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

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

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

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

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

Spreadsheet

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

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

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

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

Example

RFI’s Compared

New Drawing Compared

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

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

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

Sample Output

Sample Output Continued

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.

Spreadsheet

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)

Thanks and Questions