The 2017 Chrysler Pacifica -- 60% Better
The 2017 Chrysler Pacifica -- 60% Better
Starting in 2013 I began working with design teams to study what would become the Chrysler’s Pacifica. I trained and developed the teams, facilitators, and managers. 7FM? Design Failure Mode and Effects Analysis was used with its supporting tools such as Functional Block Diagrams, the anatomy of the function, the time sequence of actions and energy transfers, and combining software functions and physical functions, diagnostics, and mitigation strategies into the same and very simple analysis. The Power Inverter Module that controls the PHEV powertrain used Quantum Quality Matrix FMEA to provide focus for the teams. The teams found many hundreds of improvement opportunities and designs were changed. Yet, some executives still asked the questions, “Would those problems have been found anyway?” The question sounds silly but is a reasonable question and if someone does not ask it, they will be thinking the words. The answer is yes. All problems would have been found…by the customer. The early estimate is that the Pacifica was finalized at 60% better quality than any of its competitors…and it has a PHEV option that also went through the 7FM? DFMEA process.
DFMEA is theoretical problem solving. The functions are defined. Failure modes are selected. Effects of the failure modes are documented. The causes of the failure modes are determined. The activities that control the design parameters are determined (the preventions). How the failure modes will be tested are determined (the detections). Risk numbers for severity and occurrence are estimated and criticality (SXO) determines which causes have the largest potential impact. The technical solutions that create the functions are redesigned (design parameters are changed). Quality, reliability, and safety are all improved.
Detection and RPN are not as useful for product design as they are for material processing, part manufacturing, and assembly operations. My experiences is that test more...test better...has very little impact on improving quality and reliability after a company has passed from poor to good quality designs. It is needed to find gross design oversights but cannot find the poor quality designs that makes all the difference in moving from good and great. Only the statistical noise of problems are found and they are no more important than any other random event. At the point quality becomes good, general testing still needs to be performed, focused, and optimized (function and degradation testing). Testing needs to be maintained at the most professional level and at the lowest expense possible. The urge to test more...test better...needs to be resisted. Dr. Joseph Juran called this the realm of "perfectionism." In the following picture it is the right side of the front curve. Change the focus to understand the parameters that contain causes and improve the designs. Become the right side of the back curve (superior designs).
Spend money working on design parameters that control the important functions ... not the onsie and twosie effects found in the additional test more and test better strategies. I have seen companies that have increased their testing budget by a factor of ten and quality and reliability have not noticeably improved. These companies are well past the knee of the Cost of Poor Quality curve where the benefits of additional testing become wasted money. The picture above comes from an article I published with Dr. Gary Wasserman of Wayne State University. The article won a Cecil C. Craig award from the American Society of Quality (ASQ). Focus on superior designs and the only poor quality costs left are the cost of prevention and the costs of appraisal. It shows the impact of working on design parameters over time. 100% good quality can be achieved when measured against the mission time of designs. This topic is rather large and I might expand upon it in a future posting. What happens when the costs of testing approaches and passes the cost of the teams that create the designs? Dr. Deming often stated that there are two levels of optimal testing and they are 0% and 100%. This works well for producing products but not for designing products.
With DFMEA the failure modes and their related effects are simply theory. The cause to failure mode relationships are theory. DFMEA is a prevention cost of poor quality. The solutions are theory. Problems have not yet occurred. The cost of avoidance is the cost of creating the design. Hit delete and take a different approach because the study suggests problems ahead. Cost of Good Quality is small. Avoided Cost of Poor Quality is HUGE.
The design improvements for the Pacifica were created by the teams and the executives who supported the teams. A lot of very intelligent and professional people were involved and they deserve all the credit earned. The only new methods used for the design and launch was 7FM? DFMEA and the development of 7FM? DFMEA facilitators and experts who supported and guided each team as well as the development of the managers and executives. The teams, managers, and executives focused, found weaknesses, changed designs based on theory, and moved on to ensure closure and that the design and reference documents were updated. They did great and professional work. They EXECUTED.
Overall, working with the teams, mangers, and executives was a fun and rewarding experience.