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Collaboration & Teamwork: How multitasking during teamwork can reduce capacity below the bottleneck.

Biography

Kellogg School of Management
Harold L. Stuart Professor of Managerial Economics, Professor of Operations

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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. He received his Ph.D. in Business and MSEE from Stanford University, and holds an electrical engineering degree from the Katholieke Universiteit Leuven, Belgium. He is a Distinguished Fellow of the Manufacturing and Service Operations Management Society and member of the Royal Flemish Academy of Sciences and Arts of Belgium.

His research focuses on product, service and supply chain operations, and links strategy and 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 40 academic articles published in the leading international journals, and of two books: 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. He teaches courses in operations management and operations strategy in MBA, Ph.D. and executive programs and advises firms on those topics.

From 2009-2010, he served as one of the two Senior Associate Deans at the Kellogg School. From 2006 – 2009, he served as the chairman of the Department of Managerial Economics and Decision Sciences. From 2012-2017, Jan served as the Academic Director of the Kellogg Executive MBA program and of two non-degree executive programs: The Science of Lean Operations and Operations Strategy

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Areas of Expertise Capacity Management
Dynamic Stochastic Control
Operations Strategy
Supply Chain Design and Management
Tactical Operations

Education PhD, 1995, Business, 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 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

Senior Associate Dean, Curriculum and Teaching, Kellogg School of Management, Northwestern University, 2009-2010

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

Other Professional Experience , Moen (on global strategic sourcing), 2012-2012

, McKinsey & Company (on operations management and strategy), 2003-present

, Career Builder (on lean human resource management)

Honors and Awards Member, Royal Flemish Academy of Sciences and Arts of Belgium, For life

2014 Wickham Skinner Award for Best Paper Published in POM, Production and Operations Management, 2014

1st place (paper: Hospital Readmissions Reduction Program: A Financial and Operational Analysis), 2014 POMS College of Healthcare Operations Management Best Paper Award

NU Excellence in Research, Northwestern University

Sidney J. Levy Teaching Award for teaching excellence in elective classes, Kellog School of Management

Distinguished Fellow, Manufacturing and Service Operations Management Society, 2012

1st MSOM Best Paper Award, MSOM, 2007

Editorial Positions Committee Chair for Best Paper Award, Manufacturing & Service Operations Management (M&SOM), 2014-2015

Associate Editor, Manufacturing and Service Operations, 2012-present

Area Editor forManufacturing, Service and Supply Chain Operations, Operations Research, 2002-2006

Education Academic Positions Other Professional Experience Honors and Awards Editorial Positions

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Cases

Gurvich, Itai and Jan A. Van Mieghem. 2015. Collaboration and multitasking in networks: Architectures, Bottlenecks and Throughput. Manufacturing & Service Operations Management (M&SOM). 17(1): 16-33.

Motivated by the trend towards more collaboration in work flows, we study stochastic processing networks where some activities require the simultaneous collaboration of multiple human resources. Collaboration introduces resource synchronization requirements that are not captured in the standard procedure (formalized through a static planning problem) to identify bottlenecks and theoretical capacity. We introduce the notions of collaboration architecture and unavoidable idleness. In general, collaboration architectures may feature unavoidable idleness so that the theoretical capacity exceeds the maximal achievable throughput or actual capacity. This fundamental tradeoff between collaboration and throughput does not disappear in multi-server networks and has important ramifications to service-system staffing. We identify a special class of collaboration architectures that have no unavoidable idleness and present a condition on this architecture that guarantees, regardless of the processing times of the various activities, that the standard bottleneck procedure in fact identifies the actual capacity of the network. In multi-server cases this class of networks guarantees that the theoretical capacity is achievable provided one has the right number of floaters. Finally, we study the subtleties that collaboration introduces to questions of flexibility investment. Unavoidable idleness may limit the ability to materialize the benefits of flexibility. We study the interplay of flexibility and unavoidable idleness and offer remedies derived from collaboration architecture.

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

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.

Brownian networks are a class of linear stochastic control systems that arise as heavy traffic approximations in queueing theory. Such Brownian system models have been used to approximate problems of dynamic routing, dynamic sequencing and dynamic input control for queueing networks. A number of specific examples have been analyzed in recent years, and in each case the Brownian network has been successfully reduced to an "equivalent workload formulation" of lower dimension. In this article we explain that reduction in general terms, using an orthogonal decomposition that distinguishes between reversible and irreversible controls.

