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© Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License
CMMI® - Explored
HM’s Fourteen:
Essential Beliefs for Effective High Maturity Implementation
1
2 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
® CMMI and CMM are registered trademarks of Carnegie Mellon University
© Rajesh Naik, 2013 This work is licensed under a Creative Commons Attribution 3.0 Unported License. This means you are free (1) to copy, distribute, display, and perform the work, (2) to make derivative works, and (3) to make commercial use of the work so long as you give proper attribution to the author and retain the license notice. If you create derivative works using this work, they should also be made available under a similar license. For further information go to http://creativecommons.org/licenses/by/3.0/
For uses outside the scope of the license, contact Rajesh Naik at [email protected]
3 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
This presentation covers fourteen essential beliefs
that need to be internalized to implement CMMI®
high-maturity practices effectively
Contents of this Presentation
® CMMI and CMM are registered trademarks of Carnegie Mellon University
4 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
This presentation covers fourteen essential beliefs
that need to be internalized to implement CMMI®
high-maturity practices effectively
The material in this presentation is derived from the
following documents: – CMMI® for Services, Version 1.3 (CMU/SEI-2010-TR-034)
– CMMI ® for Development, Version 1.3 (CMU/SEI-2010-TR-033)
The viewer should refer to the above documents, for
the definitive requirements of High Maturity in CMMI®
Contents of this Presentation
® CMMI and CMM are registered trademarks of Carnegie Mellon University
5 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
The Fourteen essential beliefs are 1. Nothing is definite 2. Everything is interrelated 3. Nothing is permanent 4. Nobody likes variation 5. Statistics is non-intuitive 6. There are no bell-curves in real life 7. Outliers cannot be wished away 8. Skills impacts process performance 9. Statisticians should not be decision makers 10. Process performance cannot be decreed 11. Correlation is not the same as cause-effect 12. Process instability must be analyzed in real-time 13. Control of critical sub-processes impacts higher level performance 14. Simulation may be the only practical method to model reality today
HM’s Fourteen
6 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
The Fourteen essential beliefs are 1. Nothing is definite 2. Everything is interrelated 3. Nothing is permanent 4. Nobody likes variation 5. Statistics is non-intuitive 6. There are no bell-curves in real life 7. Outliers cannot be wished away 8. Skills impacts process performance 9. Statisticians should not be decision makers 10. Process performance cannot be decreed 11. Correlation is not the same as cause-effect 12. Process instability must be analyzed in real-time 13. Control of critical sub-processes impacts higher level performance 14. Simulation may be the only practical method to model reality today
HM’s Fourteen
We cover each of
these beliefs in detail
now…
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We often like to think of outcomes as definite,
single numbers
1. Nothing is definite
21
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We often like to think of outcomes as definite,
single numbers
– But that is low maturity thinking, and does not reflect reality
1. Nothing is definite
21
9 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We often like to think of outcomes as definite,
single numbers
– But that is low maturity thinking, and does not reflect reality
In reality, all activities have an inherent
variation and what we can predict is only a
probability
1. Nothing is definite
21
10 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We often like to think of outcomes as definite,
single numbers
– But that is low maturity thinking, and does not reflect reality
In reality, all activities have an inherent
variation and what we can predict is only a
probability
In HM thinking, estimates or target dates are
values that have a certain chance
(probability) of being achieved
1. Nothing is definite
21
21 +/-
11 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We often like to think of outcomes as definite,
single numbers
– But that is low maturity thinking, and does not reflect reality
In reality, all activities have an inherent
variation and what we can predict is only a
probability
In HM thinking, estimates or target dates are
values that have a certain chance
(probability) of being achieved
– Only when we attach probabilities to targets/ goals, can we start looking at ways to increase those probabilities
1. Nothing is definite
21
21 +/-
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Another simplistic assumption (low-
maturity thinking) is that each goal/
objective of a project/ service is
independent of other goals
2. Everything is interrelated
Cost
Timeliness
Quality
Other
Goals
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Another simplistic assumption (low-
maturity thinking) is that each goal/
objective of a project/ service is
independent of other goals
This gives rise to naïve decisions - E.g.,
we think that by adding more resources
we can bring forward the deadline,
expecting the cost and quality to remain
the same
2. Everything is interrelated
Cost
Timeliness
Quality
Other
Goals
