Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing; FAIM 2014

Paper presented at the Proceedings of the 24th International Conference on Flexible Automation & Intelligent Manufacturing, held May 20-23, 2014 in San Antonio, Texas, and organized by the Center for Advanced Manufacturing and Lean Systems, University of Texas at San Antonio; Includes bibliographical references; The main focus of this study is to apply the Lean concept and methods to help a compressed gas and liquid filling company find the root causes of slow production as well as identify where and how the gas and liquid are lost and suggest methods for improvement. A careful study of the production process was done by analyzing the setups and layout of current work stations, interviewing employees, conducting time study, drawing work flow charts, value stream mapping and measuring the input and output gas amount. Various wastes such as transportation, motion, and waiting were identified, and gas and liquid leaking stations were found. A new plant layout was provided to help the company improve the production efficiency. The results show that these lean manufacturing initiatives had led to a reduction of travel distance, production lead time and factory floor spac

05. Examining the use of the DMAIC process on the operational plan improvement for UPS Personal Vehicle Drivers

UPS has recently incorporated temporary seasonal personnel that deliver packages out of their personal vehicles, named Personal Vehicle Drivers (PVDs). Being able to deliver at a low cost while providing additional capacity makes them an important resource to the company, but without a guideline in place, they are being used inefficiently at some delivery centers. By utilizing the Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) process and tools included in the International Organization for Standardization (ISO) 13503, the goal of this project was to analyze how PVDs are currently being dispatched in creating a cost-effective plan to be used for future implementation. With the DMAIC method, interviews with employees directly involved in working with PVDs were first conducted and key metrics of PVD efficiency were identified. Based on delivery data collected from November 29th through December 24th, 2019, a statistical multiple regression analysis was then performed to give a predictive equation of PVD performance. This equation was then validated with the delivery data collected from the same period in 2020. These efforts in combination with two other teams’ approaches to the problem led to the creation of an operational plan and interactive tool to pinpoint the most productive areas for PVD routes. These solutions, if used, will result in betterment of UPS package delivery process by PVDs. The process and findings of this project can serve as a research model for other organizations who have an introduced resource that is being used without efficient direction and want to create a methodical guideline