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The Intelligence behind
Successful Software Projects
IT DEMAND MANAGEMENT
AND CAPACITY PLANNING:
WHY ESTIMATION IS VITAL TO
BALANCING THE SCALE
-1-
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-2-
Agenda
Challenges associated with demand
management & capacity planning:
• “Realistic” demand estimation
• Effective resource optimization
• Detailed resource planning to support
capacity utilization
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Why Is IT Development Capacity
Planning so Difficult?
-3-
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-4-
A Difficult Juggling Act in a Complex
Environment
Production CapacityBusiness Demand Technology & Business
Executive Management
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-5-
Computerworld – How to Develop an
Effective Capacity Planning Process
Requirements/Estimates
Productivity baseline
Estimates Transformed into
Resource Plans
Aggregate demand compared
to actual capacity
Recommended Best Practice
Assessing
Resource Optimization
“How to develop an effective capacity
planning process”, Rich Schiesser,
Computerworld, Mar 31, 2010
Configure skills/roles
Top-Down Estimation
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-6-
Why is Matching Demand & Capacity
so Difficult?
• Business stakeholder insatiable
appetite for competitive capability
• Poor IT estimation & poor project
stakeholder negotiation
• Ability to predict amount of resources
/skills as required over the course of a
project
• Dynamic nature of the total volume of
projects in the development pipeline
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
“Realistic” Demand Estimation
-7-
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-8-
“Realistic” Demand Estimation
• What we would like vs what is possible
• What facts can we bring to bear?
 Scope of the work (size)
 Productivity to perform the work
 Availability skilled labor
• How important is accuracy?
• How do we negotiate some realistic demand solutions?
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
9
Terminology
Targets, constraints, estimates, commitments, and plans are not the same
thing:
• Target - A goal, what we would like to do or achieve
• Constraint - Some internal or external limitation on what we are able to do
• Estimate - A technical calculation of what we might be able to do at some
level of scope, cost, schedule, staff, and probability
• Commitment - A business decision made to select one estimate scenario and
assign company resources to meet a target within some constraints
• Plan - A set of project tasks and activities that (we calculate) will give us
some probability of meeting a commitment at a defined level of scope,
budget, schedule, and staff
Organizations sometimes confuse these terms and the business practices they
represent.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-10-
Identifying Unrealistic Stakeholder
Expectations
• QSM research has found that the 2 most
common reasons projects fail is:
 Unrealistic cost & schedule expectations
 Unmanaged requirements growth
• Need an effective mechanism to quantify
stakeholder requirements - scope
• Need and effective method to translate
requirements into time and effort
• Need to provide practical alternatives when
expectations don’t meet reality
If we can’t get this
right we will never
have a effective
capacity planning
solution!
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-11-
How? Top-Down Scope-Based
Estimation
Particularly good at identifying unrealistic expectations:
• Doesn’t require a lot of detailed information
• Relatively quick
• Few hidden assumptions
• Explicitly calibrated from history (local or industry)
• Very flexible for scope, staffing, duration, etc., changes
• Considered industry “best practice”
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
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12
Two Sizing Approaches
Analogy Sizing
Comparing this system to the known sizes of
similar system(s).
Adjustments can be made by percentage or
by including/excluding functions.
Artifact Sizing
Counting and measuring system artifacts and
work products and scaling to the size of the
final system.
Different artifacts have different knowledge
“densities” adjusted by their gearing factors.
Copyright © 2014 QSM Inc
Historical
System of
known size
System
being
estimated
% difference. Functions
included or excluded
System
being
estimated
Table A
Screen1
Screen2
Model X
Gearing
Factors
Requirements,
Use Cases
Screens
Tables,
DatabasesModels,
workflows
System Work Products
and Artifacts
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-13-
Gearing Factor
 As long as we work in a single unit
for history, calibration, and
estimation, we do not need a
Gearing Factor
 If we want to compare or sum sizing
in different units or to use industry
databases, we have to normalize to
a common unit
 The Gearing Factor is the
normalizing base
 We can calculate the Gearing Factor
from history
Copyright © 2014 by QSM, Inc.
The Intelligence behind
Successful Software
Projects
14 ®
Gearing Factor
 As long as we work in a single unit for history,
calibration, and estimation, we do not need a
Gearing Factor
 If we want to compare or sum sizing in different
units or to use industry databases, we have to
normalize to a common unit
 The Gearing Factor is the normalizing base
 We can calculate the Gearing Factor from history
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-15-
Top-Down Estimation Particularly
Effective Early in the Project Lifecycle
EstimationUncertainty
Concept Reqts Design Construction Dev Testing Qualification Testing
Functional Measures:
Business Requirements
Function Points
Agile Epics/Stories
Use Cases
Component Measures:
Modules
Screens/report/forms/ETLs
RICE Objects
Package Business Process Configurations
Story Points
7X 5X 3X .75X .25X .05X
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-16-
Top-Down Estimation Particularly
Effective Early in the Project Lifecycle
EstimationUncertainty
Concept Reqts Design Construction Dev Testing Qualification Testing
Functional Measures:
Business Requirements
Function Points
Agile Epics/Stories
Use Cases
Component Measures:
Modules
Screens/report/forms/ETLs
RICE Objects
Package Business Process Configurations
Story Points
7X 5X 3X .75X .25X .05X
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-17-
Top-Down Estimation Particularly
Effective Early in the Project Lifecycle
EstimationUncertainty
Concept Reqts Design Construction Dev Testing Qualification Testing
7X 5X 3X .75X .25X .05X
Historic sizing &
performance data
is the key to
reducing
uncertainty early in
the lifecycle!
