Graduation Semester and Year

2014

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Erick C Jones

Abstract

Most of Enterprise Resource Planning (ERP) systems currently use manual entry or mass upload methods to enter data collected from warehouse operations. These methods are highly unreliable and prone to manual error affecting the data accuracy. Data accuracy and accurate real-time data are required by businesses for many critical decisions. Unreliable data becomes invalid and have a negative performance impact on ERP systems that can lead to economic loss to the organization. Automatic Identification (Auto-ID) technologies are used to track and collect data in real time or near real time. They can be integrated into ERP systems automatically to provide live updates without human intervention thereby reducing the data's inconsistency and increasing timeliness. In this research we hypothesize that by integrating Auto ID technologies, namely RFID and barcodes, into an ERP system that the data accuracy and reliability would be increased significantly by utilizing live data updates. The research begins with identifying the variables affecting the ERP data accuracy by interviewing the business ERP and warehouse users. Then a survey was conducted among, ERP specialists and users, and warehouse specialists and users from an oil company, to collect their perceived impact of these variables on the ERP data accuracy. A Multiple Linear Regression (MLR) model was developed to explain the effect of these variables on the ERP data accuracy. The model was improved by developing a model with better predictor variables. The economic impact of the implementing Auto ID with the ERP system on the data accuracy was then simulated for a variety of accuracy levels for a single product line. Our results were that a known group of experts, including data entry clerks and programmers, determined that Auto ID technologies improve ERP systems data accuracy and that the Return on Investment determined by Net Present Value (NPV) analysis indicates savings would be significant.

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

Download

Share

COinS