All related works manually performed travel management, i.e.
systems require the user interaction to initialize and finalize travels.
We believe that in logistics systems it is important to shorten
the interaction of the user to optimize time and reduce problems
caused by forgetfulness people.
Another relevant point for tracking and tracing systems is
related to the disconnection support. It is a known fact that network
might not be available in some locations. In this case the system
has to provide a way to tolerate communication’s failures, in
order to guarantee that the information will not be lost in ‘shadow
zones’ (areas without GPRS coverage that impede the device to
send data to the server). Among the studied works, just Yang
et al. (2010) did that support.
Regarding sending of alerts, just the work of Zhengxia and
Laisheng (2010) provided it automatically. The alerts are sent via
SMS for the monitoring users. That is an important issue, because
it reduces the response time and allows a quicker decision-making
without depending of an user. In their proposal, several events can
be alerted, such as overdue validity, long stayed goods, and damaged
goods. However, as the alerts were sent by SMS, the driver
may not notice the arrival of a SMS message on the mobile device.
This would cause a delay in decision-making and possibly losses
during logistics flow. Thus, an improvement of SMS alerts with
audible alerts would be interesting.
According to Musa, Gunasekaran, and Yusuf (2014), supply
chain product visibility is directly related to the capacity of the supply chain to keep on track the product’s life-cycle in different
stages, such as conception, manufacturing, distribution, delivery
to the end customer, and after customer interaction. This model
was also the only that dealt with tracking product’s life-cycle along
the entire supply chain network. This tracking was performed by
adding a smart tag into EPCglobal model, where the product’s
information was obtained at real-time, and along different stages.
With this, a full product’s visibility was obtained for whole supply
chain. However, this approach may become unpractical in cases
where the product’s volume is huge, and consequently several
smart tags are required.
The critical issue to have the interoperability of product’s information
was approached in work of Geerts and O’Leary (2014), that
designed an ontology capable to represent the entire domain of
productions chain. Geerts and O’Leary (2014) defined an ontology
that leverages the availability of an individual thing’s (object) identification
information within the context of a standard set of economic
phenomena, which supported multiple views in a range of
data architectures. This monitoring information can be spread all
over the supply chain, at any time and anywhere. The main goal
was to facilitate the visibility and improve interoperability in the
entire supply network. Consequently, their proposal was able to
recognize location and equipment as ontological primitives, integrating
three different perspectives of supply chain: the physical
flow, the chain custody, and the chain of ownership.
In practice, companies hire specialized transporters to conduct
their shipments to destination points. In order to reduce costs,
the transporters perform cargo delivery coming from different
companies, making the definition of routes fairly complex. Thus,
an optimizer integrated with tracking and tracing system would
provide significant benefits for the whole supply chain. This would
qualify the logistics operation and minimize the error rate, allowing
companies to reduce their costs and remain competitive and
profitable. In addition, there are studies in the literature that aim
to optimize deliveries (Diao & Heching, 2011; Shirazi, Hu, Singh,
Squillante, & Mojsilovic, 2009; Yefu & Zaiyue, 2010). However,
none of the related works considered supports deliveries integrated
with the monitoring system.
None of the related works had routes control, namely, they did
not provide mechanisms to identify either a vehicle is following or
left the planned route. This feature adds security, allowing quickly
detect the occurrence of carjackings and detour in planned routes.
The cargo control was also not found in related works. It allows the
management of inputs and outputs of cargo in the vehicle.
Furthermore, it identifies mistaken deliveries in depot stations,
and cases where cargo is unloaded and it is out of a depot station.
Besides the characteristics listed in Table 1 and in Oliveira et al.
(2013), we expect in our model two additional features not found
in the related works: (1) an efficient delivery management, able to
determine whether a cargo was picked up or delivered correctly;
(2) a mechanism for cargo control able to identify inconsistencies
in the logistics flow, such as mistaken deliveries and pickups and
cargo thefts.