Semantic interoperability
of Supply Chain networks
Milan Zdravković, Miroslav Trajanović
Faculty of Mechanical Engineering...
Motivation: Key issues of
traditional supply chains
• Traditional supply chains are relation-oriented
• Supplier Relations...
Examples of possible
problems
• High-speed, low-cost supply chains are unable to
respond to unexpected structural changes ...
Issues source: “Lost in
translation”
• There is NO lingua franca
for enterprises, they all
“speak” different languages
• H...
Some of the Holly Grails of supply
chains
• Loose networks (virtual enterprises)
• Flexibility
• Internal – participation ...
What is interoperability ?
• ISO/IEC 2382
• 01.01.47 interoperability: The capability to
communicate, execute programs, or...
What is semantic
interoperability ?
• system(S) ∧ system(R) ∧ semantically-
interoperable(S,R) ⇒
• ∀p (
• (transmitted-fro...
Common questions
• How can I keep my own semantics without
being forced to use those of other partners?
• How can I link t...
Good start
• Development of an enterprise message
model as a reference point for flexible
economic integration
• Message m...
But,.. which conceptualization ?
Enterprise Architecture
• The history of Enterprise Architecture goes back
20 years, but the field is still rapidly evolvi...
What are Enterprise
architectures ?
• Explicit conceptualizations of the
enterprise, specified by a language or
notation, ...
Communication (psychology)
SensationPerception
Expectatio
n
Experienc
e
Physiology
Stimulus
sensory
energy
Process of
orga...
Analogies (and what is involved)
Sensation
Perception
Cognition
Articulation
• Web service
• Database trigger
• Sensor
• C...
Communication is often
association game
• Conceptualization
• Extensional – enumeration
of the set elements
• Example – Wo...
What kind of conceptualization we
need for supply chain networks ?
• Many of them, in different contexts (inter-
related)
...
Cn
C1
C2
Implementation of
semantically interoperable
systems
OL1
OD1
OL2
ML1D1
ML2D1
MO1O2≡f(ML1D1 , ML2D1)
S1
S2
MLnD1
S...
Adding contexts improves
expressiveness of a framework
• if there exist systems S1 and S2, driven by
the ontologies O1 and...
Our approach
SCOR-based interoperability in supply
chain networks
Used assumptions
• Domain knowledge evolves at highest
rate at lower levels of abstraction, in
domain community interactio...
Problem domain (research
scope)
Formal model
of supply chain
Enterprise
semantics
Implementation
- Enterprise semantics in...
SCOR (Supply Chain
Operations Reference)
Ontological framework
SCOR-KOS OWL
Native formats
Exchange formats
SCOR-FULL OWL
SCOR-SYS OWL
SCOR-GOAL OWL
SCOR-MAP OWL
S...
SCOR-FULL
• Semantic enrichment of SCOR
• Developed by semantic analysis of SCOR Input/Output
elements, identification of ...
SCOR – FULL Concepts
• Course: prescriptions of ordered sets of
tasks: activity, process, method,
procedure, strategy or p...
SCOR – FULL Resource Items
• Information Item aggregates the atomic, exchangeable objects in
enterprise environment (Order...
Example App: Web
application for SCOR process
configuration
• Features
• Development of
complex thread
diagrams (multiple
...
Example App: EIS Databases
as local ontologies
• OWL representation of ER
model according to proposed
formalization
• Meta...
Thank you for your
attention
Milan Zdravković, Miroslav Trajanović
Faculty of Mechanical Engineering, University of Niš
mi...
Upcoming SlideShare
Loading in …5
×

Zdravković Milan, Trajanović Miroslav. Semantic interoperability of Supply Chain networks

1,057 views
929 views

Published on

Presentation from the 1st Workshop on Future Internet Enterprise Systems - FINES 2010: Ontologies and Interoperability, made at 10.11.2010 in Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
1,057
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
13
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • A sender's system S is _semantically operable_ with a receiver's system R if and only if the follow condition holds for any data p that is transmitted from S to R:
    For every statement q that is implied by p on the system S,
    there is a statement q' on the system R that
    (1) is implied by p on the system R, and
    (2) is logically equivalent to q.
    the receiver must at least be able to derive a logically equivalent implication for every implication of the sender's system.
  • Interplay of 4 physiological and psychological groups of processes:
  • Zdravković Milan, Trajanović Miroslav. Semantic interoperability of Supply Chain networks

