Ferdi Eruysal
Assistant Clinical Professor
Assistant Clinical Professor of Supply Chain & Information Systems
Ferdi Eruysal earned his Ph.D. in Management Information Systems from the University of Illinois at Chicago. He specializes in teaching data analytics and business intelligence, with a passion for interdisciplinary research on the economics of information systems. He has extensive experience in teaching undergraduate and graduate courses, advising students, and collaborating with industry.
Expertise
Ferdi Eruysal's research interests include the economics of information systems, focusing on big data and business intelligence implications, as well as the evolution of business analytics programs in higher education to meet industry needs. He employs game-theoretical models to analyze market dynamics and consumer behavior, with ongoing studies exploring technology adoption decisions and the impact of customer loyalty on firm strategies.
Education
PhD in Management Information Systems in Management Information Systems, University of Illinois at Chicago, College of Business Administration, Chicago, Illinois, USA, Business intelligence provided relatively low-cost capabilities to collect and analyze vast amount of customer information. Accumulation of customer specific information allows firms to categorize customers into segments and offer customized prices. The impact of business intelligence and smart pricing on competition and consumer purchase behavior in a game-theoretic model with two asymmetric firms is studied. We first introduce a basic game-theoretical model where customers differ in brand loyalty. Then, the basic model was extended to study segment granularity, product cost, privacy-conscious customers and classification errors. First, we extend the basic model by introducing multiple segments. Our analysis shows that firms do not necessarily become worse off. The firm dominating the industry is likely to increase its profits at the expense of the rival firm, and consumer welfare will improve with business intelligence. Two fundamental parameters, market dominance and the technology cost to industry dominance ratio, are introduced. High technology cost coupled with high dominance indicator results in a situation in which only very dominant firm acquires business intelligence. Second, we extend the basic model by incorporating product cost. Previous literature implicitly assumes that product cost differential can be aggregated into the price tolerance, and thus product cost can be assumed away. We show that a change in product cost does not translate into a correlated adjustment of the price tolerance. We identify four distinct subcases whose equilibrium conditions are significantly different in terms of market share, prices and profits. The case where product cost is considered to be negligible or equal across firms is shown to be one special case. Third, we extend the basic model by introducing privacy-conscious customers and classification errors. Privacy-conscious customers could inhibit firms’ data collection initiatives, resulting therefore in incorrect segmentation. Our analysis shows that firms offer lower prices when they make classification errors. This result is counter-intuitive. Even though, smart pricing is not considered as fair, it drastically improves competition which leads to more affordable prices. Most important contribution of this study is that an increase in information quality lower firms’ profits as this makes them more competitive., 2014-08-11 MS Information Technology in Information Technology, Bahcesehir University, Istanbul, Turkey, 2003-01-01 BS Industrial Engineering in Industrial Engineering, Middle East Techbnical University, Faculty of Engineering, Ankara, Turkey, 2000-01-01
Courses Taught
BAN 550 – Prscptve Analytics
Analytics, defined as the scientific process of using data and quantitative techniques to make better decisions, has permeated virtually all aspects of business. The widespread availability of large amounts of detailed data combined with analytics methods permits an extensive examination of the tradeoffs that inform business decision making, with the ultimate goal of choosing "best" courses of action.This course explores the use of prescriptive analytics methods in a variety of business contexts. In the early part of the course, the focus is on the tools and methods of prescriptive analytics. As the course progresses, the emphasis shifts to the effective integration and implementation of prescriptive analytics in supply-side decision making processes such as supply chain management, service management, operations, logistics and transportation. The applications areas within business will reflect the interests of the instructors and will evolve as new areas of theory and practice develop.
BAN 850 – Prescriptive Analytics for Business
Analytics, defined as the scientific process of using data and quantitative techniques to make better decisions, has permeated virtually all aspects of business. The widespread availability of large amounts of detailed data combined with analytics methods permits an extensive examination of the tradeoffs that inform business decision making, with the ultimate goal of choosing "best" courses of action. This course explores the use of prescriptive analytics methods in a variety of business contexts. In the early part of the course, the focus is on the tools and methods of prescriptive analytics. As the course progresses, the emphasis shifts to the effective integration and implementation of prescriptive analytics in supply-side decision making processes such as supply chain management, service management, operations, logistics and transportation. The applications areas within business will reflect the interests of the instructors and will evolve as new areas of theory and practice develop.
