Essays on revenue management

Abstract

Revenue Management (RM) is the process of understanding, anticipating and reacting to consumer behavior in order to maximize expected future revenue from a perishable resource. The challenge here is to sell the right product or service to the right customer at the right time for the right price. RM is of high relevance in cases where products or services are perishable, capacity is limited (cannot be changed on short notice), demand is uncertain, and the market is segmentable, in the sense that different customers are willing to pay different prices for using the same amount of resources. Revenue management has significantly altered the business process of airline industry since its inception in the mid-eighties. Applications of revenue management however are not restricted to airline industry only. Over the last one and half decade, various RM techniques have been applied to several other sectors like hotel, cargo, retail, car-rental, broadcasting, petroleum and natural gas and entertainment. Besides these, there are many other industries with similar characteristics that can benefit from RM techniques. Motivated by the success of revenue management in traditional industries, we focus on research problems applied to three different industries - (i) Airlines, (ii) Online Advertising and (iii) Restaurants. (i) Airlines Accurate demand forecasting is at the heart of revenue management system. Poor estimates of demand may lead to inadequate inventory control and suboptimal revenue performance. The available booking data used for forecasting may sometimes be constrained, as airlines stop observing demands when the entire capacity is sold out. It is preferable to unconstraint the censored observations so that they represent true demands. Literature addresses the demand unconstraining methods within the maximum likelihood framework for fare-classes. This dissertation extends the demand unconstraining methods to dependent demand classes. Our simulation study attempts to combine the effects of two hitherto separately studied techniques-expectation maximization (EM) algorithm and expected marginal seat revenue (EMSR) heuristics, to compare four different methodologies based on their revenue performances. Our results show that the opportunity cost of neglecting demand censorship is upto 1% whereas that for neglecting the dependency of demands can be of the order of 2%. Consideration of both truncated demand and dependency between fare classes can lead to a significant (of the order of 2.5%) revenue increase. (ii) Online Advertising Internet is currently the fasted growing advertising medium. Online advertising emphasizes on marketing that appeals to a specific behavior or interest, instead of broadly reaching out to a defined demography. Easy and cheap measurability of all its statistics makes the internet a potential medium for forecasting based allocation. The focus of this research is on banner advertisement and we deal only with the ‘home page’. We develop a multi-period space allocation model to find a trade-off between price – per – view (PPV) and price-per-click (PPC) pricing schemes, taking into account various characteristics of the internet like non-uniform demands by advertisers and unpredictable web traffic. We compare our revenue management model with the first-come-first-serve policy and fixed capacity bucket policy used in practice under which fixed advertising space is allocated to either of the advertising categories (PPV or PPC). Over a wide range of distributions, an RM approach yields about 16-20% more revenue when compared to the fixed capacity bucket. (iii) Restaurants Restaurants Revenue Management (RRM) is defined as ‘selling the right seat to the right customer at the right price and for the right duration’. Table-mix problem and dynamic pricing are the two important aspects of RRM. While table-mix problem attempts to determine the option number of tables of different sizes, dynamic pricing is used to shift demand to off-peak periods (e.g., discount coupons, happy-hours, early-bird dinner, etc). In this dissertation, we focus on the right mix of the tables that a restaurant should have. The basic issue in RRM is how floor-managers deal with the parties and vacant tables of same or larger size. In the sense, if there are tables of a certain size available and a party of lower sizes arrives, does the floor manager seat the party on this larger table or wait for a larger party to arrive? Thus, restaurants often come across a typical acceptance-rejection situation as in standard RM practices We develop an integer programming models to find the optimal table-mix and to address acceptance0rejection issues in a highly crowded restaurant in Ahmedabad. We analyze the implications of various manually easy- to- implement operational policies like 1 – up (practices can be seated on the tables of the same or next-higher size) and higher levels of nesting also, the high percentage of optimal revenue (using RM models) achieved by some of these policies are associated with the increase in waiting time. These analyses are aimed at enabling the floor managers to decide when and where to seat an incoming party, if at all