Excellence Through Teamwork CMMI and Six Sigma SC-SPIN February 28,2008

Excellence Through TeamworkExcellence Through Teamwork

CMMI and Six Sigma

SC-SPINFebruary 28,2008


Presentation Outline

Introductory Comments

Characteristics of High Maturity Organizations

CMMI and Value Streams

Measuring

Six Sigma


Capability Maturity Modeling Integration (CMMI)

• Develop/Document Process

• Refine Process

• LSS is a tool to assist w/ process refinement

• Standardize process across multiple areas

• Measure the process

• Modify based on measurement analysis



• A mature ML 4-5 organization can accurately, quantitatively estimate

quality and process performance, make confident commitments to those

predictions, and execute those commitments as planned.

• Statistical and other quantitative methods are used, at both the

organizational and project levels, to understand the past, control the

present, and predict future quality and process performance.

• Organizations establish quantitative objectives for quality and process

performance based on their business objectives.

• Individual projects establish their objectives based on those of the

organization and the needs of their customers and end users.



• Projects and individuals use statistical and other quantitative methods to plan, monitor, take corrective actions, and predict progress against their objectives.

• Key subprocesses are identified and statistically managed.

• The performance of key subprocesses is known and used to compose a

overall processes to meet organizational and project objectives.

• High Maturity capabilities allow organizations to:

– Use information from individual projects to understand and refine

project performance and variation for the organization’s standard

processes

– Know what their process limitations are so they do not over commit

– Target areas for continuing improvement

– Evaluate quantitatively the impact of proposed improvements.



• Prerequisites for a High Maturity organization:

– The ability to gather and use meaningful data at all levels from

individual practitioners to projects to the organization itself

– Defined processes for projects that specify how and when data are

collected for use in quantitatively managing the projects

– Consistent tailoring of the project’s defined processes from the

organization’s standard processes

– A functioning Measurement and Analysis (MA) process area that

collects data at the practitioner and project levels and elevates that

data to the Organizational Measurement Repository for use by other

projects

– An understanding of what constitutes meaningful and useful

measurement data

– An understanding of the types of variation that are present in all

processes


CMMI Maturity Level 4 Process Areas

• Organizational Process Performance (OPP)

– The purpose of Organizational Process Performance (OPP) is to establish and maintain a quantitative understanding of the performance of the organization’s set of standard processes in support of quality and process-performance objectives, and to provide the process-performance data, baselines, and models to quantitatively manage the organization’s projects.

• Quantitative Project Management (QPM)

– The purpose of Quantitative Project Management (QPM) is to quantitatively manage the project’s defined process to achieve the project’s established quality and process-performance objectives.

• OPP and QPM are very tightly coupled and require very close cooperation between the organization and the projects to realize their benefits.



• Comments on Organizational Process Performance (OPP):– The organization builds a set of Common Measures (in the Organizational

Measurement Repository) that represents actual performance of processes for individual projects. These Common Measures are statistically analyzed as to distribution and range and then applied to any individual project in the organization.

– Quantitative objectives for quality and process performance are established for the organization and are based on the organization’s business objectives and the actual past performance of projects.

– Performance Baselines and Models are established for the organization’s set of standard processes (OSSP):

• Process Performance Baselines = Measurements of actual performance for the OSSP. Include a distribution of results that can be used by all projects for estimation purposes.

• Process Performance Models = Predictors of future performance of the OSSP and of processes in a project’s lifecycle – may use simulations and other statistical methods

– A quantitative understanding of the OSSP enables individual projects to know how to compose/tailor their defined processes so that their objectives are realistic and can be met.



• Quantitative Project Management (QPM) involves the following:

– Establishing and maintaining the project’s quality and process-performance objectives

– Identifying suitable subprocesses from the OSSP baselines and models to tailor and include at the project level

– Selecting the project subprocesses that should be statistically managed and monitoring their performance

– Sending appropriate statistical and quantitative data to the Organizational Measurement Repository

– Understanding the nature and extent of the variation experienced in the project’s process performance

– Determining if the project’s quantitative objectives for quality and process-performance can be met

– Ensuring that there is not a misalignment between the Voice of the Customer [Specification Limits] and the Voice of the Process [Control Limits] -- desired results vs. capability

• The principles of QPM can be applied to projects, support groups, and functional areas


Quality Management System (QMS) Involving Value Streams

• QMS methodology must support the initiative to be CMMI ML 3 compliant.

• Value Streams (VSs) can be used to select subprocesses for statistical

management.

• Value Streams are representative of major lifecycle phases.

– SEI is recommending for QPM that at least one subprocess be statistically managed within

each major project phase and that at least one subprocess be included from each of the

four CMMI Continuous Representation Categories (Process Management, Project

Management, Engineering, and Support)

– Statistically managed subprocesses should facilitate predicting process outcomes and

tracking current project progress.

– Peer Reviews could be one of these “control knobs”/subprocesses to manage statistically

as it can appear in several Categories.

– It may take two or more years to collect sufficient data to determine the critical

subprocesses to optimize for ML 4.


Value Stream Structure

• Overview of process

• Identifies all associated sub processes

• Provides for CMMI artifact collection

• Includes other functionality (CM, ILS, etc.)

