The Effectiveness and Benefits of Implementing Dynamic Revenue Management throughout an Airline - Coursework Example

The Effectiveness and Benefits of Implementing Dynamic Revenue Management Throughout an Airline Alliance by Mohammed Z. Abudohir A Graduate Capstone Project Submitted to the College of Aviation, Department of Graduate Studies,in Partial Fulfillment of the Requirements for the Degree of Master of Science in Aeronautics Embry-Riddle Aeronautical University Daytona Beach, Florida Month 2013 The Effectiveness and Benefits of Implementing Dynamic Revenue Management Throughout an Airline Alliance by Mohammed Z. Abudohir This Graduate Capstone Projectwas prepared under the direction of the candidate’s Graduate Capstone Project Chair,Dr. Xxxxxx X. Xxxxxxx, Professor, Daytona Beach Campus,andhas beenapproved. It was submitted to the Department of Graduate Studies in partial fulfillment of the requirements for the degree of Master of Science in Aeronautics Graduate Capstone Project: ___________________________________________ Xxxxxx X. Xxxxxxx, Xx.X. Graduate Capstone Project Chair ___________________________________________ Xxxxxx X. Xxxxxxx, Xx.X. Graduate Capstone Project Committee Member ___________________________________________ John M. Lanicci, Ph.D. Program Coordinator Master of Science in Aeronautics ________________ Date Acknowledgements [The purpose of the Acknowledgements is to thank the people that were especially helpful in completing the project. This may include supervisors (committee chair/committee members), organizations that were involved and/or funded the student’s work, and sometimes friends and family.] Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx. [150 to 350 words] Abstract Scholar: Mohammed Z. Abudohir Title: The Effectiveness and Benefits of Implementing Dynamic Revenue Management Throughout an Airline Alliance Institution: Embry-Riddle Aeronautical University Degree: Master of Science in Aeronautics Year: 2013 Most airline firms are resorting to formation of airline alliances so as to streamline their operations. This means that the airline alliances put in place measures aimed at optimizing the sale of passenger seats, accurate forecasting of customer demand, and customer behavior. This paper examines the parameters linked to dynamic revenue management in which pricing and seat allocation (capacity management or inventory management of seats) is done alongside considerations for time or seasons in pricing decisions. Effectiveness and benefits of the dynamic revenue management form the core of this paper, though with few comparisons to the static revenue management model (Belobaba&d’Huart, 2011). The assumption is that the airlines within the alliances have complete perfect information concerning the industry and costs transferred from one airline to another in an alliance are noted in profit computation or sharing. Keywords: Dynamic, Revenue Management, Seat Allocation, Pricing, Static, Capacity Management. Table of Contents Page Graduate Capstone Project Committee ii Acknowledgements iii Abstract iv List of Tables vii List of Figures viii Chapter I Introduction 1 Project Definition 2 ProjectGoals and Scope 4 Definitions of Terms 7 List of Acronyms 8 II Review of the Relevant Literature # Background # Revenue Management # Airline Alliance and Revenue Management # Alliance Factor on Reservations and Revenue Management # Revenue Management Behavior …………………………. . # Effectiveness of Dynamic Revenue Management ….. # Summary # III Project Planning # Project Statement of Work # Project Steps and Schedule # Project Resources # IV Project Outcomes # V Project Conclusions # Project Presentation # Lessons Learned # References # Appendices[if only one, just state as “Appendix” and no letter designator is needed] A Tables # B Figures # List of Tables Page Table 1 Sample Table Title [Use single spacing if title exceedsone line.] # 2 Exact Table 2 Title # List of Figures Page Figure 1 Sample Figure Caption[Use single spacing if caption exceeds one line.] # 2 Exact Figure 2 Caption. # Chapter I Introduction Initial global airline alliances were formed barely than 2 decades ago and airlines have realized the power, global visibility, and market cover of synchronizing schedules, pricing, ticketing, among other activities. Huge participants in the airline industry such as British Airways, United Airlines, and AirFrance view strategic airline alliances as core factor for their growth and expansion. Airlines forming alliances are capable of augmenting their technical economic efficiency as well as increase their joint market power and attain competitive advantage (Smith & Barnhart, 2011). Based on these introductory remarks on airline alliances, it becomes mandatory for firms to have well designed revenue management (RM) schemes that will facilitate profit maximization by the airlines. Airline firms operate jointly and this is accompanied with conflicts on revenue sharing thus, the need for effective RM approaches to be implemented. This paper documents information on the effectiveness and benefits of implementing dynamic RM throughout an airline alliance. In airline alliances, most or all of the interline itineraries are sold together so as to extend the reach of each of the airlines alliance members’ network. If an appropriate RM model is not implemented, suboptimal profit or revenue results of the alliance could be attained (Brumelle&Walczak, 2003). Dynamic RM approaches adjusts its parameters as the number of available seats in the network changes and time passes. It is an effective model (though with some flaws), and this paper documents the significance that airline alliances can derive from using this dynamic RM approach in their operations. Dynamic pricing and seat allocation in an airline alliance seems to be imperative in ensuring that optimality in revenue generation is enhanced alongside sustained airline alliance operations between the members. Statement of the Problem Airlines have the tradition of selling identical seats at different prices so as to increase the revenue, a practice commonly referred to as revenue management (RM). This practice entails contemporary management concepts and schemes including overbooking, dynamic seat inventory control, dynamic pricing, and group management among other issues. RM is the process of inducing and controlling probable demand so as to match it with the fixed seat inventory and then maximize the revenue (Belobaba&d’Huart, 2011). Unlike static RM models, dynamic models do not determine a booking control policy at the start of the booking period but monitor the state of the booking process continually and then dynamically makes a decision based on particular booking request. Dynamic RM approach is aimed at optimizing the expected revenue from flights as appropriate capacity management is put in place. Project Definition Through effective matching of the supply and demand for seats, the airline alliance can carry a huge number of passengers and increase its revenue. Since the invention of the internet and its adoption in e-commerce in the early 1990s, there has been fare transparency among airlines. Before this invention, passengers had difficulties in collecting and comparing information concerning all the fares offered by different airlines (Brumelle&Walczak, 2003). The use of the internet to enhance visibility into the present fares has had the effect of moving the market toward improved informational efficiency and thus augmented the susceptibility of the consumer to assess the market so as to find the best available price. Dynamic RM provides efficient mechanisms of sharing revenue within an airline alliance and ensures that optimal benefits are reaped by all airline alliance members. Strategic airline alliances are imperative in enhancing sharing ground services such as fueling, catering, business lounges, and other services at similar prices as those offered by the local friendly carriers. The low cost carriers (LCC) only provide a single fare at any given time making it complicated for alliances that use static models to derive the full potential benefits of RM. Having a dynamic model helps in ameliorating such instances and therefore ensures that maximum revenue is earned (Hu &Vulcano, 2013). Dynamic RM models have multi-fare offerings that support airline alliances’ capability to segment demand based on the desirability of diverse features. Revenue is one of the most vital indices for the management of airline sector. In spite of these assertions, the practical situations of the revenue management in airline companies are complicated. Extensive analysis is needed though dynamic programming where diverse dimensions are considered. Aspects such as customer behavior, demand forecasting, control systems, revenue factors, variable cost factors, and fare products are put in consideration in dynamic RM in airline alliances. These issues enable members in airline alliances to develop various strategies to improve the performance of each of the airlines in form of increased seat sales and profitability in the long run. Dynamic RM tactics enable airline alliances to make accurate forecast of possible seat sales, pricing and seat inventory control (Smith &Barnhart, 2011). This means that different fare classes are effectively allocated to the passengers so as to enhance attainment of optimal revenue in their joint operations. Airline alliances that embrace dynamic RM models gain competitive advantage as they achieve a balance between demand, reservation scheduling and variable pricing. The optimal distribution of seat inventory can be mastered for airline companies to make the most effective use of seat allocation through controlling booking conditions. In practice, airlines could compete on multiple routes and provide more than two fare classes without making any RM decision concurrently or simultaneously. Dynamic RM becomes relevant in this context for the case of airline alliances in which the airlines can adjust booking limits dynamically over time. Firms can coordinate their pricing through the alliance’s policies and split profits based on predetermined criteria (Topalogu, 2012). The benefits of dynamic models in RM are immense especially in pricing where dimensions such as time are incorporated in pricing since competitive decisions do not remain fixed. Dynamic pricing is also effective as it is more flexible to adjust and control capacity or seat inventory in the airline alliances. Customers’ behavioral changes are monitored in this model meaning that the schemes used in pricing and ensuring that capacity in airlines is well managed is done to maximize the alliances’ revenue/profits. Project Goals and Scope The goals of this paper revolve around the benefits and effectiveness of implementing a dynamic RM system in airline