Different Flavors Of PPMs

-S.Sugavaneswaran

Sonata Software Ltd.

Different Flavors of PPMs

Presented at HMBP 2010

S.Sugavaneswaran

Sonata Software Limited

21-May-10

www.sonata-software.com

3

Agenda

• About Process Performance Models

• High maturity enablers

• Challenges faced in implementation

• Flavors of PPM

• How good they are

4

Need for PPM

• An Earned Value Management dashboard

• How effective is such a report in terms of triggering

process improvement actions?

• Will it help to know which controllable process

factors influence the above outcomes?

Adapted

from the SEI

paper “An

Executive

Tutorial of

CMMI

Process

Performance

Models”

“Delighting customers is what it’s all about, and that comes from

consistent, end-to-end process performance.” – Kevin Weiss

• Relate controllable factors to an outcome

o Y=f(x1,x2,x3…)

• Developed from historical data

• Predict results achieved by following a process

• With a known confidence level

• Help perform “What-if” analysis

• Compose processes for a project

5

Process Performance Models

• IT Consulting and Services company

• Customers across US, Europe, Middle East and APAC

• Services offered

• Product Engineering Services

• Application Development/ Management

• Managed Testing

• Infrastructure Management

• Quality standards adaptation

• ISO 9001

• CMM Level 5

• CMMI v1.2 Level 3

• ISO 20000-1

6

Our Context

7

High Maturity Enablers

• Standardizing size measures for projects

• To normalize process performance

• Enabling sub-process level control

• Effort to create, review and rework

• Options for each sub-process

• Data at the sub-process option level

• Capturing defect injection and detection

8

Implementation Challenges

“The truth is that you always know the right thing to do. The

hard part is really doing it.” – H. Norman Schwarzkopf

• Stakeholder buy-in

• Issues with data availability / stability

• Tool enablement constraints

• Continued involvement of practitioners

9

PPM – Healthy Ingredients

1. Statistical or probabilistic in nature

2. Predict interim and/or final project outcomes

3. Use controllable factors tied to sub-processes

4. Model the variation of predictive factors to forecast

outcome variations

5. “What-if” analysis for project planning/re-planning

6. Connect upstream with downstream activities

7. Enable mid-course corrections

10

PPM Flavors

“All models are wrong, some are useful!” – George Box

• Development project – Continuous simulation

• Sub-process wise process performance

• Prediction with confidence levels

• “What-if” analysis

• Production support – Discrete event simulation

• Process flow depiction and simulation

• Analysis of

• SLA adherence

• Resource utilization

11

Flavor 1

• About the project

• New development (Agile)

• Sprints & stories

• Sprint content decided based on experience

• Developers categorized by skill level

• Model applied

• Monte Carlo Simulation

12

Simulation Highlights

• Objective: To optimize number of stories forming

part of a sprint

• Predictive factors

• Working hours per day

• Number of stories

• Sub-process wise productivity

• Skill levels

• Size of each story

• Team size

13

The Model

• Inputs: Estimated story size and sub-process

productivity distributions

• In each simulation run,

• The model chooses values from sub-process

productivity distributions, arrives at effort

• Predicted effort = Sum of all sub-process efforts

• Effort computed is divided by the available man-hours

per day, giving the elapsed days

• Over time, a profile is built showing the distribution

of likely outcomes (number of days)

• Confidence level indicated for the output

Scenarios

Story

1

Story

2

Story

3

Story

4

Story

5

Story

6

Story

7

Story

8

Story

9

Story

10

Size 30 12 80 2 6

Skill High High High Low High

Understanding &

Analysis0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Design 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Design Review 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Coding 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Code Review 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Code Fix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Unit Test 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Units Test Fix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

FIT Testing 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

IT Fix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

14

Table 1: Model before running the simulation

15

Sample Predictions

Tool Output 1: Release Prediction – 6 Stories

Tool Output 2: Release Prediction – 5 Stories

Scenario 1: Stories

1,2,3,4,5 and 6

Scenario 2: Stories

1,2,3,5 and 6

16

Process Control

Tool Output 3: Sensitivity-Release Prediction

Sub-processes to be

closely monitored:

IT and Coding- High

skill

17

Flavor 2

• About the project

• Production Support

• High volume, short turnaround work

• SLA-driven

• Different ticket priorities

• Three different skill sets

• Model applied

• Discrete Event Simulation

18

Simulation Highlights

• Objectives: To forecast and manage SLA

adherence and Resource utilization

• Predictive factors

• Team size

• Response, analysis and development time

• Arrival pattern of tickets (by priority)

• Wait times

19

The Process Model

SLA Adherence

Tool Output 4: SLA Miss before the model

Tool Output 5: SLA Miss after the model

Probability of SLA breach brought down

21

Resource Utilization

Tool Output 6: Before

the model

Tool Output 7: After

the model

Resource utilization improved as well

Model Flavors vs Healthy

Ingredients

Ingredient Flavor 1 Flavor 2

Statistical, probabilistic… Yes Yes

Predict interim/ final… Yes Yes

Sub-process level factors Yes Yes

Model uncertainty… Yes Yes

Support “What-if”

analysisYes Yes

Connect to downstream.. Yes Yes

Enable course correction Yes Yes

22Table 2: Models vs Healthy Ingredients

23

Conclusion

“Action may not always bring happiness, but

there is no happiness without action.”

- Benjamin Disraeli

24

Thank you

Q & AEmail: sesh@sonata-software.com

www.sonata-software.com

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