## 1 Introduction  
The keyword \"Industry 4.0\" describes a new way of production that is distinguished from others through digitization of working processes and increasing integration and modularization of intelligent machines, products, and resources [1, 2]. Industry 4.0 was introduced in Germany in 2011, and one of its goals is to merge the physical and digital worlds. This new paradigm enables continuous resource productivity and efficiency gains to be delivered across the entire value network. Today, there is a worldwide trend in some of the most advanced economies seeking to improve productivity and efficiency in industrial manufacturing by incorporating the latest advances in information and communications technology (ICT). Such a trend concerns the adoption of concepts such as the Internet of Things (IoT) and services (IoS), Cyber-Physical Systems (CPS) and human-robot collaboration [3, 4], Artificial Intelligence [5], industrial automation, continuous connectivity, cybersecurity, semantic technologies, industrial big data, with the aim of improving the overall performance of the manufacturing systems [6, 7, 8]. All these technologies allow a remotely sensed, real-time monitoring and control of devices and cyber-physical production elements across network infrastructures and therefore provide a more direct integration and synchronization from the physical to the virtual world [9, 10]. Large amounts of data are processed, analyzed, and evaluated by simulation and optimization tools in order to be able to make them available for supporting decision makers in an efficient way. In this challenging context, the use of the Digital Model concept is spreading considerably. It \"is a digital representation of an existing or planned physical object that does not use any form of automated data exchange between the physical object and the digital object\" [11]. It is different from the Digital Twin (DT), which represents today a very broad concept [12] and is defined as virtual representation of multiple manufacturing elements such as workforce,processes, assets, and products, which is continuously updated as the physical counterpart changes, with the aim of representing in real-time working conditions and the resources state [13]. According to [PERSON] [14], three main concepts are part of the DT: the process of interaction between the virtual and reality, the virtualization of physical entities, and the materialization of virtual processes. At the same time, the recent COVID-19 pandemic has highlighted and is highlighting the fragility of the supply chains of various industrial sectors and consequently, of the manufacturing systems [15]. Basically, in recent years, the scientific community has been intensifying studies on the concept of resilience, with the aim of understanding how to help Industry 4.0-driven ecosystems to be better prepared for possible COVID-like crisis. However, there is still no research work capable of exploiting the potential of Digital Models combined with simulation approaches along the entire lifecycle of a manufacturing system, from its design to the assessment of the impact of disruptive events and the formulation & evaluation of possible response strategies. Therefore, this paper aims to propose and test a simulation-based framework, aimed at: (1) supporting the design of a manufacturing system, (2) evaluating its resilience in the face of unexpected and disruptive events, (3) estimating the goodness of possible response strategies to face COVID-like crises. The usefulness of the proposed framework is proved through a real-life case study in the wood sector. The remainder of this paper is organized as follows. Section 2 reviews the literature around the concept of Modeling & Simulation (M&S) and the resilience of manufacturing systems confronted with unexpected and disruptive events. In Section 3, our simulation-based framework for manufacturing design and resilience assessment is introduced and explained.