Joint Mail-In Rebate Decisions in Supply Chains Under Demand Uncertainty

This is the author's accepted manuscript. The original publication is available at http://dx.doi.org/10.1111/j.1937-5956.2010.01171.x.We study the joint decisions of offering mail-in rebates (MIRs) in a single-manufacturer–single-retailer supply chain using a game theoretic framework. Either party can offer an MIR to the end consumer if it is in his best interest. The consumer demand is stochastic and depends on the product price and the amount of MIRs. When the retail price is exogenous, we show the existence of a unique Nash equilibrium under both additive and multiplicative demand functions and characterize it completely. We show that any of the following four scenarios can be the equilibrium: both parties offer MIR, only one party offers MIR, none offers MIR. When the retail price is a decision variable for the retailer and the rebate redemption rate increases with the amount of MIR, we once again prove the existence of a unique Nash equilibrium where both the retailer and the manufacturer offer MIRs. Using a numerical study, we show that the average post-purchase price of the product is higher not only than the perceived pre-purchase price but also than the newsvendor optimal price without an MIR. This implies that an MIR makes a product look cheaper while the consumers actually pay more on average

Managing On-air Ad Inventory in Broadcast Television

This is the author's accepted manuscript. The original publication is available at http://dx.doi.org/10.1080/07408170802323026.Motivated by the experiences of the National Broadcasting Company (NBC), we present an analytical model for managing on-air ad inventory in broadcast television. The ad inventory in this industry is priced based on rating points or the number of viewers that watch a commercial. The rating points during a broadcast year are sold through two distinct processes: the Upfront, which occurs before the broadcast season, and the Scatter, which occurs during the broadcast season. A firm needs to allocate its total rating points inventory to these two markets before knowing either the performance rating of its shows or the Scatter market price, both of which are random. The networks offer ratings (performance) guarantees on the inventory that is sold in the Upfront market while such guarantees are seldom offered in the Scatter market. We propose an optimization model for the networks to manage their rating points inventory. Our model explicitly incorporates the performance uncertainty of the television shows as well as the revenue uncertainty of the Scatter market. We derive conditions for feasibility of the problem and characterize the optimal amount of rating points to sell in the Upfront market. Our model explains the current practice of selling around 60-80% of the total rating points for the season during the Upfront market and analyzes other common strategies used by the firms. In addition to providing key managerial insights, our work introduces quantitative methodologies to television networks in planning their Upfront markets

Design of Extended Warranties in Supply Chains under Additive Demand

This is the author's accepted manuscript. The original publication is available at http://dx.doi.org/10.1111/j.1937-5956.2011.01300.x.We study the design of extended warranties in a supply chain consisting of a manufacturer and an independent retailer. The manufacturer produces a single product and sells it exclusively through the retailer. The extended warranty can be offered either by the manufacturer or by the retailer. The party offering the extended warranty decides on the terms of the policy in its best interest and incurs the repair costs of product failures. We use game theoretic models to answer the following questions. Which scenario leads to a higher supply-chain profit, the retailer offering the extended warranty or the manufacturer? How do the optimum price and extended warranty length vary under different scenarios? We find that, depending on the parameters, either party may provide better extended warranty policies and generate more system profit. We also compare these two decentralized models with a centralized system where a single party manufactures the product, sells it to the consumer and offers the extended warranty. We also consider an extension of our basic model where either the manufacturer or the retailer resells the extended warranty policies of a third party (an independent insurance company, for example), instead of offering its own policy

Beamforming based Mitigation of Hovering Inaccuracy in UAV-Aided RFET

Hovering inaccuracy of unmanned aerial vehicle (UAV) degrades the performance of UAV-aided radio frequency energy transfer (RFET). Such inaccuracy arises due to positioning error and rotational motion of UAV, which lead to localization mismatch (LM) and orientation mismatch (OM). In this paper, antenna array beam steering based UAV hovering inaccuracy mitigation strategy is presented. The antenna beam does not accurately point towards the field sensor node due to rotational motion of the UAV along with pitch, roll, and yaw, which leads to deviation in the elevation angle. An analytical framework is developed to model this deviation, and its variation is estimated using the data collected through an experimental setup. Closed-form expressions of received power at the field node are obtained for the four cases arising from LM and OM. An optimization problem to estimate the optimal system parameters (transmit power, UAV hovering altitude, and antenna steering parameter) is formulated. The problem is proven to be nonconvex. Therefore, an algorithm is proposed to solve this problem. Simulation results demonstrate that the proposed framework significantly mitigates the hovering inaccuracy; compared to reported state-of-the-art the same performance can be achieved with substantially less transmit power

