EPCIS Event-Based Traceability in Pharmaceutical Supply Chains via Automated Generation of Linked Pedigrees

ISWC, 21st October 2014, Riva del Garda
Enabling EPCIS Event-Based Traceability
in Pharmaceutical Supply Chains via
Automated Generation of Linked
Pedigrees
Monika Solanki
https://w3id.org/people/msolanki
@nimonika
Aston Business School
Aston University, Birmingham, UK

Motivation
m.solanki@aston.ac.uk, @nimonika EPCIS Event-Based Traceability in Pharmaceutical Supply Chains

Visibility* in supply chains
Visibility is the ability to know exactly where
things are at any point in time, or where they
have been, and why.
*http://www.gs1.org/docs/GS1_SupplyChainVisibility_
WhitePaper.pdf

Enabiling Visibility
Data/Knowledge Sharing
Information and knowledge need to be interlinked, shared
and made available consistently along the supply chain
not least for regulatory reasons but also due to increasing
consumer demands of being able to track and trace
commodities.

Pharmaceutical supply chains
Flow of goods and flow of information (Abstraction)

Visibility in Pharmaceutical supply chains
Crucially Important!
Counterfeiting has increasingly become one of the major
problems prevalent in these chains. The WHO estimates
that between five and eight percent of the worldwide trade
in pharmaceuticals is counterfeit.

Visibility in Pharmaceutical supply chains
Legal requirements
Drug Supply Chain Security Act (DSCSA)
The Drug Quality and Security Act (DQSA), was signed
into law by President Obama in 2013.
DSCSA outlines critical steps to build an electronic,
interoperable system to identify and trace certain
prescription drugs as they are distributed in the U.S.
The system will facilitate the exchange of information at the
individual package level about where a drug has been in
the supply chain.
http://www.fda.gov/Drugs/DrugSafety/
DrugIntegrityandSupplyChainSecurity/
DrugSupplyChainSecurityAct/

GS1 standards* for Visibility
GS1: a neutral, not-for-profit organization dedicated to the
design and implementation of global standards and
solutions to improve the efficiency and visibility in supply
chains.
Core GS1 standards: EPCIS 1.1 & CBV 1.1
GS1 US Secure Supply Chain Task Force: preliminary
implementation guidelines* for applying GS1 Standards to
U.S. Pharmaceutical supply chains for track and trace.
*http://www.gs1.org/healthcare/standards
*www.gs1us.org/RxGuideline

EPC, EPCIS and CBV
The Electronic Product Code (EPC)*: provides products
with unique, serialised identities.
Electronic Product Code Information Services (EPCIS)*:
provides a set of specifications for the syntactic capture
and informal semantic interpretation of EPC based product
information.
CBV* supplements EPCIS by defining the structure of
vocabularies and specific values for the vocabulary
elements.
Events as abstractions for traceability.
*http://en.wikipedia.org/wiki/Electronic_Product_Code
*http://www.gs1.org/gsmp/kc/epcglobal/epcis
*http://www.gs1.org/gsmp/kc/epcglobal/cbv

SW & LD for Visibility in Supply chains
Problem statement
* Can we exploit EPCIS events for the automated
generation of provenance-based traceability/visibility
artifacts that can be shared across supply chain partners?
* Can we exploit EPCs of products to detect counterfeits
in the supply chain and utilise provenance to trace their
origins?

SW & LD for Visibility in Supply chains
Potential solution & Proposed framework
Based on GS1’s EPCIS 1.1 and CBV 1.1 standards.
A set of ontologies: EEM, CBVVocab, OntoPedigree.
Streams of EPCIS events.
Event-Based traceability artifact: Linked Pedigrees.
Algorithm: automated generation of linked pedigrees from
EPCIS events and counterfeit detection.

EPCIS(1.1) Events: An informal Intuition
One generic and four specific physical event types
For this talk,
EPCISEvent: the generic EPCIS event.
ObjectEvent: an event that occurred as a result of some
action on one or more entities denoted by EPCs.
AggregationEvent: an event that happened to one or more
EPC-denoted entities that are physically aggregated.
TransactionEvent: an event in which one or more entities
denoted by EPCs become associated or disassociated
with one or more identified business transactions.

Data model components
What(product(s)), Where(location), When(time), and
Why(business step and status) of events (product movement)
occurring in any supply chain.
EPCs
Time
Read Points
Business Location
Business steps
Disposition
Action

EEM*: The EPCIS Event Model
Focuses on a tight conformance with the EPCIS 1.1
standard and Simplicity.
Explicitly defines relationships with CBV entities through
CBVVocab*.
EEM has been mapped* to PROV-O*.
*http://purl.org/eem#
*www.w3.org/ns/prov-o
*http://purl.org/cbv#
*http://fispace.aston.ac.uk/ontologies/eem_prov.html

EEM Modules

EEM Entities: Mapping to PROV-O

Pedigrees
Most widely prevalent in the pharmaceutical industry.
Pedigree (e-pedigree) is an audit trail that records the path
and ownership of a drug as it moves through the supply
chain.
Each stakeholder involved in the manufacture or
distribution of the drug adds information to the pedigree.
“Event-based Linked Pedigrees”: pedigrees based on a
relevant subset of the captured EPCIS events.
cf. COLD, DeRiVE @ ISWC 2013

OntoPedigree: A CO design pattern

Flow of linked pedigrees (Abstraction)

Automated generation of Linked Pedigrees
Set of EPCIS events (cf. EEM ontology):
Etypes = fOe;Ae; Teg, where Oe is an ObjectEvent, Ae is
an AggregationEvent and Te is a
TransactionEvent.
Set of business step types (cf. CBVVocab ontology):
Bsteps = fcom; pck; shpg representing the business steps
of “commissioning”, “packing” and “shipping” respectively.