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-18-
Estimates are Uncertain By Their
Nature
 Estimates are always uncertain
 The job of the estimation process is not to
remove the uncertainty, it is to measure it
 SLIM-Estimate simply translates
uncertainty into risk
 We accept and deal with uncertainty in
many aspects of our lives
“Forecasts of the future are
inherently uncertain.”
Larry Putnam Sr.
Measures for Excellence
Yourdon Press 1992 p.207
“The key issue… is documenting
the estimate’s uncertainty…”
Steve McConnell.
Software Estimation
Microsoft Press 2006 p.251
“Prediction is difficult, especially
if it involves the future.”
Niels Bohr.
Physicist and
Nobel Laureate
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-19-
Accuracy and Early Feasibility
Estimates
• Early estimates have more uncertainty.
 Driven by what we know and what we don’t know.
– It’s good to know what you don’t know
• Good place to focus our attention to reduce uncertainty
• Some contend that without high accuracy estimates are of no use
and provide little value.
 Estimates don’t need to be 10 decimal places accurate to identify cost
and schedule proposals that are patently unreasonable.
 “It is better to be roughly right than precisely wrong.” - John Maynard
Keynes
• At this stage of the project lifecycle estimates should be judged on
their ability to help make better business decisions.
The Accurate Useful Estimate
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-20-
Trapping High Risk Proposals
• Most projects start off as proposals that need to be prioritized and
justified.
 Priorities are usually aligned with the organizations strategic goals.
 Justification may be based on benefits to the organization.
– New revenue to be realized
– Savings or cost efficiencies
 Many times the benefit is expressed in terms of ROI , IRR or payback
period.
• Most proposals have some high level description of capabilities that
relate to the size & scope to be developed and implemented.
• Most proposals have a target or desired schedule and cost.
• This is enough information to generate a useful feasibility estimate.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-21-
Typical Project Proposal
Initially these calculations are
based on what the business
would like to happen. We need
to re-evaluate them based on
the feasibility estimate of what
is likely to happen.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-22-
Identify High Risk Projects as They Enter
the Budgeting & Approval Process
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-17-
Compare Proposed Expectations to
Historical Performance
Desired
Cost &
Schedule
Historical
data and
benchmark
trends
Impossible Zone
Impossible Zone
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-17-
Compare Proposed Expectations to
Historical Performance
Desired
Cost &
Schedule
Even though
there is
variability in
the estimate it
is useful in
identifying
when desired
costs and
schedules are
not reasonable
for a given
amount of
scope
Impossible Zone
Impossible Zone
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-25-
Evaluating Alternative Plans
To get alignment between what we would like and what is likely?
• There are a finite number of options to explore:
1. Reduce scope to meet schedule and cost goals
2. Increase staffing to meet schedule
– Decreases probability of meeting cost and lowers reliability
3. Negotiate for more schedule and budget
– Historic data can help backup your case
4. Increase productivity
– Usually not able to influence much in the short term
– Should be backed up by data
5. Combination of the above
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Effective Resource Optimization
-26-
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-27-
Effective Resource Optimization
• How do we make sure we are using our scarce resources
so as to optimize the amount of work that can get done?
• What facts can we bring to bear?
 Scope of the work (size)
 Historic staffing data (internal & industry)
• Trading-off cost & schedule
 Recognize what we can influence though staffing/resources
 Unanticipated benefits of effective trade-offs
– Lower cost
– Higher throughput
– Higher reliability
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-28-
Using Benchmarking to Assess
Resource Utilization – Internal
Trends
Internal Baseline
Schedule Performance
1 10 100
Effective IU (thousands)
0.1
1
10
100
Duration(Months)
Effort Performance
1 10 100
Effective IU (thousands)
0.01
0.1
1
10
100
1,000
Effort(PHR)(thousands)
Productivity Performance
1 10 100
Effective IU (thousands)
0
5
10
15
20
25
30
35
Productivity(Index)
Staffing Performance
1 10 100
Effective IU (thousands)
1
10
100
1,000
PeakStaffing
CompanySample Projects Avg. Line Style 1 Sigma Line Style 2 Sigma Line Style 3 Sigma Line Style
With a modest amount of historic
data, we can understand key
performance trends and variability.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-29-
Using Benchmarking to Assess
Resource Utilization – External
Comparison
External Benchmark
Schedule Performance
1 10 100
Effective IU (thousands)
0.1
1
10
100
C&TDuration(Months)
Effort Performance
1 10 100
Effective IU (thousands)
0.01
0.1
1
10
100
1,000
C&TEffort(PHR)(thousands)
Productivity Performance
1 10 100
Effective IU (thousands)
0
5
10
15
20
25
30
35
PI
Staffing Performance
1 10 100
Effective IU (thousands)
0.1
1
10
100
1,000
C&TPeakStaff(People)
CompanySample Projects QSM Business Avg. Line Style 1 Sigma Line Style 2 Sigma Line Style 3 Sigma Line Style
External comparison to industry
benchmarks can highlight
opportunities for improvements
and optimization.