Project initiation

Pre-Install Phase Install Phase Closeout Phase

Site Survey

BESEP

IDPInstall prep

Install Activities

Test & Checkout

Turnover

Installation Value Stream

Sub processes

REQM PP PMC SAM MA PPQA CM RD TS PI VER VAL OPF OPD OT IPM RSKM DAR

CMMI ML 3 Process Areas


QA / CMMI Structure

Value Streams (handbooks)

Installation VS

ISEA VS

SSA VS

Acquisition VS

Repair VS

Command Level Docs RSKMP CMP SEP M&A P RMP

COI/PM Level Docs COI/Program Management Plan

Project Level Docs Project /Assignments Plan

SAM QAP ILSPPMP

Integration VS

R & D VS Project Plans tie the COI to the VS

Life Cycle


Quality Management System (QMS) Involving Value Streams

• The following Command processes complement and are embedded in the foregoing VSs:

– Finance

– Contracting

– Purchasing

– Business Processes

• CMMI

• QA

• Etc.

Note: Logistics and Configuration Management may become VSs themselves if they are separate product/service deliverables to a customer in their own right.


LSS Focused Efforts

• Black Belts will assist the Implementation of CMMI

– Work with Divisions to determine VSs

– Break VSs down into Process steps

• Green Belts will conduct events to further define and improve VS Processes

• Black Belts will support the EntPG Structure

• Basic EntPG Communication Plan will provide visibility and coordination of these efforts


Detailed Communication Plan

DivEPG

TWGChair

TWGChair

TWGChair

TWGChair

TWGChair

DeptEPG

DivEPG

TWGChair

TWGChair

TWGChair

TWGChair

TWGChair

DivEPG

TWGChair

TWGChair

TWGChair

TWGChair

TWGChair

DIPTChair

DIPTChair

DIPTChair

DIPTChair

EntPG09K

IPTChair

IPTChair

IPTChair

IPTChair

Master BB

Dept BB

Div BB


8 Step Process

• The 8 Step Process is the improvement methodology based on tools and techniques of Lean & Six Sigma

Implement&

Validate

Identify & Prioritize

Opportunities

ProjectDefinition

Document &MeasureCurrentReality

Communicate & Acknowledge

Success

Measure &Sustain

Improve &

Innovate

AnalyzeWaste &Variation

1 2 3 4

5678

ExcellenceExcellence


Purpose of Measuring

• Measurements should do any or all of the following:

– Directly tie in to the project objectives

• Assess current performance of your process against customer requirements

• Show baseline (existing) capability

• Validate project benefits and ensure visibility of improvements

– Identify relative strengths and weaknesses in and between processes

– Monitor and control long term performance

– Be concise and readily understandable

• Facilitate rapid understanding of what is happening in the process/project


Establishing a Measurement

• Establishing a measurement follows a generic process

• Five steps to establish an effective measurement:

– Select the measurement

– Define the measurement

– Identify measurement/data source

– Define a measurement collection/sampling plan

– Maintain the measurement


Select the Measurement

• The selection of effective measurements should consider data that is:

– Relevant

• Related to your purpose or goal and actually used

• Is collected real time

– Accurate

• Measures what was intended to be measured

• Obtained through careful reading and recording

– Understandable

• Clear and organized

• Summarized in tables or graphically

– Complete

• Includes every necessary detail and event

• Enables you to make decisions

– Simple

• Data is available without extraordinary effort

• Data is not expensive to collect


Measurement Effectiveness

• Need to define measures to assess project complexity. Possible candidates areas:

– Estimated engineering hours (not dollars which could change) Technology risks

– Number of requirements and their volatility

• Management has a role in driving down complexity in all phases of a project.

“More consideration must be paid to ways of reducing Project Challenge [Complexity]. Doing so is a major challenge prior to the establishment of the development project, beginning during the pre-acquisition period. Earlier application of Systems Engineering practices and principles may go a long way towards reducing that challenge.” Survey Report, p. 100


6

• In statistics, represents standard deviation, a measure of variation for process performance

• Sigma is generated by measuring processes

– Process data can be collected and evaluated to determine its impact on productivity, performance, and customer satisfaction

– The measurements provide the ability to “predict” process performance and provide a benchmark to determine if actions have produced results


“Operating at 6 Capability” implies

• Data driven decision making

• Meeting customer’s requirements

• Measurable processes

• Processes Under Control

• Variation has been reduced

• Future performance can be predicted

• Results of actions can be assessed


Utilizing the Data

• Process variation is large compared to specifications such that the process must remain centered to maintain capability

• Any shifts in process mean, or variation, will abruptly reduce the capability of the process

– Cp measures the ability of a distribution to fit within specs

• Cp = (usl-lsl / 6

– Cpk measures how well the process is centered

• Cpk = min (usl-3-lsl / 3)


Utilizing the Data

• Cp and Cpk are used together so that they may indicate if the process can meet specs and is properly centered

Cp

Low variation

Centered Cp

Cpk High variation

Not Centered

Cpk

Cp

Low variation

Not Centered

Cpk

USLLSL

meantarget


Interpreting Histograms

• Histograms can judge current performance and future performance

0

2

4

6

8

10

12

15 16 17 18 19 20 21 22 23 24

Classes

# o

f O

bse

rvat

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s

15 16 17 18 19 20 21 22 23 24

Classes

Process at Time Period A

Process at Time Period B

Detecting a process shift

13 14 15 16 17 18 19 20 21 22

Classes

Process Before Improvement

0

5

10

15

20

25

30

13 14 15 16 17 18 19 20 21 22

Classes

# o

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Process After Improvement


Wrap

• Six Sigma supports both individual process areas as well as aggregate process data

• Six Sigma statistical processes should be applied to rolled up management level aggregate data as well as singular process data

• Forecasting, modeling and prediction can be done utilizing statistically derived data

• Six Sigma provides a tool set that can be used to collect and analyze data used in high maturity organizations

Documents

Excellence Through Teamwork CMMI and Six Sigma SC-SPIN February 28,2008