PREDICTIVE ANALYTICS IN PROJECT MANAGEMENT: EVALUATING ITS ROLE IN COST CONTROL, SCHEDULE OPTIMIZATION, AND RISK MANAGEMENT
DOI:
https://doi.org/10.68156/46sjss47Keywords:
Cost Control, Lahore, Machine Learning, Predictive Analytics, Project Management, Risk Management, Schedule OptimizationAbstract
Abstract
Predictive analytics is becoming an important capability in project management because it enables organizations to forecast potential problems, improve resource allocation, and make evidence-based decisions. Nevertheless, cost overruns, schedule delays, and inadequate risk anticipation continue to challenge project delivery, particularly where project data are fragmented and analytical capabilities are limited. This study examines the perceived role of predictive analytics in cost control, schedule optimization, and risk management among project management professionals in Lahore, Pakistan. Drawing on the Technology–Organization–Environment framework and the project management literature, the study proposes a quantitative, cross-sectional survey of project managers, engineers, planning professionals, and project coordinators working across construction, information technology, manufacturing, and service sectors. A structured questionnaire measures predictive analytics adoption, perceived forecasting accuracy, cost-control effectiveness, schedule performance, risk identification, and organizational readiness. The proposed analysis uses descriptive statistics, reliability testing, correlation, and multiple regression to examine the relationships between predictive analytics adoption and project management outcomes. Existing research indicates that machine learning and data-driven forecasting can support cost estimation, schedule prediction, and risk assessment, although effectiveness depends on data quality, model validation, and implementation conditions (Lagos et al., 2024; Moussa et al., 2024; Talgata, 2023). The study is designed to generate context-specific evidence concerning analytical adoption in Lahore. Its anticipated practical contribution is a framework for integrating predictive tools into routine project monitoring, enabling earlier intervention, more informed resource allocation, and improved project governance. Actual empirical conclusions will require analysis of collected survey responses.
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