## 5 Discussion  
The results reported in Figure 3 highlight the overall capability of the proposed approach to provide good predictions for most of the considered components. The results of [29] were similar, highlighting the competitiveness of the proposed approach and the possibility of estimating delivery delays based on regression algorithms in the automotive sector. Furthermore, as shown in Figure 4, the joint adoption of both macroeconomic variables in the data management phase and an LSTM model in the model learning phase (i.e., the proposed approach) outperformed the other benchmarks for most of the forecasts related to the considered components. These results thus support the literature that reports the benefits of adopting deep learning models in other sectors and for other types of forecasts. In addition, it aligns with the need expressed by [15,16] to use external variables to predict suppliers' delivery delays. Lastly, the evidence shown in Figure 5 supports the suggestion made by [22] to consider the whole ML lifecycle when developing new approaches based on this technology. Overall, these results provide evidence of the effectiveness of the proposed approach in solving the problem formulated in Section 3.1 and its capability to cover the research gap reported in Section 2.4. Unlike other approaches [14, 15, 16, 18], the proposed approach can make predictions about delivery risks based on regression algorithms. Second, unlike the work proposed by [29], it allows for the consideration of macroeconomic indicators in predictions. Third, the new proposed approach has finally considered the autocorrelated nature of delivery delays. Lastly, the need to potentially consider macroeconomic indicators related to multiple countries and sectors for each component has been met. Indeed, as shown in Figure 2b, more than 20 different macroeconomic variables have been considered for each component. The outcome of this experiment has several theoretical and managerial implications. From the theoretical point of view, the results support two major theories connected to the field of SC risk management [46]. The higher performance of the proposed approach, composed of a data management stage that considers macroeconomic indicators and a  
Figure 5: Relative importance of the data management and model learning blocks on the results reported in different response metrics. model learning stage based on a DL model, provides support for the information processing theory and the high-reliability theory. According to the former, organizations, as information processing systems, must enhance their capabilities by gathering and processing information from the environment to mitigate SC risk [47]. Meanwhile, according to the latter, organizations must accept complexity and avoid simple explanations for problems [48]. On the other hand, the results also have several managerial implications. First, according to Figure 5, managers should prioritize investment, time, and efforts for the data management phase rather than for the model learning phase. Indeed, the results showed that this is the phase that most affects the results and thus has the highest return on investment. Moreover, two strategies should be preferred when considering the adoption of macroeconomic indicators in this phase: First, predictive models should be built based on deep learning models rather than traditional statistical ones. According to Figure 2, deep learning models have been shown to be able to lead to higher predictive accuracy. Moreover, conversely to the approach proposed in [18], macroeconomic indicators related to the specific sector or country of the supplier under investigation should not be the only indicators considered. Indeed, according to Figure 2, deep learning models reported the best results when 20 features were provided as input. This means that to increase predictive accuracy, models should be able to observe what happens to other sectors and countries.