This presentation explains the concept of CMMI for services, their expectations, tips & tricks on using the service model in project management with various inferences.

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“Hi Maturity in the CMMI Services
Context"
Chinmay Pradhan
QAI Global

2. CMMI For Services
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• Helps Build Better Services
• Looks at Services from Strategic
perspective
• Applicable across broad spectrum
of work done
• Aligned and Leverages existing
service management frameworks
eg ITIL, CoBIT etc

3. Snapshot of Services Where CMMI SVC has been applied
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• Majority is for Application Management
•Production Support
•Change Requests
•Bug Fixes
• Other Non Conventional work types
are also catching up
Work Types WIth SVC Implemented
Application Management and
Support
Training Services
Risk Consutling
ERP Configuration
ERP Support
Staff Augumentation
BPO

4. Service System
• Services are useful intangible and non-storable results
delivered through the operation of a service system.
» CMMI SVC
• Services are characterized by
–Simultaneity
–Heterogeneity
• A Service System is a combination of
–People using
•Tools and Resources to execute
–Process Steps to complete an service operation
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5. Service Components: Elements that help deliver a Service
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Work
products
Work
Processes
Tools
Infrastructure
People
Streamlined Service Delivery

6. Modeling in an Application Support
Service
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7. Typical Expectations
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Year-on-Year Savings
Technical Complexity

8. Assumed system
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No of
Requests
per Day
• Based on actual average TAT per step and priority
•Helped analyze the capacity required per day and hence plan for the month
• Was simple to use and understand
But!!!

9. In Reality….
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19
4
• Events are dynamic
with multiple factors
affecting the
performance
• The inflow of tickets
changes constantly
• The performance for
each of the steps is
not fixed, but variable
An animated view of the service system

10. In Reality….
• There were far too many dynamic factors to be considered in
the entire resolution process like
–If a high Priority ticket came in all others have to be
dropped till this is fixed
–If resources are busy then tickets will be in queue
–The Process Performance is a function of the complexity
and not just the priority,
• Additional complexity: can have high priority simple tickets and
also complex tickets.
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… to control the Key Process Outputs
Key Process Inputs:
Control these …
Output Variability
“Outputs inherit variability from inputs.”
Sub Process and its Impact

12. One approach for Modeling Such a System
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13. Let’s Take an example
The Service System we have picked up is
–Service to resolve Production support requests for large banking
application
–There are 2 different kinds of requests that come in to the queue,
Type 1 and Type 2. Each having its own priorities and SLA’s
–The Service provides a 6X18 hr support.
Some of the challenges faced by the service system are
–The resource allocation and utilization vis a vis the SLA performance
is sub optimal i.e if the SLA compliance is comfortable then there is
low utilization
–Team composition and shift allocation
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14. Creating the Model
The service system was aptly described by a queue based system. To
model such a system we required:
• Standard Simulation Modeling Tool. E.g Process Model
• Resources with the knowledge of such modeling and the skill to use the
tools
• Process maps of actual events
• Map of all possible occurring conditions i.e high priority gets picked first,
there are shifts with breaks,
• Actual data with respect to the
–Request classifications i.e no of type 1 and type 2, breakup of their
priorities.
–The Turnaround time for each process steps
–Waiting time if any
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15. The data required – Not very different from what is typically collected
• The data collected were
–A sample of the actual turnaround time for each process
steps by their priorities and types
–Request incoming patterns; quite an eye opener.
–Operating condition were
• Shifts of 9am to 6pm, 6pm to 3am
• Till now separate teams work on separate types
• If high priority request comes it is picked up immediately
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16. The Model
Type 1
Type 2
Storage
Decision
Study and
Response
Study and
Response 2
Analysis
Analysis 2
Debugg
Debugg 2
Close Request
Engineer
1
Engineer 2
Engineer
3
Engineer 4
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•Daily incoming pattern
described as statistical
distributions for each
type of request
•Data was entered for the
entire time period for
support i.e 9 am to 6pm
and then 6pm to 3 am
•Requests had priority
assigned using
probabilities
•Requests arrive at a
system before they are
assigned and allocated
across

17. The Model
Type 1
Type 2
Storage
Decision
Study and
Response
Study and
Response 2
Analysis
Analysis 2
Debugg
Debugg 2
Close Request
Engineer
1
Engineer 2
Engineer
3
Engineer 4
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•Current team assignment was
depicted using the shift
timings
•One Resource was to
complete the whole request
before he or she was free
• Before the shift started or
during breaks the queues will
build up
•If a high priority ticket enters
the system, it will get
addressed immediately
•SLA are established for all
ticket categories and
priorities

18. Using the Model with current team settings
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•Shift 1
•Engineer Group 1 (4
Resources)
•Engineer Group 3(4
Resources)
•Shift 2
•Engineer Group 2(4
Resources)
•Engineer Group 4( 4
Resources)
•Average idle time of 15%
for Eng 1 and 14% for Eng
3

19. Using the Model with current team settings
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•Average SLA at 95% CI is
predicted to be
•Type 1
•P1 92%
•P2 91%
•P3 89%
•Type 2
•P1 89%
•P2 74%
•P3 98%
With a target of 90%
compliance there could be quite
some misses

