May - Library

15
Downtime Priorities, Jack-Knife Diagrams, Business Cycle
Downtime Priorities,
Jack-knife Diagrams,
and the
Business Cycle
Peter F. Knights
Associate Professor, Mining Centre, Faculty of Engineering, Catholic University of Chile,
e-mail: knights@ing.puc.cl
Abstract
Pareto histograms are commonly used to determine maintenance
priorities by ranking equipment failure codes according to their
relative downtime contribution. However, these histograms do not
readily identify the dominant variables influencing equipment
downtime, namely the failure frequency and mean time to re s t o re
s e rvice (MTTR) associated with each failure code. This paper
advances an alternative method for analysing equipment downtime
using logarithmic (log) scatterplots. Log scatterplots pre s e rve the
ranking scheme of Pareto histograms, but provide additional
i n f o rmation content re g a rding failure frequencies and MTTR. By
applying limit values, log scatterplots can be divided into four
quadrants enabling failures to be classified according to acute or
c h ronic characteristics and facilitating root cause analysis. In
addition, the graphs readily permit the identification of pro b l e m s
a ffecting system re l i a b i l i t y, availability, and maintainability. Depending
on the relative importance of maintenance and repair costs to the
economic consequences of failures (including lost production costs),
a V-shaped limit can be established for determining downtime
priorities. These graphs have been christened “jack-knife” diagrams
after the shape of the V-shaped limit. As prices vary over the business
cycle, the jack-knife limit can change orientation, re - f o c u s i n g
downtime priorities to reflect changes in business priorities. By
graphing the trend of failure data over successive time periods, log
scatterplots provide a useful visual means of evaluating the
p e rf o rmance of maintenance improvement initiatives. Since their
i n t roduction in 1999, log scatterplots and jack-knife diagrams have
been profitably employed by a number of mining companies and
mining equipment suppliers world-wide.
1. Introduction
In the late 19 th Century, the Italian Engineer Vilfredo Pareto (1842-
1923) constructed histograms of the distribution of wealth in Italy and
concluded that 80 percent of the country’s wealth was owned by 20
p e rcent of the nation’s population. This trend was later found to be
representative of the distribution of other data populations. The 80:20
rule, and variations such as ABC analysis that uses an 80:15:5
classification rule, or “top ten” list generation, are now routinely used
in many fields of study. As applied to the field of maintenance
engineering, Pareto analysis is commonly used for identifying those
failure codes responsible for the majority of equipment maintenance
cost or downtime. Based on the failure codes identified, action plans
can be elaborated to lower maintenance costs or improve equipment
availability and reliability.
However, Pareto Analysis suffers from a number of deficiencies:
F i r s t l y, maintenance costs and downtime are the product of two
factors; the number of failures that occurred in a particular time frame
and the average associated repair cost, or mean time to re s t o re serv i c e
(MTTR). A Pareto histogram based on downtime (or cost) alone cannot
d e t e rmine which factor, or factors, are dominant in contributing to the
downtime or cost associated with particular failure codes.
S e c o n d l y, Pareto histograms pre p a red for failure codes ranked
according to repair cost, equipment downtime, failure frequency and
MTTR will generate four quite distinct lists of failure code priorities.
These four lists must be combined in some way to generate a re d u c e d ,
manageable set of downtime priorities. Which failures should be
selected in order to best improve business perf o rmance? A graphical
means for simultaneously visualizing equipment availability, re l i a b i l i t y
and maintainability problems as a function of equipment failure codes
would be extremely useful to this process.