Unbundling of Ancillary Service: How Does Price Discrimination of Main Service Matter?

We consider a setting where the firm sells a main service (e.g., air travel) and an ancillary service (e.g., baggage delivery) to two types of consumers (e.g., business travelers and leisure travelers). We study how the firm’s ability to charge discriminatory main service prices affects its decision of whether to unbundle the ancillary service from the main service and charge separate prices. Unlike a firm using uniform pricing of main service that unbundles the ancillary service if the consumers that value the main service higher have a high likelihood of purchasing the ancillary service, a firm using discriminatory pricing of main service unbundles the ancillary service if the consumers that value the main service higher have a low likelihood of purchasing the ancillary service. Moreover, discriminatory pricing of main service makes unbundling more (less) likely to be the optimal ancillary service strategy when consumers’ main service valuations and ancillary service valuations are negatively (positively) correlated. Finally, we characterize how firms’ use of main service price discrimination and consumers’ valuation structure (i.e., whether the correlation between consumers’ main service valuations and ancillary service valuations is positive or negative) jointly determine the ancillary service strategies in an industry.http://deepblue.lib.umich.edu/bitstream/2027.42/117358/1/1301_Duenyas.pd

Pricing of Conditional Upgrades in the Presence of Strategic Consumers

In this paper, we study a conditional upgrade strategy that has recently become common in the travel industry. After a consumer makes a reservation for a product (e.g., a hotel room), she is asked whether she would like to upgrade to a higher-quality (more expensive) one at a discounted price. The upgrade, however, is not fulfilled immediately. The firm fulfills upgrades at check-in if higher-quality products are still available, and the upgrade fee is only charged to the consumer if she gets upgraded. Consumers decide which product type to book and whether to accept an upgrade offer based on the anticipated upgrade probability. We model the consumers' decisions using a Poisson-arrival game framework with incomplete information and prove the existence of Bayesian Nash equilibrium. To further study the firm's optimal upgrade pricing strategy,we also analyze a fluid model which is the asymptotic version of the stochastic model. Our numerical studies validate that our theoretical results derived from the fluid model carry through to the stochastic model. Our analysis identifies multiple benefits of conditional upgrades. First, the firm is able to capture more demand by offering conditional upgrades, Second, conditional upgrades enable the firm to improve its market segmentation by inducing more consumers to purchase higher-quality products. Third, conditional upgrades give the firm more flexibility in better matching fixed capacities to stochastic demands. For a firm that has the ability to optimize product prices, conditional upgrades can generate higher revenues than dynamic pricing.http://deepblue.lib.umich.edu/bitstream/2027.42/117357/1/1300_Duenyas.pd

Should competing firms reveal their capacity?

In this article, we explore when firms have an incentive to hide (or reveal) their capacity information. We consider two firms that aim to maximize profits over time and face limited capacity. One or both of the firms have private information on their own capacity levels, and they update their beliefs about their rival's capacity based on their observation of the other firm's output. We focus on credible revelation mechanisms—a firm may signal its capacity through overproduction, compared to its myopic production levels. We characterize conditions when high‐capacity firms may have the incentive and capability to signal their capacity levels by overproduction. We show that prior beliefs about capacity play a crucial, and surprisingly complex, role on whether the firm would prefer to reveal its capacity or not. A surprising result is that, despite the fact that it may be best for the high‐capacity firm to overproduce to reveal its capacity when capacity information is private, it may end up with more profits than if all capacity information were public knowledge in the first place. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96261/1/21521_ftp.pd

Optimal Learning Algorithms for Stochastic Inventory Systems with Random Capacities

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156225/2/poms13178_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156225/1/poms13178.pd

Priority Rules for Multi‐Task Due‐Date Scheduling under Varying Processing Costs

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135700/1/poms12606.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135700/2/poms12606_am.pd

Dynamic Customer Acquisition and Retention Management

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134194/1/poms12559.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134194/2/poms12559_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134194/3/poms12559-sup-0001-Appendix.pd

Base-stock control for single-product tandem make-to-stock systems

In this paper, we consider a multiple-stage tandem production/inventory system producing a single product. Processing time at each stage is assumed to have a general stationary processing time distribution. The cost of holding work-in-process (WIP) inventory is different at each stage. Therefore, decisions on when to release work to the system as well as when to transfer WIP from one stage to another need to be made. We formulate this problem of release/production control as a Markov decision process. However, the optimal policy is rather complex, making its implementation impracticable in practice. We therefore investigate the performance of simple base stock policies. Our approach aggregates several stages into one and uses a simple approximation to compute ‘approximately optimal’ base stock levels. We present the results of a simulation study that tests the performance of our approximation in estimating the best base stock levels, and the performance of base stock policies as compared with the optimal policy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45874/1/10756_2004_Article_173348.pd

Control of an Assembly System with Processing Time and Subassembly-Type Uncertainty

We address the problem of controlling an assembly system in which the processing times as well as the types of subassemblies are stochastic. The quality (or performance) of the final part depends on the characteristics of the subassemblies to be assembled, which are not constant. Furthermore, the processing time of a subassembly is random. We analyze the trade-off between the increase in the potential value of parts gained by delaying the assembly operation and the inventory costs caused by this delay. We also consider the effects of processing time uncertainty. Our problem is motivated by the assembly of passive and active plates in flat panel display manufacturing. We formulate the optimal control problem as a Markov decision process. However, the optimal policy is very complex, and we therefore develop simple heuristic policies. We report the results of a simulation study that tests the performance of our heuristics. The computational results indicate that the heuristics are effective for a wide variety of cases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45474/1/10696_2004_Article_238699.pd

Control of manufacturing networks which contain a batch processing machine

We consider the control of a batch processing machine which is part of a larger manufacturing network of machines. Systems consisting of a batch processing machine and one or more unit-capacity machines in tandem are considered. The objective is to minimize the average time that jobs spend in the entire system. We present algorithms to determine the optimal policies for certain finite horizon, deterministic problems. We then discuss the structure of the optimal policies for infinite horizon, stochastic problems, and investigate the benefit of utilizing information about upstream and downstream unit-capacity machines in the control of the batch machine. We develop a simple heuristic scheduling policy to control the batch machine which takes into account the state of other machines in the network. Computational results demonstrate the effectiveness of our heuristic over a wide range of problem instances.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45931/1/10756_2004_Article_274353.pd

Estimating the throughput of an exponential CONWIP assembly system

We consider a production system consisting of several fabrication lines feeding an assembly station where both fabrication and assembly lines consist of multiple machine exponential workstations and the CONWIP (CONstant Work-In-Process) mechanism is used to regulate work releases. We model this system as an assembly-like queue and develop approximations for the throughput and average number of jobs in queue. These approximations use an estimate of the time that jobs from each line spend waiting for jobs from other lines before being assembled. We use our approximations to gain insight into the related problems of capacity allocation, bottleneck placement and WIP setting.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47599/1/11134_2005_Article_BF01153531.pd