Jan A. Van Mieghem - Faculty - Kellogg School of Management

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Jan A. Van Mieghem

MANAGERIAL ECONOMICS & DECISION SCIENCES; OPERATIONS
Harold L. Stuart Professor of Managerial Economics
Professor of Operations Management

Overview
Vita
Research
Teaching

Jan A. Van Mieghem is the Harold L. Stuart Distinguished Professor of Managerial Economics and Professor of Operations Management at the Kellogg School of Management at Northwestern University. During 2009-2010, he served as Senior Associate Dean: Curriculum and Teaching of the Kellogg School. From 2006 – 2009, he served as the chairman of the Department of Managerial Economics and Decision Sciences. He received a PhD in Business and an MSEE from Stanford University, and holds an electrical engineering degree from the Katholieke Universiteit Leuven, Belgium.

His research and teaching focuses on product, service and supply chain operations and studies both strategic questions as well as tactical execution. He is past editor of the operations and supply chain area of Operations Research and has served on the editorial board of several professional journals. He is the author of over 30 academic articles published in the leading journals, and of two textbooks: one on operations management and the other on operations strategy. His paper co-authored with Marty Lariviere received the first MSOM best paper award in 2007.

Areas of Expertise

Capacity Management
Cycle Time Management
Response Time Management
Tactical Operations

Recent Media Coverage

BNET: What a Bricks-and-Mortar Business Can Learn From Website Analytics

Economist Intelligence Unit: Executive Briefing: Global dual sourcing strategies

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Recent Kellogg News

Destination: India

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Intensive ‘mini MBA’ program offers new Kellogg faculty tools to excel

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Education

PhD, 1995, Graduate School of Business, Stanford University

MS, 1990, Electrical Engineering, Stanford University

MS, 1989, Applied Sciences, University KU Leuven, Belgium, Summa Cum Laude

Academic Positions

Senior Associate Dean, Curriculum and Teaching, Kellogg School of Management, Northwestern University, 2009-present

Professor (by courtesy), Robert R. McCormick School of Engineering and Applied Science, Northwestern University, 2007-present

Harold L. Stuart Distinguished Professor, Kellogg School of Management, Northwestern University, 2001-present

Professor, Kellogg School of Management, Northwestern University, 2000-present

Visiting Professor, WHU, 1998-present

Chairman of Managerical Economics and Decision Sciences Department, Kellogg School of Management, Northwestern University, 2006-2009

Visiting Professor, Chulalongkorn University, 2005-2005

Visiting Professor, KU Leuven, 2005-2005

Visiting Professor, York University, 2004-2004

Associate Professor, Kellogg School of Management, Northwestern University, 1997-2000

Assistant Professor, Kellogg School of Management, Northwestern University, 1995-1997

Editorial Positions

Editorial Board, Review of Business and Economics, 2008-Present

Area Editor forManufacturing, Service and Supply Chain Operations, Operations Research, 2002-2005

Associate Editor, Management Science, 1997-2003

Senior Editor, Manufacturing and Service Operations Management, 2000-2002

Doctoral Students

Lauren Xiaoyuan Lu, 2007, MECS

Tingliang Huang, Operations Management

Service

International MSOM-Informs Conference at Northwestern University, Committee - 12/31/2005

M&SOM journal and editor three-annual review , Committee

M&SOM Best Student Paper Competition, Committee (Co-Chair)

Utah Operations Winter Conference, Organizing Committee Member
1/01/2006

Conference Presentations

University of Utah, 1/08/2009

McGill Montreal, 11/20/2008

INFORMS, Mexico-China paper, 10/15/2008

INFORMS, Optimal flexibility configurations in Newsvendor Networks: Going beyond Chaining and Pairing, 10/14/2008

INFORMS, A little flexibility is all you need: Optimality of tailored chaining and pairing, 10/13/2008

KU Leuven, 9/05/2008

London Business School, 6/13/2008

MSOM Conference, A little flexibility is all you need: Optimality of tailored chaining and pairing, 6/05/2008

University of Chicago, 5/23/2008

Deloitte (Supply Chain Summit), 5/17/2008

Columbia University, 5/02/2008

Wharton, 4/24/2008

UC Berkeley, 4/17/2008

Research Interests

Operations management (capacity, inventory and supply chain management; queuing, newsvendor, and heavy traffic theory and optimization), operations strategy (capacity investment, network design, strategic sourcing, pricing)

Articles

Chod, Jiri, Nils Rudi and Jan A. Van Mieghem. Forthcoming. Operational Flexibility and Financial Hedging: Complements or Substitutes?. Management Science.

