The value of accurate automated data collection to manufacturing

Abstract

The purpose of this thesis is to demonstrate the value of implementing a novel, accurate automated data collection system and to describe a practical method for doing so. This thesis addresses the value of accurate automated data collection to manufacturing, focusing on a combined passive power monitoring and auto RFID data collection system implemented within the CNC turning and CNC milling departments at the Waters Corporation Machining Center in Milford, Massachusetts. A detailed study of the machining center revealed that inaccurate data was being used to plan production. This thesis demonstrates a graphical approach and an analytical method which can be used to determine manufacturing systems statistics such as average part cycle time and average state dependent setup time. This project addressed the problem by implementing a passive power monitoring and auto RFID data collection system used to monitor ten selected part types and four machines in the turning and milling departments. In order to prevent human error during the experiment, the author developed a series of training guides for Waters to use to avoid data fidelity issues. Since the Waters machining center is a highly metallic environment, many logistical issues were faced during the implementation phase. These logistical issues include an overlapping RFID antenna layout, operator training, and operator compliance. The present data collection system has an opportunity cost of approximately $85,775 per year. Based upon the implementation cost of the experimental setup used for this project, the cost to implement power monitoring and auto RFID at full-scale will be $60,000 in raw materials. The combined passive data collection system is expected to pay off 9 months after implementation in addition to data resolution and standardization benefits.