Strengthening Six Sigma

Abstract

A recent special issue of the Journal (Quality and Reliability Engineering International 2007; 23(1):1-154) contained selected papers from the Fourth International Conference on Quality and Reliability. A paper in this issue by Tang et al. 1 discussed the importance of enhancing and increasing the usefulness of Six Sigma by incorporating new techniques into the familiar DMAIC (Define, Measure, Analyze, Improve, Control) problem-solving framework. Many of these techniques are from the operations research/management science fields. The authors argue convincingly that Six Sigma and DMAIC cannot remain static if they are to sustain the value proposition for business and industry. I think that this is particularly true as Six Sigma moves beyond the traditional manufacturing and engineering applications into transactional and service industries. I would like to suggest that there are other techniques that need to be included in the Six Sigma practitioner's toolkit.Discrete-even simulation methods are critical to the implementation of Six Sigma in many environments. In transactional and service industries, it is almost impossible to experiment directly with the actual system, so most efforts directed towards improvement and studying the impact of system changes must involve a simulation model of the system. For example, if a Six Sigma team is investigating how to reduce waiting times in a hospital emergency room, it will be virtually impossible to make changes in the actual emergency room. However, with a properly developed and validated simulation model of the emergency room, changes in operating policy, staffing, and resource availability can be easily and efficiently investigated. A recent paper in the Journal by Abbas et al. 2 discusses how simulation can be used to optimize the design of clinical trials.Another important aspect of Six Sigma training is to include more information about engineer process control (EPC) methods (as opposed to process monitoring, such as control charts). EPC is widely used in the chemical and process industries to keep critical process outputs on target by making appropriate adjustments to process inputs, taking advantage of known relationships between the inputs and the outputs. These techniques deal with variability not by eliminating assignable causes but by transferring the variability in the output to the input. For example, we may keep the molecular weight near to the desired target by making regular adjustments to temperature via a technique such as feedback control. There has been considerable research on EPC methods and methods to integrate EPC and SPC are reasonably well known. However, there is usually little effort in Six Sigma training devoted to EPC, even for individuals who will practice Six Sigma in the chemical and process industries. Recent papers in the Journal devoted to EPC and SPC include Jaing et al. 3 , Runger et al. 4 , and Yang and Sheu 5 .Forecasting and time series methods are not usually sufficiently addressed in many Six Sigma training programs. Forecasti...

Dealing With Kano Model Dynamics: Strengthening the Quality Function Deployment as a Design for Six Sigma Tool

“…On top of that, this paper aims to strengthen the Six Sigma as it moves beyond the traditional manufacturing and engineering applications into transactional and service industries (Montgomery, 2007) by providing a better analysis on Kano Model, which is a prerequisite in the 'Measure' phase in the Define-Measure-Analyze-Improve-Control (DMAIC) stage (Tang et al, 2007).…”