May 1998—Key Performance Indicators From Weibull Production Plots

The cusps on each Weibull production plot tell about “mixed failure modes” from a variety of cause and effect problems that take away from consistent output. WinSMITH Weibull (WSW) probability software produced a Weibull plot of production output for the April ’98 problem of the month. The plot showed multiple cusps, and each segment of the plot was quantified with different Weibull slopes and characteristic values.

This problem of the month shows how you can build a model (you can download
instructions in an Excel file on how to perform a

__ The Problem__

The April
’98 problem of the month produced a Weibull plot with cusps shown in Figure
1.

The line segments have the following data:

·
**Segment
#1 **from ~100% reliability to 99% reliability is defined by:

b = 0.1676 and h
= 1.77822E+14

and the line segment terminates at X-value = 214 K-Lbs at 1% CDF or 99%
reliability

·
**Segment #2** from 99% reliability to 95%
reliability is defined by:

b = 4.9047 and h
= 549.996

and the line segment terminates at X-value = 300 K-Lbs at 95% reliability

·
**Segment #3** from 95% reliability to 65%
reliability is defined by:

b = 8.9347 and h
= 419.688

and the line segment terminates at X-value = 396 K-Lbs at 65% reliability

·
**Segment #4 **from 65% reliability to 25.5%
reliability is defined by

b = 1.2328 and h
= 773.336

and the line segment terminates at X-value = 996 K-Lbs at 25.5% reliability

·
**Segment #5** from 25.5% reliability to ~0
reliability is defined by

b = 37.1194 and h = 995.812

This information can be used to generate a

You can download an Excel spreadsheet ( MAY98PRB.XLS )—217Kb file size, which contains the
Monte Carlo model. You can run a
production output simulation by pressing the F9 key. The F9 key will recalculate the spreadsheet
and produce new simulated output from the plant. The plant output will be used to produce some
common key performance indicators such as the plant output ratings (of course,
every time you run the Monte Carlo simulation, you will get a different
answer). Thus the results of the KPI’s must be averaged to get the results shown in Table 1
for 10 simulations—notice the large variations in the KPI’s
when the cusps are far from the demonstrated line. This large variation could lead you to
believe the problem lies within how the KPI’s are
constructed and thus people spend too much time arguing over the non-validity
of the KPI rather than fixing the problem which causes the deviation.

Other pages you may want to visit concerning similar issue are:

·
__Production Reliability Example With Nameplate Ratings__

·
__Process Reliability Plots With Flat Line Slopes__

· Process Reliability Line Segments

·
__Papers On Process Reliability As PDF Files For No-charge
Downloads__

Return to the list of problems by **clicking here**.
Return to top of this problem statement click here.

(c) Barringer & Associates, Inc., 1999