Close to average
Close to average Higher than average
Higher than average
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Opportunity to Optimize Staffing
Levels
Staffing Comparison
Peak Staffing
1 10 100
Effective IU (thousands)
1
10
100
PeakStaff(People)
Comparison of Company Sample Projects to QSM Business
C&T Peak Staff (People) vs. Effective IU
C&T Peak Staff (People) Values
Benchmark Reference Group:
QSM Business
Comparison Data Set:
Company Sample Projects
Difference From Benchmark
at Min
Effective IU:
1200
4.33
5.92
1.59
at 25% Quartile
Effective IU:
4920
6.62
12.22
5.60
at Median
Effective IU:
8840
7.90
16.51
8.61
at 75% Quartile
Effective IU:
11100
8.46
18.55
10.10
at Max
Effective IU:
33420
11.78
32.68
20.90
Comparison breakpoints based on min, max, median and quartile values for the data set: Company Sample Projects
Company Sample Projects QSM Business Avg. Line Style
How industry staffs
How this organization staffs
Opportunity to take
on more work
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Opportunity to Lower Cost
Effort Comparison
Effort (PHR)
1 10 100
Effective IU (thousands)
0.1
1
10
100
C&TEffort(PHR)(thousands)
Comparison of Company Sample Projects to QSM Business
C&T Effort (PHR) vs. Effective IU
C&T Effort (PHR) Values
Benchmark Reference Group:
QSM Business
Comparison Data Set:
Company Sample Projects
Difference From Benchmark
at Min
Effective IU:
1200
599.37
2301.02
1701.65
at 25% Quartile
Effective IU:
4920
1591.89
5917.57
4325.68
at Median
Effective IU:
8840
2388.30
8760.07
6371.77
at 75% Quartile
Effective IU:
11100
2795.98
10202.24
7406.26
at Max
Effective IU:
33420
5996.75
21337.60
15340.85
Comparison breakpoints based on min, max, median and quartile values for the data set: Company Sample Projects
Company Sample Projects QSM Business Avg. Line Style
Industry effort cost
This organization’s effort cost
Potential savings for
redeployment on additional
demand or if we outsource
to an external vendor this
represents potential savings
to the organization
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-32-
For Almost No Schedule Penalty
Schedule Comparison
Schedule
1 10 100
Effective IU (thousands)
1
10
C&TDuration(Months)
Comparison of Company Sample Projects to QSM Business
C&T Duration (Months) vs. Effective IU
C&T Duration (Months) Values
Benchmark Reference Group:
QSM Business
Comparison Data Set:
Company Sample Projects
Difference From Benchmark
at Min
Effective IU:
1200
2.71
2.61
-0.10
at 25% Quartile
Effective IU:
4920
3.83
3.62
-0.21
at Median
Effective IU:
8840
4.42
4.15
-0.27
at 75% Quartile
Effective IU:
11100
4.68
4.38
-0.30
at Max
Effective IU:
33420
6.13
5.66
-0.47
Comparison breakpoints based on min, max, median and quartile values for the data set: Company Sample Projects
Company Sample Projects QSM Business Avg. Line Style
Industry durations
This organization’s durations
Very little difference!
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-33-
Resource Optimization Takeaways
• Use historical data to assess current resource utilization.
• Compare current resource utilization to outside industry benchmarks.
• Identify opportunities for optimization (don’t over staff!).
 Make the time-effort tradeoff relationship work for us.
– You pay almost no schedule penalty.
– Cost goes down and reliability goes up.
 For more information on this - http://www.qsm.com/research
 Document the results and benefits of making better business decisions.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Translating Estimates into
Resource Demands
-34-
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Common Practice vs Best Practice
Labor Hour Estimates
Most Common Industry Practice Today Best Practice Estimates Breakdown
Effort by Skill by Month
This approach doesn’t
help the organization
determine when these
resources are need as
the project progresses.
This approach identifies what skills are needed when and can
easily feed a PPM system where specific people can be allocated
to the project.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-36-
How Skills Flow on-off a Project
Release
• Need to understand for
any given development
methodology how the
skilled manpower builds up
and rolls off a project.
 Agile
 Waterfall
 Package Implementation
 Etc.
