Awad Hanna - Project Tracking
Content
Project Tracking to Improve
Labor Productivity:
An Earned Value Approach
By
Awad S. Hanna, Ph.D., P.E.
Professor
University of Wisconsin-Madison
Department of Civil and Environmental
Engineering
2314 Engineering Hall, 1415 Engineering Dr.
Madison, WI, 53706
Tel (608) 263-8903
[email protected]
President
Hanna Consulting Group Inc.
1314 Farwell Drive
Madison, WI 53704
Tel (608) 246-0221
Fax (608) 246-0614
No part of this publication may be reproduced or transmitted in any form, or by any
means, without written consent from Hanna Consulting Group Inc.
©
1
Biography
Awad S. Hanna, Ph.D., P.E
Awad S. Hanna is a professor and chair of the construction engineering and management program
at the University of Wisconsin-Madison, department of Civil and Environmental Engineering.
Dr. Hanna holds M. S. and Ph.D. degrees from Penn State University and he is a register
professional engineer in the U S and Canada. Awad has been an active construction
practitioner, educator and researcher for over 30 years. He has taught construction
management courses at Penn State University, Memorial University of Newfoundland, Canada,
and University of Wisconsin-Madison. Dr. Hanna has conducted several research projects for
the Electrical Contracting Foundation including landmark studies on the cumulative impact of
change orders on electrical/mechanical labor productivity, schedule compression and
acceleration, impact of stacking of trades on labor productivity, performance evaluation for
electrical supervisors, and craftsmen, and productivity factors in electrical construction. Dr.
Hanna has conducted research for other national organizations including the National Highway
Research Program, the Mechanical Contracting Foundation, the New Horizon Foundation and
the Construction Industry Institute. Dr. Hanna has taught more than 300 successful seminars
and workshops in more than 35 states on topics such as change orders impacts, project
scheduling, estimating, labor productivity, construction delay claims.
Dr. Hanna is also a national consultant representing and assisting many contractors and owners in
productivity losses related to change orders, acceleration and compression, delay, and trade
stacking.
Prof. Awad Hanna
©
2
Why Track Labor Productivity?
Ross Umentum
©
3
Productivity Improvement
y
What can’t be measured
can’t be improved
Ross Umentum
©
4
Learning Objectives
1.
Accurately measure physical project
percent complete in real time
2.
Measure project performance every
reporting period. “Performance Factor”
3.
Predict project outcomes as early as 20-25%
project complete
4.
Identify problem areas and activities
Ross Umentum
©
5
Objectives (Continued)
5.
Establish and validate Hanna’s Control Points
6.
Track change orders and quantify impact
7.
Track Project Schedule
8.
Analysis of trends
9.
Project ratios analysis (Apprentice/Journeyman
ratio)
10. Track a variety of actual mechanical/SM
construction projects
Ross Umentum
©
6
Productivity Analysis
Selected
Selected Activity
Activity
In
In Progress
Progress
Work
Work hours
hours
Quantities
Quantities
Productivity
Productivity
Calculations
Calculations
Performance
Performance
Evaluation
Evaluation
Workhour
Workhour
Forecast
Forecast
Analysis
Analysis of
of
Trends
Trends
Prof Awad S. Hanna
©
7
Estimate Activity Percent Complete
(Output)
1)
Subjective Evaluation (Observed)
2)
Quantity Installed as Measured
3)
Binary Approach
4)
Partial Complete Method
