Semantic interoperability of Supply Chain networks

Milan Zdravković, Miroslav TrajanovićFaculty of Mechanical Engineering, University of Niš

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

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

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”

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

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.

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)• )

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?

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

But,.. which conceptualization ?

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

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.

Communication (psychology)

SensationPerception

Expectation

Experience

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

CognitionMemorizin

g

Reasoning

ProblemSolving Conceptualizing

Imagining

• 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...

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• CalculationAnalysis• Trial-and-error• Root-Cause analysis• Impact-Difficulty analysis• KPI’s• ……..

Operations • Translation• Data mapping• Ontology matchingMeans• Enterprise model• Goal model• Strategy, policy, plans, standards• Reference models• Dictionaries, taxonomies• ……

Communication is often association game

• Conceptualization• Extensional – enumeration

of the set elements• Example – WordNet,

reference models

Contextualization

Communication

Levels of abstraction

• Intensional – specifying necessary and sufficient conditions for set elements

• Example – DOLCE• Cogito, ergo sum (I think,

therefore I am)

When it doesn’t work

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)

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

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

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 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_For_Return_Processes)

Problem domain (research scope)

Formal modelof supply chain

Enterprisesemantics

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

SCOR (Supply Chain Operations Reference)

Ontological framework

SCOR-KOS OWL

Native formatsExchange formats

SCOR-FULL OWL

SCOR-SYS OWL

SCOR-GOAL OWL

SCOR-MAP OWL

SCOR-CFG OWL

Domainontologies

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

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

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.

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)

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

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

Thank you for your attention

Milan Zdravković, Miroslav TrajanovićFaculty of Mechanical Engineering, University of Niš

milan.zdravkovic@masfak.ni.ac.rs

traja@masfak.ni.ac.rs

Semantic interoperability of Supply Chain networks