Dean of Springs

Do You Know Your Quality Numbers?

By Dan Sebastian

What is yourcost of qualityreturn rate in dollars as a percentage of sales? What is yourdefect rate in PPM(parts per million)? Do you know thecritical characteristics of the parts you are making? Do you use the statistical measures of the critical characteristics (standarddeviation σ)? What is the process potential index (Cp) and what is the process performance index (Cpk)?

The early 1980s was a turbulent time for the auto industry. Chrysler was being bailed out by the government, while General Motors Corp. and Ford were losing a large share of market to the Japanese auto companies because of inadequate quality from U.S. manufacturers. The problem was deeply rooted and began after World War II.

During the war, U.S. manufacturers were pressed into high volume manufacturing to support the war effort. The war boards set up by the Roosevelt administration established the Emergency Technical Committee for American War Standards. They went to W. Edward Deming and Joseph Juran to establish a quality program. Both Deming and Juran had worked with Walter Shewhart of Western Electric (also Bell Labs), who pioneered the use of statistical quality control methods. The programs were enormously successful. After the war, American companies began to be run by non-technical people, who turned to efficiency experts (modern-day lean managers) who saw the time used to perform quality checks as wasted time. In large measure, these quality checks were abandoned, which brought us to the quality crisis of the 1980s.

I arrived at the Associated Spring valve spring operation on a cold snowy day in February 1982. My assignment was to “fix a few minor problems with inventory and production issues.”

As I began to assess the problems, it was apparent that what we faced was a lot more than anyone anticipated. I went to my engineering roots and formed a Pareto diagram of the many problems we had to address. It started with an evaluation of the inventory issues. As in any good plan, I had to first deal with a significant distrust between managers and union employees. As we began the arduous process of rebuilding trust, we were hit by a notice from Ford that we were being decertified as a vendor. After a series of meetings with Ford quality and procurement people and a serious look at the inventory issue, it was apparent that the root cause was a complete absence of both an understanding and commitment to the quality of our product. As we looked at our quality measurements, we saw that there was a complete lack of control.

At first, I was confused by this issue, as we had some of the best setup people and operators in the business and they were measuring parts and adjusting for out of the center point of the characteristic being measured. We turned to the teachings of Shewhart, Deming and Juran (the Ford quality people pointed us in the right direction).

The Basics

The journey to establish a consistent quality process that gave us the parts our customers required started with the basics. As we studied the problem, we realized we did not

Probability Density 0.400.350.300.250.200.150.100.050.00µ-3σ µ-2σ

68.27%

95.45%

99.73%

µ-σ µ µ+σ µ+2σ µ+3σ

Dan Sebastian is a former SMI president and currently serves as a technical consultant to the association. He holds a degree in metallurgical engineering from Lehigh University and his industry career spans more than four decades in various technical and management roles. He may be reached by contacting SMI at 630-495-8588.

understand the normal variation. The data clearly looked like a normal distribution (see page 17).

The math to calculate the deviation σ was found on most calculators or with an easy download. As our search to make sure our customer’s requirement for parts in specification (critical characteristics) and life that exceeded 100,000 miles and in some cases 500,000 miles, we realized a simple + or - 3 σ was not good enough. The chart below shows why.

Sigma Level σ 1 2 3 4 5 6

Expected Failure Rate

691,462 308,538 66,807 6,210 233 3.4

The work by Shewhart showed that you need to allow normal variation to occur and only adjust when the parts exceeded the control limits. What this meant was our employees’ efforts to simply measure and adjust was causing more harm than good.

After we mastered the use of control charts, we made some interesting discoveries. The one that we were not expecting was that anything we measured with statistical methods got better. Use of control charts is simple, and today software can assist you in tabulating the data. Some automated measuring devices can actually adjust the equipment

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Measurement

Time or sequence (x axis)

UCL = Upper control limit; LCL = Lower control limit Mean

LCL

Shewhart Control Chart

using the SPC rules. The “Golden Rule” here is to only make adjustments when you exceed the upper or lower control limit. The upper and lower control limits are the expected tolerances for the characteristics being measured.

All the data coming in from the control charts gave us the opportunity to understand process potential, or Cp. In fact, with the information we could calculate the Cp.

Mathematically, Cp is expressed as follows:

Cp = (USL – LSL) / (6 x sigma σ)

Where: USL = upper specification limit LSL = lower specification limit

Knowing the potential was great, but we needed to understand how capable we were in meeting our potential. Again, the science of quality production gave us a way to calculate it.

Mathematically, Cpk is expressed as follows:

Cpk = min {(µ – LSL) / 3 sigma, (USL – µ) / 3 sigma}

Where: µ = Mean

All this information, as well an extensive training program, changed the manufacturing process in our plant. The commitment to quality paid off. The hard work of our employees resulted in the division being the first supplier of springs to receive Ford’s “Q1” award. Not long after that, we repeated our success becoming General Motors Corp.’s first spring supplier to receive the “Mark of Excellence” award.

Conclusion

Our journey toward quality was not over with our success. In fact, it had only just begun. We posted our quality numbers where everyone could see them. More importantly, all the managers would talk to our employees on a regular basis to make sure they were using the tools and understood why. That constant reinforcement was vital in maintaining our quality. Like many springmakers, we were under constant pressure from our customers to meet the latest program for excellence. Whatever program requirements you must meet, there is nothing more important than the quality of the products you make. So, make sure you “know your quality numbers.” n