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

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA Introduction 2. Supply Chain Management Information Systems Challenges 3. A Common Ontology for Supply Chain Management Information Systems 4. Intelligent Agents 5. The Possible Solution 6. Conclusion

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Supply Chain Management is: matching supply and demand profits and costs efficient integration

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Information is very vital in the supply chain: right place & time. efficiency (output : input) customer demand.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Integrated supply chain requires continuous information (Teigen & Fox, 1997). forward flow of materials and backward flow of information

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Companies use Supply chain management information systems for e-business business models and processes motivated by Information and Communication Technology

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 disparate software/ hardware incompatible data formats. Pools/silos of information irregularities in data interchange

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Current integration solutions: Enterprise Integration Architecture Data Layer (database replication) Business Process Management Information Portal

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Solution: Wrap / integrate heterogeneous systems Common Ontology Knowledge representation Intelligent Agents Data format conversion

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Proprietary Software applications Subsystems(ERP, ASP, PDM) vendors. business context and cultures

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 restricted access to information current environment requires shared information

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 incorporate available information to better efficient supply chain (Kadadevaramath et al, 2011). information sharing in a non- invasive manner

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 shared understanding formal Used by intelligent agents

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Objective function Peer review Learn

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Heterogeneous systems (1, 2,..., N). Native autonomous Intelligent Agents i, ii,..., n human counterparts

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 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 … …

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA EDI guidelines/Standards VS non- invasive 2. scalability & flexibility VS robust/ Learning 3. coded parameters VS inference notifications

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 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.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 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.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 This approach answers the question of where and how we can improve the supply chain management information systems interoperability.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 The impact of seamlessly passing information: Reduction of the bullwhip effect. Ease of collaboration. Catalysis of globalisation.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 The ability to make useful inferences to help supply chain executives is an added advantage of the use of intelligent agents.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 Fikes, R. & Farquahr, A. (1999) Distributed Repositories of Highly Expressive Reusable Ontologies. IEEE Intelligent Systems and their Applications, 14 (2), Gruber, T. R. (1995) Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies, 43 (5-6), 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.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 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, 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.

Supply Chain Management Information Systems: An Artificial Intelligence Perspective NUST ORSSA 2011 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.

NUST ORSSA 2011 Supply Chain Management Information Systems: An Artificial Intelligence Perspective