To discover risk as early as possible is a major demand of today’s supply-chain- risk-management. This includes analysis of internal resources (e.g. ERP and CRM data) but also of external sources (e.g. entries in the Commercial Register and newspaper reports). It is not so much the problem of getting the information as to analyze and evaluate it near-term, cross-linked and forward-looking. In the APPRIS project an Early- Warning-System (EWS) is developed applying semantic technologies, namely an enterprise ontology and an inference engine, for the assessment of procurement risks. The approach allows for integrating data from various information sources, of various information types (structured and unstructured), and information quality (assured facts, news); automatic identification, validation and quantification of risks and aggregation of assessment results on several granularity levels. For representation the graphical user interface of a project partner’s commercial supply-management-system is used. Motivating scenario is derived from three business project partners’ real requirements for an EWS with special reference to the downstream side of supply chain models, to suppliers’ company structures and single sourcing.

1. Improving Supply Chain Management
Based on Semantically Enriched Risk
Description
1.
2.
Authors:
Sandro Emmenegger (1)
Emanuele Laurenzi (1,2,3) 3.
Barbara Thönssen (2,3)
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2. Discovering risk as early as possible is a major
demand of today’s supply chain risk management
This includes analysis of
• Internal Resources
• External Resources
CHALLENGE:
Analyze and evaluate risk information from multiple
sources in a timely manner.
2

3. The Early Warning System Prototype
Analyze
Information
Assess the risk
3

4. Contents
1. APPRIS Project
2. The Early Warning System Prototype
2.1. Risk Assessment
2.2. Risk Monitor
3. Conclusions
4. Further Work
4

5. APPRIS (Advanced Procurement Performance
and Risk Indicators System) Project
Lexis Nexis
Dun&Bradstreet Müller Martini
Roche
Simmeth
..It integrates
risk, procurement
and knowledge
management
into one
Early Warning System ETH
FHNW
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6. Contents
1. APPRIS Project
2. The Early Warning System Prototype
2.1. Risk Assessment
2.2. Risk Monitor
3. Conclusions
4. Further Work
6

7. The Early Warning System Prototype
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8. Risk Event
• Business Event (supplier’s CEO leaves, …)
• Force Majeure Event (earthquake, flood, …)
(impact on the company’s supply chain risks)
Properties: Aspect of time
• Time information, (Expectation & Facts)
• Source,
• Reliability value
Reliability of
different sources
Reliability (Facts) = Reliability (source) * 1.0
Reliability (Expectation) = Reliability (source) * 0.7
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9. Scenario
Use Case Example
From newspaper:
Supplier A (SA) moves from
Singapore to Vietnam
Location: No free-trade agreement
Singapore between Vietnam and
Switzerland
«Becker AG»
Location:
Switzerland From news provider:
Supplier B (SB)
goes bankrupt
Location:
Italy
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10. Terms Extracted:
Use Case Example • Name of SupplierA and B
• Bankruptcy
D&B • Old/New location
• Country • Presence/absence of Free-Trade
Code Web Agreement
• Location Service
Status
Wrapper
• Bankruptcy
Wrapper Event
…
(Extraction) Assembler
Web
• List of free Wrapper
trade agr.
Risk Events Created and Assembled:
LexisNexis • LocationChanges
Web Reliability (Expectation)=1.0 * 0.7= 0.7
• Location
Service • CompanyBankruptcy
changes
Reliability (Facts)=1.0 * 1.0=1.0
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11. 1. APPRIS Project
2. The Early Warning System Prototype
2.1. Risk Assessment
2.2. Risk Monitor
3. Conclusions
4. Further Work
11