Let I be the set of IRI references and G the set of RDF Graphs*
An EPCIS event E is a 6-tuple hIe; to; tr ; et ; bs;Rei where,
Ie 2 I is the IRI for the event.
to is the time at which the event occurred.
tr is the time at which the event was recorded (timestamp).
et 2 Etypes is the type of event.
bs 2 Bsteps is the business step.
Re is a non empty set of EPCs associated with the event.
* J. J. Carroll, C. Bizer, P. Hayes, and P. Stickler. Named graphs, provenance and trust.
In Proceedings of the 14th International Conference on World Wide Web, WWW ’05.
ACM, 2005.

An EPCIS event named graph, Eg, is a pair (In 2 I;Ge 2 G),
where In is the name (as well as the IRI) of the event graph and
Ge is the RDF event graph.
Functions:
The event graph: eventGraph

Eg

The event IRI: eventIRI

eventGraph

Eg

Time of occurrence to:
eventOccurrenceTime

eventGraph

Eg

An EPCIS stream (Gs) is an ordered sequence of RDF triples
h(In; eventRecordedAt; tr ) : [to]i published at an IRI Is 2 I, and
ordered by to. The set of triples in Gs are valid at timestamp tr .

Two step approach for extracting events from EPCIS streams
Step 1: (Qt ) All event graphs (serialised in TRIG) within a
time interval of X hrs (tumbling windows) are selected from
the event stream
Step 2: (Qbs) All events corresponding to the business
steps of commissioning, aggregating and shipping are
extracted from each event graph.
Please refer to the paper for SPARQL queries.

Counterfeit EPC detection
All EPCs that are part of an Aggregation event have been
actually commissioned and asserted as part of an Object
event.
If the business step is “packing” for an Aggregation event,
we further check if the EPCs included in the event have
indeed been commissioned as part of an Object event with
business step “commissioning”.
SPARQL queries (to retrieve the events) + implementation
(to check for counterfeits).
Lazy approach: Counterfeit detection happens at pedigree
generation time.
Can be supplemented with other approaches for
counterfeit detection.

Automated Generation of Linked Pedigrees

Evaluation
Two critical timing requirements for pedigree generation.
The time taken to detect counterfeit products in
varying volumes of shipments: This is important as
counterfeits have to be detected either before or along with
the pedigree generation.
The time taken for pedigree generation: This time is
crucial as pedigree generation for a specific shipment must
be initiated as soon as a shipping event for the shipment is
recorded. The pedigree must be published imminently
when the shipment is dispatched.

Evaluation
EPCIS Event volumes
Data Sources: Sample EPCIS relational data, Grey
literature, interviews, surveys, EPCIS experts
Assumption: an average rate of production as 6 days per
week and 10 hours per day,
Commissioning events generated based on the number of
items ranging from 24,000 to 102,000 per day or
approximately 40 to 170 per minute.
Aggregation and shipping events generated considering
aggregated items ranging from 100 to 500 (increments of
100) per case and number of cases per pallet ranging from
20 to 100 (increments of 20).
Tumbling window sizes of 3, 5, 7 and 10 hours respectively.

Evaluation

Evaluation: Architecture and Implementation

Evaluation: Results

Summary
Semantic Web standards, ontologies and linked data can
be utilised to record and represent real time supply chain
knowledge via “linked pedigrees”.
EEM forms the basis for traceability in supply chains -
Event-based Linked Pedigrees.
Complex Event Processing over continuous streams of
semantically interlinked EPCIS event datasets enable
automated generation of linked pedigrees, detection of
exceptions and validation of integrity constraints.
The proposed approach is domain independent and can
be widely applied to most scenarios of traceability as long
as there is conformance to EPCIS 1.1 in the supply chain.

Further information
M. Solanki and C. Brewster. EPCIS event-based traceability in
pharmaceutical supply chains via automated generation of linked
pedigrees. ISWC 2014. Springer-Verlag.
M. Solanki and C. Brewster. A Knowledge Driven Approach towards the
Validation of Externally Acquired Traceability Datasets in Supply Chain
Business Processes. EKAW 2014. Springer-Verlag.
M. Solanki and C. Brewster. Modelling and Linking transformations in
EPCIS governing supply chain business processes. EC-Web 2014.
Springer-LNBIP.
M. Solanki and C. Brewster. Detecting EPCIS Exceptions in linked
traceability streams across supply chain business processes.
SEMANTiCS 2014. ACM-ICPS.
M. Solanki and C. Brewster. Consuming Linked data in Supply Chains:
Enabling data visibility via Linked Pedigrees. COLD2013 at ISWC,
volume Vol-1034. CEUR-WS.org proceedings, 2013.
M. Solanki and C. Brewster. Representing Supply Chain Events on the
Web of Data. DeRiVE at ISWC. CEUR-WS.org proceedings, 2013.
http://windermere.aston.ac.uk/~monika/ontologies.html
http://windermere.aston.ac.uk/~monika/publication.html

EPCIS Event-Based Traceability in Pharmaceutical Supply Chains via Automated Generation of Linked Pedigrees

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