Transcript Construction Industry Institute

Engineering Productivity
Measurement
Engineering Productivity
Measurement
Research Team
Bob Shoemaker
BE&K
CPI Conference 2001
Engineering Productivity
Measurement
Bob Shoemaker
BE&K
CPI Conference 2001
Engineering Productivity
Measurement Research Team
Bob Shoemaker
John Atwell
Bill Buss
Luh-Maan Chang
Glen Hoglund
Duane McCloud
Deb McNeil
Navin Patel
John Rotroff
Ken Walsh
Denny Weber
Tom Zenge
BE&K, Chair
Bechtel
Air Products
Purdue University
Ontario Hydro
FPL Energy
Dow
Chemtex
U.S. Steel
Arizona State University
Black & Veatch
Procter & Gamble
Problem Statement
• Engineering productivity
measurement is a critical element of
project performance
• Present practices do not work well in
driving the improvement that today's
design tools offer
• Surprisingly little effort has been
expended in the engineering
productivity arena
Research Objectives
• Determine present practices and why
they do not work well
• Find productivity improvement success
stories in other industries and learn
from them
• Develop an Engineering Productivity
Model that addresses shortcomings of
present methods
• Test new model with pilot study
• Develop implementation plan
Productivity Literature
• Focuses on manufacturing, construction
• Little on engineering profession
• Biased toward tools or techniques
• Abundance of conclusions; lack of data
• Service professions focus on profitbased measures
• The software industry approach has
applicability to engineering
Software Industry
Lines of Code/hour did not work well
• Defined clear starting point
• Adjusted for complexity
• Adjusted for defects
• Developed standardized scoring system
• This proven methodology has driven
significant improvement in the software
delivery process
Present Practices
Most companies:
• Track production of drawings and
specifications versus budget
• Use % TIC as target engineering budget
• Use earned value concept in some form
• Have no uniform system of
measurement
Problems with Present Practices
• Lack of standards for format and
content
• Difficulty in tracking actual effort
dedicated to each deliverable
• No correlation between number of
deliverables and installed quantities or
effectiveness
• Computer-based tools:
- Schematics and specs from database
- Physical drawings replaced by models
Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Discipline
Deliverable
Individual
Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Discipline
Deliverable
Individual
Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Discipline
Deliverable
Individual
Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Discipline
Deliverable
Individual
Disciplines
1. Civil/Structural
2. Architectural
3. Project Management
4. Procurement
5. Mechanical
6. Piping
7. Chemical Process
8. Mechanical Process
9. Electrical
10. Instrument/Controls
Engineering Productivity Model
Input
Quality
Factor
Project
Definition
Rating
Index
X
Scope &
Complexity
Factor
Project
Characteristics
X
Raw
Productivity
Hours
Installed Qty.
Focus of Piping Pilot
X
Effectiveness
Factor
% Field Rework
Engineering Productivity Model
Input
Quality
Factor
Project
Definition
Rating
Index
X
Scope &
Complexity
Factor
Project
Characteristics
X
Raw
Productivity
Hours
Installed Qty.
X
Effectiveness
Factor
% Field Rework
Engineering Productivity Model
Input
Quality
Factor
Project
Definition
Rating
Index
X
Scope &
Complexity
Factor
Project
Characteristics
X
Raw
Productivity
Hours
Installed Qty.
X
Effectiveness
Factor
% Field Rework
Engineering Productivity Model
Input
Quality
Factor
Project
Definition
Rating
Index
X
Scope &
Complexity
Factor
Project
Characteristics
X
Raw
Productivity
Hours
Installed Qty.
X
Effectiveness
Factor
% Field Rework
Engineering Productivity Model
Input
Quality
Factor
Project
Definition
Rating
Index
X
Scope &
Complexity
Factor
Project
Characteristics
X
Raw
Productivity
Hours
Installed Qty.
X
Effectiveness
Factor
% Field Rework
Testing the Model
for Piping Discipline
Projects analyzed: 40
Objectives
- Screen for dominant influence factors
- Verify input/output correlation for hrs/ft
Results
- Established number of equipment pieces as a
dominant scope/complexity variable
- Established good correlation between hrs/ft
and dominant variable
Learning
- Valuable data is being ignored in detail design
phase of projects
Summary
This quantity-based model:
• Addresses shortcomings of present methods
• Allows progress tracking with present engineering
tools
• Engineering and Construction on same project
control basis
• Focuses engineering effort on capital investment
• Uses data already collected for construction
productivity
• Is applicable to all industries and project types.
• Will continuously improve with use
What’s Next
• Call to companies with expertise and
interest in this previously neglected arena
• Develop detailed models for each discipline
• Implement on projects
• Industry use of standardized system for
internal improvement and external
benchmarking
Stake goes well beyond engineering cost
Implementation Session Panel
Deb McNeil
Dow, Moderator
John Atwell
Bechtel
Ken Walsh
Arizona State
Tom Zenge
Procter & Gamble
Implementation Session
• Learn how the software industries’
experience validates the approach
• See what benefits to effective project
delivery the future holds
• Learn the many different ways you
can contribute to a significant
improvement step in the EPC
industry