• Need to be able to
estimate the skill demands
and pass to PPM system.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
37
Product Development Lifecycles & Skills
• All software development lifecycles include four primary activities
 What, How, Do & Deploy/Fix
 Some SDLCs are more sequential and others include more concurrency in
theses activities
• Types of labor needed changes as we transition across activities
Methodology What How Do Deploy/Fix
Waterfall Concept Rqmts. &
Design
Construct &
Test
Deploy
RUP Initiation Elaboration Construction Transition
Agile Initiation Iteration
Planning
Iteration
Development
Production
SAP ASAP Project
Preparation
Business
Blueprint
Realization &
Final Prep
Go Live
Knowledge Acquisition Implementation Usage
Types of labor needed
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-38-
Top Down Estimate Skills/Role
Configuration
• You specify the
skills/roles defined in
your organization and
the rates charged for
those labor categories.
• Then you allocate the
skills across the lifecycle
appropriate to your
organization and
development
methodology(s).
These allocations can be
determined by mining
data from corporate
PPM/resource planning
tools.
Import
from
PPM
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-39-
The QSM PPM Integration Framework Works with
Any Enterprise PPM/Resource Planning Solution
QSM Webinar: From Proposal to Project:
Getting Resource Demand Early
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-40-
The QSM PPM Integration Framework Works with
Any Enterprise PPM/Resource Planning Solution
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Determining Aggregate Demand
and Matching Demand to Capacity
-41-
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
Schedule Staffing Effort & Cost
Monthly Avg Staff (L0)
< Ireland, India & US >
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
0
50
100
150
200
250
300
350
people
Monthly Avg Staff (L1)
< Ireland, India & US >
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
0
25
50
75
100
125
150
175
people
Monthly Avg Staff (L2)
< Ireland, India & US >
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
0
20
40
60
80
people
Staffing - Demand verses Capacity
IT Demand
Staffing plans
for individual
projects across
3 development
centers (35
projects
approved and
in the pipeline)
Demand at
3 Dev
centers
• India
• US
• Ireland
Aggregate
IT
resources
required =
305 FTE
staff
Capacity Limit
250 People
When the demand exceeds capacity
1. Eliminate projects
2. Slip start dates
3. Selective headcount reductions
Demand exceeds
Capacity for 9 months
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-43-
Adjust Start Dates on the Future
Projects in Order to Match Demand to
Capacity
Staffing & Schedule
Monthly Gantt Chart (L3)
< Start dates adjusted to not exceed 250 Max capacity>
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
IRELAND DEVELOPMENT...
Ireland Project 001
Ireland Project 002
Ireland Project 003
Ireland Project 004
Ireland Project 005
Ireland Project 006
Ireland Project 007
Ireland Project 008
Ireland Project 009
Ireland Project 010
Ireland Project 011
Ireland Project 012
Ireland Project 013
Ireland Project 014
Ireland Project 015
INDIADEVELOPMENT CEN...
India DC Project 001
India DC Project 002
Monthly Avg Staff (L2)
< Start dates adjusted to not exceed 250 Max capacity>
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
0
20
40
60
80
people
Projects start dates are moved out in time in
order to drop the total aggregate staffing
under 250 which is the maximum capacity.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-44-
Delaying Start Dates on 8 Projects
Matches the Demand to the Capacity
Schedule Staffing Effort & Cost
Monthly Avg Staff (L0)
< Start dates adjusted to not exceed 250 Max capacity >
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
0
50
100
150
200
250
300
people
Monthly Avg Staff (L1)
< Start dates adjusted to not exceed 250 Max capacity >
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
0
20
40
60
80
100
120
140
people
Monthly Avg Staff (L2)
< Start dates adjusted to not exceed 250 Max capacity >
3 6 9 12 15 18 21 24 27
Oct
'13
Jan
'14
Apr Jul Oct Jan
'15
Apr Jul Oct Jan
'16
0
20
40
60
80
people
8 projects needed
to be delayed to
make demand
match capacity.
The start date
delays averaged 5
months but
ranged from 2 to 8
months in
duration.
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
What Is the Demand for Skills
Across the portfolio?
-45-
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-46-
What Are the Skill Requirements
across the Portfolio?