Ductwork Installation% of Work
Detailing/Coordination
15%
Hanger Installation
15%
Duct Rough-In
50%
Finish and Trim Work20%
TOTAL
100%
Ross Umentum
©
8
Earned Value Fundamentals
Earned Hours = Qty(Installed)x(Estimated Production Rate
Earned Hours=Qty(Installed)/Qty(Estimated)xBase
Hours(Estimated)
Or
Earned Hours=Base Hours(Estimated) x Percent Complete
Prof. Awad Hanna
9
©
Develop Work Breakdown
Structure
HVAC
DUCTS
Erected
Connected
Accepted
Phase Breakdown
Area Breakdown
Hanna 2004
Prof. Awad Hanna
©
10
Set up The Earned Value Spread Sheet
Code
Description
Base % Complete
Hr
Earned
Actual
Total Estimated Hours
Prof. Awad Hanna
©
11
Simplified Earned Value Example
(1)
Activity
(2)
Budgeted
Hours
(3)
Percent
Complete
(4)
Earned
Hours
(5)
Actual
Hours
(6)
Activity
Performanc
e Factor
Ductwork 1st Floor Area A
973
78%
759
867
0.88
Ductwork 1st Floor Area B
821
59%
484
579
0.84
Ductwork 2nd Floor Area A
325
30%
98
65
1.50
Ductwork 2nd Floor Area B
151
15%
23
15
1.51
Piping 1st Floor Area A
383
100%
383
366
1.05
Piping 1st Floor Area B
574
40%
230
248
0.93
Piping 2nd Floor Area A
456
5%
23
23
0.99
Piping 2nd Floor Area B
195
0%
0
0
N/A
AHU on 2nd Floor
160
35%
56
65
0.86
Grilles/Flex
240
25%
60
55
1.09
VAV's
38
50%
19
24
0.79
Linear Diffusers 1st Floor
75
95%
71
85
0.84
Linear Diffusers 2nd Floor
58
0%
0
0
N/A
Misc. Metal Install
20
25%
5
5
1.00
Set Equipment
54
30%
16
12
1.35
4523
49%
2226
2409
0.92
PROJECT TOTALS
Ross Umentum
©
12
Example (Continued)
Job % Complete = Σ Earned = 2226 = 0.492
Σ Base
4523
Performance Factor =
Σ Earned = 2226 = 0.92
Σ Actual 2409
Predicted Hrs at Comp. = Hrs-to-date = 2409 = 4896
Job % Comp. 0.492
4896 > 4523, therefore cost overrun to date.
Ross Umentum
©
13
Typical Performance Factor Curve
1.0
25%
50%
75%
100%
% Complete
Ross Umentum
©
14
Examples: Case Studies
y
Example 3 Projects Tracked
- See below for summary of completed case studies
Project Item
Case Study #1
Case Study #2
Case Study #3
Type of Work
Mechanical
Architectural
Sheet Metal
Sheet Metal
Project Size (Man-hours)
13,731
15,268
7,344
Project Duration (Weeks)
17
29
37
Project Location
Lower Midwest
Western US
Upper Midwest
Final Labor Status
Under by 371 hrs. Over by 1,159 hrs. Under by 923 hrs.
Final Schedule Status
Behind by 2 wks Behind by 12 wks. Ahead by 13 wks.
Ross Umentum
©
15
Contractor Input Worksheet
Ross Umentum
©
16
Quantity Installed Worksheet
Ross Umentum
©
17
Method Summary Worksheet
Ross Umentum
©
18
Performance Factors: Quantity
Installed
Project Performance Factors for Quantity Installed Method
1.30
1.27
1.19
1.18
1.20
1.19
1.17
1.15
Performance Factor
1.15
1.14
1.10
1.06
1.04
1.06
1.08
1.05
1.13
1.12
1.14
1.01
1.00
1.03
1.00
0.95
0.95
0.93
0.87
0.75
20%
0.92
Case Study #1
Case Study #2
Case Study #3
Planned Productivity
0.80
10%
0.94
0.94
0.93
0.90
0.70
0%
0.95
0.99
30%
40%
50%
60%
Percent Complete
70%
Ross Umentum
80%
90%
100%
©
19
Performance Factors: Subjective
Evaluation
Project Performance Factors for Subject Evaluation Method
1.30
1.26
1.29
1.26
1.20
1.16
1.21
1.13
Performance Factor
1.18
1.10
1.07
1.02
1.07
0.98
1.00
0.96
0.96
0.97
0.88
0.98
0.96
0.92
0.88
20%
30%
40%
1.03
0.94
0.94
0.92
0.95
Case Study #1
Case Study #2
Case Study #3
Planned Productivity
0.80
10%
1.02
0.99
0.92
0.92
0.90
0.70
0%
1.13
50%
60%
Percent Complete
70%
Ross Umentum
80%
90%
100%
©
20
Forecast Error: Quantity Installed
Man-Hour Forecast Error Against Actual Final Man-Hours
15%
11.0%
10%
Forecast Error
