یک مدل ترکیبیANP در محیط فازی برای انتخاب شریک اتحاد استراتژیک در صنعت هواپیمایی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|6152||2011||10 صفحه PDF||سفارش دهید||7866 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Applied Soft Computing, Volume 11, Issue 4, June 2011, Pages 3515–3524
Strategic airline alliances are an increasingly common strategy for enhancing airline competitiveness and satisfying customer needs, especially in an era characterized by blurring industry boundaries, fast-changing technologies, and global integration. Airlines have been very active in utilizing this form of strategic development. However, the selection of a suitable partner for a strategic alliance is not an easy decision, involving a host of complex considerations by different departments. Furthermore the decision-makers may hold diverse opinions and preferences arising due to incomplete information and knowledge or inherent conflict between various departments. In this study fuzzy preference programming and the analytic network process (ANP) are combined to form a model for the selection of partners for strategic alliances. The effects of uncertainty and disagreement between decision-makers as well as the interdependency and feedback that arise from the use of different criteria and alternatives are also addressed. This generic model can be easily extended to fulfill the specific needs of a variety of companies.
Strategic alliances between airlines are now common in the aviation industry. They are frequently made in response to changing economic and regulatory conditions . Three major alliances established within the last 10 years—Star Alliance, One-world and Sky Team—now account for nearly 70% of passengers and turn-over in the global market . Strategic alliance strategies allow airlines to expand networks, attract more passengers, and take advantage of product complementarities, as well as providing cost-reduction opportunities in passenger service related areas (such as code-sharing, joint baggage handling, joint use of lounges, gates and check-in counters, and exchange of flight attendants) . A good strategic partner can further enhance the quality of their connecting services by adjusting arrival and departure flights so as to minimize waiting time between flights while providing sufficient time to make connections. On the other hand, ineffective strategic alliances can lead to the loss of core competencies and capabilities, exposure to unexpected risk and even business failure. Take for example—the fall of Swissair. Financial statements show that its airline alliance policy and investment strategy were responsible for the majority of its losses from 1997 to 2001 . Prior research suggests that the choice of alliance partner is an important variable with significant influence on the performance of the strategic alliance partners  and . An appropriate partner is one that can contribute resources and capabilities that the focal firm lacks. This ultimately determines the viability of the strategic alliance. Partner-related selection criteria require consideration to determine whether the corporate cultures of the partners are compatible, and whether trust exists between the partners’ management teams. This ensures that the selected partner and focal firm achieve organizational interdependence. Although the importance of selecting the right partner for forming strategic alliances has been recognized in literature, there have been few empirical studies on how to choose that partner which stress the interrelationship between the partners and the focal firm at the same time. The analytic network process (ANP) was proposed by Saaty  to overcome the problem of interrelation among criteria or alternatives. The ANP is a general form of the analytic hierarchy process (AHP), which releases the restrictions of the hierarchical structure. It has been successfully applied in many multi-criteria decision making (MCDM) problems , ,  and . However, due to problems such as incomplete information and subjective uncertainty, even experts find it difficult to quantify the precise ratio of weights for the different criteria. The concept of fuzzy sets has been incorporated into AHP to deal with the problem of uncertainty, although ANP has not often been used to address this type of problem in fuzzy environments. A way to cope with uncertain judgments and to incorporate the vagueness that typifies human thinking is to express the preferences as fuzzy sets or fuzzy numbers . Therefore, the objective of this study is to combine fuzzy preference programming and ANP to make a model capable of helping airlines select the best partner for strategic alliances. The rest of this paper is structured as follows: In Section 2, we summarize some of the important previous studies regarding the strategic alliance strategy, and the problem characteristics are described. In Section 3, the basic concepts of fuzzy preference programming and ANP are reviewed. In Section 4, a strategic alliance model is developed. The implementation using the proposed fuzzy ANP is presented in Section 5. Section 6 includes discussions and some conclusions.
نتیجه گیری انگلیسی
Although ANP has been widely used in various applications, it is hard for decision-makers to quantify precise judgments about criteria under conditions of incomplete information and subjective uncertainty. In this paper, we propose a hybrid model combining fuzzy preference programming and ANP, which extends the original ANP by using fuzzy judgments to compare the ratios of weights between criteria. This model can avoid the convergence problems encountered using standard fuzzy arithmetic operations in fuzzy ANP. Since standard fuzzy arithmetic operations are used to multiply and divide fuzzy numbers, the method may result in the convergence and rational problems of fuzzy global weights. We use linear programming to derive the steady-state priority vectors, and then use ANP to consider clusters/criteria dependence. The model should be more practical for actual application than ANP, which ignores the uncertain judgments often made in the real world, and conventional fuzzy ANP, which causes convergent problems. Table 8 shows a comparison of the results obtained between our proposed model and the original ANP method. Our model indicates that One-world is the best selection while the original ANP method points to Star Alliance (with a higher weight 0.100 than 0.098 of One-world) as being optimal. However, in our proposed model, we considered decision-maker uncertainty when they make a decision, which could make this model more realistic than the original method. Furthermore, we divided the experts into two groups, technical (operational, maintenance and safety departments) and non-technical (financial, marketing and service departments). The opinion of the technical group was that One-world was the best alliance, but the result for the non-technical group gave Star Alliance the highest weight. These results might be because Star Alliance has a higher marketing share, and non-technical groups deemed marketing and service to be the important criteria. On the other hand, One-World was chosen by technical groups due to the experts thinking that One-world offered more reliable technical operation. Again, this is the advantage of our model that it can integrate different opinions to come up with an optimal solution.The empirical results indicate that “One-world” is the best selection from the airline's viewpoint. However whether to join an alliance or not is not only dependent on the company's “willingness”, but also on “acceptance” of the alliance. Here we provide a tool to help airlines select an optimized strategic alliance given their own requirements. It is also worth noting that different airlines may end up with different results, based on their own specific needs. Although the present model has proven valuable, there are still some areas that need further discussion. It is acknowledged that the decision levels and criteria involved in any particular implementation may differ depending on the airlines/enterprises involved. In fact, this is one of the strengths of ANP, which can be used to construct various structures considering inner dependence and feedback effects. A set of criteria should be designed for each application, depending upon what is deemed important for that application. Decision criteria or dependence within/between clusters that a company considers to be crucial can be easily added to the generic model. Also, the weighting given each component in the model is dependent on the decision-makers evaluation of the component. This helps facilitate tailoring of the model to the company in question. For example, an airline that stresses enlarging markets would likely select criteria and weightings different from an airline seeking to provide better services/products. On the other hand, not all possible criteria and interactions are considered. Again, decision factors could be added, depending on the decision environment. Possible extensions in this area currently being explored include risk analysis of strategic alliances and different interactions between clusters. For instance, currently, only a one-way influence between motivations, alliance structures and partners is included in the model. The interactions could be modeled as two-way interactions. Perhaps a more interesting and useful extension of the model would be to include interactions within alliance structures and alternatives (partners). One of the limitations of the original ANP is its dependency on the decision-makers. The weightings obtained are based on the decision makers’ subjective opinions and many of these values are strategic, therefore, additional strategic group decision-making tools are needed. Although we can use scenario planning or the Delphi approach, these are still time-consuming and it is sometimes hard to reach a consensus. In this study, the uncertainty of judgment is removed by expressing the comparison ratios as an interval, to incorporate the vagueness inherent in human thinking. The proposed model has some further advantages. It provides opportunity for solving prioritization problems with mixed types of comparison judgments, such as intervals or crisp numbers. Also, the prioritization problem is treated as a linear program, which can easily be solved.