Download or read book Dynamic Pricing and Inventory Management in the Presence of Online Reviews written by Nan Yang and published by . This book was released on 2018 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the joint pricing and inventory management problem in the presence of online customer reviews. Customers who purchase the product may post reviews that would influence future customers' purchasing behaviors. Under the common practice of customer-generated reviews on e-commerce platforms, rigorous investigation of their operational implications offers valuable insights and guidance for both the research community and practitioners. We develop a stochastic joint pricing and inventory management model to characterize the optimal policy in the presence of online reviews. We show that a rating-dependent base-stock/list-price policy is optimal. Interestingly, the inventory dynamics of the firm do not influence the optimal policy as long as the initial inventory is below the initial base-stock level. Hence, we can reduce the dynamic program that characterizes the optimal policy to one with a single-dimensional state-space (the aggregate net rating). The presence of online reviews gives rise to the trade-off between generating current profits and inducing future demands, thus having several important implications upon the firm's operations decisions. First, online reviews drive the firm to deliver a better service and attract more customers to post a review. Hence, the safety-stock and base-stock levels are higher in the presence of online reviews. Second, the evolution of the aggregate net rating process follows a mean-reverting pattern: When the current rating is low (resp. high), it has an increasing (resp. decreasing) trend in expectation. Third, although myopic profit optimization leads to significant optimality losses in the presence of online reviews, balancing the current profits and near-future demands suffices to exploit the network effect induced by online reviews. We propose a dynamic look-ahead heuristic policy that well leverages this idea and achieves small optimality gaps which decay exponentially in the length of the look-ahead time-window.

Download or read book Optimal Inventory Control in the Presence of Dynamic Pricing and Dynamic Advertising written by and published by . This book was released on 2015 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information written by Boxiao Chen and published by . This book was released on 2020 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the periodic review dynamic pricing and inventory control problem with fixed ordering cost. Demand is random and price dependent, and unsatisfied demand is backlogged. With complete demand information, the celebrated (s,S,p) policy is proved to be optimal, where s and S are the reorder point and order-up-to level for ordering strategy, and p, a function of on-hand inventory level, characterizes the pricing strategy. In this paper, we consider incomplete demand information and develop online learning algorithms whose average profit approaches that of the optimal (s,S,p) with a tight O ̃(√T) regret rate. A number of salient features differentiate our work from the existing online learning researches in the OM literature. First, computing the optimal (s,S,p) policy requires solving a dynamic programming (DP) over multiple periods involving unknown quantities, which is different from the majority of learning problems in operations management that only require solving single-period optimization questions. It is hence challenging to establish stability results through DP recursions, which we accomplish by proving uniform convergence of the profit-to-go function. The necessity of analyzing action-dependent state transition over multiple periods resembles the reinforcement learning question, considerably more difficult than existing bandit learning algorithms. Second, the pricing function p is of infinite dimension, and approaching it is much more challenging than approaching a finite number of parameters as seen in existing researches. The demand-price relationship is estimated based on upper confidence bound, but the confidence interval cannot be explicitly calculated due to the complexity of the DP recursion. Finally, due to the multi-period nature of (s,S,p) policies the actual distribution of the randomness in demand plays an important role in determining the optimal pricing strategy p, which is unknown to the learner a priori. In this paper, the demand randomness is approximated by an empirical distribution constructed using dependent samples, and a novel Wasserstein metric based argument is employed to prove convergence of the empirical distribution.

Download or read book Dynamic Pricing in the Presence of Strategic Consumers written by Mirko Kremer and published by . This book was released on 2015 with total page 39 pages. Available in PDF, EPUB and Kindle. Book excerpt: We investigate the impact of strategic consumer behavior on retailers' dynamic pricing decisions. We present a stylized two-period model, and test the equilibrium predictions in a set of behavioral experiments in which human subjects played the role of pricing managers. Our main insight is that relative to equilibrium predictions, subjects underprice in the main selling season. Consequently, they sell more inventory and obtain higher revenue in that season. However, by doing so they significantly limit their ability to generate revenue in the markdown season, which, in the presence of strategic consumers is a major source of revenue.

Download or read book Data Driven Dynamic Pricing and Inventory Management of an Omni Channel Retailer in an Uncertain Demand Environment written by Shiyu Liu and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, omni-channel retailing has become immensely popular among both retailers and consumers. In this approach, retailers often leverage their brick-and-mortar stores to fulfill online orders, leading to the need for simultaneous decision-making on replenishment and inventory rationing. This inventory strategy presents significant complexities in traditional dynamic pricing and inventory management problems, particularly in unpredictable market environments. Therefore, we have developed a dynamic pricing, replenishment, and rationing model for omni-channel retailers using a two-level partially observed Markov decision process to visualize the dynamic process. We design a deep reinforcement learning algorithm, called Maskable LSTM-Proximal Policy Optimization (ML-PPO), which integrates the current observations and future predictions as input to the agent and uses the invalid action mask to guarantee the allowable actions. Our simulation experiments have demonstrated the ML-PPO's efficiency in maximizing retailer profit and service level, along with its generalized ability to tackle dynamic pricing and inventory management problems.

