Buch, Englisch, 248 Seiten, Format (B × H): 156 mm x 234 mm
Buch, Englisch, 248 Seiten, Format (B × H): 156 mm x 234 mm
ISBN: 978-1-041-32746-2
Verlag: Taylor & Francis Ltd
Every project carries uncertainty. Costs overrun, schedules slip, and revenues disappoint — not because project managers lack skill, but because most risk analyses rely on single-point estimates that conceal the true range of possible outcomes. This book provides a structured framework for project risk management, combines it with rigorous quantitative risk analysis, and shows how artificial intelligence makes both accessible to every practitioner, regardless of budget or technical background.
Artificial Intelligence and Risk Analysis in Projects delivers a complete, practitioner-focused framework covering the full project risk management lifecycle — from risk identification and qualitative assessment through quantitative risk analysis, response planning, and monitoring and control. The quantitative core of the book addresses Monte Carlo simulation, probabilistic NPV and IRR appraisal, decision trees, Expected Monetary Value, sensitivity analysis, and the calibration of probability distributions from real project data. A case study running through the book compares two capital investment projects under deterministic and probabilistic analysis, demonstrating concretely how single-point estimates overstate expected returns and conceal the probability of loss — in one case by more than 70%. The AI dimension is integrated throughout rather than treated as a separate topic: readers learn how large language models support risk identification and qualitative analysis, how AI enhances the accuracy of Monte Carlo simulation inputs, how structured prompt engineering directs AI toward specific risk management tasks, and how AI performs as an independent model auditor and stress-testing partner. The book addresses the governance, validation, and accountability structures that responsible AI deployment in project environments requires, and closes by demonstrating Monte Carlo simulation using AI alone — making rigorous probabilistic analysis accessible to practitioners who lack access to commercial simulation software.
Written for project risk managers, project controls professionals, and students of project management, this is the first book to unite a structured project risk management framework, rigorous quantitative risk analysis, and applied artificial intelligence in a single integrated treatment. It is both a professional reference and a practical guide — grounded in real case studies, immediately applicable to real project decisions, and positioned at the frontier of where the project risk management profession is heading.
Zielgruppe
Academic, Postgraduate, Professional Practice & Development, and Undergraduate Advanced
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Business Application Software für Projektmanagement
- Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Versicherungswirtschaft
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Wirtschaftswissenschaften Finanzsektor & Finanzdienstleistungen Finanzsektor & Finanzdienstleistungen: Allgemeines
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Risikobewertung, Risikotheorie
- Wirtschaftswissenschaften Betriebswirtschaft Management Projektmanagement
- Wirtschaftswissenschaften Betriebswirtschaft Management Risikomanagement
Weitere Infos & Material
Foreword. Introduction. CHAPTER 1. The environment of risk analysis in projects. CHAPTER 2. The key role of project managers — risk management and strategic leadership. CHAPTER 3. The risk management environment in a project. CHAPTER 4. Statistics for risk analysis: fundamentals for data-driven decision-making. CHAPTER 5. A framework for managing risks in projects. CHAPTER 6. How to create a risk plan. CHAPTER 7. Risk identification — strategies for detecting and anticipating potential problems. CHAPTER 8. The quantitative risk analysis process. CHAPTER 9. Economic evaluation of projects Monte Carlo— a case study. CHAPTER 10. Decision optimisation with decision trees, EMV analysis, and Bowtie. CHAPTER 11. The arrival of artificial intelligence in risk management. CHAPTER 12. AI-assisted Monte Carlo simulation — from deterministic model to probabilistic analysis. Bibliography. Index.
