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Project # 7
Optimizing Warehouse Picking: Evaluating Accuracy, Speed, and Workload Across Instruction Modalities
| Team Lead: | Matin Ghasempour Anaraki |
| Other Team Members: | Eshwara Prasad Sridhar | Md Abdullah |
| Faculty Advisor(s): | Mahmudur Rahman |
| Department: | IMSE |
| Project Type: | Graduate Student |
| Title: | Optimizing Warehouse Picking: Evaluating Accuracy, Speed, and Workload Across Instruction Modalities |
| Abstract: | This research investigates how different warehouse order picking strategies affect both worker performance and ergonomics, with a focus on comparing traditional paper lists and handheld devices (HHD) to an augmented reality (AR) picking system. Although order picking represents a major share of warehouse labor costs, the trade-offs between conventional tools and emerging immersive technologies remain insufficiently understood. To address this gap, the study evaluates discrete and batch picking methods in terms of productivity, accuracy, and worker well-being. Participants complete fulfillment tasks using either discrete or batch picking, with AR integrated into one experimental condition alongside paper and HHD approaches. Key performance measures include task completion time, error rates, and perceived workload. Preliminary findings indicate that discrete picking enhances accuracy but slows performance. In contrast, batch picking increases speed while raising error rates and physical demand. Overall, the results suggest that AR can help balance efficiency and ergonomics, offering practical guidance for warehouse managers seeking to optimize task design and support worker well-being. |
| Poster: | View Poster #7 (PDF) |
| Judges Scoring: | Submit Evaluation (judges only) |