Intra-firm coordination under asymmetric information: Modeling, analysis, and applications

This dissertation, motivated by the experiences of a US-based semiconductor manufacturer, presents an integrated model of incentive problems arising in forecasting and capacity allocation in a multi-plant, multi-product context. The firm studied in this dissertation allocates capacities to the competing product groups based on the demand forecasts from the product managers and the capacity forecast of the manufacturing managers. The process is characterized by incentive problems. When the capacity is scarce, a product manager inflates his forecast to gain a greater allocation of capacity. A manufacturing manager, on the other hand, deflates his forecast to cover for the uncertainties manufacturing. The aim of this thesis is to develop an incentive mechanism that induces honest forecasting by all managers. The first part of this dissertation is concerned with single statistics reporting, where each manager forecasts only the mean of his distribution. We propose a game theoretic framework to model the strategic behavior of the managers and design a mechanism (a bonus scheme and an allocation rule to allocate available capacity to the products) that elicits truthful information from all managers. We show that the structure of the incentive scheme is rather simple with easily calculable parameters. We also show that a large class of allocation rules are manipulable. A bonus is often required for elicitation of truthful information. The second part of the dissertation generalizes the results of Part I by assuming that the firm can ask the managers to report multiple statistics about their respective distributions (for example, mean and standard deviation). We seek to design a mechanism that elicits truthful forecasting. We have characterized the structure of the truthful bonus functions and allocation rules under multiple statistics reporting. We have compared and contrasted our multiple statistics results with those of single statistics. The third part of the dissertation discusses the implementation aspects of our model. We have shown that it is possible to implement our schemes within the existing structure of the firm

Intra-firm coordination under asymmetric information: Modeling, analysis, and applications

This dissertation, motivated by the experiences of a US-based semiconductor manufacturer, presents an integrated model of incentive problems arising in forecasting and capacity allocation in a multi-plant, multi-product context. The firm studied in this dissertation allocates capacities to the competing product groups based on the demand forecasts from the product managers and the capacity forecast of the manufacturing managers. The process is characterized by incentive problems. When the capacity is scarce, a product manager inflates his forecast to gain a greater allocation of capacity. A manufacturing manager, on the other hand, deflates his forecast to cover for the uncertainties manufacturing. The aim of this thesis is to develop an incentive mechanism that induces honest forecasting by all managers. The first part of this dissertation is concerned with single statistics reporting, where each manager forecasts only the mean of his distribution. We propose a game theoretic framework to model the strategic behavior of the managers and design a mechanism (a bonus scheme and an allocation rule to allocate available capacity to the products) that elicits truthful information from all managers. We show that the structure of the incentive scheme is rather simple with easily calculable parameters. We also show that a large class of allocation rules are manipulable. A bonus is often required for elicitation of truthful information. The second part of the dissertation generalizes the results of Part I by assuming that the firm can ask the managers to report multiple statistics about their respective distributions (for example, mean and standard deviation). We seek to design a mechanism that elicits truthful forecasting. We have characterized the structure of the truthful bonus functions and allocation rules under multiple statistics reporting. We have compared and contrasted our multiple statistics results with those of single statistics. The third part of the dissertation discusses the implementation aspects of our model. We have shown that it is possible to implement our schemes within the existing structure of the firm

Managing Television Commercial Inventory under Competition: An Equilibrium Analysis

This is the peer reviewed version of the following article: Geng, Q. and Mallik, S. (2019), Managing Television Commercial Inventory under Competition: An Equilibrium Analysis. Decision Sciences, 50: 170-201. https://doi.org/10.1111/deci.12317, which has been published in final form at https://doi.org/10.1111/deci.12317. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.We develop a game theoretic model for managing prime time on-air ad inventory in the television industry. The ad inventory in this industry is priced based on rating points or the number of viewers that watch a commercial. The rating points are sold through two distinct processes: the upfront, which occurs before the broadcast season, and the scatter, which occurs throughout during the broadcast season. Television networks need to allocate their total rating points inventory to these two markets before knowing either the performance rating of their shows or the scatter market price, both of which are ex ante uncertain. The television networks offer performance guarantees on the inventory that is sold in the upfront market while such guarantees are not offered in the scatter market. We consider the inventory competition between two television networks under such a setting. To the best of our knowledge, ours is the first article to consider competition in media revenue management. We establish the existence of unique Nash equilibrium under quantity competition and describe the sensitivity of the equilibrium outcome with respect to various problem parameters. We show that choosing quantity over price during the upfront is a dominant strategy for a television network. We compare our competitive model with a centralized system and discuss the managerial implications for our work