IE Seminar: Dynamic Data-Driven Adaptive Real-Time Location Tracking System for Workflow Improvement

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Date: March 25, 2022
Time: 11:00 a.m. - 12:00 p.m.
Location: Virtual event
Category: Seminar


Timely and accurate tracking of workers and materials in a production facility is critical to enhancing its productivity and overall efficiency. Workflow analysis is one of the most widely used approaches to identify improvement opportunities for productivity and efficiency.

However, the dynamic and complex nature of production systems makes utilizing traditional workflow analysis very challenging due to the following reasons. First, a detailed workflow analysis of a large production facility requires a massive amount of time measurements for every task. Second, direct observations by human observers for an indefinite time are not feasible and inherently flawed due to human error. Third, data collection based on workers’ input involves bias due to the perceived risk of losing the job. Fourth, there is a lack of algorithms enabling automatic workflow analysis for the production processes that are manual labor-intensive. 

In this talk, Dr. Chowdhury will discuss a novel dynamic data-driven framework based on a real-time location tracking system (RTLTS). The RTLTS is based on the passive radio frequency identification (RFID) and ultra-wideband (UWB) sensors that localize and track workers and materials at the macro and micro levels, respectively. The proposed frameworks provide solutions to four major problems that need to be resolved to achieve workflow analysis based on RTLTS at both levels.


Bijoy Dripta Barua Chowdhury is currently working as an Operations Research Scientist at the University of Phoenix under Business Analytics and Operations Research team. He received his Ph.D. from the Department of Systems and Industrial Engineering at the University of Arizona. Before starting his Ph.D., he served as a Lecturer of the Department of Industrial and Production Engineering at Bangladesh University of Engineering and Technology. In his current role, he analyzes and develops machine learning and predictive models with the students' enrollment, progression, and financial data to support the business stakeholders in strategic decision-making. His PhD research focuses on designing and developing the dynamic data-driven indoor localization system using the RFID system and ultra-wide band technology for workflow and layout optimization. He has co-authored several manuscripts in journals and conference proceedings. He is a member of the Institute of Operation Research and Management Sciences and Institute of Industrial and Systems Engineers.

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March 2022