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

CHAPTER FIVE Discussion After analyses in chapter one, two and three, chapter four provided detailed data and results with respect to thesis statement of this research. However, chapter five is specific in its approach. It brings in perspective of all the chapters by analyzing revenue management practices and how data and results analysed in chapter four conceptualizes thesis statement and its tandem to previous chapters. Such critical analysis of the data and result will be four-fold: review of previous topics, integrating such topics with results and data in chapter four, referring to original question of the study and provision of overall conclusion and recommendations insightful for future researches. To begin with, this research finds that there has been significant increase or rather adoption of dynamic revenue management by airline alliances. This underpinning is backed by the fact that results and data as analysed in chapter four was plausible owing to alliances such as Air France, Northwest, oneworld, Delta and SkyTeam. Considering figures in chapter four, there is general consensus that competitions in airline alliances bring overproduction and building of capacities among such alliance. This position is supported by interview regarding effects and Benefits of RDM when alliance size is increased as indicated in figure 2. Though the data was only conducted in oneworld, increasing number of airlines within an alliance benefits such airlines. Conceptualizing these findings, Graf and Kimms (2011) as reviewed in literatures contend that airlines compete on operational decisions like inventory. Therefore as this interview supports, as partners within an alliance increases, airlines such as British Airways, Cathay Pacific and American are able to consolidate their flight networks thus reducing what Netessine and Shumsky (2005) term as redundancies in their market of operations in which the aforementioned airlines operate. This interview findings further coceptualise the goals and scope of this research as identified in chapter one by identifying specific benefit of dynamic model over static RM scheme and at the same time limiting such findings within the realm of the benefits and effectiveness of the dynamic RM in airline alliances across the world. Basing on the 21% of the respondents arguing that increase in size makes no difference, this research observes that airlines will be competing more and vigorously with competitors emerging from different alliances by applying aggressive capacities and entries. Surprisingly, results from figure 2 differ significantly with benefits of Network RM and Full Information as seen in figure 3. Starting with data and results from figure 3, the research observes that adoption of dynamic revenue management leads to sub-optional control. By sub-optional control, airlines within an alliance become flexible in their operation. For instance, Partner 1 and 2 and their respective competitor must be operating in post-alliance era where there can operate and install capacity in their markets of operations especially where non-allied carriers tend to be very active. This is why cascading as seen in figure 3 leads to higher revenues comparing that to AVS (marked as red stacks). As noted above, the result if figure 3 differs significantly as those observed in figure 2 in the sense that it is robust in multiple alternative model specifications such as dynamic pricing model as explained by You (1999) in the literature review. In as much, going by results from figure 2 and 3, they both contradict what this research earlier found through Topalogu (2012) about what airlines claim would happen when airlines charge one-way segment coupon especially in markets that are shared with partners rather than competitors. On the other hand, considering the argument by scholars such as Çetiner and Kimms (2012), findings from the figures are consistent with views regarding predictions on multimarket contact theory. Findings in figures 2 and 3 are further contradicted regarding data and results from figure 5 which analyses effect of DRM on Price Volatility vis-à-vis benefits and effectiveness of dynamic revenue management on airline alliance. It is clear from the data presentation that larger percentage of the respondents contended that DRM helps the alliance during price volatility thus becoming effective when fewer passengers turn up for the booked seats. To conceptualise this data within the realm of statement of the problem as earlier identified, alliances tend to impose a credible threat of what Çetiner and Kimms (2012) term as retaliation and in so doing, tend to strengthen their multimarket contact thus high number of alliances strongly agreeing that DRM give them an advantage during price volatility. A point missing from the results in figure 5 is that it cannot help understand situations where airlines tend to strategically increase and overlap capacity on routes where other airlines are present especially when such moves solidify their alliance. In summary of figure 5, this research suspects that respondents who were against the idea were attributing their views to the events of September 11 and the consequences of financial turmoil that affected alliances such as Northwest and Delta. Basing on research from Graf and Kimms (2011), there is a proposition for a model for capacity management decisions of a partnership which is composed of two airlines partners operating on a one- leg flight. Looking at their suggestions in view of theories