2180_Nandyal

© SITARA Technologies
For Use and Distribution by the SEI in the proceedings of the 24th SEPG Conference 2012 – Albuquerque 1111
SITARA Process JewelBoxTM
© SITARA Technologies Pvt. Ltd.
How to Accelerate High-Maturity
Implementation of CMMI using Six Sigma
Raghav S. Nandyal
Chief Executive Officer
SITARA Technologies Pvt. Ltd.
raghav_nandyal@SITARATECH.com
Work-product of the 2011 High-maturity Partner Advisory Board of the SEI.
I thank: Bradley Bittorf, Eberhard Huebner and Kaliappan Marrapa for their
feedback.

LEARNER OBJECTIVES
Why should you consider Six Sigma as a
problem solving methodology to accelerate
High Maturity process institutionalization?
How can Six Sigma help in accelerating
quantitative benefits while assuring a
statistical significance?
How can a simple 4-step process help to
demystify Six Sigma application in the
context of process improvement?

Some Perspectives …
Why a blind application of Six Sigma might be wrong? (1 of 5)
Noteworthy distinctions while considering the
application of Six Sigma to a software process:
Six Sigma originated in manufacturing and extended
itself to other transactional services
Most Six Sigma analysis techniques, tools and
templates are well suited to the needs of a
manufacturing or a transactional service
Well known optimization procedures (e.g.,
DOE), approaches to assess process stability
(e.g., Western Electric rules) and capability
analysis (long-term and short-term capability)
don’t extend naturally to a software process
Quality assurance and not quality control – is
more relevant to a software development
process based on the CMMI

Noteworthy distinctions … (contd..)
Software processes are not repetitive in the same
sense as a manufacturing process:
driven by the changing “constraints of operation”.
In a manufacturing process:
– the same controlled process is applied repetitively
on raw materials yielding a finished product,
– controlled variation of input parameters to optimize
the yield or quality of the output is possible,
– major change is uncommon; change occurs only
when new machinery, or new material processing
requirements, or new products are required.
For software process improvements to be effective, it is
important to identify the “pain-points” and isolate the “real
root-cause” – which is the difficult part (controllable factors)

A software process based on the CMMI is prone to
numerous in-process and out-of-process variations
New Definition: Common cause variations,
have a source of variation assignable to
something within the process control and,
whose impact can be isolated, corrected
and possibly eliminated with effective
“process management” practices.
New Definition: Special cause variations,
have a source of variation assignable to
something outside the process control and,
whose impact can be resolved or minimized
with effective “project management”
practices.
- Lack of domain
understanding,
- Internal churn resulting
from people leaving
midway,
- Lack of budgets and
resources,
- Lack of appropriate tools
- Changing customer
requirements,
- Lack of access to the
validation environment,
- Interoperability issues
with a legacy application,
- Escape defects from the
legacy

Six Sigma emphasizes process measurement and its
relevance to the bottom-line performance with the
onset of planning
Explicit strategies tying process performance to
bottom-line results or improvements, are not quite
evident in CMMI until the maturity level 4
Without Six Sigma thinking, at levels 2 & 3, it is likely
that frivolous measures are collected which might not
help to demonstrate improved business performance
A simple 4-step process inspired from Six Sigma can
be applied at the CMMI level 2 itself
This helps to establish the right focus on
measurement and analysis at level 2

Widely accepted that “process quantification
and process performance management” in
CMMI, occurs only at levels 4 and 5
Can this focus shift downwards to the level 2?
Note that, process measurement and analysis
is addressed at level 2 itself
However, CMMI does not provide the required
rigor of practices at level 2 for analysis
Hence, the 4-step process that–
defines the required analysis practices,
serving as a strong foundation for high-
maturity practice implementation,
to improve the rigor of analyzing data,
whereby, accelerating high-process maturity
institutionalization
Quantitatively
Managed :Level4
Initial : Level 1
Managed :Level2
Optimizing
: Level 5
Defined : Level3
0
1
2
3
4
5
CapabilityLevels
CMMI-DEV V1.2 Process Areas
Target Level
Achievement
Level

AGENDA
Establish the Context
What is the 4-step process?
Establish that the process is stable & capable
Understand the underlying process
distribution
For a stable process identify the regressions
and the residuals
Generalize the regression into a model
One Example of applying the 4-Step
Process –
Conclusion, Q & A