Download or read book Dynamic Pricing Under Demand Uncertainty in the Presence of Strategic Consumers written by Yinhan Meng and published by . This book was released on 2011 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the effect of strategic consumer behavior on pricing, inventory decisions, and inventory release policies of a monopoly retailer selling a single product over two periods facing uncertain demand. We consider the following three-stage two-period dynamic pricing game. In the first stage the retailer sets his inventory level and inventory release policy; in the second stage the retailer faces uncertain demand that consists of both myopic and strategic consumers. The former type of consumers purchase the good if their valuations exceed the posted price, while the latter type of consumers consider future realizations of prices, and hence their future surplus, before deciding when to purchase the good; in the third stage, the retailer releases its remaining inventory according to the release policy chosen in the first stage. Game theory is employed to model strategic decisions in this setting. Each of the strategies available to the players in this setting (the consumers and the retailer) are solved backward to yield the subgame perfect Nash equilibrium, which allows us to derive the equilibrium pricing policies. This work provides three primary contributions to the fields of dynamic pricing and revenue management. First, if, in the third stage, inventory is released to clear the market, then the presence of strategic consumers may be beneficial for the retailer. Second, we find the optimal inventory release strategy when retailers have capacity limitation. Lastly, we numerically demonstrate the retailer's optimal decisions of both inventory level and the inventory release strategy. We find that market clearance mechanism and intermediate supply strategy may emerge as the retailers optimal choice.

Download or read book Dynamic Pricing Strategies in the Presence of Demand Shifts written by Omar Besbes and published by . This book was released on 2016 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many factors introduce the prospect of changes in the demand environment that a firm faces, with the specifics of such changes not necessarily known in advance. If and when realized, such changes affect the delicate balance between demand and supply and thus current prices should account for these future possibilities. We study the dynamic pricing problem of a retailer facing the prospect of a change in the demand function during a finite selling season with no inventory replenishment opportunity. In particular, the time of the change and the postchange demand function are unknown upfront, and we focus on the fundamental trade-off between collecting revenues from current demand and doing so for postchange demand, with the capacity constraint introducing the main tension. We develop a formulation that allows for isolating the role of dynamic pricing in balancing inventory consumption throughout the horizon. We establish that, in many settings, optimal pricing policies follow a monotone path up to the change in demand. We show how one may compare upfront the attractiveness of pre- and postchange demand conditions and how such a comparison depends on the problem primitives. We further analyze the impact of the model inputs on the optimal policy and its structure, ranging from the impact of model parameter changes to the impact of different representations of uncertainty about future demand.

Download or read book Combined Dynamic Pricing and Inventory Control written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing and Inventory Control with Learning written by Nicholas C. Petruzzi and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing With Infrequent Inventory Replenishments written by Boxiao Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a joint pricing and inventory control problem where pricing can be adjusted more frequently, such as every period, than inventory ordering decisions, which are made every epoch that consists of multiple periods. This is motivated by many examples, especially for online retailers, where price is indeed much easier to change than inventory level, because changing the latter is subject to logistic and capacity constraints. In this setting, the retailer determines the inventory level at the beginning of each epoch and solves a dynamic pricing problem within each epoch with no further replenishment opportunities. The optimal pricing and inventory control policy is characterized by an intricate dynamic programming (DP) solution. We consider the situation where the demand-price function and the distribution of random demand noise are both unknown to the retailer, and the retailer needs to develop an online learning algorithm to learn those information and at the same time maximize total profit. We propose a learning algorithm based on least squares estimation and construction of an empirical noise distribution under a UCB framework and prove that the algorithm converges through the DP recursions to approach the optimal pricing and inventory control policy under complete demand information. The theoretical lower bound for convergence rate of a learning algorithm is proved based on the multivariate Van Trees inequality coupled with some structural DP analyses, and we show that the upper bound of our algorithm's convergence rate matches the theoretical lower bound.