0.00
50.00
100.00
150.00
200.00
250.00
300.00
Oct2013
Nov2013
Dec2013
Jan2014
Feb2014
Mar2014
Apr2014
May2014
Jun2014
Jul2014
Aug2014
Sep2014
Oct2014
Nov2014
Dec2014
Jan2015
Feb2015
Mar2015
Apr2015
May2015
Jun2015
Jul2015
Aug2015
Sep2015
Oct2015
Nov2015
Dec2015
Jan2016
Feb2016
Mar2016
Architect
Database Administrator
Quality Assurance
Development
Data Architect
Business Analyst
Project Manager/Lead
The Intelligence behind
Successful Software Projects
Quantitative Software Management
Executive
Summary
-47-
Summary
• Capacity Planning and Demand
Management go hand in hand. It requires:
 Sound estimation capability early in the
lifecycle
 Effective stakeholder negotiations of
time/effort/capability
 Ability to forecast effort by skill level by
month and feed a PPM system
 Ability to perform what-if analysis on the
portfolio quickly when IT Demand exceed
Capacity

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IT Demand Management and Capacity Planning: Why Estimation Is Vital to Balancing the Scale

  • 1. The Intelligence behind Successful Software Projects IT DEMAND MANAGEMENT AND CAPACITY PLANNING: WHY ESTIMATION IS VITAL TO BALANCING THE SCALE -1-
  • 2. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -2- Agenda Challenges associated with demand management & capacity planning: • “Realistic” demand estimation • Effective resource optimization • Detailed resource planning to support capacity utilization
  • 3. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Why Is IT Development Capacity Planning so Difficult? -3-
  • 4. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -4- A Difficult Juggling Act in a Complex Environment Production CapacityBusiness Demand Technology & Business Executive Management
  • 5. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -5- Computerworld – How to Develop an Effective Capacity Planning Process Requirements/Estimates Productivity baseline Estimates Transformed into Resource Plans Aggregate demand compared to actual capacity Recommended Best Practice Assessing Resource Optimization “How to develop an effective capacity planning process”, Rich Schiesser, Computerworld, Mar 31, 2010 Configure skills/roles Top-Down Estimation
  • 6. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -6- Why is Matching Demand & Capacity so Difficult? • Business stakeholder insatiable appetite for competitive capability • Poor IT estimation & poor project stakeholder negotiation • Ability to predict amount of resources /skills as required over the course of a project • Dynamic nature of the total volume of projects in the development pipeline
  • 7. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary “Realistic” Demand Estimation -7-
  • 8. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -8- “Realistic” Demand Estimation • What we would like vs what is possible • What facts can we bring to bear?  Scope of the work (size)  Productivity to perform the work  Availability skilled labor • How important is accuracy? • How do we negotiate some realistic demand solutions?
  • 9. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary 9 Terminology Targets, constraints, estimates, commitments, and plans are not the same thing: • Target - A goal, what we would like to do or achieve • Constraint - Some internal or external limitation on what we are able to do • Estimate - A technical calculation of what we might be able to do at some level of scope, cost, schedule, staff, and probability • Commitment - A business decision made to select one estimate scenario and assign company resources to meet a target within some constraints • Plan - A set of project tasks and activities that (we calculate) will give us some probability of meeting a commitment at a defined level of scope, budget, schedule, and staff Organizations sometimes confuse these terms and the business practices they represent.
  • 10. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -10- Identifying Unrealistic Stakeholder Expectations • QSM research has found that the 2 most common reasons projects fail is:  Unrealistic cost & schedule expectations  Unmanaged requirements growth • Need an effective mechanism to quantify stakeholder requirements - scope • Need and effective method to translate requirements into time and effort • Need to provide practical alternatives when expectations don’t meet reality If we can’t get this right we will never have a effective capacity planning solution!
  • 11. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -11- How? Top-Down Scope-Based Estimation Particularly good at identifying unrealistic expectations: • Doesn’t require a lot of detailed information • Relatively quick • Few hidden assumptions • Explicitly calibrated from history (local or industry) • Very flexible for scope, staffing, duration, etc., changes • Considered industry “best practice”
  • 12. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary orem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. orem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. 12 Two Sizing Approaches Analogy Sizing Comparing this system to the known sizes of similar system(s). Adjustments can be made by percentage or by including/excluding functions. Artifact Sizing Counting and measuring system artifacts and work products and scaling to the size of the final system. Different artifacts have different knowledge “densities” adjusted by their gearing factors. Copyright © 2014 QSM Inc Historical System of known size System being estimated % difference. Functions included or excluded System being estimated Table A Screen1 Screen2 Model X Gearing Factors Requirements, Use Cases Screens Tables, DatabasesModels, workflows System Work Products and Artifacts
  • 13. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -13- Gearing Factor  As long as we work in a single unit for history, calibration, and estimation, we do not need a Gearing Factor  If we want to compare or sum sizing in different units or to use industry databases, we have to normalize to a common unit  The Gearing Factor is the normalizing base  We can calculate the Gearing Factor from history
  • 14. Copyright © 2014 by QSM, Inc. The Intelligence behind Successful Software Projects 14 ® Gearing Factor  As long as we work in a single unit for history, calibration, and estimation, we do not need a Gearing Factor  If we want to compare or sum sizing in different units or to use industry databases, we have to normalize to a common unit  The Gearing Factor is the normalizing base  We can calculate the Gearing Factor from history
  • 15. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -15- Top-Down Estimation Particularly Effective Early in the Project Lifecycle EstimationUncertainty Concept Reqts Design Construction Dev Testing Qualification Testing Functional Measures: Business Requirements Function Points Agile Epics/Stories Use Cases Component Measures: Modules Screens/report/forms/ETLs RICE Objects Package Business Process Configurations Story Points 7X 5X 3X .75X .25X .05X
  • 16. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -16- Top-Down Estimation Particularly Effective Early in the Project Lifecycle EstimationUncertainty Concept Reqts Design Construction Dev Testing Qualification Testing Functional Measures: Business Requirements Function Points Agile Epics/Stories Use Cases Component Measures: Modules Screens/report/forms/ETLs RICE Objects Package Business Process Configurations Story Points 7X 5X 3X .75X .25X .05X
  • 17. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -17- Top-Down Estimation Particularly Effective Early in the Project Lifecycle EstimationUncertainty Concept Reqts Design Construction Dev Testing Qualification Testing 7X 5X 3X .75X .25X .05X Historic sizing & performance data is the key to reducing uncertainty early in the lifecycle!