20. Evaluating Strategies :
• The Model was used to
• Simulate various scenarios and Identify
–Evaluate available strategies to improve Utilization and
SLA compliance
–Identify the potential areas of improvement in the process
steps.
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21. Evaluating Strategies
• One of the things noticed in the setup was the queue that
was getting built up in the non working period of 3 am to 9
am.
• Since the data for the inflow had not been analyzed before it
was not realized that the engineers used to start work with
the queue that was leading to SLA’s being breached
• What would be the impact if the team did not have the
queue?
• Also it was noticed that there was typically some free
resources in the day in the first shift but the second shift was
tight.
• What if we overlap shift timings?
• What if we cross skill the people in the night shift so that
anyone free can take in the other queue?
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22. Changes Made in the rules
• The inflow was modified to evaluate the impact of yanking
away the requests logged during the non working hours.
• One of the resources was cross trained
and assigned to both the queue so that requests can be
resolved from both.
• The timings of this resource were made on an overlapping
slot of 2pm to 11 pm so that there is an availability in both
the shifts.
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23. Using the Model with changes
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•Shift 1
•Engineer Group 1 (4
Resources)
•Engineer Group 3(2
Resources)
•Shift 2
•Engineer Group 2(2
Resources)
•Engineer Group 4( 1
Resources)
•Mid Shift
•Engineer 5 ( 1
Resource)
•A reduction of 6 resources
from the team
•Average un-utilization was
the highest for Eng 4 at 53%
for the only person there
Type 1
Type 2
Storage
Decision
Study and
Response
Study and
Response 2
Analysis
Analysis 2
Debugg
Debugg 2
Close Request
Engineer
1
Engineer 2
Engineer
3
Engineer 4
Engineer
5

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•Average SLA at 95% CI
Predicted
•Type 1
•P1 93%
•P2 88%
•P3 90%
•Type 2
•P1 93%
•P2 88%
•P3 100%
SLA Compliance much
healthier
Using the Model with changes

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0.00
10.00
20.00
30.00
40.00
50.00
60.00
StudyandResponse
StudyandResponse2
Analysis
Debugg
Analysis2
Debugg2
Storage
PercentofTotalMinutes
Top 7 Hot SpotsTop 7 Hot Spots
Percentage of NVA Minutes
Percentage of BVA Minutes
Percentage of VA Minutes
•The Study and Response
Process Step was seen to the
most variable process step
and identified for further
analysis and improvement
Using the Model with changes

26. Inferences
• “Yanking” the queue for the non working hours can reduce
the load on the system
• Cross training can be a significant leverage
• Overlapping shift timings can greatly impact the SLA’s as
well as the effective team utilization.
• The initial step of study and response is also identified as the
potential area for improvement
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27. Key Takeaways
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• Involve subject matter experience
–Meaningless (statistical) relationships should be
discarded
–Expected (statistical) relationships can be verified
• GIGO
–Review and feedback cycle
• Be clear on objectives and expectations from simulation
• Ensure adequate interactions between
–Model builder, Management, Practitioners
• Train personnel operating the model
–To know how to use it – make appropriate inferences
–To know when it is not working – seek help
• Check against known results

28. Tips and Tricks on Model usage in Project
Management
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29. Service Transition
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•Right turnaround time to
promise
•Right SLA
•Right Volume

30. Capacity Management
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16 People handle 71 work requests
Is this Ok
If volume increases by 20% then
how many resources?? By when??
Capacity during Service
Continuity?? SLA during SCON

31. Setting Up Service Delivery
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Type 1
Type 2
Storage
Decision
Study and
Response
Study and
Response 2
Analysis
Analysis 2
Debugg
Debugg 2
Close Request
Engineer
1
Engineer 2
Engineer
3
Engineer 4
Engineer
5
Resource Overlapping
VS
Type 1
Type 2
Storage
Decision
Study and
Response
Study and
Response 2
Analysis
Analysis 2
Debugg
Debugg 2
Close Request
Engineer
1
Engineer 2
Engineer
3
Engineer 4
No Overlapping

32. Dependency Management
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Dependency on customer answers

33. Common Examples of Controlled Factors: Enough?????
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Sl No Y Parameter
(Outputs)
X ( Controllable
factors)
Impacted Sub Processes
Monitored
1 % SLA Met by
Priority
Skill of Resources in a
team/Shift
Optimal No of
Resources in a
shift/team
Usage of Knowledge
Database
TAT of Resolution for each Incidents
TAT of Response for each of the
incidents
Assignment Time for each incident
No of backlog incidents per day
2 Utilization of
Resources
Skill of Resources in a
team/Shift
Optimal No of
Resources in a
shift/team
Usage of Knowledge
Database
TAT of Resolution for each Incidents
TAT of Response for each of the
incidents
Assignment Time for each incident
No of backlog incidents per day

34. What is Critical!!!!:2013 Malayasian GP Pitstop2.05Secs
Time Difference between Winner and 2nd :4.2secs
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Pit stops not mandatory
Critical Sub Process can be outside the chain of direct delivery process step

35. Watch Out for
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36. Misaligned Goals: Improvement in Productivity in T&M
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37. Swamped by information
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38. Measurements at in-appropriate granularity
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Measurement is a function of work complexity and Need for information not
convenience
Measurement does not always mean a permanent measurement system

39. Propagate beyond Delivery and Interweave
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24X7 support is driven by transport: Do they do
capacity management???

40. Questions
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41. References
• Using Process Performance Models to enhance CAM in CMMI for SVC-
Mukul Madan and Chinmay Pradhan; Presented at SEPG NA 2011
• Service Management: Operations, Strategy, and Information Technology-
- James A. Fitzsimmons, Mona J. Fitzsimmons
• Introduction To Operations Research- Billy Gilett
• Process Model User Guide and Tutorial. http://www.processmodel.com/
• CMMI® for Services, Version 1.3 – CMMI Product Team (CMU/SEI-
2010-TR-034)
• Improving Organizational Alignment Leveraging High Maturity Principles:
Sankararaman D: HMBP 2012
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42. Please feel free to write in.
Chinmay Pradhan
QAI Global
Chinmay.Pradhan@qaiglobal.com
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