# Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning
## 2 Materials and Methods
### Research Hypotheses  
The purpose of the present research is to examine the implications of FNN deep learning procedures for supply chain firms' transportation performance based on big data analytics. As analyzed above, the improvement of supply chain firms' transportation indexes is crucial and, as a result, should be the utmost goal for the managerial staff of supply chain firms. The selected indexes represent key transportation performance indicators for supply chain firms while providing important insights for their expense management and finance departments. It has been indicated that supply chain firms should observe any variations in their transportation performance indexes originating from various sources. The utilization of big data analytics from their corporate websites has been discerned as an important factor for their sustainability and financial performance [43; 44]. In this context, the benefits of using big data analytics from corporate websites could be extended to supply chain firms' transportation performance, given the fact that a notable and significant relationship arises among them. Therefore, the authors seek to investigate the impact of specific big data analytics from the websites of supply chain firms on key transportation performance indicators (indexes). Since the selected indexes contain elements of corporate expenses, some of the selected big data analytics focused on the organic and paid campaign costs of supply chain firms. More specifically, the following research hypotheses have been settled based on the literature review and the focus of this study, as see in Figure 1.  
The Freight Shipments Index (FSI) [45] shows the volume of cargo and shipment transportation for supply chain firms. This metric, foremost, indicates the demand of supply chain firms' clients for goods transportation, making its prediction highly valuable. For supply chain firms to discover a new context of prediction factors for their transportation volumes, multiple benefits could emerge, first and foremost for their financial performance as well as for their expenses' repercussions. The potential prediction of their customers' demand constitutes another important benefit for supply chain firms to start analyzing key big data analytics from their websites. **Hypothesis 1 (H1):** The Freight Shipments Index of supply chain firms can be reduced by the adjustment of their website's big data analytics. Regarding the Freight Expenditure Index (FEI), supply chain firms can calculate every year the expenses related to their cargo and freight transportation. For these firms, being able to simulate freight costs could prove beneficial. If a connection is being developed between freight expenditures and the big data analytics of supply chain firms, then they could proceed to a proper adjustment of the big data analytical metrics. Thus, given that supply chain firms could discern that their freight expenditures decreased with the adjustment of specific big data analytics, they would be able to predict and reduce their costs. **Hypothesis 2 (H2):** The Freight Expenditure Index of supply chain firms can be reduced through the adjustment of their website's big data analytics. Inferred Rates (IR) as an index of supply chain firms' performance show the analogy of expenses by the number of deliveries to help depict the transportation performance per activity. Knowledge of the specific big data analytics that are strongly connected with supply chain firms' Inferred Rates provides a solid framework for these firms to further try to reduce this index. Again, the proper adjustment of big data analytics could indicate changes to the Inferred Rates index, given that it is closely connected with specific metrics. **Hypothesis 3 (H3):** There is a strong connection between supply chain firms' website big data analytics and their Inferred Rates. For the next hypothesis of the paper, the authors discern the role of the Truckload Line Haul Index as another key transportation performance index.