A Machine Learning Framework for Contractor Scope Matching Using Profile Characteristics and Work Package Vector Similarity

Abstract

This paper presents a machine learning-based framework for predicting contractor performance in the construction industry by integrating contractor profile information with work package characteristics. The proposed approach addresses procurement challenges arising from the misalignment between contractor expertise and assigned scopes, a key contributor to cost overruns and schedule delays. Unlike existing models that rely on aggregated project-level indicators, the framework enables scope-aware performance prediction by leveraging the Work Breakdown Structure (WBS) to capture similarity among work packages. Contractor profiles are encoded using historical performance data, while WBS elements are vectorized to quantify scope similarity and contextualize predictions. Experimental results demonstrate strong predictive performance, with high tolerance-based accuracy and competitive error metrics, indicating that the proposed method effectively captures contractor–scope relationships. By shifting the focus from traditional contractor selection to contractor–scope matching, the framework provides practical decision support for procurement, planning, and execution, aligning machine learning capabilities with Lean Construction principles.