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Seminars
The following
speakers will visit Kellogg this Fall for the 2009-10 Kellogg
Operations Seminar Series. Please click on a date for more
information about a particular talk.
View
past seminars
"Operations Management and History"
Talk by Roger Schmenner, Indiana University
September 30th, 2009
12:00-1:00 PM, room 561
It is my contention that we students of operations management ignore what history can teach us about our discipline. Economists routinely study economic history (e.g., Ben Bernanke’s own work on the origins of the Great Depression, economic history courses), but we operations management types blithely neglect the lessons from our manufacturing, service, and supply chain past. This seminar will try to make sense of some key features of our history.
"A new risk-ratio procedure for estimating multinomial logit models with unobservable no-purchases"
Talk by Kalyan Talluri, UPF
October 1st, 2009
12:00-1:00 PM, room 561
Revenue management models in the literature, and in many implementations, make some
important assumptions such as Poisson arrivals, independence, and multinomial logit customer
purchase behavior. In this talk we describe:
1. A large scale empirical study spanning four RM industries (traditional airline, low-cost
airline, cargo and hotel) to test these assumptions (Poisson, Logit, Independence) on
transactional data. The data however does not contain information on customers who
did not purchase (no-purchases). A novel feature of the study is that we device the tests
assuming that the no-purchases are not observed.
2. We examine the standard nite-period, one-arrival-per-period dynamic program. We show
that this model is essentially unestimable as the number of periods T is a design param-
eter. Speci cally we show that the maximum likelihood estimates are always biased for
large enough T. We augment the study with simulation experiments comparing the ML
estimates vs. true parameters.
3. We propose an alternate dynamic program that operates with arbitrary variance (uncer-
tainty) in the forecasts and is still tractable (for a single resource).
4. One of the most challenging problems in RM is the estimation of customer behavior models
when one cannot observe no-purchases. We propose a new risk-ratio procedure that under
the assumption that the customers arrive over time deterministically, leads to an exact
unbiased estimator. We show conditions under which this estimator can be calculated by
solving a convex or quasi-convex program. We describe simulation experiments where the
method in many cases recovers the true parameters to the second decimal place, without
observing no-purchases.
"Innovation Tournaments"
Talk by Karl Ulrich, Wharton
October 6th, 2009
4:00-5:00 PM, Ford Motor Company Engineering Design Center (joint seminar with Segal Design Institute) [link to Segal site]*
Extremely valuable innovations are usually based on statistically exceptional opportunities. In most settings, organizations use tournaments to find these exceptional opportunities, by which I mean they generate many candidate opportunities and develop and filter them until only the very best remain. Although the basic idea of a tournament is common in industrial practice, very little science has been brought to bear on the problem of generating more, better opportunities and on more accurately evaluating and selecting the exceptional few. In this talk I lay out the beginnings of a science of innovation tournaments, illustrating how the somewhat random process of identifying and selecting opportunities can be managed more deliberately. I then link the concept of innovation tournaments to the popular notion of "design thinking," arguing that design thinking works well for some types of problems but not others.
This event is part of the Segal Seminar Series.
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Talk by Serhan Ziya, UNC
October 21st, 2009
12:00-1:00 PM, room 561
In many service systems, customers are not served in the order they arrive, but according to a priority scheme
that ranks them with respect to their relative “importance.” However, it may not be an easy task to determine
the importance level of customers, especially when decisions need to be made under limited information.
A typical example is from health care: When triage nurses classify patients into different priority groups, they
must promptly determine each patient’s criticality levels with only partial information on their conditions.
We consider such a service system where customers are from one of two possible types. The service time
and waiting cost for a customer depends on the customer’s type. Customers’ type identities are not directly
available to the service provider; however, each customer provides a signal, which is an imperfect indicator of
the customer’s identity. The service provider uses these signals to determine priority levels for the customers
with the objective of minimizing the long-run average waiting cost. In most of the paper, each customer’s signal
equals the probability that the customer belongs to the type that should have a higher priority and customers
incur waiting costs that are linear in time. We first show that increasing the number of priority classes decreases
costs, and the policy that gives the highest priority to the customer with the highest signal outperforms any
finite class priority policy . We then focus on two-class priority policies and investigate how the optimal policy
changes with the system load. We also investigate the properties of “good” signals and find that signals that
are larger in convex ordering are more preferable. In a simulation study, we find that when the waiting cost
functions are nondecreasing, quadratic, and convex, the policythat assigns the highest priorityto the customer
with the highest signal performs poorlywhile the two-class priority policy and an extension of the generalized
cµ rule perform well.
