## 5 Proposed Methodology  
### Process Modelling and Simulation Role  
In the context of the current research, process simulation model (PSM) denotes a generic model with all the related methods, algorithms, mechanisms, services and tools it directly uses, integrated into an overall modelling application or platform. In any individual model, these methods, algorithms and mechanisms do not change, but the process model (digital) representation per se is adapted to the specific needs and features of the use case. PSM interconnects and interoperates with external AI tools, optimisation tools, analytics tools, etc. These may also change from subclass to subclass. However, PSM itself does not change from application case to case. Figure 1 outlines the communication between the physical system and its digital shadow as well as the procedures taking place in the context of the implemented services. Generally speaking, the PSM can be used in conjunction with other tools and methods for predicting and cognitive purposes that serve decision making. Predicting system abilities can be used to support the physical system's dynamic adaptation, e.g., by predicting product demand or resources' availability and matching accordingly production schedule and process parameters (i.e., product supply). An entirely different type of prediction will be to anticipate the next process/system state change(s) (including potential problems), in order to perform some action or notify some operator. The cognitive behavioural enhancements of the model add elements of reasoning capacity, understanding of system knowledge and self-awareness, with the main focus put on the anticipation (and corrective action). The PSM uses the analytics service to estimate KPIs for assessing (_a_) the performance of the current system (or single processes), either at regular intervals or on request), and/or (_b_) the performance of a simulated scenario(s). In addition, analytics provide sequences of events to be simulated and approach the characteristics (delays and appearances) especially of uncontrollable and unpredictable events using statistical and AI tools. Given that the AI tools detect an anomaly, PSM can either verify or refine (or both) this AI inference running a scenario of the current system in isolation from the current shadow twin, in order to test some hypothesis about the anomaly. A simulation may be carried out either (i) in the present to repeat processes with modified parameters, (ii) in the past to retrace the system's/event's history, or (iii) in the future to predict consequences. Regarding the interaction with optimisation service, there is a bidirectional interchange of data, to be used for running both services with common assumptions in order to define the optimal policy, evaluate and cross evaluate its efficiency. In addition, the process model can be used to evaluate a subset of feasible alternative schedules through simulation in order to provide the final selection to the decision makers, especially in the cases that no optimal policy can be analytically defined. Ideally, the role of the central (production system) modelling component can be defined in relation to the cognition process, as follows:  
1. Create a dynamic digital shadow of the physical system, providing an accurate image of its operation at any time, updated by getting real-time monitoring data from the  
Figure 1: Physical system—digital shadow communication and services interoperation. analytics platform. The dynamic image can be also used by man-machine interfaces for system monitoring, control and management. 2. Accurately model all production processes as well as all entities along the product's value chain and all inputs and outputs, providing a comprehensive systemic view and operational statistics. 3. Use KPIs and business rules and benchmarks to assess the performance of the entire system and each component; also compare estimated and real-time data for self-assessment. 4.