## 2 Literature Review
### ML and DL Predictive Approaches for SCRM-Exploiting Macroeconomic Indicators  
In the data management stage design for ML and DL models, macroeconomic indicators have been widely adopted as input when estimating supply chain demand risks. In [19], the problem of providing 12 months ahead forecast data of sales has been addressed. In particular, both past historical data of product sales and 67,851 macroeconomic data have been adopted as input for a LASSO regression model. The LASSO regression model has been proposed because it can automatically perform the features selection step by identifying the most useful macroeconomic indicators and their lagged versions. This approach has been tested on five years of data related to two products of a raw material supplier for the tire industry, and the results suggest that the proposed method can lead to accuracy improvements of up to 18.8% compared to other traditional models like Holt-Winters, Exponential Smoothing, and ARIMA models. This study has been successively extended in [26; 27]. In ref [26], a bigger dataset, including 10 years of sales from five global plants of tire manufacturers, has been considered and, in addition to the investigation of the forecasting accuracy of the proposed approach, its impacts on the service level and inventory have been examined. In particular, the results reported that adopting the LASSO model leads to lower inventory costs. Lastly, in ref [27], the empirical investigation was extended by including data from two other companies: a global steel producer and a producer of composite building materials. The additional case studies confirmed the advantages of the proposed method, which reported a reduction of 25.6% in terms of mean absolute percentage error compared to the conventional forecasting method. Other studies investigating the integration of macroeconomic indicators when forecasting demand data can also be found in [20; 21; 28]. In ref [20], gross domestic product, unemployment rate, crude oil price, purchasing managers' indices, and copper price were used as input to compare the forecasting accuracy of several traditional and ML models. Conversely, in ref [21; 28], DL models started to be adopted for the task, with the results highlighting the greater capability of these models to provide more accurate forecasts.  
Conversely, supplier-related risks have been rarely predicted, relying on macroeconomic data. To the best of this author's knowledge, the only study considering macroeconomic indicators for predicting supplier delivery delays is the one performed by [18]. In this study, 54 variables, including internal to company and macroeconomic indicators, were provided as input to a deep neural network to solve the problem. However, contrary to previous studies, the problem of predicting supplier delivery delays has been formulated in a classification manner (i.e., by predicting only if a component would have been delivered late or not). Furthermore, this study did not consider autoregressive variables representing the past historical behavior of suppliers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Study**} & **Predicted** & **Predictive** & **Predictive** & **Auto** & **Macro** \\  & **Risk** & **Problem** & **Model** & **Regressive** & **Economic** \\  & & & & **Input Data** & **Input Data** \\ \hline
[14] & SD & C & ML & & \\
[15] & SD & C & ML & & \\
[16] & SD & C & ML & & \\
[17] & SD & C & ML & & \\
[18] & SD & C & DL & & \\
[29] & SD & R & ML & & \\
**This study** & **SD** & **R** & **DL** & **✓** & **✓** \\ \hline \hline \end{tabular}  
* D: Demand, SD: Supplier Delivery, R: Regression, C: Classification, ML: Machine Learning, and DL: Deep Learning.  
\end{table}
Table 1: Cont.