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Going beyond the traditional business to consumer air transport passenger perspective, this paper examines what we can learn from combining transaction marketing (TM) and relationship marketing (RM) orientations in respect to customer loyalty in the context of business to business (B2B) freight transportation markets. We model and examine post-transactional air freight express (integrator) customer behavior of 170 manufacturing companies and our results suggest that both TM and RM orientations influence customer loyalty as TM has a positive direct and indirect impact on loyalty due an mediating effect on RM outweighing potential dark side effects of RM on customer loyalty. This indicates that transaction-based marketing can support a relationship structure with the aim to nurture customer loyalty in B2B freight markets. In contrast to theoretical notions and contradictions between simultaneous competition/cooperation and interdependence/dependence of customers and service providers, we show that managers of freight and logistics companies should embrace the coexistence of TM and RM approaches and develop strategies that value long-standing transactional customers as they provide opportunities to create effective customized solutions.

Crossdocks play an important role in supply chain operations. Due to the need to decrease transportation lead time to coordinate with other supply chain activities such as just-in-time, make-to-order, or merge-in-transit strategies, shortening the total transfer time at crossdocks is increasingly important. In this research, we use real-time information about freight transferring within a crossdock to schedule waiting inbound trailers in order to reduce the time freight spends in a crossdock. We use dynamic simulation models to compare the performance of several strategies. These are first-come, first-served, look-ahead, minimum processing time, and minimum total time policies. We examine these under different trailer arrival headways, crossdock layouts, and destination distributions. Our simulation results show that our time-based algorithms save more time than the first-come, first-served and look-ahead policies. In addition, these algorithms appear to result in higher service reliability and productivity.