ERIC ED502282: Variables Control Charts: A Measurement Tool to Detect Process Problems within Housing

7 Pages·1999·3 MB·English

by  ERIC

some articlesdoexist.GraceandTemplin(1994)
useda simple form of statistical measurement to
VariablesControlCharts:
help them improve the quality of their student
servicesdepartments. Macchia (1993) usedSPC
A MeasuremenTtoolto
tomeasuretheenhancement of student learning,
and Koberna and Walter (1993) explained how
DetectProcesPsroblems
a community college used many TQM tools to
achieve quality. Thisstudyoffers another example
of how a traditional TQM tool can be used in
WithinHousing
higher education, specifically inaresidence hall.
More often than not, higher education
Andrew Luna
professionals tend to use only the process
UNIVERSITYOFLOUSIVILLE
definition tools available in TQM, and avoid
important data generation and analysis tools.
Koberna andWalter (1993) attribute thisaversion
INTRODUCTION to statistical methods to the newness of TQM in
thehigher education community. Bynot utilizing
When housing administrators discovered that Statistical ProcessControl tools, higher education
female residents were unsatisfied with theequality professionals may be creating barriers in
of hotwater coming from thecommunity showers achieving a better understanding of TQM in the
in a high-rise residence hall, they relied on the future (Seymour & Collett, 1991). Beforethey
concepts ofTotal Quality Management (TQM) to fully understood TQM, someprofessionals stated
help them identify both thecausesofand possible thatSPCtools may not be needed for achieving
remedies for the problem. TQM is a method of better quality in higher education [Vance &
achieving quality using scientific and teamwork Schipani, 1993), and some have even criticized
approaches. By utilizing quantitative TQM for its ambiguity and simplicity (Fisher,
measurement, administrators believed they could 1993). Clearly, statistical tools can provide a
pinpoint the underlying causes of the hot water reliable solution for turning theenigmatic intothe
problem-something previous focusgroups could exact (Seymour & Collett, 1991).
not. After identifying the problem, they relied on This article focuses on one of the more
various teamwork methods to discuss and intricate statistical process control tools inTQM.
implement changes toimprove the process ofhot TheControl Chart isone of the mostwidely used
water distribution. SPCtools in businessand industry and probably
Current literature concerning TQM inhigher oneoftheleastunderstood among thoseinhigher
education.
education places heavy emphasis on
nonempirical methods of achieving quality.
Although teamwork, customer satisfaction, and THECONTROL CHART
ownership are all important factors in TQM,
authors who have written about these concepts Theunderlying principle behind theControl Chart
have failed to address the significance of isthatevery process, nomatter how well executed
statistical methods in quality control. is imperfect. Because many variables are
According to some (Luna, 1996; Teeter & involved in all processes, deviations can occur
Lozier, 1993), Statistical Process Control (SPC) inoneor more of thesevariables, creating a less
istheonecomponent separating TQM from other thanperfect process. According toTague(1995),
improvement methods. Infact, quality isdefined the variables involved in a process can usually
in TQM as a measurable characteristic of a be placed into the four distinct categories of
product, process, orservice [Montgomery, 1985). personnel, machines, materials, and methods.
By using quantifiable measurement techniques, According to Shewhart (1986), variations
it is possible to synthesize and evaluate quality that occur among these elements can produce
in relationship to operational variables. deviations incentral tendency which can lead to
Furthermore, one may examine theeffectsofthese output variations in the process. Detecting
variables to any changes within a particular changes and controlling input variations from
process under study. eitherofthesecategories requires awell-identified
Although examples of quantitative methods systemfor evaluating process output (Demming,
of achieving quality are limited in the literature, 1986).