alliances. A comparison of the static and dynamic RM approaches will be provided in the paper and benefits of the dynamic model over the static RM scheme will be presented. The scope of the paper is limited to the benefits and effectiveness of the dynamic RM in airline alliances across the world. Issues relating to seat allocation, pricing, seat capacity management and inventory management will be extensively assessed in this paper. This will be done show the relationship of this factors in optimizing airline alliance members’ revenue or profitability (Hu &Vulcano, 2013). Aspects relating to dynamic pricing (setting of different fares for diverse classes of passengers) and forecasting future demand based on current purchases of air tickets will also be succinctly described in this report. The overall aim is to provide information to the airline owners concerning the benefits and effectiveness of dynamic RM in airline alliances. Conflict resolution strategies will also be proposed in this paper since firms in an airline alliance are bound to making profit making decisions independently. The paper will also provide detailed information on the effectiveness of the dynamic RM models in light of the varying market structures in the airline sector such as oligopoly depending on the itineraries offered by airlines (Topalogu, 2012). Advantages of the airline alliances in regard to route assignments, sharing of information and services, and attainment of competitive advantage will be highlighted. Significance of the Study This study is imperative to researchers and business people in the aviation field as it offers information that will act as a benchmark for conducting extensive research in revenue management approaches within the airline alliances. The paper provides succinct but vital information on issues underlying RM in airline alliances, their limitations and benefits thus making it easier for airline project managers to make well-thought-out decisions. Harmony in revenue or profit apportionment in airline alliances can easily be attained through dynamic RM. This assertion makes this study to be pivotal to all airlines in alliances or those intending to form alliances. Limitations of the Study This study is focused to only notable and huge airlines in the world that have attained wide market cover. This could be a limitation as studies on small carriers have not been considered in the documentation of the paper. Though the paper provides information on the benefits and effectiveness of dynamic RM approach, little information is offered on alliances that have embraced it. Issues relating to government policies and regulation of airlines in different nations are not considered. Information privacy issues could also impinge on the effectiveness of dynamic RM since firms intend to remain autonomous and make independent decisions on revenue maximization (Belobaba&d’Huart, 2011). The study makes use of only already documented information meaning that there could biasness among the writers on dynamic RM approach which could not be detected. Definitions of Terms Term Definition including citation, as needed. If the definition goes longer than the item is double spaced and indented to 1 inch. Term Definition including citation, as needed. Term Definition including citation, as needed. Term Definition including citation, as needed. Term Definition including citation, as needed. Term Definition including citation, as needed. List of Acronyms Acronym Abbreviations spelled out. If the acronym goes longer than the line, then the item is double spaced and indented to 1 inch. Acronym Abbreviations spelled out Acronym Abbreviations spelled out Chapter II Review of theRelevant Literature Background Revenue management is visible in the airline industry as early as the 1970s. The beginning of it coined by (Littlewood, 1972) describes the main problem. Littlewood, in his article introduced a result that depicts that only when a demand for a seat fulfills revenue that surpasses that of intended value in future, should be reserved. It is worth noting that this intuitive rule became the basis of various control policies in practice and theory. Of important, to note is the fact that most authors have continued to build on the above rule. This includes (Glover et al., 1982) who clearly argued on passenger mix issue in relation to a network environment; (Belobaba, 1989) who evaluated problems using a combination of multiple fare-restrictions; (Taluri & van Ryzin, 2004) who are associated with discrete form of demand; and (You, 1999) who assessed dynamic pricing model. In relation to the above, some literatures continue to examine the facets of airline alliances. (Brueckner, 2001) assesses the economic impacts of these alliances on traffic level, market welfare, fares and profits. (Park, 1997) also applies similar evaluation tool while (Barron, 1997) explains various legal consequences of these alliances. He mainly focuses on agreements applied in the industry such as the code sharing agreements. (Brueckner & Whalen, 2000) offer an practical analysis on the primary effect of the airline alliances on fare, citing that the interline fares are about 26 per cent lower than that being charges by those airline that have no alliances; and Ito & Lee (2007) go ahead and assess the impact of local alliances on fares. It is also important to know that before revenue management was coined, most