## 3 Proposed Simulation-Based Framework for Manufacturing Design and Resilience Assessment  
As previously mentioned, the main purpose of this study is to use simulation to support the design of a manufacturing plant, evaluating its resilience and possible strategies in the face of disruptive and unexpected events. Design concerns the identification of the most profitable combination of production parameters, considering that the starting point of the decision-maker is a poor knowledge of the domain. To achieve all these objectives, a simulation-based framework is proposed, which is shown in Figure 1.  
Basically, at the beginning, the real manufacturing system is reproduced in a virtual environment in the form of a Digital Model, to carry out all the experiments and analyze the benefits and drawbacks of multiple application scenarios. However, once the production system has been designed and put into operation, this framework can continue to be used as a monitoring and control tool. This is because the tool has a cyclic structure. More precisely, the meaning of the various blocks is explained below:  
* Real Word represents the set of machinery and equipment of the production plant under analysis.
* Data Collection is the phase in which all the real-world data are collected, which will constitute the input for the simulation model underlying the Digital Model.
* Digital Model represents the system designed in a software environment in the form of a Virtual World. In this context, a simulation model must support the testing of different operational configurations, with the aim of identifying the best in terms of effectiveness and efficiency. More specifically, we refer to discrete-event simulation through the Siemens Plant Simulation.  
Figure 1: The simulation-based framework for manufacturing design and resilience assessment.  
* Insights Analysis represents the step within which an evaluation of the information collected as output of the simulation model takes place. This phase is carried out through multiple tools and approaches such as Design of Experiment, Full Factorial Design, and ANOVA. Moreover, Minitab supports this step.
* Decision Maker is the entity responsible for the design choices of the production plant, who exploits the information acquired through the Digital Model to identify the best possible configuration in terms of machinery, operators, re-order levels, etc.
* Corrective Actions: based on the analyses carried out, the decision-maker can decide to take corrective actions on the real system. In the design phase, these corrective actions consist in making some changes about a possible configuration of the production system. However, when fully operational, these actions concern the execution of control actions on the system, once monitoring has been carried out.  
The portion of the framework highlighted in orange is devoted to assessing the resilience of the manufacturing system and the effectiveness of any response strategies in the face of disruptive and unexpected events. Basically, it is possible that the real system is affected by a disruption. The effects of this event can be studied under various scenarios using the Digital Model, which outputs an assessment of the resilience of the manufacturing system. Therefore, the Decision-Maker can evaluate the validity of different response strategies on the real system. This role of the Digital Model is crucial, also considering the recent outbreak of the Covid-19 pandemic, which has highlighted all the fragility of manufacturing ecosystems and supply chains. In this context, it is really important to highlight that the different examined response strategies can be assessed through the joint use of Siemens Plant Simulation and Minitab, with the aim of carrying out multiple statistical and what-if analyses.