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

We characterize a firm's optimal factor adjustment when any number of factors faced "kinked" linear adjustment costs so that all factor accumulation is costly to reverse. We first consider a general non-stationary case with a concave operating profit function, unrestricted form of uncertainty and a horizon of arbitrary length. We show that the optimal investment strategy follows a control limit policy at each point in time. The state space of the firm's problem is partitioned into various domains, including a continuation region where no adjustment shoudl optimally be made to factor levels. We then consider two specific model classes and exploit their special structure to derive expressions for their continuation regions.

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.

This paper addresses a correlation based nearest neighbor pattern recognition problem where each class is given as a collection of subclass templates. The recognition is performed in two stages. In the first stage the class is determined. Templates for this stage are created using the subclass templates. Assignment into subclasses occurs in the second stage. This two stage approach may be used to accelerate template matching. In particular, the second stage may be omitted when only the class needs to be determined. The authors present a method for optimal aggregation of subclass templates into class templates. For each class, the new template is optimal in that it maximizes the worst case (i.e. minimum) correlation with its subclass templates. An algorithm which solves this maximin optimization problem is presented and its correctness is proved. In addition, test results are provided, indicating that the algorithm's execution time is polynomial in the number of subclass templates. The authors show tight bounds on the maximin correlation. The bounds are functions only of the number of original subclass templates and the minimum element in their correlation matrix. The algorithm is demonstrated on a multifont optical character recognition problem

Van Mieghem, Jan A. and Gad Allon. 2014. Operations Strategy: Principles and Practice. Belmont, MA: Dynamic Ideas, 2nd.

Operations Strategy: Practices and Principles provides a unified framework for operations strategy. The book shows how to tailor the operational system to maximize value and competitive advantage. Conceptual thinking and financial optimization yield guidelines for implementation. This dual emphasis on principles and practice is reflected by analytical models that are illustrated with detailed examples and a dozen case studies of real business situations.

Allon, Gad and Jan A. Van Mieghem. 2015. FleeComm mini case.

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

HP sells configure-to-order products. With millions of part combinations going into an order, the challenge is deciding which parts to keep in the portfolio to balance costs with revenues. The case explains how one would approach this problem before product introduction, but focuses on managing the existing portfolio.

Van Mieghem, Jan A. and Gad Allon. 2011. The Mexico-China Dual Sourcing Strategy Simulation. Case 1-212-500.

This team-based simulation teaches students how to make strategic and operational decisions about sourcing. Students play the role of sourcing managers who must make strategic allocation decisions as they place day-to-day orders with two suppliers—one that is responsive but expensive (Mexico) and another that is cheaper but more remote (China). Each team must develop a sourcing strategy that will satisfy a random level of demand that is revealed over time. In each period teams place orders with both suppliers while managing inventory and attempting to maximize their bank account. Students experience the operational, financial, and service-related consequences of their sourcing decisions, and instructors have access to the strategies used by different teams along with financial and operational metrics to use as part of the debrief.

Allon, Gad and Jan A. Van Mieghem. 2010. Lean Transformation at Global Connect. Case 5-310-504 (KEL633).

Global Connect, a major telecommunications service provider, partners with national cable providers to bundle media and telecom services offered through voice over Internet protocol (VoIP). Global Connect provides the VoIP physical infrastructure that enables cable providers to offer VoIP phone service to their end customers. VoIP cable services are growing at a faster rate than anticipated, leaving Global Connect incapable of meeting contractual agreements with the cable partners and preventing them from capturing substantial VoIP market opportunities.
Students are asked to improve the configuration of work at this service organization by identifying the types of waste in the current process. Process improvements use lean tools and their impact is quantified using time and capacity analysis. 

Gurvich, Itai and Jan A. Van Mieghem. 2015. Collaboration and multitasking in networks: Architectures, Bottlenecks and Throughput. Manufacturing & Service Operations Management (M&SOM). 17(1): 16-33.

Motivated by the trend towards more collaboration in work flows, we study stochastic processing networks where some activities require the simultaneous collaboration of multiple human resources. Collaboration introduces resource synchronization requirements that are not captured in the standard procedure (formalized through a static planning problem) to identify bottlenecks and theoretical capacity. We introduce the notions of collaboration architecture and unavoidable idleness. In general, collaboration architectures may feature unavoidable idleness so that the theoretical capacity exceeds the maximal achievable throughput or actual capacity. This fundamental tradeoff between collaboration and throughput does not disappear in multi-server networks and has important ramifications to service-system staffing. We identify a special class of collaboration architectures that have no unavoidable idleness and present a condition on this architecture that guarantees, regardless of the processing times of the various activities, that the standard bottleneck procedure in fact identifies the actual capacity of the network. In multi-server cases this class of networks guarantees that the theoretical capacity is achievable provided one has the right number of floaters. Finally, we study the subtleties that collaboration introduces to questions of flexibility investment. Unavoidable idleness may limit the ability to materialize the benefits of flexibility. We study the interplay of flexibility and unavoidable idleness and offer remedies derived from collaboration architecture.