7.5%
6.1%
9.3%
5.7%
4.1%
5%
5.2%
3.6%
4.4%
3.2%
2.0%
1.8%
0%
0.0%
-0.2%
-0.8%
-3.3%
-2.7%
-2.7%
-5%
-7.2%
-4.5%
-5.5%
-6.0%
Case Study #1
Case Study #2
Case Study #3
Zero Error
-10%
-12.7%
-15%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent Complete
Ross Umentum
©
21
Forecast Error: Subjective Evaluation
Man-Hour Forecast Error Against Actual Final Man-Hours
10%
7.5%
5.9%
6.0%
5%
2.8%
2.2%
Forecast Error
0.3%
0.5%
2.4%
-2.7%
-2.3%
0.0%
-0.4%
0%
-3.2%
2.9%
3.7%
-2.8%
-1.4%
-0.7%
-2.3%
-5%
-4.5%
-5.6%
-5.5%
-6.6%
-10%
Case Study #1
Case Study #2
Case Study #3
Zero Error
-11.2%
-12.6%
-15%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent Complete
Ross Umentum
©
22
Project Tracking Benefits
y
Early Warning Signs of Labor and/or Schedule Over-runs
y
Identification of Underproductive Activities
y
Preparation of Future Estimates
y
Proving Construction Inefficiency Claims
Ross Umentum
©
23
Major Conclusions
y
The Project Performance Factor does not vary by
more than 10% at the 30% project completion mark.
y
The Quantity Installed and Subjective Evaluation
Method have a man-hour forecast error of less than
10% for the entire project duration.
y
The linear regressed man-hour forecasts become
useful (accurate) after 25% project complete.
Ross Umentum
©
24
Labor Productivity:
An Earned Value Approach
By
Awad S. Hanna, Ph.D., P.E.
Professor
University of Wisconsin-Madison
Department of Civil and Environmental
Engineering
2314 Engineering Hall, 1415 Engineering Dr.
Madison, WI, 53706
Tel (608) 263-8903
[email protected]
President
Hanna Consulting Group Inc.
1314 Farwell Drive
Madison, WI 53704
Tel (608) 246-0221
Fax (608) 246-0614
No part of this publication may be reproduced or transmitted in any form, or by any
means, without written consent from Hanna Consulting Group Inc.
©
1
Biography
Awad S. Hanna, Ph.D., P.E
Awad S. Hanna is a professor and chair of the construction engineering and management program
at the University of Wisconsin-Madison, department of Civil and Environmental Engineering.
Dr. Hanna holds M. S. and Ph.D. degrees from Penn State University and he is a register
professional engineer in the U S and Canada. Awad has been an active construction
practitioner, educator and researcher for over 30 years. He has taught construction
management courses at Penn State University, Memorial University of Newfoundland, Canada,
and University of Wisconsin-Madison. Dr. Hanna has conducted several research projects for
the Electrical Contracting Foundation including landmark studies on the cumulative impact of
change orders on electrical/mechanical labor productivity, schedule compression and
acceleration, impact of stacking of trades on labor productivity, performance evaluation for
electrical supervisors, and craftsmen, and productivity factors in electrical construction. Dr.
Hanna has conducted research for other national organizations including the National Highway
Research Program, the Mechanical Contracting Foundation, the New Horizon Foundation and
the Construction Industry Institute. Dr. Hanna has taught more than 300 successful seminars
and workshops in more than 35 states on topics such as change orders impacts, project
scheduling, estimating, labor productivity, construction delay claims.
Dr. Hanna is also a national consultant representing and assisting many contractors and owners in
productivity losses related to change orders, acceleration and compression, delay, and trade
stacking.
