Copyright 2005 Northrop Grumman Corporation0
SqueezingVariation forProfitCMMI® Technology Conference and User GroupDenver, Colorado
Thursday, November 17, 2005
D. R. CorpronDivision Manager & Six Sigma Master Black BeltNorthrop Grumman Corporation
Copyright 2005 Northrop Grumman Corporation1
Background…
� CMMI Level 4,Quantitatively Managedcovers both theorganizational and projectaspects of processstability and capability
� Stability and Capabilityare not just nobleconcepts, they haveeconomic value and areabout managing variation
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The problem…
� The economic value ofrendering processesstable and capable isoften incalculable
� And, the return oninvestment of placingmore processes underquantitative managementlikewise isindeterminable
� So, how to quantify thebenefit?
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Situations where variation manifests asschedule misses is a problem..
� Projects miss committeddelivery dates due tosystematic underestimatesof the effort to performtasks
� Projects miss committeddelivery dates due to poorexecution and control ofproject tasks
� Missed delivery datesoften have direconsequences
In Cost ofQuality lingo,
these arefailure costs
Schedule often is a majorconcern of Customers
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The concept of failure costs…� Defect correction� Budget misses� Processing discrepancy
reports (DR’s)� Retesting� Unscheduled downtime� Inventory shrinkage� Schedule misses� Invoice errors� Payroll errors� Erroneous status reporting� Lost data
Internal Failure Cost arethe costs that result from afailure to…“Do it right the first time.”
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Example…
� The Build Schedulingsubprocess was put underQuantitative Management
� Same process usedacross several projects todetermine scheduleperformance
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Data were collected for 30 samples…
20Build 2558Build 1519Build 512Build 24337Build 1410Build 415Build 2319Build 13-1Build 311Build 2220Build 121Build 289Build 2123Build 1128Build 1
95Build 3062Build 2011Build 1031Build 2910Build 1987Build 913Build 288Build 182Build 8
4Build 2753Build 1790Build 74Build 262Build 165Build 6
± DaysEarly or
LateBuild
± DaysEarly or
LateBuild
±DaysEarly or
LateBuild
Fairly clear that schedule performance isan issue
Copyright 2005 Northrop Grumman Corporation7
Histogram reveals the shape of the data…
Before
Fre
que
n cy
1501209060300
16
14
12
10
8
6
4
2
0
Histogram of Delivery Performance
Thisdistribution
clearly is non-normal (typicalfor time data)
Data that are not normal present analytic challenges
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Further analysis shows the data to be alognormal distribution…
Before+5
Perc
ent
1000100101
99
95
90
80
70
60504030
20
10
5
1
Loc 3.172Scale 1.020N 30AD 0.473P-Value 0.226
Probability Plot of Before+5Lognormal - 95% CINote that 5 is
added to eachnumber to
eliminate anynegativevalues
All points fallwithin the 95%confidenceinterval
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Charting the data shows the process to bestable…
Build
Natu
ralL
ogo f
Day
sE a
r ly
orL a
te
Bui ld
28
Build
25
Build
22
Build
19
Build
16
Build
13
Build
10
Bui ld
7
Build
4
Build
1
7
6
5
4
3
2
1
0
_X=3.172
UCL=6.224
LCL=0.121
I Chart of Before+5Using Box-Cox Transformation With Lambda = 0.00This process
is stable asno special
cause pointsappear
Number actually ise3.172 or 24 days less 5
or 19 days
Stable processes lend themselves to improvement
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The process is not capable…
654321
USL*transformed data
Sample Mean* 3.1725StDev(Overall)* 1.02886
LSL *Target *USL 5Sample Mean 42.6Sample N 30StDev(Overall) 64.4483
LSL* *Target* *USL* 1.60944
After Transformation
Process DataZ.Bench -1.52Z.LSL *Z.USL -1.52Ppk -0.51Cpm *
Overall Capability
% < LSL *% > USL 96.67% Total 96.67
Observed Performance% < LSL* *% > USL* 93.56% Total 93.56
Exp. Overall Performance
Process Capability of Before+5Using Box-Cox Transformation With Lambda = 0
Fitteddistribution
Actualdata
Can expect theprocess to belate 94% of thetime
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An improvement team went to work…
� The team conducted athorough CausalAnalysis and Resolution
� They implemented anew Build Schedulingprocess
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Data were collected for 30 new samples…
-2Build 5517Build 4524Build 35-2Build 547Build 444Build 3422Build 5311Build 434Build 33
3Build 52177Build 42-4Build 3288Build 5135Build 41-3Build 31
3Build 603Build 5038Build 40-2Build 595Build 49-3Build 39-2Build 5815Build 4828Build 3811Build 579Build 470Build 37
4Build 56-2Build 4623Build 36
± DaysEarly or
LateBuild
± DaysEarly or
LateBuild
± DaysEarly or
LateBuild
The performance looks better. But, byhow much, and what dollar benefit?