VOLUME 28, NUMBER 1 1999 49
=rII
I,
;::;
'"
1,1,
I:::
k
, The Control Chart isa tool for determining subgroup. For a range chart, the center line is
!
j" the type of variation in a process. When a the average range of samples within each
process isin control, it issubject only toa stable observation. Linesabove and below the center
I'
I,; system of random causes. When a process is line represent the upper control limits (UCL)and
I
out ofcontrol, itissubject toan assignable cause the lower control limits (LCL) of the average
I.
or cause of variation (Montgomery, 1985). respectively. The upper and lower control limits
Stated another way, TQM philosophy are based on three standard deviations above
::J assumes all processes have internal and external and below the mean (referred to as"3 sigma" in
Ii:
factors effecting efficiency and reliability. When TQM terminology). The control chart may
I'"!j these factors occur randomly, they are assumed indicate an out-of-control condition either when
I'il! to be caused by chance circumstances outside a point falls beyond the control limits, or when
1..,',"' the control of the worker or the institution. theplotted points exhibit somenonrandom pattern
,"
"', However, if thesefactors occur in a nonrandom of behavior. Ishikawa (1990) defined nonrandom
) fashion, or iftheyexceed required specified limits, causes of variation as follows:
1,";': it is assumed that there is an underlying cause 1. Runs - when several points line up
'I
r,: which can be pinpointed and corrected. These consecutively on one side of the central line.
Il' underlying causes are called "assignable" 2. Trends- when there isa continued direction
::i! because they are identified as an assessable of a series of points.
qI variable which can be manipulated and 3. Periodicity - when the points show thesame
II"',ii'; controlled (Luna, 1996). pattern of change over equal intervals.
,':: The Control Chart is a graph representing 4. Hugging ofthecontrol line - when thepoints
:1; the variability of a process variable with respect on the control chart fall close to the central line
totime (seeFigure 1). Thehorizontal axis on the or to the control limit line.
:1,
graph represents rational subgroups over a If there is no reason to suspect an out-of-
i; period oftime. Eachpoint on thehorizontal axis control condition, changes should be made to
,.I,
I'
, ii coincides with observations made on asubgroup the systemand further measurements conducted.
" If the change positively effects the process, the
data in the control chart will move toward a
ji: ---------------------
;'I" Upp<rControlLinU(3Sigma) controlled state. Ifthe change brings a negative
': 3StandardDeviationsAboveMean effect, the control chart will indicate that the
",ii
process is more out of control than before the
X(Mean)
change.
orR(Range)
Average Many control charts are available for the
-----L-ow-er-C-on-tro-l-Li-mit-(-3S-ig-ma-) --- study of a process. The major factor in
3Standard Deviations Below Mean determining which chart to useisthetype ofdata
generated from the study. Again, the most
fundamental concept ofTQM istheability tocount
Subgroups or measure aparticular characteristic. However,
(Measurements during apartiClllar timeperiod)
counting and measuring are two different
Figure 1.Example ofa Control Chart functions in statistics and should be treated
differently in TQM. The process of counting
drawn from the process at a particular point in involves a finite scale and scores generated are
.1
!,jl time. Stated another way, a subgroup isa point based on the binomial distribution. Thetool for
in time when a series of measurements are this type of data isthe Attributes Control Chart.
::1
i gathered during a particular time within the Theprocessofmeasuring involvesan infinite scale
'1
I process. The subgroup then becomes the mean and scores generated are based on the normal
I of each of the observations within that time distribution. The tool for this type of data is the
period. Variables Control Chart. Tounderstand fully how
,,'I
H.';I The center line isdrawn horizontally across distributions and Control Charts are related and
pI, the chart to represent an average value. This used, refer to Montgomery (1985).
iiil
value can be obtained from the subgroup data,
I:!'
:'i:i or it can be in the form of a specification mean METHOD
!
I evolving from longer-term historical data. Fora
.
1: means chart, the center line isderived from the Thepurposeofthis study was todetermine if the
!
sample observation averages within each current process of supplying hotwater toa high-
":
1I'
Ii
I
:
:1 50 JOURNAL OFCOLLEGEANDUNIVERSITY STUDENT HOUSING
,:'I
=raJ[
- ---
rise residence hall for women at a Southeastern subgroups thencontained observations from each
Doctoral Igranting institution was incontrol. After of the three time periods per day for 14
a series of focus groups among the residents in observations per floor. Stated another way, for
that hall, itwas determined thattheywere mostly a given floor, each subgroup represented the
concerned about theconsistency and availability average of a particular day's three temperature
of hot water in the building. readings.