BAN 850 – Prscptve Analytics
Analytics, defined as the scientific process of using data and quantitative techniques to make better decisions, has permeated virtually all aspects of business. The widespread availability of large amounts of detailed data combined with analytics methods permits an extensive examination of the tradeoffs that inform business decision making, with the ultimate goal of choosing "best" courses of action.This course explores the use of prescriptive analytics methods in a variety of business contexts. In the early part of the course, the focus is on the tools and methods of prescriptive analytics. As the course progresses, the emphasis shifts to the effective integration and implementation of prescriptive analytics in supply-side decision making processes such as supply chain management, service management, operations, logistics and transportation. The applications areas within business will reflect the interests of the instructors and will evolve as new areas of theory and practice develop.
MIS 301 – Business Analytics
Application of computer-based information systems to support management decision making; basic systems design, data organization, and data processing. A student cannot receive credit toward graduation for both M IS 100 or 100W and M I S 301.
MIS 441 – Applied AI and ML in Business
Application of Information Technology based methods and tools to analyze business data and support decision making. MIS 441 Business Intelligence for Decision Making (3) Business intelligence encompasses the IT tools for exploring, analyzing, integrating, and reporting business data for fact-based, intelligent decision making. This course primarily investigates methods and tools for exploring and analyzing large amounts of business data also called "Big Data." Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Students will be exposed to a variety of methods for analyzing both structured and unstructured data and they will work with business data sets to understand the value that can be extracted from large data sets. They will also learn how to classify and associate data to discover business rules that can be used to support decision making. The course will also cover methods to analyze social media information and about tools that can facilitate such analysis and discovery. Again they will get a chance to work with data from real social networks to gain an appreciation of how value can be obtained from such networks. Finally, they will learn about techniques for visualizing, presenting and communicating information in a useful way, e.g. through dashboards and with other technologies on various platforms.
MIS 441 – Bus Intelligence
Application of Information Technology based methods and tools to analyze business data and support decision making. MIS 441 Business Intelligence for Decision Making (3) Business intelligence encompasses the IT tools for exploring, analyzing, integrating, and reporting business data for fact-based, intelligent decision making. This course primarily investigates methods and tools for exploring and analyzing large amounts of business data also called "Big Data." Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Students will be exposed to a variety of methods for analyzing both structured and unstructured data and they will work with business data sets to understand the value that can be extracted from large data sets. They will also learn how to classify and associate data to discover business rules that can be used to support decision making. The course will also cover methods to analyze social media information and about tools that can facilitate such analysis and discovery. Again they will get a chance to work with data from real social networks to gain an appreciation of how value can be obtained from such networks. Finally, they will learn about techniques for visualizing, presenting and communicating information in a useful way, e.g. through dashboards and with other technologies on various platforms.
MIS 441 – Business Intelligence for Decision Making
Application of Information Technology based methods and tools to analyze business data and support decision making. MIS 441 Business Intelligence for Decision Making (3) Business intelligence encompasses the IT tools for exploring, analyzing, integrating, and reporting business data for fact-based, intelligent decision making. This course primarily investigates methods and tools for exploring and analyzing large amounts of business data also called "Big Data." Learning methods emphasize active learning in the application of methods and tools to real data and the presentation of the results. Students will be exposed to a variety of methods for analyzing both structured and unstructured data and they will work with business data sets to understand the value that can be extracted from large data sets. They will also learn how to classify and associate data to discover business rules that can be used to support decision making. The course will also cover methods to analyze social media information and about tools that can facilitate such analysis and discovery. Again they will get a chance to work with data from real social networks to gain an appreciation of how value can be obtained from such networks. Finally, they will learn about techniques for visualizing, presenting and communicating information in a useful way, e.g. through dashboards and with other technologies on various platforms.
MIS 494H – Research Project (H)
Supervised student activities on research projects identified on an individual or small-group basis.