improving supply chain-management based on semantically enriched risk description
DESCRIPTION
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.TRANSCRIPT
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Improving Supply Chain Management Based on
Semantically Enriched Risk Description
Authors:
Sandro Emmenegger (1) Emanuele Laurenzi (1,2,3)
Barbara Thönssen (2,3)
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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.
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The Early Warning System
Prototype
Analyze Information
Assess the risk
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Contents
1. APPRIS Project2. The Early Warning System Prototype 2.1. Risk Assessment 2.2. Risk Monitor
3. Conclusions4. Further Work
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APPRIS (Advanced Procurement Performance and Risk Indicators System)
Project
Dun&Bradstreet
Simmeth
Lexis Nexis
Roche
Müller Martini
..It integrates risk, procurement and knowledge management into one Early Warning System
ETH
FHNW
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Contents
1. APPRIS Project2. The Early Warning System Prototype 2.1. Risk Assessment 2.2. Risk Monitor
3. Conclusions4. Further Work
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The Early Warning System
Prototype
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• Business Event (supplier’s CEO leaves, …)• Force Majeure Event (earthquake, flood, …)(impact on the company’s supply chain risks)
Properties: • Time information,• Source,• Reliability value
Reliability of different sources
Aspect of time(Expectation & Facts)
Reliability (Facts) = Reliability (source) * 1.0 Reliability (Expectation) = Reliability (source) * 0.7
Risk Event
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ScenarioUse Case Example
«Becker AG»
Location: Switzerland
From newspaper:Supplier A (SA) moves from Singapore to Vietnam
No free-trade agreement between Vietnam and Switzerland
From news provider:Supplier B (SB)goes bankrupt
Location:Singapore
Location:Italy
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Use Case
Example
D&B• Country
Code• Location
Status• Bankruptcy
LexisNexis• Location
changes
Web • List of free
trade agr.
WebService
Wrapper(Extraction)
Wrapper
Wrapper
WebService
EventAssembler
Terms Extracted:• Name of SupplierA and B• Bankruptcy • Old/New location • Presence/absence of Free-Trade Agreement
Risk Events Created and Assembled:• LocationChanges Reliability (Expectation)=1.0 * 0.7= 0.7• CompanyBankruptcy Reliability (Facts)=1.0 * 1.0=1.0
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1. APPRIS Project2. The Early Warning System Prototype 2.1. Risk Assessment 2.2. Risk Monitor
3. Conclusions4. Further Work
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Risk Assessment
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Semantic Risk Model• The core risk model:
o Risk Evento Risk Indicatoro Crisis Phase
o Warning Signalo Top 10 Procurement Risk
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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• 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)
…are metrics used to monitor identified risk exposures over time.
Normalization is necessary
Risk Indicator
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Score1: Low Risk2: Medium Risk3: High Risk4: Extreme Risk
WeightedScore= 4 * 1= 4
Use case example…
RI:SupplierBWentBankrupt
RI: N°OfChangedLocationPerYear
Threshold: 3
Threshold: 1WeightedScore = Score * Reliability WeightedScore= 2 *
0.7=1.4
(Risk Event: LocationChanges)
(Risk Event: CompanyBankruptcy)
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Warning Signals
Procurement Risk Pipeline
Procurement Risk Sources
…are pointers to risks and categorized based on sources and
crisis phases
Warning Signals
Grosse-Ruyken and Wagner (2011)
Risk
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Warning signals lead to different degrees of risk severity depending on which crisis phase they belong to
… 0.2 0.5 0.8 1.0
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Use Case Example
(RI:SupplierBWentBankrupt)
(RI: N°OfChangedLocationPerYear)
(Risk Event: LocationChanges)
(Risk Event: CompanyBankruptcy)
Warning Signal:«A subsidiary company of the supplier recently filed for bankruptcy or was recently liquidated»
Warning Signal:«Shifting Production To Other Countries»
Type of Signal:Network-Related
Crisis Phase:Financial Crisis
Type of Signal:Organizational
Crisis Phase:Strategic Crisis
1.0 0.5
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…• More than one warning signal may trigger the same risk
Goal: Aggregating all the warning signals which belong to one of the top10 risks
Aggregated Risk Value
The importance of every substantiated warning signal is kept
(Bankruptcy)
Supplier Default Risk=1-(1-1) *(1-0)*(1-0)… = 1
(LocationChanges)
Supplier Capacity Risk=1-(1-0.5)*(1-0)*(1-0)… = 0.51-(1-0.5)*(1-0.8)*(1-0)… = 0.9
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How to formalize the core risk model???
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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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…• 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
Archimate not formalized enough
ArchiMeo contains relevant concepts for describing an enterprise. (Hinkelmann et al., 2012)
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Employ Background Knowledge
RDBMS
Supplier
«SupplierA» «Singapore»
CountryTriple Store / Ontology
“LocationChanges”
WS:Import custom rules changed
Law
FreetradeAgreement
Singapore
Vietnam BilateralAgreement
Free-trade Agreement?(SPARQL query)
Supplier Disruption Risk
1-(1-0.2) *(1-0)*(1-0)… = 0.2
RI:Free Trade Agreement in Force
«Vietnam»
Vietnam
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Warning Signal:Single Sourcing Market
… Risk Event: CompanyBankruptcy
How many suppliers are left ?(SPARQL)
Supplier Disruption Risk
Type of Signal:Network-Related
Crisis Phase:Strategic Crisis 1-(1-0.2) *(1-0.5)*(1-0)…(1-1) =
0.6
Risk Indicator: Single Supplier Per Product
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1. APPRIS Project2. The Early Warning System Prototype 2.1. Risk Assessment 2.2. Risk Monitor
3. Conclusions4. Further Work
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• Location of supplier and its risk value
are shown on a map
• Notification service (emails)
Risk Monitor
Calculated risk values are shown to the users on an aggregation level appropriate to their roles
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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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Further Work• Formal evaluation by the APPRIS business
partners • Integration of the prototype into supply chain
management system.
Some possible improvements…
• Opportunities
identification could
be considered as
well
• Automated
replacement for a
product
• Support to supplier
selection
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THANK YOU