  • 18. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -18- Estimates are Uncertain By Their Nature  Estimates are always uncertain  The job of the estimation process is not to remove the uncertainty, it is to measure it  SLIM-Estimate simply translates uncertainty into risk  We accept and deal with uncertainty in many aspects of our lives “Forecasts of the future are inherently uncertain.” Larry Putnam Sr. Measures for Excellence Yourdon Press 1992 p.207 “The key issue… is documenting the estimate’s uncertainty…” Steve McConnell. Software Estimation Microsoft Press 2006 p.251 “Prediction is difficult, especially if it involves the future.” Niels Bohr. Physicist and Nobel Laureate
  • 19. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -19- Accuracy and Early Feasibility Estimates • Early estimates have more uncertainty.  Driven by what we know and what we don’t know. – It’s good to know what you don’t know • Good place to focus our attention to reduce uncertainty • Some contend that without high accuracy estimates are of no use and provide little value.  Estimates don’t need to be 10 decimal places accurate to identify cost and schedule proposals that are patently unreasonable.  “It is better to be roughly right than precisely wrong.” - John Maynard Keynes • At this stage of the project lifecycle estimates should be judged on their ability to help make better business decisions. The Accurate Useful Estimate
  • 20. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -20- Trapping High Risk Proposals • Most projects start off as proposals that need to be prioritized and justified.  Priorities are usually aligned with the organizations strategic goals.  Justification may be based on benefits to the organization. – New revenue to be realized – Savings or cost efficiencies  Many times the benefit is expressed in terms of ROI , IRR or payback period. • Most proposals have some high level description of capabilities that relate to the size & scope to be developed and implemented. • Most proposals have a target or desired schedule and cost. • This is enough information to generate a useful feasibility estimate.
  • 21. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -21- Typical Project Proposal Initially these calculations are based on what the business would like to happen. We need to re-evaluate them based on the feasibility estimate of what is likely to happen.
  • 22. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -22- Identify High Risk Projects as They Enter the Budgeting & Approval Process
  • 23. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -17- Compare Proposed Expectations to Historical Performance Desired Cost & Schedule Historical data and benchmark trends Impossible Zone Impossible Zone
  • 24. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -17- Compare Proposed Expectations to Historical Performance Desired Cost & Schedule Even though there is variability in the estimate it is useful in identifying when desired costs and schedules are not reasonable for a given amount of scope Impossible Zone Impossible Zone
  • 25. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -25- Evaluating Alternative Plans To get alignment between what we would like and what is likely? • There are a finite number of options to explore: 1. Reduce scope to meet schedule and cost goals 2. Increase staffing to meet schedule – Decreases probability of meeting cost and lowers reliability 3. Negotiate for more schedule and budget – Historic data can help backup your case 4. Increase productivity – Usually not able to influence much in the short term – Should be backed up by data 5. Combination of the above
  • 26. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Effective Resource Optimization -26-
  • 27. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -27- Effective Resource Optimization • How do we make sure we are using our scarce resources so as to optimize the amount of work that can get done? • What facts can we bring to bear?  Scope of the work (size)  Historic staffing data (internal & industry) • Trading-off cost & schedule  Recognize what we can influence though staffing/resources  Unanticipated benefits of effective trade-offs – Lower cost – Higher throughput – Higher reliability
  • 28. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -28- Using Benchmarking to Assess Resource Utilization – Internal Trends Internal Baseline Schedule Performance 1 10 100 Effective IU (thousands) 0.1 1 10 100 Duration(Months) Effort Performance 1 10 100 Effective IU (thousands) 0.01 0.1 1 10 100 1,000 Effort(PHR)(thousands) Productivity Performance 1 10 100 Effective IU (thousands) 0 5 10 15 20 25 30 35 Productivity(Index) Staffing Performance 1 10 100 Effective IU (thousands) 1 10 100 1,000 PeakStaffing CompanySample Projects Avg. Line Style 1 Sigma Line Style 2 Sigma Line Style 3 Sigma Line Style With a modest amount of historic data, we can understand key performance trends and variability.