Copyright 2005 Northrop Grumman Corporation13
The “After” process is still stable…
Build
Natu
ralL
ogof
Day
sE a
r ly
orLa
te
Build
58
Build
55
Build
52
Build
49
Build
46
Build
43
Build
40
Build
37
Build
34
Build
31
6
5
4
3
2
1
0
-1
_X=2.369
UCL=5.701
LCL=-0.964
I Chart of After+5Using Box-Cox Transformation With Lambda = 0.00
Number actually ise2.369 or 11 days less 5
days or 6 days
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Comparing the “Before” to the “After”shows a change in the data distribution…
Freq
uenc
y
350300250200150100500
100
80
60
40
20
0
350300250200150100500
Before+5 After+5Loc 3.172Scale 1.020N 30
Before+5
Loc 2.369Scale 1.203N 30
After+5
Histogram of Before+5, After+5Lognormal
The newapproach
clearly reinedin the variation
Fitteddistribution
Actualdata
These parameterswill be used later
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And, a hypothesis test shows thatdifference is indeed real…
P-Value > .05;not significant
95% confidenceinterval does notcontain zero
A Mann Whitneytest is similar toa t-test but canbe applied to
non-normal data
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And more the process is more capable…
4.83.62.41.20.0
USL*transformed data
Sample Mean* 2.36853StDev(Overall)* 1.21385
LSL *Target *USL 5Sample Mean 22.0333Sample N 30StDev(Overall) 35.7601
LSL* *Target* *USL* 1.60944
After Transformation
Process DataZ.Bench -0.63Z.LSL *Z.USL -0.63Ppk -0.21Cpm *
Overall Capability
% < LSL *% > USL 70.00% Total 70.00
Observed Performance% < LSL* *% > USL* 73.41% Total 73.41
Exp. Overall Performance
Process Capability of After+5Using Box-Cox Transformation With Lambda = 0
Was 94%,now 73%
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$15,5231.8%$5040 hrs.140
:::::
$8161.7%$5040 hrs.2
$1241.0%$5040 hrs.1
FailureCost
(Before)probability
LaborCostper
Hour
Resourcesper Day
DaysLate
� Compute the probability of each possible day late using theparameters from the fitted distributions
� Compute the daily failure cost: resource hours × labor rate� Weight the daily failure costs by the probability� Sum all the daily failure costs
Next, compute the failure costs…
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The revised process cuts failure costalmost in half…
:::::::
$11,5811.4%$15,5231.8%$5040 hrs.20
0.0%
:
6.3%
4.8%
p(After)
$1,347$3,4730.1%$5040 hrs.140
$728K$1,226KCumulative Failure Cost
::::::
$5,027$8161.7%$5040 hrs.2
$1,908$1241.0%$5040 hrs.1
Overrun(After)
Overrun(Before)p(Before)
LaborCostper
Hour
Resourcesper Day
DaysLate
A net benefitof $500K
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Summary…
� The specific benefit fromsqueezing variation out ofa process can becalculated using Cost ofQuality principles and SixSigma techniques
� Knowing the payoffmakes further quantitativemanagement compelling