Download or read book Dynamic Pricing and Inventory Control with Learning written by Nicholas C. Petruzzi and published by . This book was released on 1996 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing and Inventory Management Under Fluctuating Procurement Costs written by Guang Xiao and published by . This book was released on 2015 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a periodic review joint pricing and inventory control model in which a firm faces both stochastic demand and fluctuating procurement costs. To address procurement cost fluctuation, the firm adopts a dual-sourcing strategy, under which it procures from a spot market with immediate delivery and through a forward-buying contract with postponed delivery. Our analysis offers the unique insight that a risk-neutral firm may earn higher expected profit under a more volatile procurement cost process. This is because the firm makes its pricing and sourcing decisions in response to the realized cost in each period. Moreover, we characterize how the firm should dynamically adjust its pricing and sourcing decisions in accordance to cost evolution. For example, if sourcing through the forward-buying contract is less expensive than sourcing directly from the spot market, the optimal safety stock is decreasing in the current spot market purchasing cost. However, the optimal order quantity through the forward-buying contract is, in general, not monotone in the current spot-purchasing cost. Finally, we conduct extensive numerical experiments to show that dynamic pricing and dual-sourcing may be either strategic complements or substitutes in the presence of fluctuating procurement costs and uncertain demand. This is because dynamic pricing mitigates demand uncertainty risk and exploits procurement cost fluctuation, while dual-sourcing may either intensify or dampen demand risk.

Download or read book Dynamic Pricing and Inventory Management written by Renyu Philip Zhang and published by . This book was released on 2016 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: We develop the models and methods to study the impact of some emerging trends in technology, marketplace, and society upon the pricing and inventory policy of a firm. We focus on the situation where the firm is in a dynamic, uncertain, and (possibly) competitive market environment. The market trends of particular interest to us are: (a) social networks, (b) sustainability concerns, and (c) customer behaviors. The two main running questions this dissertation aims to address are: (a) How these emerging market trends would influence the operations decisions and profitability of a firm; and (b) What pricing and inventory strategies a firm could use to leverage these trends. We also develop an effective comparative statics analysis method to address these two questions under different market trends. Overall, our results suggest that the current market trends of social networks, sustainability concerns, and customer behaviors have significant and interesting impact upon the operations policy of a firm, and that the firm could adopt some innovative pricing and inventory strategies to exploit these trends and substantially improve its profit. Our main findings are: (a) Network externalities (the monopoly setting). We find that network externalities prompt a firm to face the tradeoff between generating current profits and inducing future demands when making the price and inventory decisions, so that it should increase the base-stock level, and to decrease [increase] the sales price when the network size is small [large]. Our extensive numerical experiments also demonstrate the effectiveness of the heuristic policies that leverage network externalities by balancing generating current profits and inducing demands in the near future. (Chapter 2.) (b) Network externalities (the dynamic competition setting). In a competitive market with network externalities, the competing firms face the tradeoff between generating current profits and winning future market shares (i.e., the exploitation-induction tradeoff). We characterize the pure strategy Markov perfect equilibrium in both the simultaneous competition and the promotion-first competition. We show that, to balance the exploitation-induction tradeoff, the competing firms should increase promotional efforts, offer price discounts, and improve service levels. The exploitation-induction tradeoff could be a new driving force for the fat-cat effect (i.e., the equilibrium promotional efforts are higher under the promotion-first competition than those under the simultaneous competition). (Chapter 3.) (d) Trade-in remanufacturing. We show that, with the adoption of the very commonly used trade-in remanufacturing program, the firm may enjoy a higher profit with strategic customers than with myopic customers. Moreover, trade-in remanufacturing creates a tension between firm profitability and environmental sustainability with strategic customers, but benefits both the firm and the environment with myopic customers. We also find that, with either strategic or myopic customers, the socially optimal outcome can be achieved by using a simple linear subsidy and tax scheme. The commonly used government policy to subsidize for remanufacturing alone, however, does not induce the social optimum in general. (Chapter 4.) (d) Scarcity effect of inventory. We show that the scarcity effect drives both optimal prices and order-up-to levels down, whereas increased operational flexibilities (e.g., the inventory disposal and inventory withholding opportunities) mitigate the demand loss caused by high excess inventory and increase the optimal order-up-to levels and sales prices. Our extensive numerical studies also demonstrate that dynamic pricing leads to a much more significant profit improvement with the scarcity effect of inventory than without. (Chapter 5.) (e) Comparative statics analysis method. We develop a comparative statics method to study a general joint pricing and inventory management model with multiple demand segments, multiple suppliers, and stochastically evolving market conditions. Our new method makes componentwise comparisons between the focal decision variables under different parameter values, so it is capable of performing comparative statics analysis in a model where part of the decision variables are non-monotone, and it is well scalable. Hence, our new method is promising for comparative statics analysis in other operations management models. (Chapter 6.)

