## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline
**Id.** & **Enabling Technology** & **Description** \\ \hline
1 & Advanced Manufacturing & Autonomous, cooperating industrial robots \\  & Solutions & Numerous integrated sensors and standardized interfaces \\
2 & Additive Manufacturing & 3D printing, particularly for spare parts and prototypes \\  & & Decentralized 3D facilities to reduce transport distances \\  & & and inventory \\
3 & Augmented Reality & Augmented reality for maintenance, logistics \\  & & Display of supporting information, e.g., through glasses \\
4 & Simulation & Simulation of value networks \\  & & Optimization based on real-time data from \\  & & intelligent systems \\  & & Cross-company data integration based on data \\
5 & Horizontal/Vertical & transfer standards \\  & Integration & Precondition for a fully automated value chain \\
6 & Industrial Internet & Network of machines and products \\  & & Multidirectional communication between networked objects \\
7 & Cloud & Management of huge data volumes in open systems \\  & & Real-time communication for production systems \\
8 & Cyber-security & Operation in networks and open systems \\  & & High level of networking between intelligent machines, \\  & & products and systems \\  & & Full evaluation of available data (e.g., from ERP and \\
9 & Big Data and Analytics & machine data) \\  & & Real-time decision-making support and optimization \\ \hline \end{tabular}
\end{table} 
Table 1: Industry 4.0 enabling technologies. In manual assembly, activities to be performed are usually characterized by several phases, like the picking of the assembly components, walking within the industrial layout, the performance of the assembly tasks, etc. Past studies by [PERSON] [17], [PERSON] et al. [18] and [PERSON] [19] determined that picking activities cover a wide percentage of the cycle time, usually more than 50% of the global assembly time. Proper execution of these activities strongly influences the time needed to complete the whole assembly process and the human operator work conditions, affecting productivity and ergonomic performances [9]. To increase these performances maintaining flexibility, next-generation MASs are asked to introduce higher automation and collaboration levels to increase the skills of the operators providing the manual work [12; 20]. This issue remarks the need to designing reconfigurable, self-adaptable and collaborative assembly systems through digital technologies and intelligent automation [21; 22; 23] to accommodate the assembly of a wider range of product variants, characterized by different features, i.e., size, volume, work cycle, and the features of the human operators performing the assembly tasks, i.e., anthropometric measurements. All these aspects stress the compelling urgency to explore and determine human and technological needs of the assembly systems of the future to design and spread them into the industry. Such systems are emerging in the current literature as adaptive automation assembly systems (A3 Ss). Although some qualitative studies attempted to conceptualize such systems introducing their key features and identifying the main system requirements [10; 12; 22], so far, real industry-oriented framework, A3S design approaches and prototypes, and numerical analysis to a benchmark such innovative systems toward traditional ones are still missing and expected. In this scenario, this paper, extending a previous study proposed by [PERSON] et al. [20], presents a general framework guiding toward the effective design and validation of A3 Ss, which can efficiently cope with dynamic production conditions, including the worker variability. The proposed framework is then applied to a full-scale prototypal A3S, named Self-Adaptive Smart Assembly System (SASAS), describing its features together with the productivity and ergonomic improvements arising from its adoption.