# Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning
## 1 Introduction
### Supply Chain Transportation Indexes Importance
Are supply chain transportation firms equipped to optimize their operations by unlocking the dynamics of behavioral analytics? Despite the vast literature on supply chain transportation operations [13], descriptive, predictive, and prescriptive analytics considering the optimization of key transportation indexes through big data analytics and deep learning techniques such as SVM, KNN, CNN, LSTM models, etc., are still lacking. Existing research regarding supply chain transportation issues only concerns how to maximize profits and improve performance [14; 15], providing less information on customers' digital behavior. Since companies are now moving to customer-centricity approaches to remain competent and improve performance outcomes [16], adopting behavioral analytics metrics becomes of the essence. The accuracy of supply chain customers' demands through deep and machine learning contexts, such as SVM, naive Bayes, KNN, CNN, and LSTM models, could enhance the efficiency and performance of supply chain firms [17]. Moreover, the reduction in inventory storage costs and the increase in supply chain firms' profits are among the other benefits of deep learning procedures' capitalization (such as LSTM and LGBM models) combined with big data analytics [18]. Lastly, deep learning methodologies, such as CNN and SBO models, in the supply chain context tend to remove uncertainties that are based on firms' expenses, competitiveness, operational efficiency, etc. [19]. The purpose of the current study is to provide fruitful insights into the impact of website visibility and search engine ranking on key transportation performance indexes for supply chain firms. The authors have identified the potential of utilizing deep learning methods and opted to apply a simple version of the FNN to estimate the behavioral metrics of 100,000 supply chain firms' website visitors. Hence, with the assistance of deep learning methods (the FNN procedure), the developed hybrid simulation model showed that the more visitors supply chain websites have, the lower their transportation performance indexes (TPIs) obtained based on the FNN visitors' behavioral metrics estimation. The TPIs that indicate enhanced operational efficiency when having lower values are Freight Expenditure, Inferred Rates, and Truckload Line Haul. Big data analytics from corporate websites have been found to be capable of reducing the operation costs of supply chain firms in a cost-efficient manner. The main managerial and practical implications of the present paper are summarized below in the following categories:  
* Supply chain marketers could use the firm's website's big data analytics to optimize its search engine results and obtain a higher ranking in search engines. * The operational staff of supply chain firms could estimate specific metrics of corporate transportation costs and their impact on the firm's operational performance based on website big data analytics. * The potential of deep learning methods, such as the FNN, as an efficient decision-making tool for supply chain firms seeking to simulate multiple website visitors' behavioral metrics.