Analysis of Key Factors for Formation of Strategic Alliances in Liner Shipping Company: Service Quality Perspective on Asia/Europe Route after Global Economic Crisis

Strategic alliances generally mean the cooperation or collaboration between firms which pursue for a synergy that each member hopes the benefits from the alliances would be much more than those from individual efforts. Past researches provide us sufficient theories and considerations for alliance forming in liner shipping market. This research reviews important academic journals for the past decade regarding to the most important reasons to form the alliances. We would explain the motive of alliances and details of shipping cooperation in literature review. The paper also empirically investigates the key service quality requirements improved through alliances by using quality function deployment (QFD). Moreover, the research investigates famous shipping reports, shipping consultant websites and most recent shipping publications to find out the executive-s viewpoint of several leading carriers among top 20 to assess current shipping strategic alliance on Asia/Europe route. These comments provide meaningful managerial reasons to consider alliance formations and search if there is any gap between the theories and industrial practice. Analysis of the empirical investigation and top management-s perspective on current market situation will contribute us some meaningful managerial suggestions to evaluate these theories applied to current strategic alliances.

A Fuzzy Time Series Forecasting Model for Multi-Variate Forecasting Analysis with Fuzzy C-Means Clustering

In this study, a fuzzy integrated logical forecasting method (FILF) is extended for multi-variate systems by using a vector autoregressive model. Fuzzy time series forecasting (FTSF) method was recently introduced by Song and Chissom [1]-[2] after that Chen improved the FTSF method. Rather than the existing literature, the proposed model is not only compared with the previous FTS models, but also with the conventional time series methods such as the classical vector autoregressive model. The cluster optimization is based on the C-means clustering method. An empirical study is performed for the prediction of the chartering rates of a group of dry bulk cargo ships. The root mean squared error (RMSE) metric is used for the comparing of results of methods and the proposed method has superiority than both traditional FTS methods and also the classical time series methods.