Download or read book Optimal Policies for Dynamic Pricing and Inventory Control with Nonparametric Censored Demands written by Boxiao Chen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the fundamental model in joint pricing and inventory replenishment control under the learning-while-doing framework, with T consecutive review periods and the firm not knowing the demand curve a priori. At the beginning of each period, the retailer makes both a price decision and an inventory order-up-to level decision, and collects revenues from consumers' realized demands while suffering costs from either holding unsold inventory items, or lost sales from unsatisfied customer demands. We make the following contributions to this fundamental problem as follows:1. We propose a novel inversion method based on empirical measures to consistently estimate the difference of the instantaneous reward functions at two prices, directly tackling the fundamental challenge brought by censored demands, without raising the order-up-to levels to unnaturally high levels to collect more demand information. Based on this technical innovation, we design bisection and trisection search methods that attain an O(T^{1/2}) regret, assuming the reward function is concave and only twice continuously differentiable.2. In the more general case of non-concave reward functions, we design an active tournament elimination method that attains O(T^{3/5}) regret, based also on the technical innovation of consistent estimates of reward differences at two prices.3. We complement the O(T^{3/5}) regret upper bound with a matching Omega(T^{3/5}) regret lower bound. The lower bound is established by a novel information-theoretical argument based on generalized squared Hellinger distance, which is significantly different from conventional arguments that are based on Kullback-Leibler divergence. This lower bound shows that no learning-while-doing algorithm could achieve O(T^{1/2}) regret without assuming the reward function is concave, even if the sales revenue as a function of demand rate or price is concave.Both the upper bound technique based on the "difference estimator" and the lower bound technique based on generalized Hellinger distance are new in the literature, and can be potentially applied to solve other inventory or censored demand type problems that involve learning.

Download or read book Dynamic Pricing and Learning Under The Effect of Inventory Scarcity written by Mengyan Zhu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Revealing inventory scarcity messages to customers to trigger scarcity effect is an important and widely adopted way to promote sales in online platforms. Under such circumstances, the demand is affected by both price and scarcity messages. In this article, we study the joint dynamic pricing and learning problem under the inventory scarcity effect. Specifically, we consider three popular scarcity messages: partially revealed, fully revealed, and mixedly revealed inventory information, and we design passive learning algorithms with/without forced learning steps to learn unknown parameters in the demand function with a planning horizon consisting of many independent selling seasons. The main challenge is that there is always a strictly positive probability of no learning in one selling season, since the change of inventory status is not fully under control. To balance the learning speed and regret in this setting, we introduce the idea of endogenous and forced learning cycles, and design indices to determine when to conduct forced learning steps. Furthermore, to increase the success learning probability, we design learning steps by grouping two selling seasons together based on the MDP structure for the optimal pricing policy, which is quite different from the scenario without inventory scarcity effect. As a result, our methods have $O( log^2 T)$ regret bounds in all cases. Moreover, numerical experiments show that ignoring the scarcity effect will cause significant revenue loss. We also provide insights on when the seller should choose pure passive learning method or passive learning methods with forced learning steps. Our work sheds light on the practice of online retailing in the presence of inventory scarcity effect.

Download or read book Dynamic Pricing with External Information and Inventory Constraint written by Xiaocheng Li and published by . This book was released on 2019 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: A merchant sells a product over a selling season of T time periods in presence of a limited inventory. The merchant observes new external information at the beginning of each time period and then sets a price for that time period. Initially, the merchant does not know the distribution of the external information and the demand function, i.e., how the external information and price jointly impact the demand distribution in a single time period. The seller's decision, setting price dynamically, serves dual roles to learn the unknown demand function and to balance inventory, with an ultimate goal to maximize the expected revenue over the selling season. We characterize and prove a full spectrum of relations between the optimal revenue achieved in three decision-making regimes: the merchant's online decision-making regime, a clairvoyant regime with complete knowledge about the demand function and all the external information in advance, and a deterministic regime in which the demand function and all the uncertainties are revealed at the beginning. In the analyses, we derive an unconstrained representation of the optimality gap for generic constrained online learning problems, which renders tractable lower and upper bounds for the expected revenue achieved by dynamic pricing algorithms between different regimes. This analytical framework also inspires the design of two dual-based history-dependent dynamic pricing algorithms for the clairvoyant regime and the online regime. Numerical experiments are conducted to demonstrate the performances of our algorithms.

Download or read book Dynamic Revenue and Inventory Management Models written by Yifeng Liu and published by . This book was released on 2014 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Effective pricing and inventory controls are very important for the success of a company, especially in an environment with many uncertainties such as random demand and fluctuating cost. In this work, we first consider pure dynamic pricing. Indeed, we consider three cases: markup in which price can only go up, markdown in which price can only go down, and reversible pricing in which price can go either direction. We also consider a joint pricing and inventory control model in which the raw material price evolves as a Markov process. For this model, we suppose production is make-to-order, so that the conversion from raw material to finished product is carried out only when demand arrives. For the pure pricing model, we establish the optimality of threshold-like policies. We also develop efficient and numerically stable algorithms. For the make-to-order joint inventory-pricing model, we demonstrate the optimality of a base-stock-list-price policy. In addition, we identify conditions under which policy parameters would exhibit monotone trends. Moreover, we showed the significant benefit of adopting cost-dependent base-stock list-price policy.