An AHP-Based Decision Support Tool for Project Delivery System Selection in Construction

Introduction: Choosing the right contract type, or Project Delivery System (PDS), helps construction owners avoid cost overruns and project delays. Right now, most owners choose a contract based on gut feeling or past habits rather than a clear, data-driven system.

Methods: This study builds a step-by-step decision tool by combining the Analytical Hierarchy Process (AHP) and Multi-Attribute Utility Theory (MAUT). We split the decision-making process into five clear levels, moving from the overall project goal down to the final contract options. The tool weighs five main criteria: project cost, schedule speed, quality, environmental risks, and scope flexibility. We used expert panels to score these options and checked their judgments using Saaty's Consistency Ratio (CR < 0.10) to make sure the logic was sound. MAUT formulas then turned all qualitative opinions and quantitative measurements into simple, matching scores between 0.0 and 1.0. Finally, we mathematically added Lean Construction methods and Six-Sigma process capability indexes (Cp) into the scoring matrices to show how well each contract option handles site waste and operational errors.

Results: We tested the tool on a real $8.45 million road and bridge project in Montreal, Canada. That project suffered a major concrete retaining wall failure during construction, causing $500,000 in rework and a six-month winter delay. To avoid looking back with hindsight bias, we evaluated the project using only the baseline data and risk factors known before the failure happened. The tool correctly identified the Unit Price system as the best choice, giving it the highest score (8.35) because it best protects the owner from unpredictable ground and weather risks.

Conclusions: Combining AHP and MAUT gives owners a logical way to choose a contract type. While the case study focuses on a Canadian project, our regional analysis shows that adding Lean and Six-Sigma metrics makes this a highly useful framework for controlling project variance across different global markets like the US, East Asia, and the GCC.