Revenue management is a concept aimed to maximize capacity utilization and through that maximize revenues. It originated in the airline industry in the 70’s and due to its effectiveness  quickly spread to other sectors of the service industry. Today it is used in several industries like hotels, television and radio broadcasters, and energy transition companies to name a few. Since revenue management was developed in and for the service industry, most studies on revenue management are done on the service industry, creating a rather large research cap. Recently this concept has spread to the manufacturing industry as well. Despite this, there is very limited research done on revenue management in the manufacturing industry. Therefore, this paper’s aim is to partially filling this research gap by studying capacity management and pricing strategies (two mechanisms of revenue management), and how they have been shaped when implemented in a manufacturing company. This paper was done with a case study done on a multinational manufacturing company, who recently implemented revenue management. Interviews were conducted with people in key positions with good insight to the usage of revenue management in this company. Some of the most important result was that in this manufacturing company it is not possible to nest capacity on a customer segment level. However, in this company nesting was done on a market level instead. Also the pricing strategy differed between the service industry theory and this company. Instead of having a dynamic price that changed the total price up or down to change demand, this company had more of a fixed total price, and instead added more features to the product, decreasing the profit margin. The conclusion was drawn that the industry characteristics of the manufacturing industry have forced a rather large modification of revenue management. However, since this was a qualitative case study, no generalizing conclusions for the entire manufacturing industry can be drawn.

This thesis aims to improve sales forecasting in the context of passenger airlines. We study two important issues that could potentially improve forecasting accuracy: day-to-day price change rather than price itself, and linking flights that are likely to be considered as pairs for a round trip by passengers; we refer to the latter as the Round-Trip Model (RTM). We find that price change is a significant variable regardless of days remaining to flight in the last three weeks to flight departure, which opens the possibility of planning for revenue maximizing price change patterns. We also find that the RTM can improve the precision of the forecasting models, and provide an improved pricing strategy for planners. In the study of the effect of price change on sales, analysis of variance is applied; finite regression mixture models were tested to identify linked traffic in the two directions and the linked flights on a route in reverse directions; adaptive neuro-fuzzy inference system (ANFIS) is applied to develop comparative models for studying sales effect between price and price change, and one-way versus round-trip models. The price change model demonstrated more robust results with comparable estimation errors, and the concept model for the round-trip with only one linked flight reduced estimation error by 5%. This empirical study is performed on a database with 22,900 flights which was obtained from a major North American passenger airline.

Practical inventory settings often include multiple generations of the same product on hand. New products often arrive before old stock is exhausted, but most inventory models do not account for this. Such a setting gives rise to the possibility of inter-generational substitution between products. We study a retailer that stocks two product generations and we show that from a cost perspective the retailer is better off stocking only one generation. We proceed with a profit scheme and develop a price-setting profit maximization model, proving that in one and two generation profit models there exists a unique solution. We use the profit model to show that there are cases where it is more profitable to stock two generations. We discuss utility and preference extensions to the profit model and present the general n-product case.

The study aims at investigating how revenue management techniques can be applied in industries which offer multiple products. Most of the companies nowadays trend to produce multiperoducts and they try to find the best method of selling. Therefore, revenue management can be considered as a new direction which should be developed for these firms. In this study, multi-product firms are mainly referred as firms offering a bundle of products or substitute products. In this regard, models and techniques applied in multiproduct firms are discussed and it is tried to provide basic models to better understand the problems, variables, customer choice models and constraints. The main methodology in this study is literature review. In order to carry out the research first revenue management applications and techniques are discussed to find a fit to this kind of industries. The main findings of this study are (1) identifying and analyzing the most important factors affecting decision making regarding managing of bundling and substitute products and ultimately total revenue of multiproduct firms. (2) Summarizing the results and knowledge obtained from various studies within fields of bundling and substitute products. (3) Discussing the possibility of applying different revenue management techniques to these fields. (4) Identifying potentials and new directions for future study with respect to both revenue management techniques and multiproduct firms.