researchers on revenue mainly focused on controlled overbooking which primarily depended on a prediction on the number of passengers that were likely to board the a flight at a given time. This triggered more research on forecasting given that passengers behavior toward a particular flight was unpredictable; that is, some passengers would cancel a reservation at any time affecting the expected outcome (Brueckner & Whalen, 2000). It is worth noting that controlled booking and forecasting led to an established credibility towards reservation control. Other strategies introduced such as offering low fares to passengers who booked early. This kind of innovation allowed the airlines to obtain revenue from some seats that could be empty during the flight. However, it raised a problem on the number of seats left for a late booking. The above issues resulted to different approaches of revenue management that progressed from single leg control, segment control and finally the origin destination control. The success of these approach lead to the development of other dynamic systems in this industry (Brueckner, 2001). Revenue Management Of most important to note is the fact that there is little attention paid on ways in which revenue management is established and implemented by air alliances. Boyd (1998) does not only discuss on technical and methodological challenges and problems facing revenue management but also in his unpublished work considers a formal analysis on the same. Under this, he creates an inert linear program that primarily describes problems related to alliance revenue management. (Wynn, 1995) discusses some simple transfer price arrangements based on local fares value. As (Vinod, 2005) argues that the many airlines consider different alliance coordination mechanisms that do not clearly depict the advantages and disadvantages after their analysis, (Shumsky, 2006) asserts that in order to increase proportion of the traffic most of the low cost competitors in the industry are taking the network airline into other levels of relying on the alliances. Notably both (Fernandez de la Torre, 1999) and (Shumsky, 2006) go ahead and discuss the necessity of carrying out more research on the impact and advantage of alliance agreements. Airline Alliances and revenue Management Initial streams of studies consider alliances involving two airlines partners. This includes (Netessine & Shumsky, 2006) who studied alliance involving two flight legs. Under this revenue is primarily shared through a transfer price mechanism that mainly rely on the ratio of interline passengers and the total number of expected passengers. On the other hand, (Wright et al., 2010) clearly discusses total revenue expected by an alliance that is operating on multi-leg network through various schemes of sharing revenue. Not only do they consider static proration rates but also movement of bid prices, an approach of sharing revenue as coined by (Vinod, 2005). There is an argument that revenue expected using various approaches of sharing schemes is less when compared to that expected from a centralized system of revenue management. In their study, (Graf & Kimms, 2011) broadly propose a model for capacity management decision of a partnership that is composed of two airlines partners operating on a one-flight leg. Similarly, (Cetiner & Kimms, 2012) considered an alliance composed of more than two airlines and having multi leg network. Under this, they developed a mechanism of sharing revenue from a nucleolus approach; a notion borrowed from the cooperative game theory. This mechanism is fair since the airline alliance need to earn more revenue than when operating alone or when operating in a subset arrangement with other partners. This is a result of the fact that the incentive is appealing for any partner to consider pulling out of the alliance. It is important to note that this study clearly considers centralized approach that allows the partners to maximize the revenue together. Not until recently did it emerge that it is possible for alliances to share revenue through an independent decision making approach. (Topaloglu, 2012) proposed a revenue sharing mechanism for alliances operating from a multi leg approach. Under, this, the airlines partners are able to make their capacity management decision from an independent approach. In his approach, Topaloglu clears problem faced in revenue shares among airlines in an alliance by formulating linear programs that each airline should solve. Revenue shares of the participating airlines are primarily obtained using dual multipliers. As such, the scholar assumes that a decision on whether to accept or reject a request is a decision made by the airline that is selling tickets rather than all the airlines partners taking part in the decision-making. Indeed, one may argue that this is a fair revenue sharing mechanism on a selfish setting. In relation to the above, (Cetiner & Kimms, 2012) evaluate fairness of sharing revenue from a selfish setting. They apply a simulation model that considers the process of booking which permits reallocation of seats. In addition, they propose a new mechanism of sharing revenue from a nucleolus based approach. However, it is significant to point out that the above authors find it necessary to consider other realistic approaches that will counter the problems faced by airlines while using different type of sharing mechanism. Indeed, this