Proposed Actions

In order to advance the work on the Logistics Innovation Action Plan and to ensure the user involvement, the action plan will be developed by using an agile development approach, we here describe some of the types of actions to be envisaged to complete the channeling of logistic innovation uptake. A key for the long term logistic innovation is the exploration and organization of future research directions. It will be critical for the logistics domain to fully accompany its future research directions with clear objectives, ensuring that research outcomes are indeed taken into account and fully exploited.

 The uptake of logistics innovation is prevented or limited by the lack of research or proper management. In the end, finance plays a core role within logistics. Hence, the very nature of goods transport is not only about an unavailable good, but rather about obtaining it at the lowest price, even if it is already available at the desired location. As an example some countries are exporting high quality fruits while importing lower quality ones at the same time, requiring very competitive shipping costs. In this perspective, the proposition and emergence of more adapted financial mechanisms would become both a driver for innovation accomplishment and a key element fostering new innovation. (Ivanov and Sokolov, 2010 books of supply chain management courses).

 Even if innovation has to be fostered and protected, it cannot be done at all costs. As illustrated in the previous sections, some of the barriers faced by the further uptake of logistics innovation are linked to the lack of proper regulatory measures that would channel their application. In this perspective, logistics innovation and policies have to be considered together with proposals of regulatory measures on a transnational level.

According to many supply chain institutes who are offering supply chain management degree.Structural model estimations in this study were conducted with SmartPLS 2.0 software (Ringle, Wende and Will, 2005). We used mean-centered data and the path weighting scheme, missing data were excluded case-wise.