  • 29. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -29- Using Benchmarking to Assess Resource Utilization – External Comparison External Benchmark Schedule Performance 1 10 100 Effective IU (thousands) 0.1 1 10 100 C&TDuration(Months) Effort Performance 1 10 100 Effective IU (thousands) 0.01 0.1 1 10 100 1,000 C&TEffort(PHR)(thousands) Productivity Performance 1 10 100 Effective IU (thousands) 0 5 10 15 20 25 30 35 PI Staffing Performance 1 10 100 Effective IU (thousands) 0.1 1 10 100 1,000 C&TPeakStaff(People) CompanySample Projects QSM Business Avg. Line Style 1 Sigma Line Style 2 Sigma Line Style 3 Sigma Line Style External comparison to industry benchmarks can highlight opportunities for improvements and optimization. Close to average Close to average Higher than average Higher than average
  • 30. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Opportunity to Optimize Staffing Levels Staffing Comparison Peak Staffing 1 10 100 Effective IU (thousands) 1 10 100 PeakStaff(People) Comparison of Company Sample Projects to QSM Business C&T Peak Staff (People) vs. Effective IU C&T Peak Staff (People) Values Benchmark Reference Group: QSM Business Comparison Data Set: Company Sample Projects Difference From Benchmark at Min Effective IU: 1200 4.33 5.92 1.59 at 25% Quartile Effective IU: 4920 6.62 12.22 5.60 at Median Effective IU: 8840 7.90 16.51 8.61 at 75% Quartile Effective IU: 11100 8.46 18.55 10.10 at Max Effective IU: 33420 11.78 32.68 20.90 Comparison breakpoints based on min, max, median and quartile values for the data set: Company Sample Projects Company Sample Projects QSM Business Avg. Line Style How industry staffs How this organization staffs Opportunity to take on more work
  • 31. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Opportunity to Lower Cost Effort Comparison Effort (PHR) 1 10 100 Effective IU (thousands) 0.1 1 10 100 C&TEffort(PHR)(thousands) Comparison of Company Sample Projects to QSM Business C&T Effort (PHR) vs. Effective IU C&T Effort (PHR) Values Benchmark Reference Group: QSM Business Comparison Data Set: Company Sample Projects Difference From Benchmark at Min Effective IU: 1200 599.37 2301.02 1701.65 at 25% Quartile Effective IU: 4920 1591.89 5917.57 4325.68 at Median Effective IU: 8840 2388.30 8760.07 6371.77 at 75% Quartile Effective IU: 11100 2795.98 10202.24 7406.26 at Max Effective IU: 33420 5996.75 21337.60 15340.85 Comparison breakpoints based on min, max, median and quartile values for the data set: Company Sample Projects Company Sample Projects QSM Business Avg. Line Style Industry effort cost This organization’s effort cost Potential savings for redeployment on additional demand or if we outsource to an external vendor this represents potential savings to the organization
  • 32. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -32- For Almost No Schedule Penalty Schedule Comparison Schedule 1 10 100 Effective IU (thousands) 1 10 C&TDuration(Months) Comparison of Company Sample Projects to QSM Business C&T Duration (Months) vs. Effective IU C&T Duration (Months) Values Benchmark Reference Group: QSM Business Comparison Data Set: Company Sample Projects Difference From Benchmark at Min Effective IU: 1200 2.71 2.61 -0.10 at 25% Quartile Effective IU: 4920 3.83 3.62 -0.21 at Median Effective IU: 8840 4.42 4.15 -0.27 at 75% Quartile Effective IU: 11100 4.68 4.38 -0.30 at Max Effective IU: 33420 6.13 5.66 -0.47 Comparison breakpoints based on min, max, median and quartile values for the data set: Company Sample Projects Company Sample Projects QSM Business Avg. Line Style Industry durations This organization’s durations Very little difference!
  • 33. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -33- Resource Optimization Takeaways • Use historical data to assess current resource utilization. • Compare current resource utilization to outside industry benchmarks. • Identify opportunities for optimization (don’t over staff!).  Make the time-effort tradeoff relationship work for us. – You pay almost no schedule penalty. – Cost goes down and reliability goes up.  For more information on this - http://www.qsm.com/research  Document the results and benefits of making better business decisions.
  • 34. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Translating Estimates into Resource Demands -34-
  • 35. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Common Practice vs Best Practice Labor Hour Estimates Most Common Industry Practice Today Best Practice Estimates Breakdown Effort by Skill by Month This approach doesn’t help the organization determine when these resources are need as the project progresses. This approach identifies what skills are needed when and can easily feed a PPM system where specific people can be allocated to the project.
  • 36. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -36- How Skills Flow on-off a Project Release • Need to understand for any given development methodology how the skilled manpower builds up and rolls off a project.  Agile  Waterfall  Package Implementation  Etc. • Need to be able to estimate the skill demands and pass to PPM system.
