Ozge Islegen
Ozge Islegen

Assistant Professor of Operations

Print Overview

Ozge Islegen is an Assistant Professor of Operations. After receiving her PhD in Operations, Information and Technology at Stanford University, she joined Kellogg in 2011. Professor Islegen's research interests include supply chain management, capacity management, environmental sustainability and energy-related operations; specifically, energy supply chains, and capacity investment strategies under environmental regulations.

Areas of Expertise
Capacity Management
Environmental Sustainability
Supply Chain Design and Management

Print Vita
Ph.D., 2011, Operations, Information and Technology, Stanford Graduate School of Business, Stanford, CA
B.S., 2005, Industrial Engineering, Bilkent University, Ankara, Turkey

Academic Positions
Donald P. Jacobs Scholar/Assistant Professor, Managerial Economics and Decision Sciences, Kellogg School of Management, Northwestern University, 2011-present

Print Research
Research Interests

Supply chain management, capacity management and environmental sustainability; specifically, energy supply chains, and capacity investment strategies under environmental regulations.

Islegen, Ozge and S. J. Reichelstein. 2011. Carbon Capture by Fossil Fuel Power Plants: An Economic Analysis. Management Science. 57(1): 21-39.
Islegen, Ozge and S. J. Reichelstein. 2009. The Economics of Carbon Capture. The Economists' Voice. 6(12): Article 5.
Working Papers
Islegen, Ozge, Baris Ata and A.Serasu Duran. 2016. An Analysis of Time-Based Pricing in Electricity Supply Chains.
Islegen, Ozge and Erica L. Plambeck. 2013. Capacity Leadership. Under revision.
Book Chapters
Islegen, Ozge, Erica L. Plambeck and Terry A. Taylor. 2015. "Variability in Emissions Cost: Implications for Facility Location, Production and Trade." In Environmentally Responsible Supply Chains, edited by Atalay Atasu, Springer.
Islegen, Ozge. "Going Green Can Be Good for the Bottom Line.." Kellogg Insight, January 4th, 2016.
Islegen, Ozge. "Why Power Companies Love Smart Meters.." Kellogg Insight, September 8th, 2015.

Print Teaching
Teaching Interests

Operations Management

Energy Markets

Environmental Sustainability

Full-Time / Evening & Weekend MBA
Operations Management (OPNS-430-0)
1Ys: This course is typically waived through the admissions process or the equivalent course Operations Management (Turbo) (OPNS-438A) was completed during the Summer term. MMMs: This course is equivalent to the MMM core course Designing and Managing Business Processes (OPNS-440) Operations management is the management of business processes--that is, the management of the recurring activities of a firm. This course aims to familiarize students with the problems and issues confronting operations managers, and to provide the language, concepts, insights and tools to deal with these issues to gain competitive advantage through operations. We examine how different business strategies require different business processes and how different operational capabilities allow and support different strategies to gain competitive advantage. A process view of operations is used to analyze different key operational dimensions such as capacity management, cycle time management, supply chain and logistics management, and quality management. Finally, we connect to recent developments such as lean or world-class manufacturing, just-in-time operations, time-based competition and business re-engineering.

Analytical Decision Modeling (OPNS-450-0)
This course focuses on structuring, analyzing and solving managerial decision problems on Excel spreadsheets. We address problems of resource allocation (how to use available resources optimally), risk analysis (how to simulate the effects of uncertainty in problem parameters), decision analysis (how to analyze sequential decisions involving uncertainty), data analysis (how to synthesize the available data into useful information) and forecasting (how to extrapolate past observations into the future). In each area, we pose specific problems from operations, finance and marketing, structure them on Excel spreadsheets, and analyze and solve them using the available Excel commands, tools and add-ins. The course involves a hands-on, in-class learning experience in modeling and analyzing a variety of business decision problems on a common spreadsheet platform. It should, therefore, enhance one's problem-solving capabilities as well as spreadsheet skills. A good working knowledge of Microsoft Excel is required.

Analytical Decision Modeling (OPNSM-450-0)

Emerging Areas in Operations Managements (OPNS-525-0)
This course offers an advanced introduction to topics at the intersection of statistical (machine) learning and sequential decision-making. A tentative course plan is as follows. We will begin by covering classic work on optimal hypothesis testing when data can be gathered sequentially and interactively. The second part of the class focuses on bandit learning and the design and analysis of algorithms that balance exploration/exploitation. The last part of the course introduces reinforcement learning, including methods for value function approximation and algorithms for efficient exploration.