Dr. Van Mieghem received three degrees: ir. in Electrical Engineering from KU Leuven, M.S. in Electrical Engineering and Ph.D. in Operations, Information and Technology Management from Stanford University. From 2009-2010, he served as one of two Senior Associate Deans at the Kellogg School. From 2006 – 2009, he was chairman of the Department of Managerial Economics and Decision Sciences. From 2011-2017, he served as the Academic Director of the Kellogg Executive MBA program. He is the director of the PhD program in Operations Management and of three non-degree executive programs: Lean Operations, Operations Strategy and Operations Management: Digital Strategy.
This book 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.
For graduate level courses in Operations Management or Business Processes. This book provides a structured, data-driven approach to understanding core operations management concepts.
| Degree Courses | Executive Education Programs | Online Courses |
|---|---|---|
| Operations Management (MBA) | Lean Operations: Managing Risk and Uncertainty | Operations Management: Digital Strategy |
| Strategic Decisions in Operations (MBA) | Operations Strategy: Designing operations to maximize value | Operations Strategy (Henry Stuart) |
| Foundations of Operations (PhD) | Scaling Operations (Coursera) |
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Collaboration is important in services, but may lead to interruptions. Professionals exercise discretion on when to preempt individual tasks to switch to collaborative tasks. Discretionary task switching can introduce changeover times when resuming the preempted task and thus can increase total processing time.
We analyze and quantify how collaboration, through interruptions and discretionary changeovers, affects total processing time.
We introduce an {\em episodal workflow model} that captures the interruption and discretionary changeovers dynamics---each switch and the episode of work it preempts---present in settings where collaboration and multitasking is paramount. A simulation study provides evidence that changeover times are properly identified and estimated without bias.
We then deploy the model in a field study of hospital medicine physicians---``hospitalists.''
The hospitalist workflow includes visiting patients, consulting with other caregivers to guide patient diagnosis and treatment, and documenting in the patient's medical chart.
The empirical analysis uses a dataset assembled from direct observation of hospitalist activity and pager-log data. We estimate that a hospitalist incurs a total changeover time during documentation of 5min per patient per day. This represents a significant $20\%$ of total processing time per patient: caring for 14 patients per day, a hospitalist spends more than one hour each day on changeovers. This provides evidence that task switching causally leads to longer documentation time.
View paper.
Motivated by the trend towards more collaboration in team work, we study networks where some tasks require the simultaneous processing by multiple types of multitasking human or indivisible resources. The capacity of such networks is generally smaller than the bottleneck capacity. In Gurvich and Van Mieghem (2015) we proved that both capacities are equal in networks with a hierarchical collaboration architecture, which define a {\em collaboration level} for each task depending on how many types of resources it requires relative to other network tasks. This paper studies how task prioritization impacts the capacity of such hierarchical networks using a conceptual queuing framework that formalizes coordination and switching idleness.
To maximize the capacity of a team, highest priority should be given to the tasks that require the most collaboration. Otherwise, a mismatch between priority levels and collaboration levels inevitably inflicts a capacity loss. We demonstrate this essential trade-off between task prioritization and capacity in a basic collaborative network and in parallel networks.
To manage this trade-off, we present a hierarchical threshold priority policy that balances switching and coordination idleness. View paper.
This paper studies how service providers can design social interaction among participants and quantify the causal impact of that interaction on service quality. We focus on education and analyze whether encouraging social interaction among students improves learning outcomes in Massive Open Online Courses (MOOCs), which are a new service delivery channel with universal access at reduced, if not zero, cost. We analyze three randomized experiments in a MOOC with more than 30,317 students from 183 countries. Two experiments study large-group interaction by encouraging a random subset of students to visit the course discussion board. The majority of students treated in these experiments had higher social engagement, higher quiz completion rates, and higher course grades. Using these treatments as instrumental variables, we estimate that one additional board visit causally increases the probability that a student finishes the quiz in the subsequent week by up to 4.3%. The third experiment studies small-group interaction by encouraging a random subset of students to conduct one-on-one synchronous discussions. Students who followed through and actually conducted pairwise discussions increased their quiz completion rates and quiz scores by 10% in the subsequent week. Combining results from these three experiments, we provide recommendations for designing social interaction mechanisms to improve service quality. View paper.