Six Sigma as a problem solving methodology can
accelerate implementation of High-Maturity
practices of the CMMI constellations
When business goals are defined quantitatively, Six
Sigma alongside CMMI has proven useful in
minimizing the cycle time to transition maturity levels
A systematic and a consistent integrated approach
of Six Sigma with CMMI can help in rapid
acceleration of maturity levels from chaotic or level 1
mode to an optimizing or level 5 mode of operation
in about 3-years timeframe
Without Six Sigma, it would typically takes anywhere
between 12-18 months per level of maturity
(1 of 4)

Typical organizational contexts where such dramatic
results were observed–
small- to medium-sized organizations or business
units with less than 300 individuals in size,
with annual staff attrition rates less than 5% with
negligible impact to competency erosion,
projects executed on mature domains/technologies
with sufficient organizational memory of execution,
good understanding of project requirements with an
ability to manage requirements changes to within
10% effort deviation in the requirements-phase
when at least every individual has a mandatory white-
belt certification within the first 6 months
upwards of 50% have either a green-belt or a black-
belt certification
(2 of 4)

Essence of Six Sigma methodology in the CMMI
context …
Uses the DMAIC approach (Define-Measure-
Analyze-Improve-Control)
by identifying business goals in quantitative terms,
using voice of customer (VoC) that,
establishes performance criteria or, the
critical to quality (CTQ) requirements,
adding value to the customer
Also ensures:
Identification of critical business requirements that
are in alignment with the organizational goals,
by gathering voice of business (VoB) that,
are critical to process (CTP)
(3 of 4)

Essence of high-maturity focus or mindset in the
CMMI context can be summarized as …
No conclusions can be made without objective proof.
The proof is in the data.
There is no guarantee that a process change,
contributes towards improvements unless verified
and validated statistically.
(4 of 4)

What is the 4-step
process?
Establish that the process
is stable & capable
Understand the underlying
process distribution
For a stable process
identify the regressions
and the residuals
Generalize the regression
into a model

1. Establish that the process
is stable and capable–
Key Point: every stable and
capable process, can be
made more stable and more
capable
Use control charts/time-series
charts to analyze outliers
Example: 5-Whys to arrive at
a process cause
Key Point: When using
sample data, consider the
mean value of a large number
of observations from
independent homogenous
projects
Central Limit Theorem →
normal distribution
(1 of 4)

2. Understand the underlying
process distribution–
Key Point: Anderson-Darling
test of normality is the most
widely used test by statistical
software (E.g., Minitab)
High p-value shows no
statistically significant
departure from normality
Null hypothesis is that the
data is normal – so a high p-
value > 0.05 is indicative of a
normal distribution
Key Point: If the “statistical
mean” of derived measures of
a number of homogenous
projects is used, there is no
need for the normality test
(2 of 4)

Key Point: Every derived
measure uses some
combination of the basic
metric data
Key Point: A derived
measure can itself be a
combination of several phase-
wise factors (sub-process
control)
(3 of 4)
3. For a stable process identify the regressions and the
residuals–
Key Point: Regressions or, the factors are picked based on
process knowledge, cost, risks and ease of measurement
Key Point: Residuals are a
measure of the error in a
model; difference between a
fit (predicted value) and the
actually observed value

4. Generalize the regression into a model–
Key Point: Statistical models help to tie process inputs with the
process output using statistically relevant data
Key Point: Models establish a structure using which, estimation
and hypothesis testing are enabled
Key Point: Model can be deterministic or predictive
Deterministic: simple math equation: E = mc2
Probabilistic: Uses underlying probability distribution/density
functions of random variables that defines the dependent
variable
Example: Z = A + P + F (for one sample of a project)
What if there are 10 samples from 10 different projects?
For each project, Z = A + P + F may still be valid
Taken together, Z = 0.199 + 0.658*A + 0.789*P + 0.345 F+ e
Regression model must be iteratively improved by fine-tuning it
using the “actual” value
Using large samples makes the regression equation better
(4 of 4)

One Example of applying the 4-Step
Process –
Do we need this measure?
Cost of Poor Quality : An important level 4
metric which is able to quantify the extent of
rework

