كنفرانس بين المللی مهندسی صنايع و سيستم ها

صفحه اصلی / یازدهمین كنفرانس بين المللی مهندسی صنايع و سيستم ها

Adoption and Practice of Machine Learning in Project Parameter Estimation

نویسندگان :

Ali Jooyafar¹ Milad Balouch² Parnian Rafeeyan³

1- Petroleum University of Technology 2- Petroleum University of Technology 3- Yazd University

کلمات کلیدی :

Project management،machine learning،estimation methods،weight factor،duration،risk score،Technology Acceptance Model،Innovation Diffusion Theory

چکیده :

Accurate estimation of early project parameters, Weight Factors (WF), activity durations, and risk scores, are critical to effective scheduling, resource allocation, and risk management. While machine learning (ML) estimation tools offer theoretically superior predictive capabilities, their uptake in Project Management (PM) practice remains limited. This study examines prevailing estimation methods, experience with ML tools, trust levels, and perceived adoption barriers among PM practitioners, using a survey grounded in the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT). The instrument, refined through piloting, was deployed via professional networks and messaging platforms, yielding 111 complete responses from 1,256 invitations. Results show that expert judgment and organizational standards dominate estimation practice, with less than 40% of respondents reporting ML experience. Trust in ML outputs is mixed, and adoption barriers are primarily organizational, most notably infrastructure readiness, practitioner awareness/training, and data accessibility, rather than purely individual or cost-based. The findings underscore that ML adoption in PM estimation is contingent on aligning technical capability building with organizational change management, and these findings highlight targeted pilot projects, demonstrable performance gains, and MLOps integration as practical steps toward broader diffusion.