## 5 Discussion and Conclusions  
The multiperiod SSOA problem has always received considerable attention in the literature due to its practical relevance. Recently, DLMs (i.e., deterministic integer linear  
Figure 4: Economic comparison between the proposed approach and the Zero Exploration approach. Figure 5: Economic comparison between the proposed approach and the Continuous Exploration approach. Figure 3: Economic comparison between the proposed approach and the Horacle approach. programming models solved based on values provided by forecasting modules) have started to be adopted to solve the problem due to the benefits that recent advancements in ML and DL have brought to the predictive accuracy of forecasts. However, approaches proposed in the literature often assume static forecast values over time and do not consider that in the new AI era, where data are the fuel, allocating orders to suppliers can also be seen as a way to acquire new data and thus obtain updated, more accurate forecasts. Considering the current state of the literature, two main contributions are proposed in this study. First, a new approach implementing a parametrized DLM to solve the multiperiod SSOA problem is designed and proposed. For the first time, a parametrized DLM is explicitly designed to consider the trade-off between data acquisition benefits and order emission costs when forecasts about the future delivery performances of suppliers are considered to vary as new data are acquired over time. Moreover, empirical results describing the performances reached by the proposed approach and benchmarks in a real automotive case study are reported. These results provide a starting point for understanding the potential benefits of the proposed approach and, more generally, the advantages of adequately balancing data acquisition benefits against order emission costs when solving the multiperiod SSOA problem. In particular, compared to a decision-maker with perfect knowledge of the future, the proposed approach led to increased overall costs that spanned from a minimum increase of 7% to a maximum increase of 56%. The comparison with a decision-maker without perfect knowledge of the future, assuming no benefit of data acquisition, indicated the advantages of the proposed approach, which, on average, led to a 20% reduction in the overall cost. Similar results were observed in the comparison between the proposed approach and one simulating a decision-maker without perfect knowledge of the future, but assuming that it is always positive to send orders to two suppliers to exploit the benefit that new data can provide to the forecasting accuracy. The empirical results obtained from the analysis of the case study thus led to two main implications. First, the results proved that the proposed approach could lead to good supplier selection and order allocation decisions, therefore supporting adopting a parametrized DLM to solve the multiperiod SSOA problem. Secondly, they highlighted the necessity to balance data acquisition benefits and order emission costs over time. Indeed, according to the results, neither assuming zero data acquisition benefits nor assuming that data acquisition benefits are always present resulted in the best choice. Acquiring new data can allow forecast value updates; however, acquiring new supplier-related data is expensive, and updating forecast values does not always lead to changes in order allocation decisions. The observed results are subject to limitations. First, a single case study was adopted to investigate the performance of the proposed approach, thus limiting the generalizability of the findings. Secondly, the MILP model integrated into the proposed approach relied on the assumption that no relationship exists between the delivery performance of suppliers and the ordered quantity. While this assumption can be true for certain components, it cannot hold for others.