Quality Progress - January 2015 - 52

STATISTICS ROUNDTABLE BY LYNNE B. HARE EVOP: An Underused Method A path to productivity and quality-and the technique is free EVOP? AN ACRONYM for a new zero contain the necessary or sufficient informa- defects automotive program called "every tion for process improvement. The Box strategy, then, was to take vehicle operates perfectly"? No. "Each advantage of the process's continual then what is it? operation and to induce slight and repeated Evolutionary operation (EVOP) is a tech- deviations of key operating variables, all nique for process improvement based on the within specification and all centered on principle that processes generate products or the center of the specification limits. The services together with data useful for provid- resulting data do contain information for ing guidance for improvement. process improvement. Recognizing that it is highly inefficient Administratively, this is accomplished as and wasteful to ignore these data, George a joint R&D and manufacturing effort, pool- E.P. Box devised a plan to put them to ing product and process knowledge, with use and published it with crystal clarity in full awareness, involvement and blessings 1957.1 He dubbed it "evolutionary opera- of the plant manager, R&D leadership and tion" because of its analogy with genetic organizational stakeholders. evolutionary processes whereby the drive Technically, it is accomplished by an op- to survive through mutations encourages erational team of workers in close contact improvements in the physical characteris- with the process. They induce and monitor tics of organisms. the results of systematic, small and itera- 130 Temperature (°C) variable obfuscates production"? No. Well, Schematic diagram of a simple EVOP program / FIGURE 1 5 125 120 3 1 2 115 4 110 0.20 0.25 0.30 0.35 0.40 Catalyst concentration (%) EVOP = evolutionary operation run in the order shown by the circled numbers, with yield data collected at each setting. After five cycles, suppose the data are as presented in Table 1. These data should be analyzed and tive process changes. Then, they report interpreted following every cycle after the variable is changed, Box knew that it was their findings back to the administrators initial few to reflect the current process necessary to change it. While that sounds periodically. state and potential. An updated summary To measure what happens when a simple enough, it is important to recall that Here's a simple example. Suppose two board, similar to that shown in Table 2, there are many who believe incorrectly key factors thought to have major influence should be posted for all to see. Notice that that reliable causative relationships can be on the process yield are catalyst concentra- the mean yields, by setting, are posted in a discovered through the analysis of passive tion and temperature. The specification pattern similar to that in Figure 1. data. Those are the "quick check" data range for catalyst concentration is 0.20 to Below that is the standard deviation. used to monitor and guide the process. 0.40%, while temperature is 110 to 130°C. Its estimation can be a bit tricky. The one Ordinarily, the process is centered on They are not research aids: They do not shown here is based on fitting a simple a concentration of 0.30% and Five cycles of a 2 + center point EVOP program / TABLE 1 2 Setting Concentration Temperature Cycle 1 Cycle 2 Cycle 3 Cycle 4 Cycle 5 Mean 1 0.30 120.0 72.3 71.9 72.9 71.3 71.5 72.0 2 0.25 115.0 71.0 71.1 72.5 72.5 71.2 71.7 EVOP = evolutionary operation 52 QP * www.qualityprogress.com 3 0.35 125.0 72.5 72.8 72.0 72.3 73.3 72.6 4 0.35 115.0 72.1 72.0 73.2 72.2 74.0 72.7 5 0.25 125.0 72.5 71.8 71.1 71.5 71.3 71.6 factorial model in concentration, tempera- a temperature of 120ºC. It is ture and the concentration-by-temperature believed that serious losses interaction, along with a single degree-of- could be incurred if the pro- freedom term to measure lack of fit. (Sig- cess is permitted to run out nificant lack of fit might signify curvature, of specification, so the levels suggesting the need for a more elaborate chosen for them are 0.25% and design or the arrival at a local maxima or 0.35%, and 115°C and 125°C, minima.) Other good candidate models respectively-well within the will give similar, but not exactly the same, specification limits. estimates of the standard deviation. Figure 1 represents this scheme. The cycle of settings is Error limits for factor effects are shown next. These are calculated as ±ts / √n, in http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - January 2015

Up Front
LogOn
Expert Answers
Keeping Current
Mr. Pareto Head
Total Quality’s Leader
Narrow(er) Focus
Dissecting the Differences
Separate Steps
Innovation Imperative
Measure for Measure
Quality in the First Person
Career Corner
Statistics Roundtable
Standards Outlook
QP Toolbox
QP Reviews
One Good Idea
Back to Basics

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