analysed earlier in previous chapters, dynamic revenue management may be not as effective and beneficial to alliances as posited. The reason is that there can be situations where there are loss of market shares and supply or demand control in the short run---good example of such scenario has been discussed by Çetiner and Kimms (2012) where alliances such as Northwest and Delta were cited. The case studies done by Çetiner and Kimms argument can well be supported by figure 4 which presents data on Local and Y-Prorate Valuation test on the effectiveness and benefits of dynamic revenue management on airline alliances. Figure 4 shows a case where Y-Prorate when used to airlines in an alliance, brings some level of benefits and or effectiveness for the alliance. Furthermore, the data indicates that airlines which are under major alliances such as Comair American Eagle have the higher tendency to avoid vertical competition with regard to Local and Y-Prorate Valuation. Still on figure 4, it can be observed that dynamic codeshare valuation especially when bid prices are involved tends to lead to even greater revenues thus benefits for the alliance or specific airlines within the alliance. The reason for this is what the research identified earlier as ‘differential pricing in airline industry.’ Differential pricing in airline industry is further supported by this figure in the sense that two or more airlines can be considered to have overlapped in a segment in cases where they operate direct flights. The advantage of this is that vertical competition is minimized. As earlier noted in chapter two, airlines Cathay Pacific and American tend to adopt differential pricing with an aim of utilizing all the available seats in a given time limit of operation so as to utilize revenue thus supporting earlier researches such as Smith and Barnhart (2011) who notes that dynamic revenue management enables airline alliances to make accurate forecast of possible seat sales, pricing and seat inventory control. While results from figure 4 helps this research conceptualises advantages of alliances can accrue with respect to rout assignments and sharing ground services as far as dynamic revenue management is concerned, figure 6 (percent of first-best by static schemes optimal in a network with β=0.5) on the other hand is two-fold in its significance. Firstly, alliances have the ability to gain a high level of information sharing to a central dynamic revenue management process and secondly, that alliance may as well lose some revenues through dynamic revenue management especially when government policies are stringent coupled with sluggish enforcement of such policies from bodies such as IATA and ICAO. From the same figure, the line indicated as ‘Best Dynamic’ shows the highest revenue that can be achieved when considering the three dynamic schemes with the bid price scheme as the winner. Secondly, this figure can show the partner price scheme thus giving this research ability to assess the effect in case every partner go against reported bid prices within its own gain. The remaining dotted lines give the revenue from the 3 static mechanisms indicated and for every static scheme; this research finds optimal proportion ratios, thus allowing the figure to report the maximum revenue that can be achieved by static scheme. Comparing these findings with dynamic schemes as postulated by Belobaba and Jain (2013) and even as earlier shown in figures 2 and 3 that DRM makes it effective for rout assignments and sharing ground. As also noted in the project definition section, dynamic RM has the ability to make airline alliances make accurate forecast of possible seat inventory control, results from figure 7 underscores this understanding by showing that the value ratio have the ability to determine the relative incentives these airlines may need when reserving their inventory for future interline requests instead of selling inventory for usage in intra-line itineraries. That is, from the figure above, when the value ratio of interline itinerary are not 0.5 then one airline values the inventory more than the other thus requiring a bigger share of revenues as incentive so as to be assured that the decision made is for the interest of the alliance. This finding further supports Hu and Vulcano (2013) research that dynamic RM models have multi-fare offerings that support airline alliances’ capability to segment demand. Looking at the left side of figure 6, there is a 58% share that goes to airline 1 for both itineraries can be seen to be optimal in as much as the performances of these optimal universal static schemes are indeed inferior to the performance of the best dynamic scheme. This is complicated by the fact that as the value ratios get heterogeneous, proration rate (universal) no longer give the best incentives to maximize revenues of the alliance and it is the point where inputs from ICAO and IATA are really needed. On the right side of the same figure, the performances of the static proration schemes (universal) have fell below the bit price (the best dynamic scheme) and even below partner price. The reason for this is the divergence of value ratios. The performance of marketing (Universal SP) scheme also shows that while such a scheme can be used to give incentive to every airline within the alliance to generate demand (an issue not tackled in this research), it fails to give the proper incentives as far as revenue management decisions are concerned. This is something that has further been proved in figure 7 where the figure brings the