a real-world. Federated simulation worked faster in comparison with non-federated simulation with respect to execution times. Another important contribution of this research is to provide an understanding of Digital Twins concept using the simulation framework (along with enhancements) to a wider audience. We be- lieve that this theoretical understanding of Digital Twins concept is necessary to provide a basis for further research into an area which can improve the current simulation approaches in a dy- namic environment such as industry 4.0 based systems. We also provide a set of simulation scenarios using federated simulation framework to make supply chains resilient particularly focusing on industry 4.0. These simulation scenarios, with the help of core industry 4.0 technologies like IoTs, Artificial Intelligence, cloud computing can help each stakeholder in a supply chain with up to date data to make decisions in near real-time. We only provide the abstract details of the simulation scenarios because the implementation of these scenarios was out of scope for this project. Finally, we developed a method to determine optimal simulation parameters using digital twins and machine learning. Using our proposed digital twins architecture, we propose that by pre- dicting the parameters that has more significant impact on the quality of the simulation, we can enhance the simulation systems to be more accurate representation of a particular process. We also provide a proof-of-concept using a simplistic case study of assembly lines from the literature. The proposed federated simulation framework provides key functionalities to simulate complex, collaborative processes with minimum restrictions. The framework also ensures that the confi- dentiality of processes among the collaborators is maintained while using the existing simulators. Using formalism and implementation, we show the working of the components of the framework in context of real world applications. The proposed framework improves on current federated sim- ulation systems to support simulation of complex and collaborative processes to mitigate errors before the implementation while having minimum restrictions and support for existing simulators, paving way for significant financial savings without compromising on the quality of information available for decision-making stakeholders.