Engineering Supply Chain Transportation Indexes through Big Data Analytics and Deep Learning

Abstract

Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms’ transportation operations, among others. Concerning the indexes of transportation operations of supply chain firms, it has been found that the contribution of big data analytics could be crucial to their optimization. Due to big data analytics’ variety and availability, supply chain firms should investigate their impact on their key transportation indexes in their effort to comprehend the variation of the referred indexes. The authors proceeded with the gathering of the required big data analytics from the most established supply chain firms’ websites, based on their (ROPA), revenue growth, and inventory turn values, and performed correlation and linear regression analyses to extract valuable insights for the next stages of the research. Then, these insights, in the form of statistical coefficients, were inserted into the development of a Hybrid Model (Agent-Based and System Dynamics modeling), with the application of the feedforward neural network (FNN) method for the estimation of specific agents’ behavioral analytical metrics, to produce accurate simulations of the selected key performance transportation indexes of supply chain firms. An increase in the number of website visitors to supply chain firms leads to a 60% enhancement of their key transportation performance indexes, mostly related to transportation expenditure. Moreover, it has been found that increased supply chain firms’ website visibility tends to decrease all of the selected transportation performance indexes (TPIs) by an average amount of 87.7%. The implications of the research outcomes highlight the role of increased website visibility and search engine ranking as a cost-efficient means for reducing specific transportation costs (Freight Expenditure, Inferred Rates, and Truckload Line Haul), thus achieving enhanced operational efficiency and transportation capacity.