Prioritization and ranking: making trade-offs visible
A ranking is useful only when the criteria, weights, and sensitivity of the result remain visible.
Decision science · Applied AI · Business analytics
Prof. Dr. Ömer Faruk Rençber
From data to decisions.From decisions to measurable outcomes.
I develop decision models, machine-learning applications, and explainable analytical approaches that help organizations make better choices under uncertainty.
01 · Evidence02 · Model03 · Outcome
Guest lecture · Applied AIVerified impact2026
600+Citations12h-index30+Publications
Professor · Decision science · Applied AIProfessor · Decision science · Applied AI
33Publicationspeer-reviewed record
6Funded projectsgrant-backed work
28Academic presentationsacademic exchange
14Open materialsPDF access for teaching
Expertise
Three areas of work that improve decision quality.
Methods are selected around the structure of the decision, the quality of available data, and how the result will be used—not around technology fashion.
- 01
Decision system design
Transparent, defensible models for decisions with competing criteria, limited resources, and multiple stakeholders.
- 02
Applied AI and analytics
Statistical and machine-learning approaches that turn organizational data into usable evidence for action.
- 03
Executive learning and capability
Evidence-led workshops that help leaders reason about data, models, AI opportunities, and decision quality.
Decision science notes
Concise scientific writing that explains the concept and situates the method.
02Concept guide
Performance assessment beyond a single ratio
Performance becomes actionable when outcomes are connected to resources, risk, quality, and operating context.
03Concept guide
Alternative and supplier selection under uncertainty
The cheapest alternative may create the highest total risk; structured selection makes that trade-off auditable.
For Organizations
Analytical accuracy is not enough. A model must connect to a real decision.
The organizational approach combines precise problem framing, a fit-for-purpose method, and communication leaders can actually use.
Explore ways to work together →Research program
Decision models, machine learning, and measurable business performance.
The work connects statistical modeling with multi-criteria decision making, financial performance analysis, and AI-enabled business practice.
- Multi-criteria decision makingTOPSIS, PROMETHEE, VIKOR, WASPAS, CoCoSo, BWM, and hybrid ranking designs for complex managerial choices.
- Machine learning in businessClassification, clustering, ensemble learning, and predictive modeling for finance, banking, logistics, and entrepreneurship.
- Statistical education and open materialsAccessible PDF lecture notes and applied statistics resources that turn quantitative methods into usable practice.
- 2024
Advances in Transportation Studies: An International Journal
- 2024
Mathematical Modeling and Numerical Simulation with Applications
- 2024
Sakarya University Business Institute Journal
Putting knowledge into circulation
Research, teaching, and international exchange are parts of one program.
Open teaching materials and applied talks make quantitative methods usable beyond papers—in real learning and decision environments.
Web platforms
Turning scientific methods into digital systems people can use.
Open resources
Usable knowledge and tools built for practice.
Data-Driven Sustainability: AI Agents, Big Data and Innovative Business Practices
Data Is the New Oil: Fueling the Future Economy
İstatistik 1. Konu — İstatistiğe Giriş, Temel Kavramlar ve Grafikler
Collaboration
Let’s discuss the decision problem before the list of methods.
Share the context for a research partnership, organizational application, talk, or learning program.
Contact