IMPLEMENTATIONS….

1. IMPLEMENTATIONS…. • Product design co-ordination • Modeling and simulation of information flow for performance enhancement: • coal mine • steel processing • hospital operation • maintenance • manufacturing • Concurrent engineering philosophy • Information systems development for environmental issues

2. SUPPORT FOR INTEGRATION TECHNOLOGY EMBODIMENT OF INTEGRATED APPROACH TO INFORMATION PROBLEMS GENERALLY AND INFORMATION SYSTEMS DESIGN SPECIFICALLY

3. SIMULATIONFORPERFORMANCE ENHANCEMENT

4. PROJECTS • steel processing • manufacturing • hospital operation • mining • preventive maintenance • servicing

5. VISUAL SLAM & AWESIM The computer simulation software package that we use is called Visual SLAM. “Visual SLAM supports the modeling of systems from diverse points of view through a graphical based interface. AweSIM is a simulation problem-solving environment for Visual SLAM. AweSIM provides a database, project maintainer, interactive execution environment, standard textual and graphical reports and concurrent and post process animation facilities.”

6. “AIRBORN” SIMULATION

7. SOLVING COMPLEX PROBLEMS COMPLEX SYSTEM DECOMPOSITION REPRESENTATION INTEGRATION INTEGRATED SOLUTION

8. DECOMPOSITION INTO FUNCTIONS AND TASKS

9. CONDITION MONITORING

10. OPEN-CUT COAL MINE

11. SIMULATION REPORTS

12. SIMULATION… • Simulation Modelling can be Applied to the Whole Life Cycle of a Typical Industrial Systems Project. • Probably one of the most important advantages of simulation modelling is its adaptability. It can be easily applied concurrently to all project stages as the project evolves. The stages usually involve (i) concept design, (ii) detailed design, (iii) implementation, and (iv) operation. Using simulation models developed concurrently with each stage we can: • understand basic system operation at the concept design level, • select the best concept to proceed with to the detailed design, • test all proposed operating and control procedures, • test the impact of all design changes made during the implementation stage, • test the impact of all proposed changes during the operation stage, • predict necessary changes in the system operation to follow the envisaged changes in the external and internal environments of the system

13. SIMULATION… • Do not simulate when: • the problem can be solved using common sense analysis, • the problem can be solved analytically, • it's easier to change or perform direct experiment on the real system, • the cost of the simulation exceeds possible savings, • there are not proper resources available for the project, • there is not enough time for the model results to be useful, • there is no data - not even estimates, • the model can not be verified or validated, • project expectations can not be met, • the system's behaviour is too complex or can't be defined.

14. RESEARCH TOWARDS THE DEVELOPMENT OF A PLANNING SYSTEM FOR CONCURRENT ENGINEERING DESIGN

15. SEQUENTIAL PRODUCT DEVELOPMENT PROCESS MARKETING DESIGN PRODUCTION DISTRIBUTION

16. Concurrent Engineering (CE) has been described as(IDA Report 1988): “a systematic approach to the integrated design of products and their related processes, including manufacture and support. This approach is intended to cause the developers from the outset, to consider all elements of the product life cycle from conception through disposal, including quality, cost, schedule and user requirements.”

17. CE BENEFITS: US DEPARTMENT OF DEFENCE • Engineering Change Orders reduced by 50% or greater. • Product development cycle time reduced 40-60%. • Cost to Manufacture reduced 30-40%. • Rework and scrap reduced by up to 75%.

18. KEY FEATURES OF CE: • Cross-Functional Teams (CFT’s) • Concurrent Product Realisation Process Activities • Incremental Information Sharing and Use • Design for “X” (DFX) • Integrated Project Management • Early and Continual Supplier Involvement • Early and Continual Customer Focus

19. CE BUILDING BLOCKS AND CORE PHILOSOPHY

20. THE MAIN ELEMENTS OF CE MANAGEMENT • COLLABORATION • COMMUNICATION • COORDINATION • CONTROL • INFORMATION DEPENDENT SYSTEM