Bassamboo, Achal, Ramandeep S. Randhawa and Jan A. Van Mieghem. 2010. Optimal Flexibility Configurations in Newsvendor Networks: Going Beyond Chaining and Pairing. Management Science. 56(8): 1285-1303.

Allon, Gad and Jan A. Van Mieghem. 2010. Global Dual Sourcing: Tailored Base Surge Allocation to Near and Offshore Production. Management Science. 56(1): 110-124.

Lu, Lauren Xiaoyuan, Jan A. Van Mieghem and R. Canan Savaskan-Ebert. 2009. Incentives for Quality through Endogenous Routing. Manufacturing and Service Operations Management. 11(2): 254-273.

Ata, Baris and Jan A. Van Mieghem. 2009. The Value of Dynamic Resource Pooling: Should a service network be integrated or Product-focused?. Management Science. 55(1): 115-131.

Diermeier, Daniel and Jan A. Van Mieghem. 2008. Coordination and Turnout in Large Elections. Mathematical and Computer Modeling. 48: 1478-1496.

Lu, Lauren Xiaoyuan and Jan A. Van Mieghem. 2008. Multimarket Facility Network Design with Offshoring Applications. Manufacturing & Service Operations Management. 11(1): 90-108.

Diermeier, Daniel and Jan A. Van Mieghem. 2008. Voting with Your Pocket Book: A Stochastic Model of Consumer Boycotts. Mathematical and Computer Modeling. 48: 1497-1509.

Van Mieghem, Jan A.. 2006. Risk Mitigation in Newsvendor Networks: Resource Diversification, Flexibility, Sharing, and Hedging. Management Science. 53(8): 1269-1288.

Maglaras, Constantinos and Jan A. Van Mieghem. 2005. Queueing Systems with Leadtime Constraints: A Fluid-Model Approach for Admission and Sequencing Control. European Journal of Operational Research. 167(1): 179-207.

Schmidt, Glenn and Jan A. Van Mieghem. 2005. Seagate-Quantum: Encroachment Strategies. INFORMS Transactions on Education. 5(2)

Van Mieghem, Jan A.. 2004. Commonality Strategies: Value Drivers and Equivalence with Flexible Capacity and Inventory Substitution. Management Science. 50(3): 419-424.

Lariviere, Martin and Jan A. Van Mieghem. 2004. Strategically Seeking Service: How Competition Can Generate Poisson Arrivals. Manufacturing & Service Operations Management. 6(1): 23-40.

Van Mieghem, Jan A.. 2003. Capacity Management, Investment and Hedging: Review and Recent Developments. Manufacturing & Service Operations Management. 5(4): 269-302.

Van Mieghem, Jan A.. 2003. Due Date Scheduling: Asymptotic Optimality of Generalized Longest-Queue and Generalized Largest Delay Rules. Operations Research. 51(1): 113-122.

