1. Engineering Productivity Measurement
Research Team
Bob Shoemaker
BE&K
CII Annual Conference 2001

2. Engineering Productivity Measurement Research Team
Bob Shoemaker BE&K, Chair
John Atwell Bechtel
Bill Buss Air Products
Luh-Maan Chang Purdue University
Glen Hoglund Ontario Hydro
Duane McCloud FPL Energy
Deb McNeil Dow
Navin Patel Chemtex
John Rotroff U.S. Steel
Ken Walsh Arizona State University
Denny Weber Black & Veatch
Tom Zenge Procter & Gamble

3. 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

4. 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

5. 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 profit- based measures
The software industry approach has applicability to engineering

6. 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

7. 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

8. 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

9. Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Deliverable
Individual
Discipline

10. Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Deliverable
Individual
Discipline

11. Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Deliverable
Individual
Discipline

12. Levels of Productivity
Company EPC Work Process
Project
Overall Engineering
Deliverable
Individual
Discipline

13. 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

14. Engineering Productivity Model
Input Quality Factor
Scope & Complexity Factor
Raw
Productivity
Effectiveness Factor X X X
Project
Definition
Rating
Index
Project Characteristics
Hours
Installed Qty.
% Field Rework
Focus of Piping Pilot

15. Engineering Productivity Model
Input Quality Factor
Scope & Complexity Factor
Raw
Productivity
Effectiveness Factor X X X
Project
Definition
Rating
Index
Project Characteristics
Hours
Installed Qty.
% Field Rework

16. Engineering Productivity Model
Input Quality Factor
Scope & Complexity Factor
Raw
Productivity
Effectiveness Factor X X X
Project
Definition
Rating
Index
Project Characteristics
Hours
Installed Qty.
% Field Rework

17. Engineering Productivity Model
Input Quality Factor
Scope & Complexity Factor
Raw
Productivity
Effectiveness Factor X X X
Project
Definition
Rating
Index
Project Characteristics
Hours
Installed Qty.
% Field Rework

18. Engineering Productivity Model
Input Quality Factor
Scope & Complexity Factor
Raw
Productivity
Effectiveness Factor X X X
Project
Definition
Rating
Index
Project Characteristics
Hours
Installed Qty.
% Field Rework

19. 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

20. 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

21. Stake goes well beyond engineering cost
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

22. Implementation Session Panel
Deb McNeil Dow, Moderator
John Atwell Bechtel
Ken Walsh Arizona State
Tom Zenge Procter & Gamble

23. 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