The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Service quality assessment and improvement methods and tools
Author:	Moriarty, Kevin M.
View Online:	njit-etd2021-078 (xv, 123 pages ~ 0.0 MB pdf)
Department:	Department of Mechanical and Industrial Engineering
Degree:	Doctor of Philosophy
Program:	Industrial Engineering
Document Type:	Dissertation
Advisory Committee:	Ranky, Paul G. (Committee chair) Abdel-Malek, Layek (Committee member) Abdou, George Hanna (Committee member) Bengu, Golgen (Committee member) Cai, Selina (Committee member) Caudill, Reggie J. (Committee member) Pittinski, Todd L. (Committee member)
Date:	2021-05
Keywords:	Analytical hierarchical process CNC Life cycle maintenance Predictive attributes Quality service Risk management
Availability:	Unrestricted
Abstract:	The nucleus of this research concept and system is being applied to turret lathe and milling machine Computer Numerical Control (CNC) tool systems. The research has a generic application to the service of broad array of sophisticated computer controlled / integrated machines, devices / equipment such as industrial robotics, medical equipment, surgical robots, and similar types of engineered system. Quality design review for quality service systems is a unique concept. Standard product service systems are qualitative and subjective in nature. The quantitative system identifies Key Predictive Attributes (KPAs) and applies quantitative methods to these attributes to develop a systematic process of analyzing and monitoring the system. This research is reviewing the specific projection of service outcomes for Machine tool CNC machining centers (Lathes and Milling Machines) for which significant data has still been available for research conducted during the covid-19 pandemic. The specific Key Predictive Attributes are the attributes to be utilized in the newly created modular function in this research. This is a unique application of the creation of diverse service factors. The examples of these KPA factors are, performance utility “Experience Performance Factor”, an ingenious “Delta Sum” forecasting technique, a “service life cycle” analysis factor, and the creation of a “Service Risk Factor”. The KPA factor performance utility “experience performance factor” for this application (utilizing a unique percentage specifically developed for this application). The forecasting factor of delta sum forecasting technique uniquely created for this modular function. The service life cycle analysis method for this equipment has been created to add this as a KPA for the function. The service risk factor utilizes an equation to assign a quantitative value to risk assessment of equipment interruption and down time as well. These are all applied to the process through Quantified Service Quality. This project is unique in that currently there is no system which utilizes methods or tools which proactively gather, analyze, assess, and project outcomes of equipment “Down Time” of the Service Quality process. What makes this research unique additionally is the system is pre-service and not post service reporting of actual down time of the equipment. This research is much more than pro-forma estimates of service outcomes. Another unique aspect of this method is that it establishes tangible tolerances to assess the performance of the Design Review and Service Quality process and does not just rely on subjective nominal values. Mathematical Upper Control Limits (UCL) and Lower Control Limits (LCL) are programmatically developed based upon the system data. This system tool has developed programming algorithms which successfully propel the current process from a subjective qualitative process to become a robust quantitative projection tool. The emphasis of this research is the development of a quality index through the creation of the Moriarty/Ranky Transform approach.