    1. 1. Semantic interoperability of Supply Chain networks Milan Zdravković, Miroslav Trajanović Faculty of Mechanical Engineering, University of Niš
    2. 2. Motivation: Key issues of traditional supply chains • Traditional supply chains are relation-oriented • Supplier Relationship Management is 80% human effort and 20% information technology • Establish, maintain, develop, discontinue • There is a tendency to reduce number of suppliers because of possible relation cost reductions • Relations are dyadic – rarely expanded to include vendors’ vendors or customers’ customers • Relationships are simple – arm’s length • Next levels are -> Limited coordination -> Activities of multiple divisions are integrated -> Each company views the other as an extension of itself
    3. 3. Examples of possible problems • High-speed, low-cost supply chains are unable to respond to unexpected structural changes in (customized) demand or supply • High level of integration reduces flexibility of small and medium enterprises • Simple capacity increase does not improve the capability of SME to participate in many supply chains, always • Investments in technical framework for enterprise integration, which could maximize the efficiency and productivity cannot be justified in a short term
    4. 4. Issues source: “Lost in translation” • There is NO lingua franca for enterprises, they all “speak” different languages • However, some are “less different” than the others: • Enterprise models (loose alphabets) • Reference models (strict alphabets) • Ontologies (formal alphabets) English translation of Welsh: “I am not in the office at the moment. Please send any work to be translated”
    5. 5. Some of the Holly Grails of supply chains • Loose networks (virtual enterprises) • Flexibility • Internal – participation in as many supply chains as possible • External – respond to market (supply and demand) changes • Interoperability, not integration • Based on • Technology (semantics) • Trust • Commitment
    6. 6. What is interoperability ? • ISO/IEC 2382 • 01.01.47 interoperability: The capability to communicate, execute programs, or transfer data among various functional units in a manner that requires the user to have little or no knowledge of the unique characteristics of those units.
    7. 7. What is semantic interoperability ? • system(S) ∧ system(R) ∧ semantically- interoperable(S,R) ⇒ • ∀p ( • (transmitted-from(p,S) ∧ transmitted-to(p,R)) ∧ • ∀q(statement-of(q,S) ∧ p⇒q) ∃q’(statement- of(q’,R) ∧ p⇒q’ ∧ q’⇔q) • )
    8. 8. Common questions • How can I keep my own semantics without being forced to use those of other partners? • How can I link the semantics of my documents and messages with those of my customers? • How can I create new data structures which are based on real world use rather than theory? • How is it possible I can trade with any party with minimal reconfiguration no matter what their country, language, systems, models?
    9. 9. Good start • Development of an enterprise message model as a reference point for flexible economic integration • Message model is based on enterprise model • Model is based on enterprise conceptualization
    10. 10. But,.. which conceptualization ?
    11. 11. Enterprise Architecture • The history of Enterprise Architecture goes back 20 years, but the field is still rapidly evolving • Why ? • The complexity of IT systems have exponentially increased, while the expectations for deriving real value from those systems have decreased • Lack of work in making existing architectures compatible • Research in formalizing existing architectures, models, frameworks, etc. and defining correspondences between those very much needed
    12. 12. What are Enterprise architectures ? • Explicit conceptualizations of the enterprise, specified by a language or notation, in different, inter-related contexts (views, perspectives) • Are Enterprise architectures ≡ ontologies ? • Are Enterprise architectures ≡ formal theories ? • We need those to communicate.
    13. 13. Communication (psychology) SensationPerception Expectatio n Experienc e Physiology Stimulus sensory energy Process of organizing, analyzing and providing meaning to various sensations Selection of sensations – which information is worth percepting Culture Transduction, transformation of stimulus energy to electrochemical one Psychology Consciousness Cognition Memorizin g Reasoning Problem Solving Conceptualizing Imaginin g • Attributes or features (can be general, defining or characteristic) are combined into concepts. • Concepts are combined into propositions. • Multiple propositions are combined to build mental models. • Mental models are combined into schemas. Mental processes involved in gaining knowledge and comprehension (information processing) What is ontology ? Explicit specification of...
    14. 14. Analogies (and what is involved) Sensation Perception Cognition Articulation • Web service • Database trigger • Sensor • Camera, Microphone • Phone, Fax • Software agent • User interface • RFID interrogators • GPS device • ….. Content • Work order (manufacturing, purchase, deliver, replenish, refund, …) Language •XML message • SQL query • ……. Operations • Storage • Inference • Calculation Analysis • Trial-and-error • Root-Cause analysis • Impact-Difficulty analysis • KPI’s • …….. Operations • Translation • Data mapping • Ontology matching Means • Enterprise model • Goal model • Strategy, policy, plans, standards • Reference models • Dictionaries, taxonomies • ……
    15. 15. Communication is often association game • Conceptualization • Extensional – enumeration of the set elements • Example – WordNet, reference models Contextualization Communication Levelsofabstraction • Intensional – specifying necessary and sufficient conditions for set elements • Example – DOLCE • Cogito, ergo sum (I think, therefore I am) When it doesn’t work
