Process Reliability and Six - Sigma

Reliability of manufacturing processes can be obtained from daily production data when process failure criteria are established. Results of the analysis are displayed on Weibull probability plots. Losses are categorized and identified for corrective action based on a demonstrated production criterion, which gives a point estimate for the daily production value. Concepts from six-sigma methodology are used to establish the effective nameplate capacity rating for the process. The differences between the nameplate rating and the demonstrated production are labeled as efficiency and utilization losses. What’s the concept of process reliability? Process reliability is a method for identifying problems, which have significant cost reduction opportunities for improvements. It started with the question: “Do I have a reliability problem or a production problem?” The author has reviewed hundreds of processes and found only one that did not need significant improvements—thus the chance for finding a process not requiring improvement is very small. Sometimes the problems are identified with a root for maintenance improvements. Very often the problems have roots in the operations area. The Weibull process reliability technique is a look down method. It uses a Weibull probability plot. Weibull plots, from the field of reliability, are well-known tools. Details about Weibull analysis are explained in an authoritative engineering book. (Abernethy 1998) The Weibull technique presents important facts as an engineering graphic which is useful for people solving business problems. On one side of one sheet of paper, the Weibull plot tells the story. Often the patterns displayed on Weibull probability plots are helpful for understanding the problem and give insight in the solution. One-page summaries are very important for busy people—particularly managers. Managers always look down on the process from a high altitude, and they see matters differently than the line personnel. Line personnel always look up the process from a low altitude where the view is overwhelming from a maze of problems. The considerable different viewpoints of management levels, stresses both the organization and the people. The hardest part of any reliability analysis is getting the data. However, process reliability techniques use data available at any plant---daily output of prime quantities produced. Production quantities are precursors for money, and thus restriction in output is very important for every profit driven operation. The Weibull technique aids in solving business problems as the cost of unreliability for processes are important. The importance requires quantification of process reliability. Reliability is about making businesses better. Definitions are listed at the end of the paper. The Weibull process reliability techniques help define a strategic course of action for making improvements. The look down technique provides opportunities for developing a strategy to solve problems. The method tells the nature of problems and quantifying the losses. Supplying details for each problem involves the tactics at lower levels. Figure 1 shows a Weibull plot using production data from a troubled process. The probability plot is constructed using a common tool in reliability known as WinSMITHTM Weibull software. (Fulton 2000) Process Reliability and Six-Sigma by H. Paul Barringer The troubled process operates on a five-day per week schedule. During the one-year interval, the process could not operate for five days. Note the plot has 250 data points (5*52 – 10 = 250) as the plant had ten scheduled holidays—this is a view of convenience. The view in Figure 1 is based only on the data reported. Notice the reliability of the process is defined at the point where the trend line, in the upper reaches of the production, began their losses at a cusp. A portion of the losses appear as cutbacks. Another portion of the losses appear as very severe problems characterized by a zone labeled crash and burn---both zones are associated with reliability problems. Figure 1 shows the process has a substantial reliability problem starting at ~26% reliability (finding this value involves both art and science and the answer is rarely the same exact value each time it is evaluated). Figure 1, shows the cutback losses are 18,262 (for the zone between 26% and 92%), the crash and burn losses are 6,530 (for the zone between 63% and upward) for a total reliability problem of 24,792 units of production compared to the total output of 113,915 units. On a Pareto basis, the major reliability problem is cutbacks in output for Figure 1. Also notice the five down days are plotted as a “small” number reflecting zero output---this ploy is used as zero values cannot be plotted on the Weibull probability plot’s logarithmic scale. Typically a value is selected 1/100 (two logs smaller) of the smallest actual production value recorded as representing zero--thus errors in the cumulative output are small. However, for presentation purposes here, the value is selected, as 4 so the extra decade of log values on the chart are not shown. Figure 2 shows a production Weibull plot from the investor’s viewpoint, requiring accountability for 365 days of output. From Figure 2, the cutback losses are 20,567 (for the zone between 18% to 62%), the crash and burn losses are 61,154 (for the zone between 62% and upward) for a total reliability problem of 81,721 units of production. compared to the total output of 114,415 units (remember the zero output is shown at 4 units and thus the slight increase in total output for Figure 2). On a Pareto basis, the major reliability issue is a crash and burn problem from an idle plant only running on a 5-day per week schedule. This represents a substantial change in outlook compared to Figure 1. The view from Figure 2 is preferred compared to Figure 1 because it avoids the provincial outlook of running a gentleman’s plant, and it incorporates the 99.9 99.5