Which basic metric and derived measure should
we consider in the 4-step process?
Related to this question are the following questions –
Why do we need to understand this measure?
What do we do with this understanding once we have
gained it?
What type of a “process input” does this measure
cater to? Is this a –
Controllable factor ?
Standard Operating Procedure ?
Noise ?
Key Point: The 4-step process applies only to a
“controllable factor”; standard operating procedures and
noise cannot be quantified reliably
(1 of 2)

(2 of 2)
To answer these questions meaningfully –
A thorough design and understanding of the process
is required,
because, it is in the lifecycle phases that, time is
consumed, errors or defects injected and detected
Key point: Measurement process should therefore
be an integral part of the development lifecycle
Key point: Data must be collected and analyzed in
real-time using simple techniques for it to be useful
Key point: There is little meaning in using software
data analysis as in a typical post-mortem – after the
fact

One Example of applying
the 4-Step Process
Step 1: Establish that the process is stable and capable
Cost of poor quality (%): Ratio of the sum of internal and
external failure fixing effort (major and minor fault) over
total project effort
Each observation is a rolled up data (mean) of different
projects in execution after establishing data homogeneity
For outliers (point 4), determine cause using a structured
5-Whys approach
(1 of 4)

the 4-Step Process
Step 2: Understand the underlying process distribution
Anderson-Darling test of normality is the most widely used
test by statistical software
Normality is required for continuous data prior to
measuring the process capability
Is the confidence interval for mean acceptable?
(2 of 4)
Variance measures
the degree to which
the probabilities of
a random variable
is spread out –
more the spread,
more the uncertainty
to predict or guess
the value.

the 4-Step Process
Step 3: For a stable process identify the regressions and
the residuals
(3 of 4)
Use One-way
ANOVA for analysis
of different phase-
wise contributors
P-value is < 0.05;
reject the null
hypothesis that the
mean COPQ % of
the different phases
are equal
Identify the
regressions for
improvements (CUT
and Test)

the 4-Step Process
Step 4: Generalize the regression into a model
(4 of 4)

A simple 4-step process which can be applied to –
establish an early focus on measurement and
analysis of software data with a Six Sigma mindset,
based on emphasizing the identification of business
objectives,
while considering the voice of business (CTP) and
voice of customer (CTQ) was explored
This approach emphasizes identification of “pain-points”
and the “real-root causes” to target the quantification
and improvement effort (using controllable factors)
Special cause: with project management practices
Common cause: with process management practices
Conclusion

From the experience gained from small- to medium-
organizations,
Six Sigma at the tactical level with,
model-based improvements using CMMI at the
strategic level,
offers the benefit of cycle-time-reduction to advance
in process maturity
This approach –
can target individual processes (continuous
representation) or,
a group of process areas targeting practices involved
in a maturity level (staged representation)
Conclusion

Six Sigma techniques relevant to a software process
can be applied by organizations at any level of
process maturity
Organizations that delay Six Sigma consideration
until the level 4 may miss significantly useful
opportunities to streamline improvement effort
Without a Six Sigma mindset, frivolous measures are
collected which might not help to demonstrate
improved business performance
May require to revamp their measurement program
after attaining level 3
Conclusion

One of the most useful insights to take-away from this
presentation–
every stable and capable process can be made more
stable and more capable
start your high-maturity journey early, drive
carefully using the 4-step process and, reach
safely!
Conclusion

References
[Chrissis 2011] Chrissis, Mary Beth et al, “CMMI
for Development®: Guidelines for Process
Integration and Product Improvement, Third
Edition”, Addison-Wesley Professional. 2011
[Nandyal 2012] Nandyal, Raghav, “Building and
Sustaining High-maturity Software
Organizations”, Tata McGraw-Hill Education.
2012

DISCUSSIONS, Q&A
Raghav S. Nandyal
Chief Executive Officer
SITARA Technologies Pvt. Ltd.
#54, Sri Hari Krupa
6th Main Road
Malleswaram
Bangalore KA 560 003
Telephone: +(91-80) 2334-3222
Mobile: + 984-523-3222
Email: raghav_nandyal@SITARATECH.com
URL: http://www.SITARATECH.com
Thank You!

2180_Nandyal

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