simply calls for more research in this area. Alliance factor on reservations and revenue management It is beyond doubt that the process of revenue management provide incremental revenue by introducing a selective mechanism that rejects and accepts reservation demands in order to maximize revenue. In order for this to happen, there is a need to meet an optimal mix of short and long haul passengers which achieved through selling a right seat to the right client and at the right price while considering the right time. In relation to this, (Boyd, 2005) asserts that partnership between airlines has a great effect on revenue management that is also influenced by seats availability and flowing of traffic through the alliances. It has been considered that arrival of airline alliances together with the practice of destination and origin revenue management has become more significant given that by making sure that there is optimal traffic flow in the network, the airlines in a given alliance maximizes revenues. As such, there is a particular optimal environment where the sharing of data between the partners, one inventory control environment for airline collaborates in the alliance and a collaborative system approach to revenue management. However, critiques argue that this owing to geographical, organizational, antitrust immunity, revenue sharing and options related to exit, this environment might not be near the reality. In his work (Shumsky, 2006) illustrates the likely position of revenue management in a given alliance where the airlines partners share inventory in order to maintain a network equilibrium that is observed between the individual operational network.. Revenue Management Behavior (Wright et al., 2010) arguably the first authors to analyze the revenue management behavior of different partners subjected under several proration mechanisms. In the discussion, they cite that there is no Markovian transfer arrangement that guarantee an optimal revenue due the fact that sharing of the interline revenue may not affect the agreement of some decisions for the intraline itineraries directly. They went ahead to show that the success to inert proration scheme merely depends on the stability and homogeneity of values that every airline plan to place or already has placed on the inventory is primarily used in the interline itineraries. It is important to note that despite adapting to the changes in value rations, there is no one dynamic scheme that is best and stands out from the rest. For instance, according to (Wright et al., 2010) dynamic schemes that are primarily based on bid prices are expected to generate most favorable acceptable decisions for the interline itineraries; however, they fail particularly in offering operating airline enough incentives and motivation to align the decisions with those of intraline ones. As such, it may force an operating airline to set its own operating prices which may result to inflation of price transfer distorting the acceptance decisions of interline. In evaluating their experiment, (Wright et al., 2010) noted that static scheme with an optimal with a specific itinerary proration rates may outperform the above dynamic schemes. However, they noted that computing and updating the optimal static proration could be a huge challenge for the alliance airlines partners. In addition, as per their experiment, the performance of these static schemes may decline steadily when there matching between the proration rates and network parameters lack (Vinod, 2005). It is important, to note that there are few studies that have considered problem in revenue management especially in an alliance setting. (Boyd, 1998) was one of the first scholars to discuss issued faced by airlines alliance in revenue management. Indeed, one may argue that an ideal decision in addressing such issues lies on the airlines partners who are to treat the partnership as a single airline and consider the revenue management decisions appropriately. Nevertheless, this to some extent is not viable due to the technical requirements and legal barriers faced among the airlines. Indeed, it as a result of the selfish behavior depicted by individual airlines, when they allocate seats individually; suboptimal decisions are visible in the alliances. In relation to the above, (Boyd, 1998) clearly addresses the issue of sharing revenue fairly between airlines in respect to revenue management. In their studies, (Cetiner & Kimms, 2012) describe the results of decisions made on seat allocation by sharing mechanisms of revenue; consequently affecting revenues obtained by the airlines partners. They continue to argue that a rule in revenue sharing may lead to unequal sharing or distribution of the intended revenues among the partners; and as such, being in the partnership may result to some airlines partners losing rather than gaining from the association. In addressing the above (Wright et al., 2010) they assert that there are special arrangements that airlines make; that is, special prorate agreements before booking of horizon is done. These agreements simply define the revenue from the shared arrangements are expected to be shared among the partners. The special prorate agreements include proration rates or transfer prices that are based on allocation rules. This means that the value of each flight varies between airlines and this depends on the sharing mechanism arranged for