14 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Another simplistic assumption (low-maturity thinking) is that each goal/ objective of a project/ service is independent of other goals
This gives rise to naïve decisions - E.g., we think that by adding more resources we can bring forward the deadline, expecting the cost and quality to remain the same
In real situations, everything is interrelated, and HM is about understanding these relationships and taking more informed decisions
2. Everything is interrelated
Cost
Timeliness
Quality
Other
Goals
Cost
Timeliness
Quality
Other
Goals
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Since change is constant, past performance
cannot be blindly used for predicting the
future
3. Nothing is permanent
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Since change is constant, past performance
cannot be blindly used for predicting the
future
– Past performance can only be taken as one of the inputs for future performance
3. Nothing is permanent
17 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Since change is constant, past performance
cannot be blindly used for predicting the
future
– Past performance can only be taken as one of the inputs for future performance
This needs to be combined with expected
change in performance due to evolving
technology, familiarity, skills, and risks
3. Nothing is permanent
18 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Since change is constant, past performance
cannot be blindly used for predicting the
future
– Past performance can only be taken as one of the inputs for future performance
This needs to be combined with expected
change in performance due to evolving
technology, familiarity, skills, and risks
While forecasting the future, we also need to
predict the impacts of our potential actions, to
take informed decisions
3. Nothing is permanent
19 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Since change is constant, past performance
cannot be blindly used for predicting the
future
– Past performance can only be taken as one of the inputs for future performance
This needs to be combined with expected
change in performance due to evolving
technology, familiarity, skills, and risks
While forecasting the future, we also need to
predict the impacts of our potential actions, to
take informed decisions
(All this in a probabilistic, interrelated way )
3. Nothing is permanent
20 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Human beings (and institutions run by
human beings) like consistency, and
dislike surprises/ variations
4. Nobody likes variation
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Human beings (and institutions run by
human beings) like consistency, and
dislike surprises/ variations
– Think of what happens when the summer is hotter or cooler than what has been in the past
– Why do people visit fast food chains? – not for the great taste or nutritional value; but just for the consistency of experience
4. Nobody likes variation
22 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Human beings (and institutions run by
human beings) like consistency, and
dislike surprises/ variations
– Think of what happens when the summer is hotter or cooler than what has been in the past
– Why do people visit fast food chains? – not for the great taste or nutritional value; but just for the consistency of experience
Our customers, employees, and vendors
also expect minimal variations from us
4. Nobody likes variation
23 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Human beings (and institutions run by human beings) like consistency, and dislike surprises/ variations
– Think of what happens when the summer is hotter or cooler than what has been in the past
– Why do people visit fast food chains? – not for the great taste or nutritional value; but just for the consistency of experience
Our customers, employees, and vendors also expect minimal variations from us
So, a key HM principle is to identify and reduce variation, wherever it is unacceptable
4. Nobody likes variation
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Let us do a quick exercise
5. Statistics is non-intuitive
25 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Let us do a quick exercise
If you throw a dice, what are the possible outcomes?
– An integer between 1 and 6 (both included), with a probability of 1/6 for each result
5. Statistics is non-intuitive
26 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Let us do a quick exercise
If you throw a dice, what are the possible outcomes?
– An integer between 1 and 6 (both included), with a probability of 1/6 for each result
If you throw the dice twice, and add the two results, what
are the possible outcomes?
5. Statistics is non-intuitive
+
27 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Let us do a quick exercise
If you throw a dice, what are the possible outcomes?
– An integer between 1 and 6 (both included), with a probability of 1/6 for each result
If you throw the dice twice, and add the two results, what
are the possible outcomes?
Without adequate time, most people come up with the
wrong answer
The right answer is
– An integer between 2 and 12 (both included), with different probabilities for each result – see chart on the left
5. Statistics is non-intuitive
+
0
0.05
0.1
0.15
0.2
1 2 3 4 5 6 7 8 9 101112
28 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Let us do a quick exercise
If you throw a dice, what are the possible outcomes?
– An integer between 1 and 6 (both included), with a probability of 1/6 for each result
If you throw the dice twice, and add the two results, what
are the possible outcomes?