In this thesis, airline network revenue management problem is considered for the case with no cancellations and overbooking. In literature, there exist several approximate probabilistic and deterministic mathematical models developed in order to maximize expected revenue at the end of the reservation period. The aim of this study is to develop models considering also the risks involved in the proposed booking control policies. Two linear programming models are proposed which incorporate the variance of the revenue. The objective of the models is to effectively balance the tradeoff between the expectation and variance of the revenue. The performances of the proposed models are compared to the previous models through a numerical study. The seat allocations resulting from the mathematical models are used in a simulation model working with several booking control policies. The probability distributions of the revenues are investigated and the revenues are compared in terms of expectation, standard deviation, coefficient of variation and probability of poor performance. It is observed that the use of the proposed models decreases the variability of the revenue and thereby the risk of probability of poor performance. Also, the expected revenues obtained by implementing the solutions of the proposed models with nested booking control policies turn out to be higher than other probabilistic models as long as the degree of variance incorporation is within some interval. When compared with the deterministic models, the proposed models provides for the decision makers with alternative, preferable policies in terms of the expectation and the variability measures.

In this study, the seat inventory control problem is considered for airline networks from the perspective of a risk-averse decision maker. In the revenue management literature, it is generally assumed that the decision makers are risk-neutral. Therefore, the expected revenue is maximized without taking the variability or any other risk factor into account. On the other hand, risk-sensitive approach provides us with more information about the behavior of the revenue. The risk measure we consider in this study is the probability that revenue is less than a predetermined threshold level. In the risk-neutral cases, while the expected revenue is maximized, the probability of revenue being less than such a predetermined level might be high. We propose three mathematical models to incorporate the risk measure under consideration. The optimal allocations obtained by these models are numerically evaluated in simulation studies for example problems. Expected revenue, coefficient of variation, load factor and probability of the poor performance are the performance measures in the simulation studies. According to the results of these simulations, it shown that the proposed models can decrease the variability of the revenue considerably. In other words, the probability of revenue being less than the threshold level is decreased. Moreover, expected revenue can be increased in some scenarios by using the proposed models. The approach considered in this thesis is especially proposed for small scale airlines because risk of obtaining revenue less than the threshold level is more for this type of airlines as compared to large scale airlines.

The most important market segmentation in sports and entertainment industry is the competition between customers that buy bundle and single tickets. A common selling practice is starting the selling season with bundle ticket sales and switching to selling single tickets later on. The aim of this practice is to increase the number of customers that buy bundles, to create a fund before the season starts and to increase the load factor of the games with low demand. In this thesis, we investigate the effect of time dependent demand on dynamic switching times and the potential revenue gain over the case where the demand rate is assumed to be constant with time.

We build a variety of customer booking choice models for a major airline that operates in a very competitive origin-destination market. Some of the models are aimed at incorporating unobserved heterogeneous customer preferences for different departure times. The estimation results show that including these factors into choice models dramatically affects price sensitivity estimates, and therefore matters. We present a stochastic trust region algorithm for estimating ML-type models that involve high-dimensional integrals. The algorithm embeds two sampling processes: (i) a data sampling process and (ii) a Monte Carlo sampling process, and the algorithm dynamically controls sample sizes based on the magnitude of the errors incurred due to the two sampling processes. The first-order convergence is proved based on generalized uniform law of large numbers theories for both the average log-likelihood function and its gradient. The efficiency of the algorithm is tested with real data and compared with existing algorithms. We also study how a specific behavioral phenomenon, called the decoy effect, affects the decisions of sellers in product assortment competition in a duopoly. We propose a discrete choice model to capture decoy effects, and we provide a complete characterization of the Nash equilibria and their dependence on choice model parameters. For the cases in which there are multiple equilibria, we consider dynamical systems models of the sellers responding to their competitors using Cournot adjustment or fictitious play to study the evolution of the assortment competition and the stability of the equilibria. We provide a simple geometric characterization of the dynamics of fictitious play for 2×2 games that is more complete than previous characterizations.

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