the revenue. As a result, seat allocation among airlines partners rarely coincide leading to suboptimal result for the partnership. Effectiveness of Dynamic Revenue Management Of important, to note is the fact that dynamic models do not settle on a booking control policy at the start of the booking period but actually it monitors the state of the booking process in a continuous manner. Consequently, it makes a decision based on specific booking request. Therefore, dynamic revenue management approach aims at optimizing the intended revenue from flights as appropriate capacity management flows in place (Cetiner & Kimms, 2010). With the realization that clients are opting for better services accompanied by reasonable prices, many airlines are entering into collaborative relationships with each other as noted by (Shumsky, 2006). Indeed, agrees to the above and adds that this is one of the ways of producing what the client wants and be in a position to realize the efficiency in the operations. This therefore, introduces the effectiveness of dynamic revenue management in alliance. Indeed, given the fact that the clients are now able to interline resulting to less fare compared to dealing with individual airline. As such, it becomes a convenience not only to the consumer but also to the alliance given that there are able to have higher revenue to the increased demand of the service that is boosted lowered fares and prices. A dynamic revenue management also ensures that clients receive high quality services through code sharing agreements where a partnering airline adds its ticket code on the partners’. This indeed, provides a client with seamless services combined with coordinated schedule ad access to regular flights programs. As Park (1997) asserts, cooperation between the airlines alliance is likely to improve the flight schedules with higher route frequency. Reduced connecting ties, and coordinated departures and arrival schedules likely accompany this. The benefits of dynamic models in RM are visible in pricing where important dimensions including time incorporated in pricing. Still, according to (Topalogu, 2012) dynamic pricing is effective due to its flexible not only to adjust but also to control capacity in the airline alliances. In addition, it is possible to monitor customers’ behavioral changes using this model Of important to note is the fact that dynamic formulation of problems related to revenue management are needed to model some real word factors properly including overbooking interspersed arrivals, batch booking, and cancellation. It has argued that there is a need to identify and exploit certain settings in order to gain optimal solutions. This is because of the increase of unmanageable dynamic programming formulations due to their growth in size when some real world executions are attempted (Boyd, 1998). Summary As seen, many scholars have largely discussed issues revolving revenue management is visible in various airlines for a long time. The introduction of alliances leads to various models of revenue generation as forwarded by various authors. Of important, to note is that various models used result to generation revenue among airlines; however, most of them have had various drawbacks. Dynamic model in relation to revenue management are effective and greatly benefit airline alliances. Chapter III Project Planning Project Statement of Work [Now that you know what it is you are doing (Scope), you need to capture details on how you are going to do it. The Statement of Work (SOW) defines the work to be done. You should use your SOW as a communications tool to explain the work of the project to your GCP Chair.Your next step will be to have your GCP Chair approve the SOW and it will become the official contract for your project.] Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx. Project Steps and Schedule [The project steps derive from the SOW and from the course syllabus. Each step of the project must be completed according to the guidelines in the course syllabus and the project must be completed within a single semester.] Xxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx. Project Resources [Project resources are items that you will use to complete the project. Certain resources (computer, Microsoft Word, etc.) that are used every project, are not mentioned. Other resources (hardware, books, software, web sites, databases, records, special materials, etc.) should be stated and referenced, if appropriate.] Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxx. Chapter IV Project Outcomes [Project outcomes are the processes and/or products of your project. Examples are: results,processes: training, products: books, brochures, handbooks, models, project websites, case studies, curricula, description of competences, procedures, samples of materials, web pages, CDs, recommendations, collections, examples, best practices, etc. Outcomes are usually the definition of the deliverables that were defined in the Project Scope (Chapter I).] Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Table 1 shows … Table 1 Table Title [Use quadruple spacing (three blank lines) before the table heading and after the table note.] Valid Missing Mean Median Mode SD Min. Max. AirlYrs 64 0 4.15 3 1a 3.50 0 13 TT 62 2 6,372.10 6,137 5,000 2,373.74 2,424 12,023 Attitude 64 0 3.31 3 3a .77 2 5 SftyAwr 64 0 3.11 3 3 .65 2 4 Note. Adapted from “Title of Source,” by X. X. Author and Y. Y. Author, 20##, Journal of Xxxxxxxxxx, ##, p. #. Copyright by Xxxxx xx Xxxxxxxx. AirlYrs = Airline Years, TT = Total Time, SftyAwr = Safety Awareness, Profess = Profession, SA = Situational Awareness, ADM = Aeronautical Decision Making. [This note is an example of the table attribution, placement, and format of a general note.] aMultiple modes exist. The smallest value is shown. [This note is an example of the contents, placement, and format of a specific note.] *p< .05. **p< .01. ***p< .001. [This note is an example of the contents, placement, and format of a probability note. Use double daggers (††) to denote one-tailed tests.] Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx. Chapter V Project Conclusions Project Presentation [If your presentation is in the form of a PowerPoint presentation, print it as a PowerPoint handout, six slides per page, and place the entire presentation in an appendix.] See Appendix X. Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx. Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx. Lessons Learned [Even in the most successful project there is always something that could have been done better. Being able to capture those lessons will enable you and others to complete more complex and challenging projects in less time, for less effort. If you are uncertain what to consider, reflect on the following three areas: (a) What training or skills could improve your ability to complete this or more challenging projects? (b) How could you measure the progress of each project step?and (c) How can technology improve this project?] Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.Xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx. References Topalogu, H (2012). A Duality Based Approach for Network Revenue Management in Airline Alliances. Journal of Revenue and Pricing Management. Vol. 11, 5, 500–517. MacMillan Publishers Ltd. Hu, X and Vulcano, R(2013). Revenue Sharing in Airline Alliances. Management Science, Vol 59, No.5. May 2013, pp.1177-1195. INFORMS. Brumelle, S and Walczak, D (2003). Dynamic Airline Revenue Management with Multiple Semi-Markov Demand. Operations Research, Vol.51, No.1, January 2003, pp.172-174. Belobaba, P and d’Huart, O (2011). A Model of Competitive Airline Revenue Management Interactions. Journal of Revenue Management and Pricing Management. Vol. 11, 1, 109-124. Macmillan Publishers Ltd. Smith, B and Barnhart, C (2011). Quantitative Problem Solving Methods in the Airline Industry: A Modeling Methodology Handbook:Volume 169 of International series in operations research & management science. Springer Publishers. Belobaba, P (1989), Application of probabilistic decision model to airline seat inventory control, Operations Research, 37 (2): 182-197 Boyd, E (1998), Airline alliance, OR/MS Today. October 28-31 Brueckner, J & Whalen, W (2000) ‘The Price Effects of International Airline Alliances’, The Journal of Law and Economics, 43, 78-132 Cetiner, D & Kimms, A (2012), Approximate nucleolus-based revenue sharing in airline alliances. European Journal of Operational Research, 220: 510-521 Fernandez de la Toure, P (1999), Airline alliances: the airline perspective. Master’s thesis, Massachusetts Institute of Technology, Cambridge, MA Glover, F, Glover, R, Lorenzo, J & McMillan, C (1982), The passenger mix problem in the scheduled airlines, Interface, 12 (3): 73-79 Graf, A & Kimms, A (2011), An option-based revenue management procedure for strategic airline alliance. European Journal of Operational Research, 215: 459-469 Ito, H & Lee, D (2005), Domestic code sharing, alliances and airfares in the US airline industry, Forthcoming in the Journal of Law and Economics Littlewood, K (1972), Forecasting and control passenger bookings, AGFORS Technology. 164 (19): 44 Netessine, S &Shumsky, R (2004), Revenue management games: Horizontal and vertical competition, Management Science 51: 831 Park, J (1997), The effect of airline alliance on markets and economic welfare, Transportation Research Part E, 33 (3): 181-195 Shumsky, R (2006), The southwest effect, airline alliances and revenue management, Journal of Revenue and Pricing Management, 5: 83-89 Talluri, K & van Ryzin, G (2004), The theory and practice of revenue management. New York: Springer Topalogu, H (2012). A Duality Based Approach for Network Revenue Management in Airline Alliances, Journal of Revenue and Pricing Management. Vol. 11, 5, 500–517. MacMillan Publishers Ltd. Vinod, B (2005), Alliance revenue management. Journal of Revenue and Pricing Management, 4:66-82 Wright, C, Groenevelt, H & Shumsky, R (2010), Dynamic revenue management in airline alliances, Transportation Science, 44: 15-37 Wynne, R (1995), From queen airs to RJS; evolution in major regional airline alliances and code sharing. New York: McGraw-Hill You, P (1999), Dynamic pricing in airline seat management for flights with multiple legs, Transportation Science, 33 (2); 192-206 Appendix A Tables A1 Some Table Title [Use this appendix for oversized tables that span more than one page or overflow into the page margins.] A2 Some Table Title Insert Table A1 here and subsequent tables on subsequent pages. Appendix B Figures B1 Some Figure Caption Title [Use this appendix for oversized graphics that span more than one page or overflow into the page margins.] B2 Some Figure Caption Insert Figure B1 on this page and subsequent figures on subsequent pages. Read More