Engineering Productivity Measurement

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

2. 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 Engineering Productivity Measurement Research Team

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

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