Optimal Monopolist Pricing Under Demand Uncertainty in Dynamic Markets

Raman, Kalyan; Chatterjee, Rabikar

doi:10.1287/mnsc.41.1.144

Cited by 85 publications

(60 citation statements)

References 33 publications

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“…Demand uncertainty is caused by several factors such as preference dynamics [29], demand model misspecification [30][31], choice context [32][33], response variability [34][35], and sampling errors associated with the estimation procedure [36]. As a result, several researchers have considered the impact of demand uncertainty on optimal pricing strategies [29][30][37][38].…”

Section: Literature Review: Product Design and Pricing Under Demand Umentioning

confidence: 99%

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

Resende

Heckmann

Michalek

2011

Volume 5: 37th Design Automation Conference, Parts a and B

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In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1-α) chance of exceeding, given the distribution of possible outcomes. The parameter α [0,1] INTRODUCTIONOver the last three decades, a significant portion of the new product development (NPD) literature has been dedicated to the integration of engineering design and marketing processes for differentiated markets. Simple models to determine the most profitable characteristics of a single new product [1-2] have progressed to account for issues such as product-line design and preference heterogeneity [3][4][5][6][7], competitor reactions [8-10], cost structure [11][12], distribution channels [9,13-16], choice-set-dependent preferences [17], and coordination with constrained engineering design decisions [18][19][20][21][22][23][24][25][26].As Hsu and Wilcox [27] argue, the trend towards estimating marketing models at lower levels of aggregation that are more structural 1 in consumer behavior representation has led to models with many parameters and consequently greater uncertainty of those parameters. However, despite the advances in NPD methods, the research has not given much consideration to the intrinsic parameter uncertainty of the demand models. Demand uncertainty directly affects the risk of introducing a new product into the market, and firms evaluate potential projects not only in terms of expected return, but also in terms of risk.The purpose of this work is threefold. First, we define a robust a-profit metric and propose a general framework to incorporate demand uncertainty arising from choice model parameter estimation into the design decision process such that it accounts for varying levels of loss tolerance. Second, we apply the delta method to approximate the a-profit function in closed form for multinomial logit (MNL) demand models to be used efficiently in numerical optimization routines. Finally, we show how ignoring demand uncertainty can lead to suboptimal decisions for risk averse firms.We do not intend to consider all the various sources of demand model uncertainty [28], and several questions will remain open. In particular, we assume the discrete choice model is correctly specified and ignore uncertainty due to model misspecification, and we assume that the model parameters do not change over time or from the context in which the data were collected to the context in which predictions will be made. Nevertheless, the proposed methodology can be useful, and it serves as a first step in addressing design for profit maximization...

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Section: Literature Review: Product Design and Pricing Under Demand Umentioning

confidence: 99%

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

Resende

Heckmann

Michalek

2011

Volume 5: 37th Design Automation Conference, Parts a and B

View full text Add to dashboard Cite

show abstract

“…More importantly, the pricing research in marketing has typically ignored the supply chain implications -for example, Raman & Chatterjee (1995) consider the impact of demand uncertainty on the optimal price path without incorporating the corresponding upstream supply chain implications.…”

Section: Literature Reviewmentioning

confidence: 99%

Static and Dynamic Pricing of Excess Capacity in a Make-to-Order Environment

Hall¹,

Kopalle

Pyke

2002

SSRN Journal

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Recent years have seen advances in research and management practice in the area of pricing, and particularly in dynamic pricing and revenue management. At the same time, researchers and managers have made dramatic improvements in production and supply chain management. The interactions between pricing and production/supply chain performance, however, are not as well understood. Can a firm benefit from knowing the status of the supply chain or production facility when making pricing decisions? How much can be gained if pricing decisions explicitly and optimally account for this status? This paper addresses these questions by examining a make-to-order manufacturer that serves two customer classes -core customers who pay a fixed negotiated price and are guaranteed job acceptance, and "fill-in" customers who make job submittal decisions based on the instantaneous price set by the firm for such orders.We examine four pricing policies that span a range of complexity and required knowledge about the status of the production system at the manufacturer, including the optimal policy of setting a different price for each possible state of the queue. We demonstrate properties of the optimal policy, and we illustrate numerically the financial gains a firm can achieve by following this policy vs. simpler pricing policies. The four policies we consider are (1) state-independent (static) pricing, (2) allowing fill-in orders only when the system is idle, (3) setting a uniform price up to a cut-off state, and (4) general state-dependent pricing. Although general statedependent pricing is optimal in this setting, we find that charging a uniform price up to a cut-off state performs quite well in many settings and presents an attractive trade-off between ease of implementation and profitability. Thus, a fairly simple heuristic policy may actually out-perform the optimal policy when costs of design and implementation are taken into account.2

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“…Additionally, MARI relates to work in operations research done in the domain of dynamic pricing of inventories [19,20,21]. Specifically, we address the issue of how sellers should dynamically shift their valuations when demand is price sensitive and stochastic, and the seller's objective is to maximize expected revenues.…”

Section: Related Workmentioning

confidence: 99%

“…We are keen to explore what algorithms and technologies our system can leverage in the process of information integration and representation, decision analysis, modeling and reasoning about utilities, heuristical learning and inference from user-interaction, facilitating inter-agent communication and negotiation, and attuning pre-existing knowledge bases in developing and managing shared product ontologies. Further, in the near future, we would like to explore the usage of standardized agent communication languages, we would like to see how machine learning techniques can be used in assisting decision support and negotiation, and will also facilitate dynamic alteration of bids and asks based upon stochastic demand and supply patterns over finite horizons [19,20,21].…”

Section: Future Workmentioning

confidence: 99%

Design and implementation of an agent-based intermediary infrastructure for electronic markets

Tewari

Maes

2000

Proceedings of the 2nd ACM Conference on Electronic Commerce

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This paper describes MARI (Multi-Attribute Resource Intermediary), a project which proposes to improve online marketplaces, specifically those that involve the buying and selling of non-tangible goods and services. MARI is an intermediary architecture intended as a generalized platform for the specification and brokering of heterogeneous goods and services. MARI makes it possible for both buyers and sellers alike to more holistically and comprehensively specify relative preferences for the transaction partner, as well as for the attributes of the product in question, making price just one of a multitude of possible factors influencing the decision to trade. Ultimately, we expect that the ability to make such specifications will result in a more efficient, richer, and integrative transaction experience.

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Optimal Monopolist Pricing Under Demand Uncertainty in Dynamic Markets

Cited by 85 publications

References 33 publications

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

Robust Design for Profit Maximization Under Uncertainty of Consumer Choice Model Parameters Using the Delta Method

Static and Dynamic Pricing of Excess Capacity in a Make-to-Order Environment

Design and implementation of an agent-based intermediary infrastructure for electronic markets

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