Teaching Notes This chapter describes concepts of statistics, statistical thinking, statistical methodology, sampling, experimental design, and process capability. Students should be encouraged to take a “big picture” perspective on this framework, rather than the approach of: “How do I get the right answer?”
There is a Bonus Materials file on the Premium website that reviews the basic concepts and techniques of statistics that are relevant to the technical areas of statistical process control (SPC). These topics are typically covered in business or engineering statistics course that students should have had prior to taking a course using this text. Key objectives for this chapter include:
To establish the importance of statistics as the "bridge" between quality of design and quality of conformance. The proper use of statistics is highlighted as a quality improvement tool.
To help students appreciate the importance of statistical thinking in order to understand inter-related processes, process variation, and the need to reduce it in order to assure quality in operations.
To review definitions and concepts of statistics, and relate them to quality control applications. Spreadsheet techniques for statistical analysis with Excel® software are also emphasized.
To introduce the use of DOE as a tool for drawing conclusions regarding controllable process factors and/or comparing methods for process development or improvement.
To help students to understand the concept of process capability and its effects on quality and conformance to specifications.
To introduce the concept of statistical measurement of service quality
The Instructor’s Resource folder on the course website has a number of Baldrige video clips which give an inside view of organizations that have received the Baldrige award. A couple of those, that are especially appropriate for this chapter, have scenes that show how statistical thinking and concepts can enhance an organization’s quest for world-class quality.
ANSWERS TO QUALITY IN PRACTICE QUESTIONS
Improving Quality of a Wave Soldering Process Through the Design of Experiments
1. The first experimental design at the HP plant did not achieve the true optimum combination of factors, because not all combinations were tested. It is theoretically possible that a better combination of factors exists among those that were not tested. Thus, the ones that were tested could be considered a “random sampling” of all of the possibilities. It is also likely that some interaction effects were at work, so some of the combinations that produced a higher number of defects had to be eliminated.
2. Experimental design allows the experimenter to systematically evaluate two or more methods to determine which is better, or to determine the levels of controllable factors to optimize process yields or minimize variation of a response variable. Therefore, it is generally faster and more efficient than using one at a time, trial-and-error methods.
Applying Statistical Analysis in a Six Sigma Project at GE-Fanuc
1. This case showed how wave soldering technology was applied to electronic circuit boards in a process that is very similar to the “Improving Quality of a Wave Soldering Process Through the Design of Experiments” case, above. There are many variables that must be taken into account in order to improve the process, some of which are the “true” root causes of the problems in the process, but many are not. Thus, the “critical” X’s (independent variables) must be separated from the “insignificant many,” in order to solve the process problems. Many individuals were involved because this was a cross-functional process problem. No one team member had all of the knowledge, but together, they had all that was needed.
2. The Ni-Au boards could have originally been selected for any number of reasons. Reports on the advantages of using Ni-Au boards could have
