Graduation Semester and Year
Summer 2025
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Industrial Engineering
Department
Industrial and Manufacturing Systems Engineering
First Advisor
Dr. Mahmudur Rahman
Abstract
Objective: This dissertation evaluates the manual order-picking (OP) task from a human factors point of view with the ultimate goal of improving worker safety, performance, and satisfaction. Background: Manual OP tasks involve identifying, searching for, locating, and confirming the selection of specific items listed for picking. Various methods, such as paper instructions, pick-by-voice, and pick-by-light, have been employed to enhance OP efficiency. Advanced technologies like augmented reality smart glasses have demonstrated promise in improving OP efficiency, while Virtual Reality (VR) has shown potential for enhancing performance through immersive training environments. However, the existing literature lacks comprehensive comparative evidence to support or refute the adoption of these emerging technologies. This research conducts a comparative study of three different pick types, such as paper-picking, Handheld Device (HHD)-picking, and vision-picking, using Microsoft HoloLens 2.0. It also explores the efficacy of VR training for manual order picking using Meta Quest 3.0. Methods: This research was done in three phases: (1) a systematic literature review identifies the methods of OP tasks, the use of technology in OP tasks, and human factors issues related to OP tasks (2) an empirical study (study #1) involving human subjects to evaluate the efficacy of three pick types, namely paper-based picking, HHD based picking, and augmented reality (AR) in facilitating manual order-picking tasks, and (3) a second empirical study (study #2) to assess the effectiveness of virtual reality (VR) training for manual OP tasks. In both empirical studies (#1 and #2), objective measures such as task completion time, error rates, distance travelled, and heart rate were collected, alongside subjective evaluations such as NASA TLX, Situation Awareness Rating Technique (SART), and Technology acceptance. Results: The systematic literature review identified various methods and technologies used in OP, along with related human factors concerns. Among the 31 selected papers, task completion time and accuracy emerged as the most common objective measures, while NASA TLX was the predominant subjective measure. In terms of technology, Vuzix, HoloLens, and Google Glass were the most frequently used devices for AR picking. The results of study #1 found that vision picking using HoloLens 2.0 (AR headset) demonstrated better performance in terms of the number of items picked, error rate, NASA TLX, and SART score. The results of study #2 demonstrated the same level (the difference was not statistically significant) of performance for VR training as compared to traditional training for paper picking and vision picking in terms of number of items picked, error count, whereas it showed statistically better performance for NASA TLX, SART score with VR training. The output from the technology acceptance model revealed a positive attitude and behavioral intention towards using VR training for manual order picking. Finally, the system usability score of 71.4 showed that VR training for order picking was relatively easy to use and acceptable to users. Conclusion: The findings of the dissertation identified that vision picking with AR headsets provides better performance in terms of the number of items picked and error count. At the same time, the study identifies that VR training can provide the same or better performance as compared to traditional training for manual order picking.
Keywords
Manual Order Picking (OP), Human Factors, Worker Safety, Augmented Reality (AR), Virtual Reality (VR), Situation Awareness, Technology Acceptance
Disciplines
Ergonomics | Industrial Engineering
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Abdullah, Md, "ENHANCING WORKER SAFETY AND PERFORMANCE IN MANUAL ORDER PICKING WITH EMERGING TECHNOLOGIES AND IMMERSIVE TRAINING" (2025). Industrial, Manufacturing, and Systems Engineering Dissertations. 246.
https://mavmatrix.uta.edu/industrialmanusys_dissertations/246