NUST ORSSA 2011 K. R. Chilumani, S. B. Mangena, E. G. Mtetwa.

NUST

ORSSA 2011Supply Chain

Management Information Systems: An Artificial

Intelligence PerspectiveK. R. Chilumani, S. B. Mangena, E. G.

Mtetwa

Supply Chain Management Information Systems: An Artificial Intelligence PerspectiveNUST

ORSSA 2011

1. Introduction2. Supply Chain Management Information

Systems Challenges3. A Common Ontology for Supply Chain

Management Information Systems4. Intelligent Agents 5. The Possible Solution6. Conclusion

Presentation Contents:


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Supply Chain Management is:matching supply and demand

profits and costsefficient integration

1. Introduction


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Information is very vital in the supply chain:

right place & time.efficiency (output : input)

customer demand.


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Integrated supply chain requires continuous information (Teigen & Fox, 1997).

forward flow of materials and backward flow of information


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Companies use Supply chain management information systems for e-business

business models and processes motivated by Information and Communication Technology


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disparate software/ hardware

incompatible data formats.Pools/silos of informationirregularities in data interchange


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Current integration solutions:Enterprise Integration Architecture◦Data Layer (database replication)

Business Process Management◦Information Portal


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Solution: Wrap / integrate heterogeneous systems

Common Ontology◦Knowledge representation

Intelligent Agents◦Data format conversion


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2. Supply Chain Management Information Systems Challenges


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Proprietary Software applications

Subsystems(ERP, ASP, PDM) vendors.

business context and cultures


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restricted access to information

current environment requires shared information


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incorporate available information to better efficient supply chain (Kadadevaramath et al, 2011).

information sharing in a non-invasive manner


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3. A Common Ontology for Supply Chain Management Information Systems


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shared understandingformalUsed by intelligent agents


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4. The Intelligent Agents


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Objective functionPeer reviewLearn


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5. The Possible Solution: supply chain management software and intelligent agents


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Heterogeneous systems (1, 2, ..., N).

Native autonomous Intelligent Agents i, ii, ..., n

human counterparts


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Integrated Heterogeneous Systems Architecture

System 1

System N

Intelligent Agent i

Intelligent Agent n

Common Ontology

Artificial Intelligence

System

Intelligent Agent ii

System 2

E-Business System

…

…


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1. EDI guidelines/Standards VS non-invasive

2. scalability & flexibility VS robust/ Learning

3. coded parameters VS inference notifications

Enterprise Integration Architecture or Business Process Management feature VSImprovement offer by Intelligent Agents


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4. switch to other internet applications VS profitable features such as searching and filtering of documents on the internet.

5. past with restricted access VS current shared information environment.

Enterprise Integration Architecture or Business Process Management feature VSImprovement offer by Intelligent Agents


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information flow challenges in e-business systems that have heterogeneous architectures can be circumvented by using intelligent agents that are aware of a common ontology.

6. Conclusion


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This approach answers the question of where and how we can improve the supply chain management information systems interoperability.

6. Conclusion (cont.)


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The impact of seamlessly passing information:

Reduction of the bullwhip effect.

Ease of collaboration.Catalysis of globalisation.


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The ability to make useful inferences to help supply chain executives is an added advantage of the use of intelligent agents.

6. Conclusion (end)


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Fikes, R. & Farquahr, A. (1999) Distributed Repositories of Highly Expressive Reusable Ontologies. IEEE Intelligent Systems and their Applications, 14 (2), 73-79.

Gruber, T. R. (1995) Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies, 43 (5-6), 907-928.

Iosif, V. Mika, P. Larsson, R. Akkermans, H. & Sure, Y. (2003) Handbook on Ontologies in Information Systems. In: Ontology-based Content Management in a Virtual Organization, Series International Handbooks on Information Systems, Verlag, Berlin D, Springer, 447–471.

References


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Kadadevaramath, R., Mohanasundaram, K.M., Rameshkumar, K., Chandrashekhar, B. (2011) Multi Echelon Supply Chain Optimization Using Particle Swarm Intelligence Algorithm, Journal for Manufacturing Science and Production, 8(2-4), 199–212.

Pena, J. (2008) e-Business and the Supply Chain Management, Business Intelligence Journal, 1, 77-90.

Rosse, C. & Mejino, J. L. V. Jr. (2003) A Reference Ontology for Bioinformatics: The Foundational Model of Anatomy, Journal of Biomedical Informatics, 36, 478–500.

References (cont.)


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Sato G. Y., Silva de Azevedo H. J., Barthès J. A. (2011) Agent and multi-agent applications to support distributed communities of practice: a short review, Autonomous Agents and Multi-Agent Systems, 23.

Teigen, R. & Fox, M. S. (1997), Agent Based Design and Simulation of Supply Chain Systems. Proceedings of WET-ICE, IEEE Computer Society Press.

References (end)

NUST

ORSSA 2011

Thank You

Supply Chain Management Information Systems: An Artificial

Intelligence Perspective

NUST ORSSA 2011 K. R. Chilumani, S. B. Mangena, E. G. Mtetwa.

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