Search

Handling uncertainty in information is one of the biggest challenges faced by logistics management and freight forwarding agencies. The uncertainty may be in user needs, availability of raw materials, Estimated Time of Arrivals (ETA) of shipments, or occurrence of any natural calamities. These agencies are increasingly adopting intelligent ways of handling uncertainties in the information. In this paper, we describe a machine learning-based approach to deal with the uncertainty in the Estimated Time of Arrivals (ETA) of shipments. The approach uses multiple regression techniques to predict the ETA of a shipment at the destination port. The approach uses data from two different sources. First is the in-house internal data about shipments maintained by a company. Second is the marine traffic data collected through the Automatic Identification System (AIS). The approach combines these two data to predict the ETA of a shipment. The approach predicts the ETA of the shipment with 89% accuracy. The prediction model described in the paper is being in use and the predicted ETA has been found helpful to effectively manage resources in the downstream supply chain such as human sources at port, transporter and customers inquiries.

Although most logistics and freight forwarding organizations, in one way or another, claim to have core values. The engagement of employees is a vast structure that affects almost every part of the company's core environmental values. There is little theoretical knowledge about the relationship between firms and the engagement of employees. Based on research literature, this paper aims to provide a novel approach for insight around employee engagement in a logistics organization by implementing deep natural language processing concepts. The artificial intelligence-enabled solution named Intelligent Pulse (I-Pulse) can evaluate hundreds and thousands of pulse survey comments and provides the actionable insights and gist of employee feedback. I-Pulse allows the stakeholders to think in new ways in their organization, helping them to have a powerful influence on employee engagement, retention, and efficiency. This study is of corresponding interest to researchers and practitioners., Comment: 11 Pages 7 Figures. International Journal of Information Management Data Insights (Elsevier) 2021