"Seasonal Storage Asset Valuation: Uncovering the Value of Limited Flexibility"
Talk by Owen Wu, University of Michigan
November 4th, 2009
12:00-1:00 PM, room 561
The value of a seasonal commodity storage asset depends not only on the seasonal price spread, but also on its operational flexibility: The maximum storing and delivering rates depend on the inventory level in the storage, and thus the firm has limited flexibility in choosing when and how much inventory to procure or sell. Using the heuristics in practice, the firm would pick the periods with the most favorable prices to procure and sell. We characterize the optimal strategy, analyze the underlying tradeoffs under limited flexibility, and decompose the value of flexibility. We show that, contrary to intuition and the heuristics, it may be sub-optimal to buy or sell when all future prices are expected to be worse than the current price, because delaying operations captures the value of flexibility in the future. On the other hand, over-delaying operations would reduce flexibility and forgo the value of counter-season operations, and striking a balance is thus necessary. Also contrary to intuition, we show that even if the storage can be filled up or emptied at better prices later in the season, it may be optimal to buy or sell some inventory at the current least favorable price, because this allows the firm to buy less at the adverse price in the future. We also show that more flexibility is not necessarily beneficial when heuristic policies are used.
"Quick Response and Retailer Effort"
Talk by Harish Krishnan, UBC
November 11th, 2009
12:00-1:00 PM, room 561
The benefits of supply chain innovations such as quick response (QR) have been extensively investigated. This paper highlights a potentially damaging impact of QR on retailer effort. By lowering downstream inventories, QR may compromise retailer incentives to exert sales effort on a manufacturer’s product and may lead instead to greater sales effort on a competing product. Manufacturer-initiated quick response can therefore backfire, leading to lower sales of the manufacturer’s product and, in some cases, to higher sales of a competing product. Evidence from case studies and interviews confirms that some manufacturers view high retailer inventory as a means of increasing retailer commitment (“a loaded customer is a loyal customer”). By implication, manufacturers should recognize the effect we highlight in this paper: the potential of QR to lessen retailer commitment. We show that relatively simple distribution contracts such as minimum-take contracts, advance-purchase discounts, and exclusive dealing, when adopted in conjunction with QR, can remedy the distortionary impact of QR on retailers’ incentives. In two recent antitrust cases we find evidence that, consistent with our theory, manufacturers adopted exclusive dealing at almost the same time that they were making QR-type supply chain improvements.
"Blind Fair Routing in Large-Scale Parallel-Server Systems "
Talk by Amy Ward, USC
November 18th, 2009
12:00-1:00 PM, room 561
In a call center, there is a natural trade-off between minimizing customer delay costs and fairly dividing the workload amongst agents of different skill levels. The relevant control is the routing and scheduling policy. The routing component specifies which agent should handle an arriving call when more than one agent is available, and the scheduling component decides which class a newly idle agent should serve when there are waiting customers in more than one class.
We formulate an optimization problem whose objective is to minimize the sum of class-dependent convex delay costs subject to a constraint that requires a “fair” division of the total idle time amongst the agents. We solve this optimization problem in the Halfin-Whitt many-server heavy-traffic limit regime. However, there is an important objection to the routing and scheduling policy that arises: its implementation requires extensive system parameter information. Therefore, we relax our original objective of finding a routing and scheduling policy that is optimal as the number of servers becomes large to finding a blind policy that is close to optimal. By blind, we mean that the implementation of the policy does not require system parameter information such as arrival and service rates.
* This is joint work with Mor Armony from NYU.
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Talk by Francis De-Vericourt, ESMT
December 2nd, 2009
12:00-1:00 PM, room 561
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Talk by Anita Tucker, HBS
December 9th, 2009
12:00-1:00 PM, room 561
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