Using Six Sigma to Evaluate Automatic Identification Technologies to Optimize Broken-Case Warehousing Operations

Authored by: Christopher A. Chung , Erick C. Jones

Supply Chain Engineering and Logistics Handbook

Print publication date:  December  2019
Online publication date:  November  2019

Print ISBN: 9781138066519
eBook ISBN: 9781315159096
Adobe ISBN:


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Evaluating the impact of radio frequency identification (RFID) technology on broken pallet case customer returns in this project, we sought to use research scientific technology in order to evaluate where improvements in the manual picking area could be implemented. The problem is that the information associated with a stretch wrapped full pallet License Plate Number (LPN) is lost when a “full” pallet has to be broken or taken apart to fulfill orders with individual cases on that pallet. The cases associated with the full pallet do not have the information such as lot, expiration date, or other relevant information that was included with the original LPN. This is causing customer returns of cases that are approximated at 15% of the outbound volume. It is expected that the volume of “broken” pallet case picking will be increasing in the near future, which with the current methods may lead to increase in labor hours, high turnover, and lower worker productivity. In an attempt to solve this problem, we decided to use the DFSS-R Lean Six Sigma Methodology in which there are three major phases: Plan, Predict, and Perform. These three phases are further expanded to include steps under each. Within the Plan phase, we define and measure the problem; in the Predict Phase, we analyze the problem as well as design solutions to the problem and identify which solution would best fit the company. The last stage Perform includes optimizing the solution and verifying its results; we were not able to follow these steps due to limitations. Our team was able to come up with three solutions that would help Company XYZ’s current problem; however, we were able to narrow it down to one. Of the three solutions, Scenario 3, which is a combination of Gen 2 RFID systems and Engineering Work Process Redesign (EWPR), had the best payback period. In this scenario, we recommend the implementation of Gen 2 RFID Systems, Facility Redesign based on worker productivity, a change in the height of the conveyor, an evaluation of employee work methods, and optimization of worker movements with training and with minimal facility upgrades. In conclusion, we greatly appreciate the opportunity to work with a research team and learn the scientific research approach using RFID and Logistics Technology. We would like to express our gratitude to the National Science Foundation (NSF) to allow us the opportunity to be a part of the NSF-IRES in Mexico UT Arlington Program. We have had the opportunity to enhance our Spanish language skills and experience the Mexican culture. We are also grateful to Company XYZ’s Latin America Distribution Center, Queretaro facility for allowing us to have a hands-on engineering experience.

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