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Forecasting Models

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Forecasting Models

Forecasting Change in the Construction Industry

By: David Walls

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

- 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

- Discussions with Austin’s Management
- Two main indicators of change in a construction project
- RFI’s (Request for Information)
- New drawings issued

- Two main indicators of change in a construction project
- Austin’s needs:
- A way to predict potential changes at any given point in project
- Impact of those changes on cost

- 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

- 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

- 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

- 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 (polynomial) 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

- 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

- 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

- 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

- 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.

- 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