# Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning
## 1 Introduction
### Supply Chain Operations' Engineering through Deep Learning  
Supply chain operations engineering involves the design, analysis, and optimization of the supply chain elements and processes to ensure the efficient flow of goods and services from suppliers to end customers [30]. With the revolution of smart technologies, supply chain engineering should be seen as an innovation able to achieve radical business transformation [31]. In other words, this is a common ground for applied sciences, information technology, and supply chain management to increase the effectiveness of supply chain demand while simultaneously adding flexibility and decreasing costs [32].  
During the past years, numerous companies have been rocked by unforeseen supply-chain vulnerabilities and disruptions, leading to revenue loss and poor customer satisfaction [33]. In an attempt to change the current landscape and balance competing goals such as risk, vulnerability, and financial risk against reliability and operational efficiency [34], deep learning methods such as variational autoencoders (VAEs) can be utilized. By using applied sciences, firms could leverage the dynamics of immersive technology to analyze and optimize elements and processes of the supply chain to meet specific business requirements. When applied to the supply chain, deep learning algorithms, such as deep learning-augmented decision-making (DLADM), enhance the efficiency of companies and improve the decision-making process [35].  
Recently, the deep learning algorithm of the multilayer feedforward artificial neural network (MLFANN) has been used in a study to estimate customers' demand [5]. The authors highlight the importance of using deep learning in developing an improved demand forecasting model based on customers' behavior through historical sales data. Identifying when customers obtain it is the key factor for business performance in the digital age [36]. For example, businesses can use algorithms powered by deep learning to examine past sales data, purchasing behaviors, and other key information to forecast supply chain demand. This data can be utilized to discover new behavioral patterns, ensuring the overall advancement and sustainability of the supply.  
Despite their great importance, deep learning methods, such as deep neural networks (DNNs), are challenged by the need to use large quantities of data to validate the outcome [37]. [PERSON] [38] has introduced the concept of combining big data, deep learning methods (SVM, KNN, CNN, and LSTM models), and simulation as a holistic approach to supporting observation, analysis, and application for unbiased data evaluation by repeatable mechanisms. To this end, the current paper has developed an agent-based model based on an ever-increasing number of agents to validate the outcomes, reduce potential errors, and optimize the simulation results. The agents represent the visitors of a supply chain firm's website, and the hybrid simulation model applies FNN deep learning processes by adjusting the number of website visits and the number of website visitors to optimize the paper's transportation indexes.