12. Risk Assessment
12

13. Semantic Risk Model
• The core risk model:
o Risk Event o Warning Signal
o Risk Indicator o Top 10 Procurement Risk
o Crisis Phase
In order to be able to measure the risk exposure of a risk
event, we have linked the latter to risk indicators.
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14. Risk Indicator
…are metrics used
to monitor identified
risk exposures over
time.
Normalization is
necessary
• counts the number of events
(e.g. n° of earthquake in the last year in certain area)
• considers the latest event and its value
(e.g. latest company rating delivered by Dun&Bradstreet)
Threshold substantiates warning signal(s)
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15. Use case example… Score
1: Low Risk
(Risk Event: CompanyBankruptcy) 2: Medium Risk
3: High Risk
RI:SupplierBWentBankrupt 4: Extreme Risk
(Risk Event: LocationChanges)
RI:
N°OfChangedLocationPerYear
Threshold: 3
WeightedScore= 4 * 1= 4
WeightedScore = Threshold: 1
Score * Reliability
WeightedScore= 2 * 0.7=1.4
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16. Warning Signals
Procurement Risk Pipeline
…are pointers to risks
and categorized based Risk
on sources and
crisis phases
Procurement Risk Sources
Warning Signals
Grosse-Ruyken and Wagner (2011)
16

17. … 0.2 0.5 0.8 1.0
Warning
signals lead
to different
degrees of
risk severity
depending
on which
crisis phase
they
belong to
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18. Use Case Example
(Risk Event: CompanyBankruptcy)
(RI:SupplierBWentBankrupt) (Risk Event: LocationChanges)
(RI:
Warning Signal: N°OfChangedLocationPerYear)
«A subsidiary company of
the supplier recently filed Warning Signal:
for bankruptcy or was «Shifting Production To
recently liquidated» Other Countries»
Type of Signal: Type of Signal:
Network-Related Organizational
Crisis Phase: Crisis Phase:
Financial Crisis Strategic Crisis
1.0 0.5
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19. …
• More than one warning signal may trigger the same risk
Goal: Aggregating all the warning signals which belong
to one of the top10 risks
The importance of every substantiated warning signal is kept
(Bankruptcy) (LocationChanges)
Supplier Default Risk Supplier Capacity Risk
= =
1-(1-1) *(1-0)*(1-0)… = 1 1-(1-0.5)*(1-0.8)*(1-0)… 0.5
1-(1-0.5)*(1-0)*(1-0)… = = 0.9
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20. How to formalize the core risk model???
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21. Using an Ontology for Enterprise
Modelling…
• No standard yet
• Requirements:
• Formally represented
• Computationally tractable for practical use
• Linked to external data sources
• Based on standards
• Easy to use
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22. …
• None of the existing ontologies have met our requirements
• A new ontology has been developed:
o It is about an enterprise and its relations to its suppliers.
o needs concepts representing relevant aspects of an
enterprise.
The modelling notation Archimate not formalized
ArchiMate enough
ArchiMeo contains relevant
concepts for describing an
enterprise.
(Hinkelmann et al., 2012)
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23. Employ Background Knowledge
“LocationChanges”
Free-trade Agreement?
RDBMS
(SPARQL query)
«SupplierA» «Singapore»
«Vietnam»
RI:Free Trade
Supplier Vietnam Singapore Agreement in Force
Triple Store /
Ontology Country
Law
Vietnam BilateralAgreement FreetradeAgreement
WS:Import custom
rules changed
Supplier Disruption Risk 1-(1-0.2) *(1-0)*(1-0)… = 0.2
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24. … Risk Event:
CompanyBankruptcy
How many suppliers are left ?
(SPARQL)
Risk Indicator:
Type of Signal: Single Supplier Per Product
Network-
Related
Warning Signal:
Crisis Phase: Single Sourcing Market
Strategic Crisis
1-(1-0.2) *(1-0.5)*(1-0)…(1-1) = 0.6
Supplier Disruption Risk
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25. 1. APPRIS Project
2. The Early Warning System Prototype
2.1. Risk Assessment
2.2. Risk Monitor
3. Conclusions
4. Further Work
25

26. Risk Monitor
Calculated risk values are
shown to the users on an
aggregation level appropriate
to their roles
• Location of supplier and its risk value are
shown on a map
• Notification service (emails)
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27. Conclusions
• Detecting risks as early as possible is of vital interest
for all enteprises
• Yet, risks are often detected too late due to
o late publication,
o not recognized importance or
o hidden impacts
• Our approach..
o addresses this problem combining the analysis of different
information sources, types and formats in order to early
identify and assess risks in the supply chain
o contributes significantly to improving risk management in
the supply chain
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28. Further Work
• Formal evaluation by the APPRIS business partners
• Integration of the prototype into supply chain
management system.
Some possible
improvements…
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29. THANK YOU
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