Open AccessAn Intelligent System to Automate the Inquery in Logistics Industry using AI and Machine Learning
Author(s) -
Leo Liao,
Ang Li
Publication year - 2022
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5121/csit.2022.120109
Subject(s) - computer science , workflow , obstacle , profit (economics) , process (computing) , automation , operator (biology) , work (physics) , database , industrial engineering , artificial intelligence , operations research , engineering , operating system , mechanical engineering , biochemistry , chemistry , repressor , political science , transcription factor , law , economics , gene , microeconomics
Operator and sales employees in the logistics industry often have to submit the same inquiry repetitively to different vendors and opt in for the quotation that will generate the greatest profit for the company [4]. This process can be very laborious and tedious. Meanwhile, for smaller companies that do not have a well-constructed database for quotation information, monitoring employee’s work is simply difficult to achieve [5]. To increase the efficiency of sales’ workflow in this particular industry, this application devises a platform that automates the inquiry process, analyzes quotations from different vendors, retrieves the most profitable one, and documents all inquiries an employee has committed [6]. The results, after a series of intensive testing, prove to be promising and satisfying. The machine learning model can successfully fetch the most cost-effective price after analyzing a list of emails containing common languages used in the industry. All histories of an employee’s inquiry can be correctly displayed on any front-end device. Overall, the obstacle presented above is largely solved.