FUBUTEC'2005

March  10-11, 2005 -  INSEAD, Fontainebleau, France

Conference Themes

Integrating Simulation and Knowledge Management for Business Advantage

ETI

ISA

Conference Themes

STATISTICAL ANALYSIS AND DATA MINING OF BUSINESS PROCESSES

Advanced statistical techniques for Business: dependency networks, influence diagrams, data envelopment analysis, data mining
Design of Methodological Studies
Machine Learning Techniques with applications in business
Data warehouses, OLAP, meta-data related issues and such
Neural Networks and Support-vector machines in Business
Genetic algorithms in Business
Decision/Classification/Regression Trees and rule-based systems in Business
K-Nearest Neighbours, case-based reasoning and other Methods
Fuzzy-related methodology in Business
Verification and/or validation techniques
Adaptive systems related to machine learning/data mining
Clustering methodologies with applications in business
Time-series methods/applications for Business
Other identification-methods applicable for Business

APPLICATIONS IN DATA MINING

Data Mining or fuzzy-related Tools
Text-mining related to Business
Web Data mining and information retrieval
Fraud-detection, CRM, SRM, ERM

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DISCRETE EVENT SIMULATION AND QUEUEING SYSTEMS IN BUSINESS AND ECONOMICS

AGENTS IN BUSINESS AUTOMATION AND ECONOMICS

Knowledge Management, Intelligent Business Agents, Human and Agent Interaction, Virtual-Agent Based Marketplaces, Automated Shopping and Trading Agents, Agents and E-Commerce, Legal Aspects of agents in e-commerce, performance measurement of e-commerce agents, rational information agents and electronic commerce, Agent-Based Trade and Mediating Services; Virtual Trading Institutions

Agent-Based Interface for Business Automation
Agent-Based Data Retrieval for Business Automation
Agent-Based Automatic Demand/Supply Formation for Business Automation
Agent-Based Demand/Supply Matching for Business Automation
Agent-Based Transaction Formation for Business Automation
Agent-Based Transaction Implementation for Business Automation
Agent-Based Logistics and Workflow Management for Business Automation
Modeling; System Design and Validation for Agent-Based Business Automation

Applications

Agent Methodology

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SIMULATION IN BUSINESS AND ECONOMICS

Business process engineering and simulation
Simulation in customer-oriented service processes
Decision support systems
Simulation in Finance and Risk Management
Simulation in workflow management
Process mapping and simulation
Simulation of production processes and equipment
Business Information Modeling Methods and Methodologies
UML and UP in Business Modelling
UML and UP for Enterprise Modelling

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SIMULATION IN BUSINESS GAMES

Enhancing strategic thinking and decision making capabilities using simulation games
Simulating Key Management and Strategic Issues
Customized Business Simulation Applications
Systems Thinking and System Dynamics
Visualizing, analyzing and solving challenges and opportunities
Using games to achieve strategic objectives
Simulation with "man in the loop"
Virtual Reality systems
Realistic presentation of simulation results
Simulation for training and education
Web-based simulation
Multi-site Group simulation
Simulation of Emergency procedures (Disaster gaming)

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The skill of the management scientist lies in the ability to balance the reality of the developed model with the effort that it will take to find an answer to the problem. Many decision support systems (DSS) use representational models to predict the impact of possible decisions. There are two major types of model: simulation and analytical. A more subjective approach to decision-making is needed. While information and some form of data processing are essential, the decision-maker should adopt a less structured approach to the way the data is used. The development of interactive software has given more flexibility to the way in which the decision-maker uses the data and the applied models. This is the essence of decision support systems. Simulation models as used in business, policy, conflict situations, and and crisis management through, in almost all cases, dynamic, discrete-event, and stochastic models. According to the application area, the researcher or designer should model the real-world situation very carefully. An incorrect, incomplete, or inconsistent model could result in a bad decision being made by the decision-maker possibly resulting in economic loss, physical damage or injury. The simulation models and their applications should take into account uncertainty and thus contain procedures for risk analysis. We are therefore interested in research presentations on the science of designing and developing simulation models to solve decisional problems in policy, management, conflict situations, and anti-disaster actions. In particular (but not exclusively), we welcome presentations and reports in the following areas:

- new developments and sound practice in the use of interactive simulation in building decision support systems for conflict situations (including simulation games);
- practice in the use of hybrid systems (artificial intelligence in simulation environments) as decision support in political, conflict and anti-disaster problems;
- simulation based logistics decision support systems;
- model based simulation in risk analysis for decision makers;
- application of simulation based pattern recognition in complex adaptive systems to identify and classify a situation to solve decision problems.
- Project Management
- Project Intelligence
- From Modelling to Management
- Consumer Demand Behaviour
- Modelling Future Demand
- Data Modelling Tools
- Sustainable Development Simulation
- Business Process: Integration, Workflow and Process Management
- Representation of Processes to Business Management
- Achieving Flexibility
- Encapsulating Processes the influence of the OO Revolution
- From Business Model to Operational Performance Support
- Models with the end users in mind
- Best practice Processes
- Business Process Modelling including information architecture and persuasive
- Web design
- Creating and sharing knowledge

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EMERGENCY MANAGEMENT AND RISK ANALYSIS MANAGEMENT

This part of the conference concerns itself more with the more “extreme” aspects of management, extreme managerial situations or management problem solving.

This track covers

-Risk identification, analysis and assessment
-Risk sensoring
-Risk scenario modelling, procedures formulation and planning: (Prevention, Preparedness, Mitigation, Response, Recovery)

Crisis management – extreme events or total disaster

- Environment (forest fires, marine risk – tanker disaster, flood, earthquakes, drought, volcanic eruptions
- Industrial (Hazards, Accidents (nuclear meltdown), Technology (asbestos))
- Terrorist, Extreme events (September 11), Civil Protection, Conflicts risk management - security (Petersberg tasks)
- Project risk management (Investment decisions, Research projects risk management, Extremely risky projects risk management (space exploration), Software development risk management.
- Enterprise risk management (Decision-making, Venture capital)
- Financial risk management (Stocks, Portfolio selection, Options, Credit Risk Assessment, Insurance sector)
- Crisis management (Extreme events, Total disaster)
- Financial Modelling and simulation (Black-Scholes Model, Real Options, Wiener process, Risk measures
- Business Process Reengineering (BPR)
- Probabilistic Risk Assessment (PRA)
- Fault Tree Analysis (Static and Dynamic FTA)
- Event Tree Analysis (ETA)
- HAZIP (Hazard identification) tools (HAZOP (Hazard and Operability Study), Judgement, FMEA (Failure Modes and Effects Analysis), SWIFT (Structured What-IF Checklist Technique)
- Human Error Analysis (HEA)

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SIMULATION IN E-MANAGEMENT, E-GOVERNMENT, E-COMMERCE AND E-TRADE

e-learning/ education management, knowledge management, knowledge economy, multimedia in manufacturing companies, maintenance of technical facilities and maintenance service management
UML applications in Risk Management Modeling. E-democracy, electronic local government, the digital divide, e-governors, citizen centric services, GovMl Description Language, Government Portals, Shared contact centres, Costs and Risks, Customer relationship management, Models for automatic evaluation of public sector sites, the role of standards, knowledge management, community portal dynamics, diversity, creativity, Innovation and Knowledge Management in the Service Sector, Integrated Electronic Records, from e-government to e-regulation.

Knowledge Management and Dynamic Portals
Learning Networks: Policy making through portals
Portals with Purpose Case Studies
e-government portal design and Evolution
Risk Based Testing of E-government Portals

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Paper Presentation Example

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