Vision and Challenges for Realising the Internet of Things

monitoring that manufactured goods are likely to reach specific customers by particular deadlines
- or whether there is any need to intervene.
The model may appear as a rather abstract mathematical model for predicting flows of individual
objects within supply chains. We therefore took the decision as a work package to develop
a working software prototype of a modular Track & Trace Analytics Framework, so that
it could be available for use by the business application work packages, for monitoring their
supply chains. At the lowest level of the framework, an Event Gathering Layer interfaces with
the EPC Network architecture and makes queries or registers standing queries with Discovery
Services and EPCIS repositories in order to gather all the available event information about an
individual object from all resources across the supply chain. Furthermore, we recognised that
as objects move across supply chains or throughout their product lifecycle (including the usage
phase), there can be significant changes of aggregation, such as when raw materials and
components are embedded within products, which in turn are aggregated into cases, pallets,
totes and vehicles for distribution and storage. Conversely, in a number of sectors such as the
food or chemical industries, large volumes of bulk product are manufactured and later broken
down into smaller packages for use. The team developed techniques for automatically following
changes of aggregation, to ensure end-to-end tracking, where necessary switching to tracking
a different identifier, such as the identifier of the pallet, vehicle or the identifiers of multiple
products that were broken down from the original bulk product.
Figure 4.7-4: Conceptual diagram of Track & Trace Analytics Framework
Having gathered the event information, it is necessary to build a model of the supply chain in
order to make sense of the movements of the objects. A Supply Chain Modeller was developed,
which was able to analyse the received event information and automatically construct
graphs of nodes and transitions between those nodes. Further manual refinement of the models
is also enabled through the provided graphical user interface.
Track and trace algorithms (both non-probabilistic and probabilistic) were developed in order
to learn flow patterns, filter out false positives and false negatives and provide predictions and
probability estimates about where an object is now, which path it is likely to have taken and
where it is likely to be in the future.
Finally, the modules were integrated and tested using real-time event information from the
automation lab in Cambridge, tracking the movement of autonomous shuttles moving around
a conveyor track. We also used the track & trace analytics framework to analyse data from the
pharmaceutical traceability pilot of BRIDGE.
Deliverable 3.2 on the software prototype was completed at the end of 2008 and the final six
months have mainly focused on completion of the contextual models, which examine how
such a Track & Trace Analytics Framework can be applied to improve manufacturing processes
and traceability, assist with the management of reusable assets (in particular, returnable
transport items such as pallets, roll cages, trays, reusable plastic containers, beer kegs etc.)
and how it can be further extended to support sensor-based condition monitoring as well as
alerting and notification about operational problems within supply chains, such as monitoring
of delays and shrinkage, probabilities of non-arrival of goods or supplies or detection of unusual
or suspicious flow patterns. Based on the analysis within various deliverables of real-
CERP-IoT – Cluster of European Research Projects on the Internet of Things
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