Without adequate time, most people come up with the
wrong answer
The right answer is
– An integer between 2 and 12 (both included), with different probabilities for each result – see chart on the left
When we combine distributions, our gut-feel falters
So, we need to start using stats to understand and predict
performance (and keep our guts aside )
5. Statistics is non-intuitive
+
0
0.05
0.1
0.15
0.2
1 2 3 4 5 6 7 8 9 101112
29 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We (including statisticians) would like to fit the
world into neat, symmetrical bell-curves
6. There are no bell-curves in real life
30 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We (including statisticians) would like to fit the
world into neat, symmetrical bell-curves
But human activity does not typically result in neat
bell curves
6. There are no bell-curves in real life
31 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We (including statisticians) would like to fit the
world into neat, symmetrical bell-curves
But human activity does not typically result in neat
bell curves
E.g., the air travel from Bangalore to Delhi takes
typically 2:40 hrs. On a real good day it can take
2:20 hrs. On bad days, it can take 3:00 hrs, or 3:30
hrs (or till they run out of fuel!). So, there is no
symmetry around the typical time
6. There are no bell-curves in real life
32 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We (including statisticians) would like to fit the
world into neat, symmetrical bell-curves
But human activity does not typically result in neat
bell curves
E.g., the air travel from Bangalore to Delhi takes
typically 2:40 hrs. On a real good day it can take
2:20 hrs. On bad days, it can take 3:00 hrs, or 3:30
hrs (or till they run out of fuel!). So, there is no
symmetry around the typical time
In other words – “There is a limit to how well you can do; but no limit to how badly you can screw up ”
6. There are no bell-curves in real life
33 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
We (including statisticians) would like to fit the
world into neat, symmetrical bell-curves
But human activity does not typically result in neat
bell curves
E.g., the air travel from Bangalore to Delhi takes
typically 2:40 hrs. On a real good day it can take
2:20 hrs. On bad days, it can take 3:00 hrs, or 3:30
hrs (or till they run out of fuel!). So, there is no
symmetry around the typical time
In other words – “There is a limit to how well you can do; but no limit to how badly you can screw up ”
HM organizations will not blindly assume smooth,
neat distributions for their plans and estimates
6. There are no bell-curves in real life
34 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
If I prepare a chart of the flight time taken
for all my trips from Bangalore to Delhi over
the past 10 years, the chart is odd;
there are times the plane diverted to Jaipur
till the fog/ dust/ smog cleared
7. Outliers cannot be wished away
Fog diversions
35 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
If I prepare a chart of the flight time taken
for all my trips from Bangalore to Delhi over
the past 10 years, the chart is odd;
there are times the plane diverted to Jaipur
till the fog/ dust/ smog cleared
I cannot ignore these events just because
they make it difficult to handle the math/
stats
– Unless, climate-change eliminates fog in Delhi altogether
7. Outliers cannot be wished away
Fog diversions
36 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
If I prepare a chart of the flight time taken
for all my trips from Bangalore to Delhi over
the past 10 years, the chart is odd;
there are times the plane diverted to Jaipur
till the fog/ dust/ smog cleared
I cannot ignore these events just because
they make it difficult to handle the math/
stats
– Unless, climate-change eliminates fog in Delhi altogether
So, in HM thinking, outliers are a part of the
process (with low probability), unless the
root-cause is eliminated
7. Outliers cannot be wished away
Fog diversions
37 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Competence makes a significant difference to
performance (speed, quality, throughput)
8. Skills impacts process performance
38 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Competence makes a significant difference to
performance (speed, quality, throughput)
That is why managers fight to get the right
people in the their teams
– “Processes make us people independent” (who said that?) - is misunderstood and misused
8. Skills impacts process performance
39 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Competence makes a significant difference to
performance (speed, quality, throughput)
That is why managers fight to get the right
people in the their teams
– “Processes make us people independent” (who said that?) - is misunderstood and misused
In HM organizations, estimates, plans, and
forecasts consider the skills of the people
doing the work
– Process performance baselines also factor the skill levels
8. Skills impacts process performance
40 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Often, decision makers get over-whelmed
(and terrified) by analytics and abdicate
decision making to statisticians
9. Statisticians should not be decision makers
41 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Often, decision makers get over-whelmed
(and terrified) by analytics and abdicate
decision making to statisticians
Statisticians can analyze data and prove or
disprove some hypothesis, but the decision
making still rests with the managers and
executives
9. Statisticians should not be decision makers
42 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Often, decision makers get over-whelmed