Prof. Awad Hanna
©
2
Why Track Labor Productivity?
Ross Umentum
©
3
Productivity Improvement
y
What can’t be measured
can’t be improved
Ross Umentum
©
4
Learning Objectives
1.
Accurately measure physical project
percent complete in real time
2.
Measure project performance every
reporting period. “Performance Factor”
3.
Predict project outcomes as early as 20-25%
project complete
4.
Identify problem areas and activities
Ross Umentum
©
5
Objectives (Continued)
5.
Establish and validate Hanna’s Control Points
6.
Track change orders and quantify impact
7.
Track Project Schedule
8.
Analysis of trends
9.
Project ratios analysis (Apprentice/Journeyman
ratio)
10. Track a variety of actual mechanical/SM
construction projects
Ross Umentum
©
6
Productivity Analysis
Selected
Selected Activity
Activity
In
In Progress
Progress
Work
Work hours
hours
Quantities
Quantities
Productivity
Productivity
Calculations
Calculations
Performance
Performance
Evaluation
Evaluation
Workhour
Workhour
Forecast
Forecast
Analysis
Analysis of
of
Trends
Trends
Prof Awad S. Hanna
©
7
Estimate Activity Percent Complete
(Output)
1)
Subjective Evaluation (Observed)
2)
Quantity Installed as Measured
3)
Binary Approach
4)
Partial Complete Method
Ductwork Installation% of Work
Detailing/Coordination
15%
Hanger Installation
15%
Duct Rough-In
50%
Finish and Trim Work20%
TOTAL
100%
Ross Umentum
©
8
Earned Value Fundamentals
Earned Hours = Qty(Installed)x(Estimated Production Rate
Earned Hours=Qty(Installed)/Qty(Estimated)xBase
Hours(Estimated)
Or
Earned Hours=Base Hours(Estimated) x Percent Complete
Prof. Awad Hanna
9
©
Develop Work Breakdown
Structure
HVAC
DUCTS
Erected
Connected
Accepted
Phase Breakdown
Area Breakdown
Hanna 2004
Prof. Awad Hanna
©
10
Set up The Earned Value Spread Sheet
Code
Description
Base % Complete
Hr
Earned
Actual
Total Estimated Hours
Prof. Awad Hanna
©
11
Simplified Earned Value Example
(1)
Activity
(2)
Budgeted
Hours
(3)
Percent
Complete
(4)
Earned
Hours
(5)
Actual
Hours
(6)
Activity
Performanc
e Factor
Ductwork 1st Floor Area A
973
78%
759
867
0.88
Ductwork 1st Floor Area B
821
59%
484
579
0.84
Ductwork 2nd Floor Area A
325
30%
98
65
1.50
Ductwork 2nd Floor Area B
151
15%
23
15
1.51
Piping 1st Floor Area A
383
100%
383
366
1.05
Piping 1st Floor Area B
574
40%
230
248
0.93
Piping 2nd Floor Area A
456
5%
23
23
0.99
Piping 2nd Floor Area B
195
0%
0
0
N/A
AHU on 2nd Floor
160
35%
56
65
0.86
Grilles/Flex
240
25%
60
55
1.09
VAV's
38
50%
19
24
0.79
Linear Diffusers 1st Floor
75
95%
71
85
0.84
Linear Diffusers 2nd Floor
58
0%
0
0
N/A
Misc. Metal Install
20
25%
5
5
1.00
Set Equipment
54
30%
16
12
1.35
4523
49%
2226
2409
0.92
PROJECT TOTALS
Ross Umentum
©
12
Example (Continued)
Job % Complete = Σ Earned = 2226 = 0.492
Σ Base
4523
Performance Factor =
Σ Earned = 2226 = 0.92
Σ Actual 2409
Predicted Hrs at Comp. = Hrs-to-date = 2409 = 4896
Job % Comp. 0.492
4896 > 4523, therefore cost overrun to date.
