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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.

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Forecasting models

Forecasting Models

Forecasting Change in the Construction Industry

By: David Walls

Austin commercial
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
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
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
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


  • 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
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
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
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

Multiple regression models
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
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
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


  • 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.

Assumptions and limitations
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)