The Hospital Readmissions Reduction Program (HRRP), a part of the US Patient Protection and Affordable Care Act, requires the Centers for Medicare and Medicaid Services (CMS) to penalize hospitals with excess readmissions. We take an economic and operational (patient flow) perspective to analyze the effectiveness of this policy in encouraging hospitals to reduce readmissions. We develop a game-theoretic model that captures the competition among hospitals inherent in HRRP's benchmarking mechanism. We show that this competition can be counter-productive: it increases the number of non-incentivized hospitals, which prefer paying penalties over reducing readmissions in any equilibrium. We calibrate our model with a dataset of more than 3,000 hospitals in the United States and show that under the current policy, and for a large set of parameters, 4% to 13% of the hospitals remain non-incentivized to reduce readmissions. We also validate our model against the actual performance of hospitals in the three years since the introduction of the policy. We draw several policy recommendations to improve this policy's outcome. For example, localizing the benchmarking process -- comparing hospitals against similar peers -- improves the performance of the policy. View paper.
We provide an empirical and theoretical assessment of the value of information sharing in a two-stage supply chain. The value of downstream sales information to the upstream firm stems from improving upstream order fulfillment forecast accuracy. Such an improvement can lead to lower safety stock and better service. Based on the data collected from a CPG company, we empirically show that, if the company includes the downstream sales data to forecast orders, the improvement in the mean squared forecast error ranges from 7.1% to 81.1% across all studied products. Theoretical models in the literature, however, suggest that the value of information sharing should be zero for over half of our studied products. To reconcile the gap between the literature and the empirical observations, we develop a new theoretical model. While the literature assumes that the decision maker strictly adheres to a given inventory policy, our model allows him to deviate, accounting for private information held by the decision maker, yet unobservable to the econometrician. This turns out to reconcile our empirical findings with the literature. These "decision deviations" lead to information losses in the order, process, resulting in a strictly positive value of downstream information sharing. Furthermore, we empirically quantify and show the significance of the value of the downstream replenishment policy. View paper.
After decades of offshoring production across the world, companies are rethinking their global networks. Local sourcing is receiving more attention, but it remains challenging to balance the offshore sourcing cost advantage against the increased inventories due to its longer leadtime, and against the cost and (volume-)flexibility of each source's capacity. To guide strategic allocation in this global network decision, this paper establishes reasonably simple prescriptions that capture the key drivers. We adopt a conventional discrete-time inventory model with a linear control rule that smoothes orders and allows an exact and analytically-tractable analysis of single and dual sourcing policies under normal demand. Distinguishing features of our model are that it captures each source's leadtime, capacity cost and flexibility to work overtime. We use Lagrange's inversion theorem to provide exact and simple square-root bound formulae for the strategic sourcing allocations and the value of dual sourcing. The formulae provide structural insight on the impact of financial, operational and demand parameters, and a starting point for quantitative decision making. We investigate the robustness of our results by comparing the smoothing policy with existing single and dual sourcing models in a simulation study that relaxes model assumptions. View paper.
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 and also share some resources with other activities. We introduce the notions of collaboration architecture and unavoidable bottleneck idleness to study the maximal throughput or capacity of such networks. Collaboration and resource sharing introduce synchronization requirements that may inflict unavoidable idleness of the bottleneck resources: even when the network is continuously busy (processing at capacity), bottleneck resources can never be fully utilized. The conventional approach that equates network capacity with bottleneck capacity is then incorrect because the network capacity is below that of the bottlenecks.
Our positive result is that networks with nested collaboration architectures have no unavoidable bottleneck idleness. Then, regardless of the processing times of the various activities, the standard bottleneck procedure correctly identifies the network capacity. We also prove necessity in the sense that, for any non-nested architecture, there are values of processing times for which unavoidable idleness persists.
The fundamental tradeoff between collaboration and capacity does not disappear in multi-server networks and has important ramifications to service-system staffing. Yet, even in multi-server networks, a nested collaboration architecture still guarantees that the bottleneck capacity is achievable. Finally, simultaneous collaboration, as a process constraint, may limit the benefits of flexibility. We study the interplay of flexibility and unavoidable idleness and offer remedies derived from collaboration architectures.
View paper.
We consider firms that feature their products on the Internet but take orders offline. Click and order data are disjoint on such non-transactional websites and their matching is error-prone. Yet, their time separation may allow the firm to react and improve its tactical planning. We introduce a dynamic decision support model that augments the classic inventory planning model with additional clickstream state variables. Using a novel data set of matched online clickstream and offline purchasing data, we identify statistically significant clickstream variables and empirically investigate the value of clickstream tracking on non-transactional websites to improve inventory management. We show that the noisy clickstream data is statistically significant to predict the propensity, amount, and timing of offline orders. A counterfactual analysis shows that using the demand information extracted from the clickstream data can reduce the inventory holding and backordering cost by 3% to 5% in our data set. View paper.
Mail: Managerial Economics, Decision Sciences and Operations Department
Kellogg School of Management
Northwestern University, 2211 Campus Drive Rm 4151,
Evanston, Illinois 60208, USA.
Electronic: vanmieghem ...at... northwestern.edu
Genealogy: : Family trees of Van Mieghem 's in the world