## 6 Results and Discussion
Indeed, the minimum value is considered the best according to the impact on space and the HRC relevance, the middle value is the best solution according to SMART and the maximum value is the best for the time. Some consideration can be done to confirm the above results. The closer the resources are, less the shop floor space utilisation is, as emerged in [35], where two different configurations present different values of impact on space. The configuration with the resources placed closer each other has the less space utilization. Furthermore, the closeness of the resources, as well as the higher robot speed, lead to a less time cycle to perform the task schedule [35; 65]. On another hand, the position of the logistic spaces is an input parameter considered by [65], but not investigated. Due to the provided considerations, apparently, the best designing should follow the minimization of the distance among the resources and the positioning of the logistic spaces at the same side. About the robot speed, it should be set according to the interest of the designer and the company. Nevertheless, an _ANOVA analysis_ conducted on the results leads us to discover the magnitude of the impact that the three parameters have on the functions, in order to find out which of them is the most influential. The analysis is carried out on the utility value and three on the four KPIs. The initial investment function is not considered because its value does not change for all the configurations. The main effect of the parameters as well as the combined effects are considered. The resume of the analysis is presented in Table 8. The final considerations are the following:  
* The relative position of the logistic spaces is very significant on the impact on space and the SMART utility value (88.88% and 90.68%);
* The robot speed is very significant on the collaborative time and total time of execution (99.96% and 98.60%);
* The minimum distance has a very low impact on all the performances (less than 9% on all the functions);
* No interaction effect is significant for all the performance (less that 1%). Therefore, from this analysis it emerges that the minimum distance is not a significant factor for all the evaluation criteria. Indeed it has a very low impact on all the evaluation functions and it could be not considered as input for the generation of the scenarios. Its value can depend only on the economic and feasibility needs. On another hand, the robot speed is one of the factors that impacts mostly on the total time and the collaborative time, as expected. Indeed, if the robot moves at a lower speed, there are two different aspects that come out: (i) it is allowed to human to stay close to the robot area and (ii) the shared time could increase or decrease its percentage on the total time depending on the task schedule. In contrast, if the robot move at the higher speed, it is forbidden to stay close to it, therefore the collaboration is penalized, even though the total cycle time decreases. Furthermore, if the robot task is carried out at the highest speed during the inspection, the control by the human gets worse. Finally, the relative position of the logistic space is the most influential factor for SMART and impact on space. That is reasonable because the different layouts need different distribution of the resources on the space. Moreover, the impact on the space is the function with the highest weight in the SMART together with the cost. Therefore, the factor with the highest impact on such performances, has a comparable impact even on the SMART utility value. Figure 11 shows the trend of the functions according to their main influential factors. The diagrams present all the functions reported on a scale from 0 to 100 where 0 corresponds to the worse condition and 100 the best condition. The trends of the SMART utility value and the impact on the space are reported according to the relative position of the logistic spaces. They have pretty similar trends as explained before (Figure 11a).