Browsing by Subject "Revenue Management"

Now showing 1 - 5 of 5

Open Access
Bundle pricing of inventories with stochastic demand
(2004) Bulut, Zümbül
In this study, we consider the single period pricing of two perishable products which are sold individually and as a bundle. Demands come from a Poisson Process with a price-dependent rate. Assuming that the customers’ reservation prices follow normal distributions, we determine the optimal product prices that maximize the expected revenue. The performances of three bundling strategies (mixed bundling, pure bundling and unbundling) under different conditions such as different reservation price distributions, different demand arrival rates and different starting inventory levels are compared. Our numerical analysis indicate that, when individual product prices are fixed to high values, the expected revenue is a decreasing function of the correlation coefficient, while for low product prices the expected revenue is an increasing function of the correlation coefficient. We observe that, bundling is least effective in case of limited supply. In addition, our numerical studies show that the mixed bundling strategy outperforms the other two, especially when the customer reservation prices are negatively correlated.
Open Access
The effect of demand uncertainty on the decisions and revenues in the two-class revenue management model
(Bilgesel Yayincilik San. ve Tic. Ltd., 2015) Kocabiyikoglu, A.
We explore the impact of changes in market conditions on optimal allocation decisions and revenues, within the standard two-class revenue management framework, using stochastic dominance relations. We show that an increase in market size leads to higher revenues, and the number of units allocated to the high-end class increases in its market size. The direction of the change in optimal allocation and revenues in response to changes in the variability of the high-end market depends on the relationship between the high and lowend prices. Our structural and numerical results suggest higher variability in the market is generally detrimental to revenues.
Open Access
On pricing of perishable assets with menu costs
(Elsevier, 2009-10) Berk, E.; Gürler, Ü.; Yıldırım, G.
We consider dynamic pricing of perishable assets in the presence of price-sensitive renewal demand processes. Unlike the existing works in the literature, we explicitly incorporate non-negligible price change costs which reflects the revenue management practice more realistically. These costs are also known as menu costs in the economic literature. The objective is to maximize the discounted expected profit for an initial inventory of Q items by determining the selling prices dynamically. We employ a dynamic programming approach and formulate a model that captures the price– demand relationship. We establish some theoretical results on the properties of the problem at hand. Specifically, we establish the sufficient conditions under which the within-period profit is concave in the selling price and in the remaining shelf life and, furthermore, show the structure of the myopically and asymptotically optimal pricing policy. In a numerical study, we investigate the impact of various system parameters and, in particular, the existence of menu costs, on pricing decisions. We observe that ignoring menu costs may be significantly misleading for the implementation of revenue management. We also propose four implementable policy heuristics and examine their performances. Our findings support some results previously obtained in settings with continuous pricing and negligible price change costs; and, contradict some others.
Open Access
Optimal bundle formation and pricing of two products with limited stock
(2005) Öztop, Salih
In this study, we consider the stochastic modeling of a retail firm that sells two types of perishable products in a single period not only as independent items but also as a bundle. Our emphasis is on understanding the bundling practices on the inventory and pricing decisions of the firm. One of the issues we address is to decide on the number of bundles to be formed from the initial product inventory levels and the price of the bundle to maximize the expected profit. Product demands follow a Poisson Process with a price dependent rate. Customer reservation prices are assumed to have a joint distribution. We study the impact of reservation price distributions, initial inventory levels, product prices, demand arrival rates and cost of bundling. We observe that the expected profit decreases as the correlation coefficient increases. With negative correlation, bundling cost has a significant impact on the number of bundles formed. When the product prices are low, the retailer sells individual products as well as the bundle (mixed bundling), when they are high, the retailer sells only bundles (pure bundling). The expected profit and the number of bundles offered decrease as the variance of the reservation price distribution increases. For high starting inventory levels, the retailer reduces bundle price and offers more bundles. The number of bundle sales decreases and the number of individual product sales increases when the arrival rate increases since the need for bundling decreases. Impacts of substitutability and complementarity of products are also investigated. The retailer forms more bundles, or charges higher prices for the bundle or both as the products become more complementary and less substitutable.
Open Access
Style goods pricing with demand learning
(Elsevier, 2009-08-01) Şen, A.; Zhang, A. X.
For many industries (e.g., apparel retailing) managing demand through price adjustments is often the only tool left to companies once the replenishment decisions are made. A significant amount of uncertainty about the magnitude and price sensitivity of demand can be resolved using the early sales information. In this study, a Bayesian model is developed to summarize sales information and pricing history in an efficient way. This model is incorporated into a periodic pricing model to optimize revenues for a given stock of items over a finite horizon. A computational study is carried out in order to find out the circumstances under which learning is most beneficial. The model is extended to allow for replenishments within the season, in order to understand global sourcing decisions made by apparel retailers. Some of the findings are empirically validated using data from U.S. apparel industry.