    16. 16. What kind of conceptualization we need for supply chain networks ? • Many of them, in different contexts (inter- related) • Approach compatible with enterprise architectures • Access control, security • Many of them, in different levels of abstraction • Abstractions and specializations are used to correlate concepts • Experts communicate in “their own” language • Managers communicate in “abstract” languages • Intensional conceptualization • “Easier” perception (ontology matching)
    17. 17. Cn C1 C2 Implementation of semantically interoperable systems OL1 OD1 OL2 ML1D1 ML2D1 MO1O2≡f(ML1D1 , ML2D1) S1 S2 MLnD1 Sn OLn MO1On≡f(ML1D1 , MLnD1) OD2 Si OLi MLiD2 MD1D2 MO1Oi≡f(ML1D1 , MD1D2, MLiD2) • S1-Sn – Enterprise Information Systems • OL1-OL2 – Conceptualizations of EIS-s – local ontologies • OD1,2 – Domain ontologies • MLiDi – Mappings between local and domain ontologies
    18. 18. Adding contexts improves expressiveness of a framework • if there exist systems S1 and S2, driven by the ontologies O1 and O2, • and if there exist alignment between these ontologies O1≡O2, • the competence of O1 will be improved and S1 will be enabled to make more qualified conclusions about its domain of interest
    19. 19. Our approach SCOR-based interoperability in supply chain networks
    20. 20. Used assumptions • Domain knowledge evolves at highest rate at lower levels of abstraction, in domain community interaction • Consensus is more likely to be reached • It is relatively easy to make a common agreement on thesaurus, relationships between concepts and business rules • This is not the case with generalizations and abstractions • Bottom-up approach • Implicit semantics of specific models is too implicit for automated semantic matching • Matching of ontologies on same level of abstraction produce best results • Thus, coherence between creation, evolution and use of specific, highly contextualized knowledge and development of formal expressive models is a very important • business-rule(x) ∧ return-process(y) ∧ has-rule(y, x) ⇒ • SameAs(x, Business_Rules_F or_Return_Proces ses)
    21. 21. Problem domain (research scope) Formal model of supply chain Enterprise semantics Implementation - Enterprise semantics in context of supply chain - Ontology matching - Semantic annotation of services and resources - Ontology matching - Rule-based process configuration - Local ontologies - Web services, exposing focal concepts - Enterprise architectures and models are formalized - on different levels of abstraction - in different contexts - Supply chain model is formalized - on different levels of abstraction
    22. 22. SCOR (Supply Chain Operations Reference)
    23. 23. Ontological framework SCOR-KOS OWL Native formats Exchange formats SCOR-FULL OWL SCOR-SYS OWL SCOR-GOAL OWL SCOR-MAP OWL SCOR-CFG OWL Domain ontologies Implicit semantics of SCOR elements SCOR’s semantic enrichment - identifies common enterprise notions, - maps those to SCOR entities - classifies them into more general concepts Semantics of SCOR-FULL concepts is defined externallyHelper ontology Design goals -> Problem ontologies
    24. 24. SCOR-FULL • Semantic enrichment of SCOR • Developed by semantic analysis of SCOR Input/Output elements, identification of core terms and their categorization • Scope is strictly limited to using the common enterprise notions for expressing the existing elements of SCOR model • Semantics of the common enterprise notions is defined externally • It extends SCOR-SYSTEM ontology, which formalizes the SCOR System element • It is extended by SCOR-GOAL ontology, which semantically maps its concepts to SCOR Performance Metrics element
    25. 25. SCOR – FULL Concepts • Course: prescriptions of ordered sets of tasks: activity, process, method, procedure, strategy or plan • Quality: general attribute of a course, agent or function which can be perceived or measured: capability, capacity, availability, performance, cost or time/location data, etc. • Setting: aggregates semantically defined features of the context in which course take place - rules, metrics, requirements, constraints, objectives, goals, assumptions, etc. • Function: entails elements of the horizontal business organization, such as stocking, shipping, control, sales, replenishment, return, delivery, disposition, maintenance, production, etc.
    26. 26. SCOR – FULL Resource Items • Information Item aggregates the atomic, exchangeable objects in enterprise environment (Order, Forecast, Budget, Contract, Report, Proposal, Bill-of-Material, etc) • Configured Item • (Phy-Item(x) ∨ Inf-Item(x)) ∧ has-state(x,state(y)) ⇒ Conf-Item(x) • y∈(Adjusted, Approved, Authorized, Completed, Delivered, Installed, Loaded, Planned, Released, Returned, Updated, Validated,..) • Communicated Item • Course(x)∧Conf-Item(y)∧issue(x,z)∧communicates(z,y) ⇒ Comm-Item(z) • z∈(Request, Response, Notice, Signal, Receipt)
    27. 27. Example App: Web application for SCOR process configuration • Features • Development of complex thread diagrams (multiple tiers, additional participants) • Generation of process models and workflows (including PLAN activities) • Generation of implementation roadmap
    28. 28. Example App: EIS Databases as local ontologies • OWL representation of ER model according to proposed formalization • Meta-model, which classifies future OWL concepts and domains and ranges of the object and data properties • Generated local ontology, with mappings to a meta-model instances • Algorithm for selection of focal concepts and generation and deployment of web services • Synchronization scenarios and implementations Rules for conceptualization of database schema patterns
    29. 29. Thank you for your attention Milan Zdravković, Miroslav Trajanović Faculty of Mechanical Engineering, University of Niš milan.zdravkovic@masfak.ni.ac.rs traja@masfak.ni.ac.rs Semantic interoperability of Supply Chain networks

    ×