This article presented a model that allows companies to manage their vehicles and loads using a merging of the information technologies and geofencing algorithms.
The SafeTrack allows those companies to monitor at real-time the moment when a detour occurs.
The integration between off-the-shelf mobile devices and the SafeDuino makes possible to identify, also at real-time, deliveries and pickups of loads made by mistake, or potential cargo theft. Moreover, using the SafeTrack, it is possible to manage travels and load’s deliveries in an automatic way.
This is important to shorten the user’s interaction, optimizing time and reducing problems caused by human fails.
The main scientific contribution of this work is the automatic delivery management of loads, without user’s interaction.
In that way, SafeTrack can optimize deliveries and also avoid human mistakes.
Comparing to the related works considered, only eTracer (Papatheocharous & Gouvas, 2011) tried to handle an automatic delivery management.
However, eTracer only detected when the load reaches the destination. Differently from that, our model can further identify if the product was in fact delivered, avoiding potential deliveries inconsistencies.
Another contribution of SafeTrack is the real-time monitoring of the delivery process, triggering alerts to highlight several inconsistencies, including unauthorized shutdown of mobile devices or even potential cargo thefts.
These contributions are a consequence of merging information technologies and geofencing algorithms, and they were not found in the related works.
The SafeTrack’s automatic provisioning of alerts speeds up decision- making, possibly reducing losses and costs for the logistics flow.
This feature was also provided by related work of Zhengxia and Laisheng (2010).
Complementary to their approach, in which alerts are sent through SMS messages, SafeTrack also uses audibly alerts.
That feature helps the driver in rapidly detecting the arrival of alerts.
Another SafeTrack’s differential is the provision of a complete and functional system.
We developed this prototype in partnership with Sawluz company and Taggen RFID solutions company.
The prototype was evaluated in a controlled environment, testing several conditions. We created the test scenario with several situations that may not occur in a typical single travel.
Even so, in the 20 times that we executed the scenario, all SafeTrack’s modules
worked as planned.
The evaluation result confirmed that the system is reliable, since all planned events occurred during the execution.
Therefore, we concluded that SafeTrack was able to efficiently manage deliveries and vehicle fleets in the proposed scenario.
The evaluation showed that SafeTrack can improve logistics operation, optimizing decision-making, avoiding losses during the logistics flow, and also allowing companies to remain competitive in market.
The main limitation of SafeTrack regards information flow.
The alerts triggered do not reach the entire supply chain. This is because we restricted our efforts in a specific problem for a logistics stage, having a partial supply chain product visibility.
However, we found the study of Geerts and O’Leary (2014) that proposed a solution regarding this matter.
There are points to be studied and improved in future work.
The SafeTrack and most of related studies in the literature only perform monitoring during the stage of distribution of loads.
So it is not possible to track a particular product or component in earlier stages of logistics chain, such as supplying and production.
In fact there are studies, such as EAGLET (Geerts & O’Leary, 2014) that approach this matter, to track and trace the entire life-cycle of a product, beginning in supplying, through production and distribution, until the final consumer.
However, none of these studies use an ontology that can represent the entire domain of productions chain.
Another area for improvement is regarding security.
We did not address the question of vulnerability and security of mobile devices in SafeTrack.
We also have to consider the possible vulnerabilities of the RFID technology to minimize potential attacks, such as in authentication phase or in its contents.
The use of cryptographic algorithms in RFID can also be further studied. As one of the motivation of SafeTrack is to reduce logistics costs, the cloud computing rises as an interesting research field, and can be explored as future work. According to Subramanian Abdulrahman, and Zhou (2014), cloud computing adoption, brings enhancements in integration, green collaboration, and sustainability of supply chain management. Finally, another future work is related to delivery optimization.
There are algorithms that aim to calculate the best route to be followed by a vehicle, considering information such as weight and volume supported by the vehicle, pickup and delivery spot of the set, time-window, and weight and volume of the set.
However, these algorithms do not consider the shadow zones, which must be avoided, or context information.
In the future, we intend to deal with this type of algorithm in an updated version of SafeTrack model.