## 2 Literature Review  
### Some Considerations about Manual Assembly  
According to [PERSON] and [PERSON] [24], manual assembly \"involves the composition of previously manufactured components or sub-assemblies into a complete product or unit of a product, primarily performed by human operators using their inherent dexterity, skill and judgment\". [PERSON] et al. [19] describe manual assembly as a spatial problem-solving activity that requires that workers build a mental model to understand and manipulate spatial information. The quality of the information contained in work instructions, the way this information is presented and how the worker interacts with this information are particularly important in manual assembly processes [25; 26; 27; 28]. To take full advantage of the assemblers' cognitive abilities, work instructions should clearly and unambiguously describe which components to use and how they should be assembled [27]. One accepted principle is that these instructions must be presented in such a way that anyone can understand them and conduct assembly accordingly [29]. This way, work instructions can help to reduce the assemblers' cognitive workloads, particularly by minimizing dynamic complexity. In modern manual assembly processes, work instructions are generally provided electronically and presented on a computer screen, as text supported by visual information [25; 28]. However, according to [PERSON] et al. [30], instructions need to be more perceptual, which means that richer and more immediate sensory inputs should be provided to the assembler. Using three-dimensional models in work instructions can show the assembly process in a more realistic, accurate and intuitive fashion. Such model-based instructions (MBI) may present multiple views and easy-to-follow assembly procedures [31; 32]. Recent technologies, such as augmented reality (AR), seem to promise even better means of delivering work instructions to assemblers. In a recent literature review, AR was identified as one of the two most promising technologies to support humans in manufacturing contexts, along with collaborative robots [33]. However, applications of AR are still in development and the technology has not reached full technological maturity [34; 35]. Choosing the right parts during the assembly requires a certain amount of information processing and decision making, which is why the use of kitting systems has been explored as a strategy to minimize this cognitive load [36]. Parts kitting is a logistics technique used to deliver groups of parts together in one or more kit containers [37]. Generally, kits are prepared in a warehouse and delivered to the assembly line, to specific workstations, according to the production schedule. When kits are prepared properly, parts are supposed to be easily available, checked, and prepositioned so that they can be removed rapidly from the container [38]. According to [PERSON] [25], a kit can be considered as a \"carrier of information\" for assembly, meaning that work instructions are embedded in the kit itself. [PERSON] [39] argues that appropriately structured kits can support assemblers and even facilitate learning. According to [PERSON] et al. [37], kitting systems provide the opportunity for in-process quality control and additional checks, both in the kitting room or at the workstation. Kitting can therefore reduce the risk of a wrong part being assembled or of a part being omitted by providing direct feedback to the assembler. Past research suggests that kitting may yield better quality and productivity when compared to other parts feeding policies [25; 39; 40]. It has been acknowledged that errors cannot be entirely eliminated because they are considered to be a normal consequence of human variability [41]. Thus, during manual assembly, inspection is required to verify that a product is free of defects before it is transferred to the next level of assembly or shipped to the customer [42]. In this way, inspection allows the system to recover from human error.