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

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.

Brownian networks are a class of linear stochastic control systems that arise as heavy traffic approximations in queueing theory. Such Brownian system models have been used to approximate problems of dynamic routing, dynamic sequencing and dynamic input control for queueing networks. A number of specific examples have been analyzed in recent years, and in each case the Brownian network has been successfully reduced to an "equivalent workload formulation" of lower dimension. In this article we explain that reduction in general terms, using an orthogonal decomposition that distinguishes between reversible and irreversible controls.

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

We characterize a firm's optimal factor adjustment when any number of factors faced "kinked" linear adjustment costs so that all factor accumulation is costly to reverse. We first consider a general non-stationary case with a concave operating profit function, unrestricted form of uncertainty and a horizon of arbitrary length. We show that the optimal investment strategy follows a control limit policy at each point in time. The state space of the firm's problem is partitioned into various domains, including a continuation region where no adjustment shoudl optimally be made to factor levels. We then consider two specific model classes and exploit their special structure to derive expressions for their continuation regions.

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.

This paper addresses a correlation based nearest neighbor pattern recognition problem where each class is given as a collection of subclass templates. The recognition is performed in two stages. In the first stage the class is determined. Templates for this stage are created using the subclass templates. Assignment into subclasses occurs in the second stage. This two stage approach may be used to accelerate template matching. In particular, the second stage may be omitted when only the class needs to be determined. The authors present a method for optimal aggregation of subclass templates into class templates. For each class, the new template is optimal in that it maximizes the worst case (i.e. minimum) correlation with its subclass templates. An algorithm which solves this maximin optimization problem is presented and its correctness is proved. In addition, test results are provided, indicating that the algorithm's execution time is polynomial in the number of subclass templates. The authors show tight bounds on the maximin correlation. The bounds are functions only of the number of original subclass templates and the minimum element in their correlation matrix. The algorithm is demonstrated on a multifont optical character recognition problem

Van Mieghem, Jan A. and Gad Allon. 2014. Operations Strategy: Principles and Practice. Belmont, MA: Dynamic Ideas, 2nd.

Operations Strategy: Practices and Principles provides a unified framework for operations strategy. The book shows how to tailor the operational system to maximize value and competitive advantage. Conceptual thinking and financial optimization yield guidelines for implementation. This dual emphasis on principles and practice is reflected by analytical models that are illustrated with detailed examples and a dozen case studies of real business situations.

Allon, Gad and Jan A. Van Mieghem. 2015. FleeComm mini case.

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

HP sells configure-to-order products. With millions of part combinations going into an order, the challenge is deciding which parts to keep in the portfolio to balance costs with revenues. The case explains how one would approach this problem before product introduction, but focuses on managing the existing portfolio.

Van Mieghem, Jan A. and Gad Allon. 2011. The Mexico-China Dual Sourcing Strategy Simulation. Case 1-212-500.

This team-based simulation teaches students how to make strategic and operational decisions about sourcing. Students play the role of sourcing managers who must make strategic allocation decisions as they place day-to-day orders with two suppliers—one that is responsive but expensive (Mexico) and another that is cheaper but more remote (China). Each team must develop a sourcing strategy that will satisfy a random level of demand that is revealed over time. In each period teams place orders with both suppliers while managing inventory and attempting to maximize their bank account. Students experience the operational, financial, and service-related consequences of their sourcing decisions, and instructors have access to the strategies used by different teams along with financial and operational metrics to use as part of the debrief.

Allon, Gad and Jan A. Van Mieghem. 2010. Lean Transformation at Global Connect. Case 5-310-504 (KEL633).

Global Connect, a major telecommunications service provider, partners with national cable providers to bundle media and telecom services offered through voice over Internet protocol (VoIP). Global Connect provides the VoIP physical infrastructure that enables cable providers to offer VoIP phone service to their end customers. VoIP cable services are growing at a faster rate than anticipated, leaving Global Connect incapable of meeting contractual agreements with the cable partners and preventing them from capturing substantial VoIP market opportunities.
Students are asked to improve the configuration of work at this service organization by identifying the types of waste in the current process. Process improvements use lean tools and their impact is quantified using time and capacity analysis. 

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