Quality can be defined as the degree or extent of excellence. Everyone wants to improve process quality. However, it can be difficult to quantify and evaluate a product's quality.
The key parameters commonly used to evaluate instrument quality are device reliability and repeatability. Tough questions remain, however, incuding:
How do you quantify the value of improved reliability and repeatability?
Which specifications are critical to instrument reliability and repeatability?
Are specifications an "apples-to-apples" comparison across suppliers?
How do suppliers prove their specifications?
What programs are available to continuously improve specifications?
Typical Work Orders for Pressure Transmitters1
Quantifying quality's value
From a broad perspective, quality improvements impact plant profitability through increased production and reduced cost of goods sold. More specifically, improved reliability translates into:
Increased production. Reducing the risk of costly unscheduled shutdowns results in improved availability (hours available to produce divided by total hours available). Improved device repeatability translates into increased production and/or reduced cost of goods sold through better measurements.
Better device repeatability provides users with the opportunity ," at no increased environmental or safety risk ," to reduce raw material costs for control applications, improve billing accuracy for custody transfer applications, or improve decision-making for monitoring applications. Therefore, one dollar of reduced flow uncertainty yields one dollar of opportunity to reduce operating expenses.
Reduced cost of goods sold. Costs are reduced through the prevention or elimination of unnecessary work. In the case of instrumentation, unnecessary work equates to work orders and trips to the field. The figure provides an industry benchmark for pressure transmitter work orders. This graph illustrates the various opportunities to reduce the time and cost of preventive maintenance programs. The obvious benefit of improved product quality is to reduce product failures, calibration shifts, plugged lines, etc., which account for roughly one-third of work orders. However the larger opportunity, which represents roughly two-thirds of work orders, is to reduce the frequency or need for quality validation from routine checks or "no problem found." This is accomplished by documenting product quality improvements and subsequently challenging the existing practices based upon earlier product quality standards.
The two parameters central to instrumentation reliability are quality control and product robustness. Quality control is a measure of product performance under normal operating conditions, while product robustness is a measure of device reliability under abnormal conditions. Many users attempt to compare and quantify robustness and quality control for different manufacturers by evaluating designs and the vendors' professed commitment to concepts such as continuous improvement, International Standards Organization (ISO) 9000, Six-Sigma quality, total quality management (TQM) and other quality intiatives. Unfortunately, such comparisons are not typically "apples to apples," so they are not always useful for decision-making.
Quality control. Perhaps a better tool for evaluating device and manufacturer quality is mean-time-between-failure (MTBF) data. MTBF is defined as a measure of random constant failures that are not reducible through material selection or routine maintenance and affect only the wearout failure modes. MTBF is not a measure of how long a device will function under field conditions.
Although MTBF is a quantitative benchmark for evaluating device and manufacturer quality, the data are difficult to compare because different suppliers use varying approaches to determine MTBF.
MTBF example for
a pressure transmitter:
Observed MTBF = 1200 years, predicted MTBF = 285 years and demonstrated MTBF = 175 years at a 95 percent confidence level. At a 50 percent confidence level for that same device, the demonstrated MTBF increases to more than 368 years.
Observed MTBF based on user-reported failures. Because most users do not report all failed transmitters, MTBF is inflated, and suppliers with better customer service and communications are actually penalized.
Predictive MTBF is a component analysis provided by suppliers and usually validated by independent third parties such as Factory Mutual (FM) and/or TV. The analyses are performed a laboratory environment. Based on the author's experience, "real-world" installed reliability always is much worse for field devices such as transmitters. Therefore, users can evaluate the real-world reliability and life-cycle-cost impact of specific technology and user practice alternatives only by quantifying installed reliability.
Demonstrated MTBF is probably the most useful MTBF method for predicting installed reliability. It is based on highly-accelerated life-cycle testing (HALT).2 However, this approach requires the destructive testing of many transmitters and is prohibitively expensive for all but the highest-volume suppliers.
It is important to verify that the MTBF numbers are measured consistently and the specifications are given at the same confidence level. As the confidence level increases, the MTBF number decreases.
Product robustness. If MTBF is a measure of random constant failures, product robustness is a measure of how a device performs under unplanned or abnormal events. Often, product robustness is used to describe the installed reliability of an instrument.