Ross Umentum
©
13
Typical Performance Factor Curve
1.0
25%
50%
75%
100%
% Complete
Ross Umentum
©
14
Examples: Case Studies
y
Example 3 Projects Tracked
- See below for summary of completed case studies
Project Item
Case Study #1
Case Study #2
Case Study #3
Type of Work
Mechanical
Architectural
Sheet Metal
Sheet Metal
Project Size (Man-hours)
13,731
15,268
7,344
Project Duration (Weeks)
17
29
37
Project Location
Lower Midwest
Western US
Upper Midwest
Final Labor Status
Under by 371 hrs. Over by 1,159 hrs. Under by 923 hrs.
Final Schedule Status
Behind by 2 wks Behind by 12 wks. Ahead by 13 wks.
Ross Umentum
©
15
Contractor Input Worksheet
Ross Umentum
©
16
Quantity Installed Worksheet
Ross Umentum
©
17
Method Summary Worksheet
Ross Umentum
©
18
Performance Factors: Quantity
Installed
Project Performance Factors for Quantity Installed Method
1.30
1.27
1.19
1.18
1.20
1.19
1.17
1.15
Performance Factor
1.15
1.14
1.10
1.06
1.04
1.06
1.08
1.05
1.13
1.12
1.14
1.01
1.00
1.03
1.00
0.95
0.95
0.93
0.87
0.75
20%
0.92
Case Study #1
Case Study #2
Case Study #3
Planned Productivity
0.80
10%
0.94
0.94
0.93
0.90
0.70
0%
0.95
0.99
30%
40%
50%
60%
Percent Complete
70%
Ross Umentum
80%
90%
100%
©
19
Performance Factors: Subjective
Evaluation
Project Performance Factors for Subject Evaluation Method
1.30
1.26
1.29
1.26
1.20
1.16
1.21
1.13
Performance Factor
1.18
1.10
1.07
1.02
1.07
0.98
1.00
0.96
0.96
0.97
0.88
0.98
0.96
0.92
0.88
20%
30%
40%
1.03
0.94
0.94
0.92
0.95
Case Study #1
Case Study #2
Case Study #3
Planned Productivity
0.80
10%
1.02
0.99
0.92
0.92
0.90
0.70
0%
1.13
50%
60%
Percent Complete
70%
Ross Umentum
80%
90%
100%
©
20
Forecast Error: Quantity Installed
Man-Hour Forecast Error Against Actual Final Man-Hours
15%
11.0%
10%
Forecast Error
7.5%
6.1%
9.3%
5.7%
4.1%
5%
5.2%
3.6%
4.4%
3.2%
2.0%
1.8%
0%
0.0%
-0.2%
-0.8%
-3.3%
-2.7%
-2.7%
-5%
-7.2%
-4.5%
-5.5%
-6.0%
Case Study #1
Case Study #2
Case Study #3
Zero Error
-10%
-12.7%
-15%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent Complete
Ross Umentum
©
21
Forecast Error: Subjective Evaluation
Man-Hour Forecast Error Against Actual Final Man-Hours
10%
7.5%
5.9%
6.0%
5%
2.8%
2.2%
Forecast Error
0.3%
0.5%
2.4%
-2.7%
-2.3%
0.0%
-0.4%
0%
-3.2%
2.9%
3.7%
-2.8%
-1.4%
-0.7%
-2.3%
-5%
-4.5%
-5.6%
-5.5%
-6.6%
-10%
Case Study #1
Case Study #2
Case Study #3
Zero Error
-11.2%
-12.6%
-15%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Percent Complete
Ross Umentum
©
22
Project Tracking Benefits
y
Early Warning Signs of Labor and/or Schedule Over-runs
y
Identification of Underproductive Activities
y
Preparation of Future Estimates
y
Proving Construction Inefficiency Claims
Ross Umentum
©
23
Major Conclusions
y
The Project Performance Factor does not vary by
more than 10% at the 30% project completion mark.
y
The Quantity Installed and Subjective Evaluation
Method have a man-hour forecast error of less than
10% for the entire project duration.
y
The linear regressed man-hour forecasts become
useful (accurate) after 25% project complete.
Ross Umentum
©
24