Consider the following due-date scheduling problem in a multiclass, acyclic, single-station service system: any class k job arriving at time t must be served by its due date t+D_{k}. Equivalently, its delay ¦Ó_{k} must not exceed a given delay or lead-time D_{k}. In a stochastic system the constraint ¦Ó_{k}¡ÜD_{k} must be interpreted in a probabilistic sense. Regardless of the precise probabilistic formulation, however, the associated optimal control problem is intractable with exact analysis. This article proposes a new formulation that incorporates the constraint through a sequence of convex-increasing delay cost functions. This formulation reduces the intractable optimal scheduling problem into one for which the Generalized c¦Ì (Gc¦Ì) scheduling rule is known to be asymptotically optimal. The Gc¦Ì rule simplifies here to a generalized longest queue (GLQ) or generalized largest delay (GLD) rule, which are defined as follows. Let N_{k} be the number of class k jobs in system, ¦Ë_{k} their arrival rate and a_{k} the age of their oldest job in the system. GLQ and GLD are dynamic priority rules, parameterized by ¦È: GLQ(¦È) serves FIFO within class and prioritizes the class with highest index ¦È_{k}N_{k}, whereas GLD(¦È) uses index ¦È_{k}¦Ë_{k}a_{k}. The argument is presented first intuitively, but is followed by a limit analysis that expresses the cost objective in terms of the maximal due-date violation probability. This proves that GLQ(¦È_{∗}) and GLD(¦È_{∗}), where ¦È_{∗,k}=1/¦Ë_{k}D_{k}, asymptotically minimize the probability of maximal due-date violation in heavy traffic. Specifically, they minimize liminf_{n¡ú¡Þ}Pr{max_{k}sup_{s¡Ê[0,t]}((¦Ó_{k}(ns))/(n^{1/2}D_{k}))¡Ýx} for all positive t and x, where ¦Ó_{k}(s) is the delay of the most recent class k job that arrived before time s. GLQ with appropriate parameter ¦È_{¦Á} also reduces "total variability" because it asymptotically minimizes a weighted sum of ¦Á^{th} delay moments. Properties of GLQ and GLD, including an expression for their asymptotic delay distributions, are presented.

Van Mieghem, Jan A. and Nils Rudi. 2002. Newsvendor Networks: Inventory Management and Capacity Investment with Discretionary Activities. Manufacturing & Service Operations Management. 4(4): 313-335.

We introduce a class of models, called newsvendor networks, that generalizes the well-known newsven- dor model along three dimensions. Newsvendor networks model the flow of multiple products through multiple processing and storage points over multiple time periods. Such models provide a parsimonious framework to study various problems of stochastic capacity investment and inventory procurement. Newsvendor networks can feature commonality, flexibility, substitution or transshipment in addition to assembly and distribution. Newsvendor networks are stochastic models with recourse that are characterized by linear revenue and cost structures and a linear input-output transformation. While capacity and inventory decisions are locked in before demand uncertainty is resolved as usual, some managerial discretion can remain via ex-post input-output activity decisions. This discretion is captured through non-basic activities that model input- or resource-substitution and that result in subtle pooling effects. Non-basic activities are never used in a deterministic environment; their value stems from the discre- tionary flexibility to meet stochastic demand deviations from the operating point. The optimal capacity and inventory decisions balance overages with underages. Continuing the classic newsvendor analogy, the optimal balancing conditions can be interpreted as specifying multiple "critical fractiles" of the multivariate demand distribution. This paper shows that the properties of optimal single-period newsvendor network solutions extend to a dynamic setting under plausible conditions. Indeed, we establishes dynamic optimality of inventory and capacity policies for the lost sales case. Depending on the non-basic activities, this also extends to the backordering case. Analytic and simulation-based solution techniques and graphical interpretations are presented and illustrated by a comprehensive example that features input substitution and a flexible processing resource.

Van Mieghem, Jan A. and Piet Van Mieghem. 2002. Price-coupled Scheduling for Differentiated Services: GC-mu vs. GPS. International Journal of Communication Systems. 15(5): 429-452.

Van Mieghem, Jan A.. 2000. Price and Service Discrimination in Queuing Systems: Incentive Compatibility of Gcmu Scheduling. Management Science. 46(9): 1249-1267.

Chopra, Sunil and Jan A. Van Mieghem. 2000. Which e-business is Right for Your Supply Chain?. Supply Chain Management Review.

Van Mieghem, Jan A.. 1999. Coordinating Investment, Production and Subcontracting. Management Science. 45(7): 954-971.

Harrison, J. Michael and Jan A. Van Mieghem. 1999. Multi-Resource Investment Strategies: Operational Hedging under Demand Uncertainty. European Journal of Operational Research. 113(1): 17-29.

Van Mieghem, Jan A. and Maqbool Dada. 1999. Price Versus Production Postponement: Capacity and Competition. Management Science. 45(12): 1631-1649.

Van Mieghem, Jan A.. 1998. Investment Strategies for Flexible Resources. Management Science. 44(8): 1071-1078.

Harrison, J. Michael and Jan A. Van Mieghem. 1997. Dynamic Control of Brownian Networks: State Space Collapse and Equivalent Workload Formulations. Annals of Applied Probability. 7(3): 747-771.