One resident assistant (RA)from the fourth,
ninth, and twelfth floors was involved with the RESULTS
study. These floors were predetermined so that
the building was separated into lower, middle, The resultsof the initial testindicated a problem
and upper areas. Each RA measured the with hugging of the mean within the central line
temperature of hot water (in Fahrenheit) coming of the x or means chart (see Figure 2). This
from a common area bathroom faucet on their showed that the majority of observations fell
floor. Thiswas done during the morning, noon, within a close proximity of the mean, indicating
and evening at specified times during the day that more than one factor was being measured.
for 14 days. Therefo(e, each RA recorded 42 Observations within the R or Range Chart,
observations for a total of 126 observations. however, indicated anout-of-control statebecause
Because the data measurement was based on a of a possible run problem for the first six
continuous scale, the x (pronounced x-bar) R observations, a possible trend problem, for
Control Chart was usedwhich showschanges in observations 19 through 27, and two
the mean of the process (x) and changes of the observations, 26 and 32, which exceeded the
dispersion of observations (xand R). lower and upper control limits respectively. The
Ifthe subgroup observations tended to hug Rchart in this example exhibits an out-of-control
around the mean, it was inferred that multiple state because observations either exceeded the
factors were mixed into asubgroup and that the control limitsof3sigma orgroups fell into patterns
of nonrandom behavior.
process ofsupplying hotwater was notconsistent
throughout the building. Therefore, the same The results of the initial test also indicated
individual temperature observations were that because ofthehigh variance of observations
stratified by floor and each floor was re- in the R or Range Chart, water temperature
examined. Ifeach floor were treated individually, variance problems may exist between the three
UCL-l.fZ.O
1<IG
'" lJO
<: MU-lz's.Q
'"
"
::;; 120
t.CL-!09.0
110
Subgroup 5 10 15 20 25 30 35 40
10
=-40.83
..0
.'". JO
C) It=<1~.1!6
<:
'"
II: ZO
IQ t.CL-o.OO
5 10 15 20 25 30 35 40
Figure 2.Control Chart for all Floors
VOLUME28, NUMBER11999 51
--
Im
floors. Thisassumption was later supported by and 10 through 14 indicated possible run
independent floor tests. One of the major keys problems and observations 5 through 10
in reading this chart was looking atobservations exceeded the lower control limits. Additionally,
26 and 32 in the range chart. Observation 26 theseobservations indicated periodicity problems
indicated that no variance existed within this starting after the 5th subgroup. By looking at
subgroup while observation 32 exceeded arange thischart, itwas apparent thatwater temperatures
of more than 40 degrees. Because the resultsof were significantly lower for this floor during
thistestindicated high variance among subgroups Thursdays and Fridays and that, on average, the
within theRchart and hugging ofthemean within water temperatures were lower than on theother
the x chart, the test was rerun after separating two floors. Furthermore, observations 5 and 10
thefloorsand redesigning thesubgroup tocontain on the meanschart also indicated wide variation
observations from each ofthe three time periods within the subgroups. Specifically, the control
for each day. chart indicated that a wide variation existed
The observations for the fourth floor (see between theobservations within thesesubgroups.
Figure 3) indicated that the Range Chart was in After observing the raw water temperature data,
control because observations did notfall outside it was determined that the water temperatures
of the 3 sigma control limits and because no for the afternoon and evening hours were
patterns existed among groups of observations. significantly lower than during themorning hours.
The means chart, however, was out of control. Each of the three floors indicated that the
Observations 5 through 10 on the means chart Range Chart was in a controlled state. This
indicated a possible trend problem and showed that the source of variation inthe water
observation 10 exceeded theupper control limits. temperature on any floor during the morning,
Although mostoftheobservations from themeans noon, and evening hours was not attributed to
chart tended to hug the center line, the range patterns ofchange on that floor. Thex or means
chart indicated that the variance was within chart for each floor, however, indicated an out-
acceptable limits. It was clear to seethat the of-control state because observations exceeded
fourth floor experienced higher water the control limits or formed patterns of behavior
temperatures than the other two floors and that, over a day-to-day period.
in some cases, these higher temperatures far
DISCUSSION
exceeded the 125 degree mean. Additionally,
observations 3 and 10 on the means chart
indicated that the higher temperatures occurred This study demonstrated the useof the Variables
on Wednesdays and, according to the Range Control Chart for detecting problems with a
Chart, littletemperature variation occurred among residence hall hot water supply. Through this
the three time periods in these days. study, a better understanding of the hot water
The observations for the ninth floor (see supply was achieved and recommendations for
Figure 3)also indicated thattheRangeChartwas the improvement of the systemwere made.
