Decision-Making and Analytics

Data-Driven Business Intelligence and Optimization
1. Auflage 2026
ISBN: 978-1-041-23733-4
Verlag: Taylor & Francis Ltd

Buch, Englisch, 328 Seiten, Format (B × H): 156 mm x 234 mm

In today's data-driven business world, organizations struggle to extract actionable insights from vast amounts of information. This textbook bridges theory and practice, offering structured approaches to leverage data for enhanced decision-making through machine learning, AI, and optimization modeling—transforming raw data into competitive business intelligence.

Decision Making and Analytics: Data-Driven Business Intelligence and Optimization features advanced analytical methodologies paired with real-world implementation studies, creating a progressive learning path from customer behavior analysis through hybrid decision models. By integrating fuzzy logic, neural networks, text mining, and reinforcement learning, it provides a comprehensive toolkit applicable across business scenarios, turning abstract concepts into practical solutions.

Serving multiple audiences, this resource bridges the gap between classroom theory and industry practice for students in industrial engineering, business analytics, and management information systems. Practitioners gain applicable methodologies to improve organizational decision-making. At the same time, researchers benefit from cutting-edge approaches across various domains, and policymakers can develop data-informed strategies—addressing the growing demand for professionals who translate data into strategic decisions.

The textbook employs open-ended questions that develop analytical reasoning skills without predetermined solutions, better preparing students for real-world complexity. Supporting materials include high-quality figure slides, as well as PowerPoint slides, for qualified adopters, enabling engaging learning environments that effectively communicate complex concepts while building practical, data-driven decision-making skills.

Zielgruppe

Undergraduate Advanced

Autoren/Hrsg.

Fazlollahtabar, Hamed

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Mathematik | Informatik Mathematik Operations Research

Inhaltsverzeichnis

1. Regression-Based Fuzzy Expert System for Customer Behavior Analysis. 2. Customer Online Shopping Analysis using Social Network and Levenshtein Distance. 3. Clustering and Automatic Cell Diagram for Customer Segmentation. 4. Integrated Association Rules and Neural Networks for Customer Churn in Social Media. 5. Text Mining for Loyal Customer Relationship Management. 6. Social Network Analysis for Product Recommender System in Online Retailing. 7. Factor Analysis for New Product Development and Multi-Channel Sales. 8. Cloud Processing in Sustainable ERPs for Small and Medium Enterprises. 9. Business Intelligence for Insurance System using Social Network Analysis. 10. Loss Function Integrated with Internet of Things for Telecommunications Supply Network. 11. Reinforcement Learning for Smart Energy Management. 12. Integrated Analytic Network Process and Best-Worst Method for Online Shopping Security.

Über Autor(innen)

Dr. Hamed Fazlollahtabar is an Associate Professor in the Department of Industrial Engineering at Damghan University, Iran, where he has served since June 2017. He earned his BSc and MSc in Industrial Engineering from Mazandaran University of Science and Technology in 2008 and 2010, followed by a PhD in Industrial and Systems Engineering from Iran University of Science and Technology. and completed a postdoctoral research fellowship in reliability engineering for complex systems at Sharif University of Technology from October 2016 to March 2017. A globally recognized researcher, Dr. Fazlollahtabar has been consistently ranked among the top 2% of scientists worldwide from 2021 through 2025, was named the best researcher across all engineering disciplines in Iran in 2022, and received the distinguished young researcher award in Industrial Engineering in Iran for 2023. His extensive research portfolio encompasses robotic production systems, reliability engineering, sustainable supply chain planning, and business intelligence and analytics, resulting in over 300 research papers published in international books, journals, and conferences, as well as twelve authored books, eight of which have international academic distribution. Dr. Fazlollahtabar further contributes to his field through service on editorial boards of academic journals and participation in technical committees for conferences.