Eberly, Janice C and Jan A. Van Mieghem. 1997. Multifactor Dynamic Investment Under Uncertainty. Journal of Economic Theory. 75(8): 345-387.

Van Mieghem, Jan A.. 1995. Dynamic Scheduling with Convex Delay Costs: The Generalized c-mu Rule. Annals of Applied Probability. 5(3): 809-833.

Avi-Itzhak, Hadar, Roger Melen and Jan A. Van Mieghem. 1995. Straight Line Extraction Using Iterative Total Least Squares Methods. Journal of Visual Communication and Image Representation. 6(1): 59-68.

Avi-Itzhak, Hadar, Leo Rub and Jan A. Van Mieghem. 1995. Multiple Subclass Pattern Recognition: A Maximin Correlation Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 17(4): 418-431.

Schwerer, Elizabeth and Jan A. Van Mieghem. 1994. Brownian Models of Closed Queueing Networks: Explicit Solutions for Balanced Three-Station Systems. Annals of Applied Probability. 4(2): 448-477.

Working Papers

Huang, Tingliang and Jan A. Van Mieghem. 2009. Are You Willing to Click? Advance Demand Information and Price Discrimination under Strategic Consumer Behavior.

Bassamboo, Achal, Ramandeep S. Randhawa and Jan A. Van Mieghem. A Little Flexibility is All You Need: On the Asymptotic Value of Flexibility in Parallel Queuing Systems with Linear Capacity Sizing Costs.

Chod, Jiri, Nils Rudi and Jan A. Van Mieghem. 2009. Mix, Time, and Volume Flexibility: Valuation and Corporate Diversification.

Books

Van Mieghem, Jan A.. 2008. Operations Strategy: Principles and Practice. Belmont, MA: Dynamic Ideas.

Anupindi, Ravi, Sunil Chopra, Sudhakar D. Deshmukh, Jan A. Van Mieghem and Eitan Zemel. 2006. Managing Business Process Flows: Principles of Operations Management. Prentice Hall, 2nd edition.

Anupindi, Ravi, Sunil Chopra, Sudhakar D. Deshmukh, Jan A. Van Mieghem and Eitan Zemel. 1999. Managing Business Process Flows. Prentice Hall.

Cases

Allon, Gad, Jan A. Van Mieghem and Ilya Kolesov. 2010. HP Product Variety Management. Case 5-310-511 (KEL571).

Teaching Interests

Operations management; operations strategy; operations economics

Teaching Materials
Global Dual Sourcing Game

Full-Time / Part-Time MBA

Operations Management (OPNS-430-0)

This course counts toward the following majors:Operations.

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.

Operations Strategy (OPNS-454-0)

This course counts toward the following majors: Managerial Analytics, Operations.

In this course, students learn how operations strategy can add value by tailoring a set of core principles to a specific business setting. The course provides a framework to formulate an operations strategy and analyze, value, and optimize the key decisions involved in operations strategy. The key evaluation metric is how operations strategy impacts the net present value of the firm. The key decisions studied are choosing competitive operational competencies and benchmarking; capacity expansion, timing, flexibility and location; sourcing and contracting; risk management and operational hedging; revenue management; improvement and learning. This course builds on the core operations class. Students should also be familiar with the basics of finance, economics and strategy, as the strategic decisions studied in this course require a detailed analysis and understanding of the underlying operations. Thus this course has a greater amount of concreteness and detail than a competitive strategy class, and uses a combination of in-depth case analysis, mini-lectures, presentations and qualitative discussions of other examples. The course is intended for students interested in operations and supply chain management, general management, or management consulting.

Doctoral

Foundations of Operations Management (OPNS-521-0)

Executive MBA

Operations Management (OPNSX-430-0)
Operations Management examines the basic principles of managing the production and distribution of goods and services. The course approaches operations as a managerial integration function and provides frameworks and tools to target and implement improvements in business processes.

Strategic Decisions in Operations (OPNSX-455-0)
This course builds on the core operations management class with an emphasis on strategic level decisions. It emphasizes the long-term, "big" decisions firms face in structuring their operations. Topics covered range from evaluating flexible technologies to designing supply chains.

CONTACT INFO:
PHONE: 847-491-5481
FAX: 847-467-1220

EMAIL:
vanmieghem
@kellogg.northwestern.edu

OFFICE:
Jacobs Center Room 569

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