Judging Category
Basic or Experimental Research
Student Rank
Graduate
College
Business
Faculty Sponsor
Dr. Mello jmello@astate.edu
Description
ABSTRACT:
Order picking is the most labor-intensive activity in warehousing, accounting for approximately 55% of total operating expenses. This study evaluates the efficiency and quality trade-offs among three primary picking methods: Single, Batch, and Zone. Conducted via an educational simulation, the study utilized a 60-SKU alphanumeric Lego inventory system where four participants executed picking cycles to measure operational lead time and quality variance.
The results demonstrated a significant correlation between picking methodology and output quality. Single Picking achieved the highest quality standards, yielding only three errors alongside the fastest completion time (4:21). Conversely, Batch Picking resulted in the lowest quality, producing six errors and physical handling incidents (dropped items); these quality failures likely stem from the high cognitive load required to sort multiple simultaneous orders (7:20). Zone Picking showed moderate quality with four errors but proved the least efficient (8:35) due to coordination bottlenecks.
These findings indicate that while advanced picking strategies aim to reduce travel time, they introduce human-factor risks, specifically "noise" in communication and sorting, that can degrade overall quality and offset efficiency gains. Beyond operational insights, this study demonstrates the value of hands-on simulations for understanding the delicate balance between speed and quality. By identifying how process design influences error rates, this research provides a framework for students and managers to mitigate the hidden costs of quality failures in supply chain dynamics.
Disciplines
Business Administration, Management, and Operations | Operations and Supply Chain Management
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Tran, Mien Thi Thao and Magar, Samita Rana, "Investigating Various Methods of Order Picking on Efficiency and Quality of Distribution Center Picking Operations" (2026). Create@State. 18.
https://arch.astate.edu/evn-createstate/2026/posters/18
Included in
Business Administration, Management, and Operations Commons, Operations and Supply Chain Management Commons
Investigating Various Methods of Order Picking on Efficiency and Quality of Distribution Center Picking Operations
ABSTRACT:
Order picking is the most labor-intensive activity in warehousing, accounting for approximately 55% of total operating expenses. This study evaluates the efficiency and quality trade-offs among three primary picking methods: Single, Batch, and Zone. Conducted via an educational simulation, the study utilized a 60-SKU alphanumeric Lego inventory system where four participants executed picking cycles to measure operational lead time and quality variance.
The results demonstrated a significant correlation between picking methodology and output quality. Single Picking achieved the highest quality standards, yielding only three errors alongside the fastest completion time (4:21). Conversely, Batch Picking resulted in the lowest quality, producing six errors and physical handling incidents (dropped items); these quality failures likely stem from the high cognitive load required to sort multiple simultaneous orders (7:20). Zone Picking showed moderate quality with four errors but proved the least efficient (8:35) due to coordination bottlenecks.
These findings indicate that while advanced picking strategies aim to reduce travel time, they introduce human-factor risks, specifically "noise" in communication and sorting, that can degrade overall quality and offset efficiency gains. Beyond operational insights, this study demonstrates the value of hands-on simulations for understanding the delicate balance between speed and quality. By identifying how process design influences error rates, this research provides a framework for students and managers to mitigate the hidden costs of quality failures in supply chain dynamics.
