Hi Maturity in the CMMI Services Context

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“Hi Maturity in the CMMI Services

Context"

Chinmay Pradhan

QAI Global

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

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

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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Service Components: Elements that help deliver a Service

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Work

products

Work

Processes

Tools

Infrastructure

People

Streamlined Service Delivery

Modeling in an Application Support

Service

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Typical Expectations

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Year-on-Year Savings

Technical Complexity

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!!!

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

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

One approach for Modeling Such a System

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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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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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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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The Model

Type 1

Type 2

StorageDecision

Study and Response

Study and Response 2

AnalysisAnalysis 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

The Model

Type 1

Type 2

StorageDecision

Study and Response


AnalysisAnalysis 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

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


Resources)

•Engineer Group 4( 4

Resources)

•Average idle time of 15%

for Eng 1 and 14% for Eng

3

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

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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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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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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Using the Model with changes

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•Shift 1

•Engineer Group 1 (4

Resources)


Resources)

•Shift 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

StorageDecision

Study and Response


AnalysisAnalysis 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


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0.00

10.00

20.00

30.00

40.00

50.00

60.00

Stu

dy a

nd R

esponse

Stu

dy a

nd R

esponse 2

Analy

sis

Debugg

Analy

sis

2

Debugg 2

Sto

rage

Percent of T

ota

l M

inute

s

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


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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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

Tips and Tricks on Model usage in Project

Management

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Service Transition

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•Right turnaround time to

promise

•Right SLA

•Right Volume

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

Setting Up Service Delivery

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Type 1

Type 2

StorageDecision

Study and Response


AnalysisAnalysis 2

Debugg

Debugg 2

Close Request

Engineer

1

Engineer 2

Engineer

3

Engineer 4

Engineer

5

Resource Overlapping

VS

Type 1

Type 2

StorageDecision

Study and Response


AnalysisAnalysis 2

Debugg

Debugg 2

Close Request

Engineer

1

Engineer 2

Engineer

3

Engineer 4

No Overlapping

Dependency Management

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Dependency on customer answers

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

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

Watch Out for

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Misaligned Goals: Improvement in Productivity in T&M

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Swamped by information

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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

Propagate beyond Delivery and Interweave

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24X7 support is driven by transport: Do they do

capacity management???

Questions

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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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http://www.processmodel.com/

Please feel free to write in.

Chinmay Pradhan

QAI Global

[email protected]

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Business

Hi Maturity in the CMMI Services Context