PurposeDespite an extensive body of knowledge on the importance of customer orientation in the marketing and management literature, the impact of customer orientation and interactive system infrastructure throughout enterprise networks is not fully understood. The purpose of this paper is to present a model linking customer orientation, interactive system infrastructure, value chain practices, and network performance outcomes.Design/methodology/approachThe prior literature on customer orientation and supply chains is reviewed and a framework is presented which shows the relationship between customer orientation and network performance outcomes, along with other variables.FindingsThe conclusion supports the importance of customer orientation in the context of the proposed value chain framework.Research limitations/implicationsThe framework introduced in this paper provides a review of customer orientation in the enterprise network and a basis for further empirical validation.Practical implicationsThe research framework suggests that customer orientation practices may have a positive impact on network infrastructure design, practices, and performance outcomes. Implementation of customer orientation practices and outcomes within this research framework may allow management to meet customer requirements more effectively.Originality/valueThis paper expands the concept of customer‐orientation in the extended enterprise network context.

Vessel Traffic Services operators have kept sharp monitoring and provided the appropriate information to ensure safe and effective navigation. While attending the tasks, analysis of traffic patterns and navigational data is required to conduct accurate situation assessment in decision-making process of VTS operators (VTSO). Unfortunately, there are problems in the process of data analysis such as appropriateness of time, VTSO's personal error and improper judgment. Therefore, objective and proper data analysis is necessary to solve above matters. However, it is virtually impossible to monitor all vessels because there are many vessels in the VTS area and at the same time complex traffic situations are produced. In this study, we proposed a machine learning algorithms for objective and accurate pattern recognition and data modeling. Support Vector Regression algorithm was used for data learning and modeling. The optimal parameters were selected through v-fold cross validation and grid search. The machine learning was conducted with virtual route and ship tracks that are similar with real navigational environment. As a result, we presented reference route and navigational patterns. We expect that the proposed modeling methods could be utilized for relevant tasks as the useful information to VTSO and/or ship's mater.

Vessel Traffic Service (VTS) is being used at ports and in coastal areas of the world for preventing accidents and improving efficiency of the vessels at sea on the basis of " IMO RESOLUTION A.857 (20) on Guidelines for Vessel Traffic Services." Currently, VTS plays an important role in the prevention of maritime accidents, as ships are required to participate in the system. Ships are diversified and traffic situations in ports and coastal areas have become more complicated than before. The role of VTS operator (VTSO) has been enlarged because of these reasons, and VTSO is required to be clearly aware of maritime situations and take decisions in emergency situations. In this paper, we propose a prediction table to improve the work of VTSO through the Cognitive Work Analysis (CWA), which analyzes the VTS work very systematically. The required data were collected through interviews and observations of 14 VTSOs. The prediction tool supports decision-making in terms of a proactive measure for the prevention of maritime accidents.

A ship's sailing route or plan is determined by the master as the decision maker of the vessel, and depends on the characteristics of the navigational environment and the conditions of the ship. The trajectory, which appears as a result of the ship's navigation, is monitored and stored by a Vessel Traffic Service center, and is used for an analysis of the ship's navigational pattern and risk assessment within a particular area. However, such an analysis is performed in the same manner, despite the different navigational environments between coastal areas and the harbor limits. The navigational environment within the harbor limits changes rapidly owing to construction of the port facilities, dredging operations, and so on. In this study, a support vector machine was used for processing and modeling the trajectory data. A K-fold crossvalidation and a grid search were used for selecting the optimal parameters. A complicated traffic route similar to the circumstances of the harbor limits was constructed for a validation of the model. A group of vessels was composed, each vessel of which was given various speed and course changes along a specified route. As a result of the machine learning, the optimal route and voyage data model were obtained. Finally, the model was presented to Vessel Traffic Service operators to detect any anomalous vessel behaviors. Using the proposed data modeling method, we intend to support the decision-making of Vessel Traffic Service operators in terms of navigational patterns and their characteristics.

Understanding a ship's present position has been one of the most important tasks during a ship's voyage, in both ancient and modern times. Particularly, a ship's dead reckoning (DR) has been used for predicting traffic situations and collision avoidance actions. However, the current system that uses the traditional method of calculating DR employs the received position and speed data only. Therefore, it is not applicable for predicting navigation within the harbor limits, owing to the frequent changes in the ship's course and speed in this region. In this study, planned routes were applied for improving the reliability of the proposed system and predicting the traffic patterns in advance. The proposed method of determining the dead reckoning position (DRP) uses not only the ships' received data but also the navigational patterns and tracking data in harbor limits. The Mercator sailing formulas were used for calculating the ships' DRPs and planned routes. The data on the traffic patterns were collected from the automatic identification system and analyzed using MATLAB. Two randomly chosen ships were analyzed for simulating their tracks and comparing the DR method during the timeframes of the ships' movement. The proposed method of calculating DR, combined with the information on planned routes and DRPs, is expected to contribute towards improving the decision-making abilities of operators.