Multi-Agent Based ERP

1. Multi-Agent Based ERP 2003. 10. 10. MAI Lab. Joon Kim

2. A prototype multi-agent ERP system:an integrated architecture and a conceptual framework Bih-Ru Lea, Mahesh C. Gupta, Wen-Bin Yu Department of Business Administration, School of Management and Information Systems, University of Missouri Department of Management, College of Business and Public Administration, University of Lousville Department of Information, Science and Technology, School of Management and Information Systems, University of Missouri Technovation(2003)

3. Contents • Problems with existent ERP systems • Software agent • MAERP architecture MAI Lab. Oct. *MAERP: Multi-Agent based ERP

4. Problems with ERP implementation • Technical aspects • Technology readiness of an organization • Complexity of commercial ERP software • Data loss due to the compatibility of data architectures • Adequacies of redesigned business process • Organizational factors • Resistance to change • Inadequate training • Underestimated implementation time and cost • Strategic view of technology adoption MAI Lab. Oct.

5. Software agent • Properties • Autonomy • Social ability – communication • Reactivity • Pro-activeness MAI Lab. Oct.

6. Software agent • Capabilities • Exploiting domain knowledge • Tolerating error • Using symbols and abstractions • Exhibiting goal-oriented behavior • Learning from the environment • Operating in real time • Communicating using natural language MAI Lab. Oct.

7. Software agent • Types of software agents • Collaborative • Interface • Mobile • Information/Internet • Reactive • Hybrid • Smart MAI Lab. Oct.

8. MAERP* architecture Users in department j Users in department i Interface Agent j Interface Agent i Intranet/network … … Coordination Agent j Coordination Agent i Data Collection Agents j Data Collection Agents i … … Task Agents j1 Task Agents jn Task Agents i1 Task Agents in MAI Lab. Oct. *MAERP: Multi-Agent based ERP

9. MAERP architecture • Coordination agent • Data collection agent • Task agent • Interface agent MAI Lab. Oct.

10. MAERP architecture • Coordination agent • Receiving instruction from / reporting to human users via an interface agent • Assigning data collection / receiving data from a data collection agent • Relaying the dataset • Assigning tasks to / receiving feedback from task agents • Communicating with other coordination agents MAI Lab. Oct.

11. MAERP architecture • Coordination agent • Data collection agent • Retrieving information requested by its coordination agent • Querying specific DBs within the department • Performing data warehousing • Preparing dataset on request from coordination agent MAI Lab. Oct.

12. MAERP architecture • Coordination agent • Data collection agent • Task agent • Receiving data from coordination agent • Performing data analysis • Reporting the results back to coordination agent MAI Lab. Oct.

13. MAERP architecture • Coordination agent • Data collection agent • Task agent • Interface agent • Communicating b/w human users and a coordination agent • Interpreting results • Preparing reports for human users MAI Lab. Oct.

14. MAI Lab. Oct.

15. Revolutionising Plant Automation –The PABADIS* Approach PABADIS White Paper Project by the European Community under the “Information Society Technology” Programme(1998-2002) *PABADIS: Plant Automation Based on Distributed Systems

16. PABADIS project overview • International IMS research project • Partners • Greece, France, Austria, USA, Canada, Switzerland & Germany • Objective • To enable an innovative plug-and-participate environment with scalable and context driven adaptability and flexibility from the ERP-System to the single machine control in single piece production plants MAI Lab. Oct.

17. Motivation Transition from rigid to reconfigurable system MAI Lab. Oct.

18. Motivation • Reconfigurability and flexibility • Advantages of mobile agents • Reducing network load • Actual condition of a plant decided by agents • No permanent network connection needed • Increasing the flexibility of the system MAI Lab. Oct.

19. General idea • Automation using distributed systems • Flattening network hierarchy • IP-based networks down to the control level Plug-and-participate environment • Agents assigned to each physical instance Tightly connected information MAI Lab. Oct.

20. System topology * MAI Lab. Oct. *CMU: Cooperative Manufacturing Units

21. Agent types • Residential agents • Interface b/w CMUs and the agent community • Tied to specific CMU • Product agents • Associated with actual work pieces • Control the manufacturing process • Scheduling, resource allocation, reporting • Plant management agents • Organize the system-wide mfg. Process • Quality management, reporting MAI Lab. Oct.

22. Workflow in a PABADIS plant * MAI Lab. Oct. *RA: Residential Agent

23. Communication b/w LUS* & agents MAI Lab. Oct. *LUS: Look Up Service

24. Benefits • System flexibility improvement • Vertical flexibility • Distribution of MES functions • Use of mobile product agents • Horizontal flexibility • Easy system redesign • Simplification of system design • Reduction of the production process control • Plug-and-participate technology • Refinement of the SCM • Increased system clarity and system openness • Extending the system to the whole supply chain MAI Lab. Oct.

25. Application areas • Production with a high variation and small lot size • Furniture industry • Car manufacturing • Auto industry suppliers • Ancillary industry of aerospace industry • Ancillary industry of machine building MAI Lab. Oct.