in a controlled state. However, the means chart The Variables Control Chart indicated three
for theninth floor indicated a possible periodicity problems: (a)high variation ofwater temperatures
problem. Thistype of problem isharder todetect from floor to floor, (b) much higher water
than other out-of-control states and is usually temperatures on the 4th floor than on the 12th
based upon experience and a greater floor, and (c) lower temperatures or higher
understanding of the process (Ishikawa, 1990). demands for hot water during the afternoon and
Theperiodicity problem was apparent after every evening hours on Wednesdays, Thursdays, and
"::i'J 3rd observation and continued over equal Fridays.
,
Ii:! intervals during the entire length of time Thefirstrecommendation concerned thefirst
::1
'I' measured. According tothe means chart, water two problems. High variation of water
!o,! temperature for both weeks increased on temperatures from floor to floor and higher
'I',:!
"'I Thursday through Saturday and decreased on temperatures on lower floors indicated potential
W:I Monday through Wednesday. water flow problems. Increasing theoverall water
f,:i
Although theobservations forthetwelfthfloor temperature piped into the building will increase
I'HI indicated thattheRangeChart was inacontrolled the temperature for the higher floors, however it
1i"'1 state, the means chart for this floor showed an will not address the variation problems between
out-of-control state more severethattheothertwo floors and will lead to even hotter water on the
l1i':~ floors (seeFigure 3). Observations 3 through 7 lower floors. Remedies for these variation
11
i,il
1
:IJ.,,"il 52 JOURNAL OFCOLLEGEAND UNIVERSITY STUDENTHOUSING
i:!1
IlL - -- -- --
4th Floor
A .J\
::l
<n
<:: I'.''''
<tS --- MtJ>UlO
d)
:;; IZO
I
ILCL-1I4,1
!to
Subgroup 0 j 10
JQ
I !VCL-2Hl
O<<<I:nJJ: :a I - \ {" ---- 'I II'--.. -- I1t-1Q.>9
<tS
a: to
I \l '\/
LCL-o,QQ
MTWT F S S MTWT F S S
9th Floor,
I
-,
14G "" VCL-I<C,O
<n \JQ
<<t:S: I ./ -.... ,/ 'MU-m,o
<IJ
:;; l:a
J
no , . ILCL-llQ.O
I I
Subgroup 0 5 10
..,
I
!VCL-J7.1S
JQ
<n -
dO<<):JI:I o I / I ...-. IR:-1...,;7
a: 10-
kCL-o,OO
oj
MTWTF S S MTWT F S s
12thFloor
l.ol
1ua.-\31<6
IJQ-
<<:n: '-l. Lu--"" . 'n;-Izs.o
<tS
<IJ 120-
:;;
J / /
110 0I 7, 1I0 '" ISI ILCL-I\Z.4
Subgroup
JOl IUCt.2UI
<n jQ
d)
<OJ:: I A / \ / '\. /\ lit-to..
<tS
a: 10 I -- ----....
t.CI.><IOO
MTW T F S S MTWT F S S
Figure3.Control Chart ByFloors
VOLUME28, NUMBER1 1999 53
--
~ -1:\
I'
I
:,
;,
i:
I:
I
",
I, problems could include, but are not limited to, values inforecast models tolocate patternswhich,
,:,
I better pipe insulation, repairing or replacing ifdetected and changed, could improve theability
I;
/, internal pumping systems,adding booster systems ofthe model tomake more accurate predictions.
which would reheat water on the higher floors, Although the Variables Control Chart may
i: and redesigning of the hot water supply system, seem complicated and foreboding at first, with
I" The quality team charged with the initial practice it can be a relatively simple tool to use.
I,' investigation of the hot water supply process Many software companies offer packages that
I, '
I recommended that Physical Plant staff explore make entering data and interpreting Control
remedies for improving the process. These Charts effortless. Asseeninthisstudy,theControl
, remedies would be classified according to cost Chart isa powerful tool in measuring the quality
'
and perceived effectiveness and be introduced ofa process and assessing theeffects ofchanges
into the systemone at a time. By including cost to it. Because theVariables Control Chart works
as a factor in the order of remedy, lower cost with continuous data, it can be used to evaluate
I
changes would be introduced to the systemfirst. any process for which this type of data can be
I, ' Thus, ifthesystementered into acontrolled state, collected.
