1. Forecasting Models Forecasting Change in the Construction Industry By: David Walls

2. Austin Commercial • Large Construction Manager • Based in Texas • Operations throughout the United States • Past customers include: • Intel • Texas Instruments • Exxon • EDS • FED • SMU

3. Austin Commercial’s Problem • Problem: Dealing with change in construction • Large amount of changes taking place • Not taking into account the impact the changes have later in a project • Uncertainty in knowing what was causing change and what impacts change had

4. Problem Analysis • Discussions with Austin’s Management • Two main indicators of change in a construction project • RFI’s (Request for Information) • New drawings issued • Austin’s needs: • A way to predict potential changes at any given point in project • Impact of those changes on cost

5. Research and Data Collection • Designed a spreadsheet to collect data • RFI’s and new drawings broken down monthly over the life of the project • Broken down into Divisions • Architectural • Structural • Civil • Mechanical • Electrical • Cost Information • Compiled a list of Project that were wanted

6. Spreadsheet

7. Projects • Akin, Gump, Strauss, Hauer, and Field Project • Alcon Laboratories Building G Project • Austin Ventures Project • CarrAmerica Project • Clark, Thomas, and Winters Project • Crossmark Project • CTW Storage/Fitness Center Project • Ft. Worth Convention Center Phase 1 Project • Ft. Worth Convention Center Phase 2 Project • Hall Office Project • Love Field CUP Project • Mabel Peters Caruth Center Project • Terrace V Project (RFI info only) • TriQuint Semiconductor Project • University of North Texas Recreation Center Project • University of Texas Southwestern Medical Center Project

8. Situation Analysis • Calculated the Percentage of RFI’s and new drawings that were complete at key points in a project (10% 25% 50% 75% 100%) • By division and Total • Decided to use regression modeling • Could obtain the most accurate fit of the relationship between the inputs and outputs of the project

9. Regression Models • Two Regression Models • Cubic (polynomial) Regression Model • “best fit” line (cubic) for the relationship between the percentage complete in the job and the percentage of the RFI’s or new drawings issued out of the total • Multiple Regression Model • Best fit (linear) for the relationships between the costs of a project and totals for RFI’s and new drawings and initial budget

10. Cubic Regression Model • Used Minitab to solve a cubic regression model • For total RFI’s and by division • For total new drawings and by division • Allow forecast of total RFI’s and total new drawings by division at the end of the project

11. Example

12. RFI’s Compared

13. New Drawing Compared

14. Multiple Regression Models • Two Models were solved: • The total change in cost on a project • The overall total cost of a project • Based on the historical totals for RFI’s and new drawings by division, and Austin initial forecasted budget

15. Two Models • Using both models together • Forecast total for RFI’s and new drawings based on initial input of percent complete of the job and current totals of RFI’s and new drawings • Use those forecast to forecast the total change in cost of the project and overall total cost of project

16. Model Output • Cubic Regression Models • High R-squared terms • Civil models had highest R-squared terms – also had largest confidence intervals • Architectural models had S-squared terms of 100% - had the smallest confidence intervals • Multiple Regression Models • High R-squared terms • Total RFI and Civil RFI variables seemed too have largest influence in both models • Low probability that there terms where zero • Total cost model more accurate that total change in cost model • Significantly higher F-ratio and corresponding P-value (probability) • Overall both models were strong and did good job of representing the data

17. Sample Output

18. Sample Output

19. Sample Output Continued

20. Recommendations • Austin should use these models to help forecast RFI’s and new drawings issued and their cost impacts • Spreadsheet to allow Austin use these forecast models • Austin should collect monthly RFI and new drawings issued information on all of its jobs • Give current information to run forecasts with • Provide historical data to add to current models to make more accurate • Conclusion: With the help of these models and Austin Commercial’s realization and efforts to solve this change management problem, I believe Austin Commercial can set itself apart from its competitors and better serve its customers in the long run.

21. Spreadsheet

22. Assumptions and Limitations • Only uses data from last 3 years – assumes last 3 years is indicative of future • Projects that were used for data ranged from cost of about $500,000 to $50,000,000 – model may not be accurate for extremely large projects • Assumes RFI’s and new drawings issued are the best indicators for change in a project • Amount of projects used - Would have like to included more projects in the data (hopefully more data will be available in the future)

23. Thanks and Questions