Development of a Data-Driven Performance Evaluation Framework for Identifying Inefficiencies in Traditional Construction Project Management Systems

Abstract

The complexity of construction projects is growing because of numerous stakeholders involved, uncertainties in working environments, and necessity of coordination between different planning, scheduling, safety, communication, and resource management activities. Conventional methods of managing construction projects mostly rely upon empirical decision-making and routine monitoring that led to delays in work, increased costs, inefficient use of resources, and poor performances. The main objectives of this study are to identify problems and inefficiencies of traditional construction project management systems and to suggest a new framework for evaluating their performances based on statistical data. The study utilizes a quantitative research design where questionnaires were sent to 200 construction workers including project managers, site engineers, constructors, consultants, and supervisors. The questionnaire was structured according to five important management dimensions: planning, scheduling, safety monitoring, communication, and resource management. The collected data set was analyzed by means of reliability assessment, descriptive statistics, Relative Importance Index (RII), Principal Component Analysis (PCA), correlation, and regression. Reliability assessment of the evaluation framework proved high reliability due to obtaining a Cronbach's Alpha score equal to 0.92. According to the obtained results, unrealistically tight scheduling (RII = 0.90), lack of materials (RII = 0.90), poor planning (RII = 0.88), and delayed information exchange (RII = 0.88) are the most crucial sources of inefficiency. As a result of performing PCA, it can be said that four major factors account for 85.6% of the total variance, and failures related to planning and scheduling constitute 34.8%. Correlation analysis revealed high dependency between scheduling issues and delays (r = 0.86), as well as between problems related to resources and budget overruns (r = 0.81). Thus, the suggested performance evaluation framework involves statistical analysis to reveal important management obstacles. By implementing this method, construction industry players will be able to move from traditional management methods to evidence-based project performance improvement practices.