;'!'
it would do so atthe lowest possible cost.
'.",Ii, There was a chance that the third problem REFERENCES
I
ii'i of day and time might be controlled by finding a
i:
J solution to the first two problems, but it was Demming, W. E. (1996). Out of the crisis.
II recommended that the same team investigate Cambridge:CenterforAdvancedEngineer-
I ingStudy,Mil
adjusting thedelivery of hotwater tothe building
Fisher,J (1993). TQM: A warning for higher
' to increase the temperature when the system is education. EducationalRecord,74(2), 15-
II: mainly in use, such as Thursdays and Fridays. 19.
I,I"' likewise, the delivery of hot water could be Ishikawa, K. (1990). Guide to quality control.
I . decreased when demand is less, such as Tokyo:AsianProductivityOrganization.
I Wednesdays. Grace, R. E., & Templin,1 J (1994). QSS:
i'! Thethird recommendation concerned future Quality Student Services. NASPAJournal,
I/I,',.l,,'' mprooncietosrsingm.ust uFnodreTrgQoMcotnotinrueomuasinmeefafescutriveem,enat Kobe3rn2a(1,)S, .7,4&-8W0.alter, P.(1993). Usingtotal
:1':
to determine if changes increase quality or ifthe quality managementtoolstoimproveorga-
!', nizational wellness. InG.G.lozier & D.J
systemfalls back into an out-of-control state. For
,',,/ Teeter(Eds.), Pursuit of quality in higher
"Ii this reason, it was recommended that the water
:i, education: Case studies intotalqualityman-
Ii temperature be continually measured for two agement. San Francisco: Jossey-Bass.
I
Iii weeks for both fall and spring semesters. luna, A. l. (1996). Workordernonconformity
:i
and the TQM process. Journal ofCollege
;;ii CONCLUSION
",t and UniversityStudent Housing, 25(2), 31-
I~Ii 37.
I';'
Statistical Process Control is an important Macchia,P.[1993}.Totalqualityeducationand
L'!
I1,1' application toTotalQuality Management. Tofully instructional systems development. Educa-
utilize the concepts of TQM, one must define, tional Technology, 32{7}; 17-21.
ii'i Montgomery, D.(1985). Introduction to statisti-
I,'!iil synthesize, and evaluate quality. The Variables calqualitycontrol.New York:Wiley.
"I' Control Chart isone of many tools for achieving
Seymour,D.,& CollettC. (1991). Totalquality
lj these goals. Because it is used with variables
management inhigher education: Acritical
that are measured, this tool can be usedfor any assessment. Methuen,MA: GoaI/QPc.
iiil continuous repeating variable such as money, Shewhart,W. A. (1986). Statisticalmethod from
::'/ time, scores on a surveyor evaluation, weights, theviewpointofqualitycontrol.New York:
1::1 distance, or volume. Dover.
,Ii For example, housing personnel could use Tague, N. R.(1995). TheQuality Toolbox.
Ii Milwaukee: ASQC.
this chart to measure utilities' output within the
U" halls in conjunction with energy saving changes Teeter,D.J, & lozier, G. G. (Eds.).(1993).
:; Totalquality management inhigher educa-
I to the buildings. It also could be used to track
': tion: Case studies in total quality manage-
", survey responses of residents each week over a
, ment. San Francisco: Jossey-Bass.
period oftime, orto track thecompletion rate for
work orders This chart also has been used to
I:
(I", track thedifference between predicted and actual
" "
I,:
"
':II 54 JOURNAL OF COLLEGEAND UNIVERSITY STUDENTHOUSING
", I
ii ~
DI1
Vance,C. L.,&Schipani,P.D.(1993). TQRE-
total quality residential experience. Jour-
nal ofCollege and University Student Hous-
ing, 23(2), 3-7.
Foradditional information, contact Andrew Luna,
Research Analyst, University of Louisville, Louis-
ville, KY40292.
VOLUME 28, NUMBER 1 1999 55
"