(and terrified) by analytics and abdicate
decision making to statisticians
Statisticians can analyze data and prove or
disprove some hypothesis, but the decision
making still rests with the managers and
executives
In HM organizations, managers/ executives
make effort to understand enough stats/
analytics to remain on top of decision making
9. Statisticians should not be decision makers
43 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Processes do not perform differently just because executive
management “decrees” a certain performance
10. Process performance cannot be decreed
44 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Processes do not perform differently just because executive
management “decrees” a certain performance
In low maturity organizations, the distinction between
performance baseline (actual performance) and performance
target (desired performance) is not clear – and desired
performance is used for estimation and planning
10. Process performance cannot be decreed
45 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Processes do not perform differently just because executive
management “decrees” a certain performance
In low maturity organizations, the distinction between
performance baseline (actual performance) and performance
target (desired performance) is not clear – and desired
performance is used for estimation and planning
In true HM organizations, baselines are not dictated by
management, but derived from past performance. And
management sets process performance targets (for driving
process changes)
10. Process performance cannot be decreed
46 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
11. Correlation is not the same as cause-effect
• Here is an example:
– One may be able to find a good correlation between the number of people carrying umbrellas/ raincoats to work in the morning, and whether it rained during the day
– But that does not mean that carrying umbrellas and raincoats causes rainfall
47 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
11. Correlation is not the same as cause-effect
• Here is an example:
– One may be able to find a good correlation between the number of people carrying umbrellas/ raincoats to work in the morning, and whether it rained during the day
– But that does not mean that carrying umbrellas and raincoats causes rainfall
• Correlations can be established statistically,
but cause-effect is based on logical thinking
and requires domain knowledge (which is why
statisticians cannot be the decision makers )
48 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
11. Correlation is not the same as cause-effect
• Here is an example:
– One may be able to find a good correlation between the number of people carrying umbrellas/ raincoats to work in the morning, and whether it rained during the day
– But that does not mean that carrying umbrellas and raincoats causes rainfall
• Correlations can be established statistically,
but cause-effect is based on logical thinking
and requires domain knowledge (which is why
statisticians cannot be the decision makers )
• HM thinking requires us to separate
correlation from cause-effect
49 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
12. Process instability must be analyzed in real-time
• Identifying process instability and doing root-
cause analysis must be as close to the event as
possible
50 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
12. Process instability must be analyzed in real-time
• Identifying process instability and doing root-
cause analysis must be as close to the event as
possible
• If the root-cause analysis is done too far from the
event (more like a post-mortem), there is lesser
likelihood of identifying the true root cause
51 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
12. Process instability must be analyzed in real-time
• Identifying process instability and doing root-
cause analysis must be as close to the event as
possible
• If the root-cause analysis is done too far from the
event (more like a post-mortem), there is lesser
likelihood of identifying the true root cause
– E.g., If you find out today that the time to reach office (from home) was out-of-control 6 weeks ago, you may not be able to recall the conditions that caused this
52 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
12. Process instability must be analyzed in real-time
• Identifying process instability and doing root-
cause analysis must be as close to the event as
possible
• If the root-cause analysis is done too far from the
event (more like a post-mortem), there is lesser
likelihood of identifying the true root cause
– E.g., If you find out today that the time to reach office (from home) was out-of-control 6 weeks ago, you may not be able to recall the conditions that caused this
• HM organizations create the infrastructure, tools,
and processes to collect, analyze and report data
on a real-time basis to effectively debug their
process instability
53 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
13. Control of critical sub-processes impacts higher level performance
• If you want your weight in control, measuring and
monitoring your weight frequently (and putting it on a
control chart) is not going to bring your weight under
control
54 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
13. Control of critical sub-processes impacts higher level performance
• If you want your weight in control, measuring and
monitoring your weight frequently (and putting it on a
control chart) is not going to bring your weight under
control
• To control weight, you may need to monitor and measure:
– The amount and type of exercise that you do
– The amount and type of calories that you eat
You may even have to measure the calories consumed and expended at various times during the day
55 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
13. Control of critical sub-processes impacts higher level performance
• If you want your weight in control, measuring and
monitoring your weight frequently (and putting it on a
control chart) is not going to bring your weight under
control
• To control weight, you may need to monitor and measure:
– The amount and type of exercise that you do
– The amount and type of calories that you eat
You may even have to measure the calories consumed and expended at various times during the day
• Similarly, (in a project) measuring schedule variance
alone is not likely to bring the schedule under control
56 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
13. Control of critical sub-processes impacts higher level performance
• If you want your weight in control, measuring and
monitoring your weight frequently (and putting it on a
control chart) is not going to bring your weight under
control
• To control weight, you may need to monitor and measure:
– The amount and type of exercise that you do
– The amount and type of calories that you eat
You may even have to measure the calories consumed and expended at various times during the day
• Similarly, (in a project) measuring schedule variance
alone is not likely to bring the schedule under control
• HM organizations identify and control sub-processes that
are critical to overall performance
57 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
14. Simulation may be the only practical method to model reality today
• Given that:
– There are multiple, interrelated goals to achieve
58 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
14. Simulation may be the only practical method to model reality today
• Given that:
– There are multiple, interrelated goals to achieve
– Inputs and processes have their own variations (probabilities)
59 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
14. Simulation may be the only practical method to model reality today
• Given that:
– There are multiple, interrelated goals to achieve
– Inputs and processes have their own variations (probabilities)
– Most of these variations do not fit neat symmetrical bell curves
60 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
14. Simulation may be the only practical method to model reality today
• Given that:
– There are multiple, interrelated goals to achieve
– Inputs and processes have their own variations (probabilities)
– Most of these variations do not fit neat symmetrical bell curves
– We need to predict the potential impact of our choices on the outcomes (multiple objectives)
61 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
14. Simulation may be the only practical method to model reality today
• Given that:
– There are multiple, interrelated goals to achieve
– Inputs and processes have their own variations (probabilities)
– Most of these variations do not fit neat symmetrical bell curves
– We need to predict the potential impact of our choices on the outcomes (multiple objectives)
• Simple, deterministic mathematical equations
alone do not suffice to reflect reality
62 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
14. Simulation may be the only practical method to model reality today
• Given that:
– There are multiple, interrelated goals to achieve
– Inputs and processes have their own variations (probabilities)
– Most of these variations do not fit neat symmetrical bell curves
– We need to predict the potential impact of our choices on the outcomes (multiple objectives)
• Simple, deterministic mathematical equations
alone do not suffice to reflect reality
• In HM organizations, random number based
computer simulations (e.g., Monte Carlo)
provide the best platform to model reality
63 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
1. Nothing is definite
2. Everything is interrelated
3. Nothing is permanent
4. Nobody likes variation
5. Statistics is non-intuitive
6. There are no bell-curves in real life
7. Outliers cannot be wished away
8. Skills impacts process performance
9. Statisticians should not be decision makers
10. Process performance cannot be decreed
11. Correlation is not the same as cause-effect
12. Process instability must be analyzed in real-time
13. Control of critical sub-processes impacts higher level performance
14. Simulation may be the only practical method to model reality today
(Recalling…) HM’s Fourteen
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Related Presentations
– CMMI® Explored – Concept of Maturity
– CMMI® - SVC Explored – Process Area Overview
65 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
® CMMI and CMM are registered trademarks of Carnegie Mellon University
© Rajesh Naik, 2013 This work is licensed under a Creative Commons Attribution 3.0 Unported License. This means you are free (1) to copy, distribute, display, and perform the work, (2) to make derivative works, and (3) to make commercial use of the work so long as you give proper attribution to the author and retain the license notice. If you create derivative works using this work, they should also be made available under a similar license. For further information go to http://creativecommons.org/licenses/by/3.0/
For uses outside the scope of the license, contact Rajesh Naik at [email protected]
66 © Rajesh Naik, 2013 Released under Creative Commons Attribution 3.0 Unported License AlignMentor
Thank You!
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