percentage of the first-best revenues for alliance for the Itinerary Specific, Universal SP and Best Dynamic schemes. Since there is no change in the relative performances of the identified schemes, similar results can hold for larger, even more realistic horizon lengths and inventory levels. This research turns to analyse variables of main concern which include the change of competitor effect, and partner effect. The research turns to table 1 (data illustrating benefits of premiums within Star Alliance) and figure 1 (interview of the Lufthansa Airline Alliance). The fact that over 70% of the respondents strongly agreed to the benefits of dynamic revenue management in airline alliance the hypothesis of the two figures have been supported (the hypothesis is that the change of competitor effect could be negative to the alliance and changing partner effect can be positive.) Figure 1 and table 1 consistently demonstrate that after alliance, airlines have the tendency of operating in a market where their partners have a strong presence. The benefit accrued as a result of this has been explained by Adelman (2007) when he asserts that, “operating in market where they feel stronger helps these airlines adopt differential pricing which in turn makes them utilize the available seats to maximize revenues” (p. 35). With regard to table 1 where airlines tend to dispose its last sit so as to prevent the alliance from disposing even more valuable interline itinerary, the reason is that some airlines becomes cautious as was the case with Air France in 2010 where they became pro-active so as not to lose airline revenue to other airlines within the alliance. Conclusion This paper sought to investigate benefits and effectiveness of dynamic revenue management on airline alliances. While this was the core objective of the research, literatures on the same topic has brought multifaceted perspective of dynamic revenue management. First, this research has recognized that airline alliances are selling growing number of interline itineraries thus creating the need to understand the effect of revenue sharing models on alliance network performance---an aspect this research learns to be intertwined with benefits and effectiveness of DRM on such alliances. While a few literatures have equally recognized challenges facing dynamic revenue management and how such challenges negatively impact on airline alliances, this paper is the first to recognize different models and integrate such with case studies from the identified airline alliances so as to understand dynamic revenue management behavior on airline alliance. Analysis of proration mechanisms and models has given the research fundamental insights regarding the alliance dynamic revenue management problems and benefits it gives to such airline alliances. For instance, it was under such approach that the research realized the most effective benefit of dynamic revenue management on airline alliance to be the transfer-price scheme which actually give first-best or optimal revenue since the sharing of inter-line revenue has been found not to be able to directly affect acceptance decisions when it comes to intra-line itineraries. In connection to this, the research is further able to show readers that static proration schemes are dependent on the stability and homogeneity of the relative values each airline can place on the inventory it used in interline itineraries. On the other hand, airline in alliances should not always find dynamic revenue management to be effective and or beneficial since there are some cases where dynamic schemes can adapt to certain changes. In some strange scenarios, the research realizes that dynamic schemes have the potential to fail in their provision of the operating airline with needed incentives to align intra-line decisions. Commenting on the data and results, most poignant issue is that with dynamic revenue management, alliances have the abilities to reduce competition thus bringing scenarios where partners within a given alliance tend to overlap among themselves thus giving them capacities in their market of operation and doing the other way round in markets operated by their competitors. Therefore, to enjoy the benefits of dynamic revenue management, such airlines have the advantage of choosing the market to operate in considering the multimarket contacts at their disposals. Recommendations In the process of integrating research topic with literatures reviewed and results and data obtained, there are assumptions that have been made and one of such is that airline partners can sometime share demand revenue forecasts regarding current inventory levels. In that regard, the assumption is that every current inventory levels can calculate value function of every partner which is unlikely scenario even when working with alliances with smaller number of airlines. This research therefore recommends that future research be based on effects of this assumption. Secondly, this research has considered and analysed different schemes. In as much, such schemes have technical limitations when it comes to implementation. For instance, there can be a situation of computational and conceptual barriers that makes alliances operating under dynamic revenue management difficult to predict. Therefore analyzing the performance of dynamic trading is recommended for future research so as to understand how such findings can be integrated with the barriers. References *****There are five new references included in this chapter and will be reflected in the entire document***** Read More