  • 37. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary 37 Product Development Lifecycles & Skills • All software development lifecycles include four primary activities  What, How, Do & Deploy/Fix  Some SDLCs are more sequential and others include more concurrency in theses activities • Types of labor needed changes as we transition across activities Methodology What How Do Deploy/Fix Waterfall Concept Rqmts. & Design Construct & Test Deploy RUP Initiation Elaboration Construction Transition Agile Initiation Iteration Planning Iteration Development Production SAP ASAP Project Preparation Business Blueprint Realization & Final Prep Go Live Knowledge Acquisition Implementation Usage Types of labor needed
  • 38. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -38- Top Down Estimate Skills/Role Configuration • You specify the skills/roles defined in your organization and the rates charged for those labor categories. • Then you allocate the skills across the lifecycle appropriate to your organization and development methodology(s). These allocations can be determined by mining data from corporate PPM/resource planning tools. Import from PPM
  • 39. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -39- The QSM PPM Integration Framework Works with Any Enterprise PPM/Resource Planning Solution QSM Webinar: From Proposal to Project: Getting Resource Demand Early
  • 40. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -40- The QSM PPM Integration Framework Works with Any Enterprise PPM/Resource Planning Solution
  • 41. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Determining Aggregate Demand and Matching Demand to Capacity -41-
  • 42. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary Schedule Staffing Effort & Cost Monthly Avg Staff (L0) < Ireland, India & US > 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 0 50 100 150 200 250 300 350 people Monthly Avg Staff (L1) < Ireland, India & US > 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 0 25 50 75 100 125 150 175 people Monthly Avg Staff (L2) < Ireland, India & US > 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 0 20 40 60 80 people Staffing - Demand verses Capacity IT Demand Staffing plans for individual projects across 3 development centers (35 projects approved and in the pipeline) Demand at 3 Dev centers • India • US • Ireland Aggregate IT resources required = 305 FTE staff Capacity Limit 250 People When the demand exceeds capacity 1. Eliminate projects 2. Slip start dates 3. Selective headcount reductions Demand exceeds Capacity for 9 months
  • 43. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -43- Adjust Start Dates on the Future Projects in Order to Match Demand to Capacity Staffing & Schedule Monthly Gantt Chart (L3) < Start dates adjusted to not exceed 250 Max capacity> 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 IRELAND DEVELOPMENT... Ireland Project 001 Ireland Project 002 Ireland Project 003 Ireland Project 004 Ireland Project 005 Ireland Project 006 Ireland Project 007 Ireland Project 008 Ireland Project 009 Ireland Project 010 Ireland Project 011 Ireland Project 012 Ireland Project 013 Ireland Project 014 Ireland Project 015 INDIADEVELOPMENT CEN... India DC Project 001 India DC Project 002 Monthly Avg Staff (L2) < Start dates adjusted to not exceed 250 Max capacity> 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 0 20 40 60 80 people Projects start dates are moved out in time in order to drop the total aggregate staffing under 250 which is the maximum capacity.
  • 44. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -44- Delaying Start Dates on 8 Projects Matches the Demand to the Capacity Schedule Staffing Effort & Cost Monthly Avg Staff (L0) < Start dates adjusted to not exceed 250 Max capacity > 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 0 50 100 150 200 250 300 people Monthly Avg Staff (L1) < Start dates adjusted to not exceed 250 Max capacity > 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 0 20 40 60 80 100 120 140 people Monthly Avg Staff (L2) < Start dates adjusted to not exceed 250 Max capacity > 3 6 9 12 15 18 21 24 27 Oct '13 Jan '14 Apr Jul Oct Jan '15 Apr Jul Oct Jan '16 0 20 40 60 80 people 8 projects needed to be delayed to make demand match capacity. The start date delays averaged 5 months but ranged from 2 to 8 months in duration.
  • 45. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary What Is the Demand for Skills Across the portfolio? -45-
  • 46. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -46- What Are the Skill Requirements across the Portfolio? 0.00 50.00 100.00 150.00 200.00 250.00 300.00 Oct2013 Nov2013 Dec2013 Jan2014 Feb2014 Mar2014 Apr2014 May2014 Jun2014 Jul2014 Aug2014 Sep2014 Oct2014 Nov2014 Dec2014 Jan2015 Feb2015 Mar2015 Apr2015 May2015 Jun2015 Jul2015 Aug2015 Sep2015 Oct2015 Nov2015 Dec2015 Jan2016 Feb2016 Mar2016 Architect Database Administrator Quality Assurance Development Data Architect Business Analyst Project Manager/Lead
  • 47. The Intelligence behind Successful Software Projects Quantitative Software Management Executive Summary -47- Summary • Capacity Planning and Demand Management go hand in hand. It requires:  Sound estimation capability early in the lifecycle  Effective stakeholder negotiations of time/effort/capability  Ability to forecast effort by skill level by month and feed a PPM system  Ability to perform what-if analysis on the portfolio quickly when IT Demand exceed Capacity

Notes de l'éditeur

  1. Can do this now with the history and proper groupings