21. SOLVING COMPLEX PROBLEMS COMPLEX SYSTEM DECOMPOSITION REPRESENTATION INTEGRATION INTEGRATED SOLUTION

22. 4W2H TYPE DECISIONS ON TASK PROCESSING

23. GDDI PLANNING CYCLE • Plan generation, • Plan decomposition, • Plan distribution, and • Plan integration

24. PLANNING INTERACTION STRUCTURE

25. BLACKBOARD DATABASE CONTROL STRUCTURE BLACKBOARD DATABASE KNOWLEDGE SOURCES PLAN INTEGRATION PLAN DECOMPOSITION KNOWLEDGE SOURCES PLAN DISTRIBUTION KNOWLEDGE SOURCES PLAN GENERATION KSAR CONTROL SOURCE

26. SOME MAIN CHALLENGES FOR FUTURE RESEARCH: • TRANSFORMATION OF INFORMATION INTO KNOWLEDGE • KNOWLEDGE REPRESENATION (Davis, Davenport, Prusak, Grudzewski, Hejduk, Probst, Takeuchi, Minsky, Boahene, Ditsa, Mitchell, Gonzalez, Pukszta)

27. Knowledge management is an emerging, interdisciplinary business model dealing with all aspects of knowledge within the context of the firm, including knowledge creation, codification, and sharing, and using these activities to promote learning and innovation. It encompasses both technological tools and organizational routines of which there are a numberof components. These include generating new knowledge; acquiring valuable knowledge from outside sources; using this knowledge in decision making; embedding knowledge in processes, products, and/or services; coding information into documents, databases, and software; facilitating knowledgegrowth; transferring knowledge to other parts of the organization; and measuring the value of knowledge assets and/or the impact of knowledge management.

28. PHASE 1 (FINISHED) – 2005-07, Project supported by grants from AAS and DAAD; University of Newcastle, Fraunhofer Berlin, University of Hannover, RWTH Aachen, Gdansk University of Technology, UC Berkeley • Knowledge Management • Proposed Platform • Five Fundamentals • KSCS

29. PHASE 2 – 2007-10, Project supported by grants from AAS and NSF; University of Newcastle, UC Berkeley, Technical University of Gdansk • KR • Proposed KR • Set of Experience • Converting Set of Experience • Set of Experience in XML

30. PHASE 3 – 2010-13, Project supported by grants from AAS, DAAD, MARIE CURIE EU Program • e-Community development

31. Proposed Platform… • Multi-SourceKnowledge-Experience Management System. • Integrated tool of rule-based systems, experts systems, numerical models, and self-learning technology to help in the decision-making process. • Interrelated net of similar systems supplying knowledge and sharing experiences and perceptions of their own worlds. KNOWLEDGE SUPPLY CHAIN SYSTEM (KSCS)

32. U V If X>70 then K = good R If W<2 then Z = 2 Z = 0.78 If G=blue then B = high K = average X = 100 H = good RtÈRlÈRtl W = 1.5 G = blue Y = 210 B = high V = 8451.54 C 2X+3Y-V <= 3450 Vl Vt H>=Excellent G<>blue AND Y+70X<2500 F Max P=3X-2Y+RQ CtÈCl Max K=Excellent Min C=YQ AND B=high FtÈFlÈFtl Set of Experience Knowledge Structure The four components are variables, functions, constraints, and rules, and constitute the knowledge structure. Set of Experience Ei = (Vij, Fi, Ci, Ri)

33. Image credit U.S. Department of Energy Human Genome Program (http://www.ornl.gov/hgmis). Variables Rules Functions Constraints Set of Experience Knowledge Structure • Surrogate • Set of Ontological Commitments • Fragmentary Theory of Intelligent Reasoning • Medium for Efficient Computation • Medium of Human Expression

34. ? KNOWLEDGE INFORMATION DATA

35. WISDOM KNOWLEDGE INFORMATION DATA

37. Wisdom versus Knowledge: What's the Difference? "I don't know and so, I don't do.I now know, but I haven't learned how.I've learned how and so, I do and I teach others." - Rodolfo Neiva de Sousa

38. TRUST KNOWLEDGE INFORMATION DATA

40. Trust Knowledge Security Decisional DNA RO Decisional Chromosome Ontology SOEKS Rules Constraints Functions Variables Decisional Trust