Conceptual Modeling Course Personal Research Presentation

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Conceptual Modeling CoursePersonal Research
Presentation

• Souleïman Naciri
• 16.01.07

Laboratoire de Gestion et Procédés de
Production Laboratory for Production Management
and Processes
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Outline

• Research Features
• Research goals definition
• Problem explanation
• Challenges
• Methodology
• Current Progress
• Conclusion

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Research features

• Goals
• Understand and model Human decision making
  processes in a supply chain context for the
  tactical level planning
• Possible applications
• Benefit from more realistic simulations by
  implementing human decision making processes
• Improving existing management tools by providing
  support to the decision maker

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Research goals_Definitions

• Supply chain (SC) set of operational entities
  that synchronize their processes in order to buy
  raw material, components, transform them into
  finished products and sell them to end customers
  (5)
• Tactical Planning set of actions to create a
  plan that guide purchasing and production in
  order to meet customers needs.
• Human being it is the tactical planner. He is in
  charge of updating the master production schedule
  (MPS) that fix for each time period the quantity
  to produce for each product.
• Level concept
• Strategic level long term decisions. Concern
  company evolution during the next years. Ex
  decide to outsource one production process in
  order to concentrate on core competencies.
• Tactical level middle term decisions. Meet the
  objectives of strategic level with the
  constraints of the operational level. Ex.
  increase ipods inventory levels before Christmas
  period
• Operational level short term decisions.
  Execution of production processes, scheduling.
  Ex prioritize one fabrication order for the most
  important customer.
• These concepts can be represented in a SEAM
  conceptual model

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Problem explanation

• Supply chain management is complex due to
• Supply chain structure
• Spatial repartition
• complex material and information flows
• Stochastic events (breakdowns, delays, etc.)
• Human beings
• Positive aspectsExperience, competencies,
  knowledge
• Negative aspects Errors, non theoretical
  behavior,etc.
• One solution to improve SC performances is
• to use simulation to handle SC complexity and
  stochastic events
• to consider human decision making

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Challenges

• To include Human decision making in simulations,
  we must
• Understand human decision making processes (HDMP)
• Model HDMP
• Identify what are the elements involved in Human
  decision making
• Create Human Decision making ontology

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Methodology_Steps_1

• Step 1.
• Creation of the SC model
• Creation of VIS interfaces
• Step 2
• Creation of simulation scenarios (events
  creation)
• Step 3
• Simulation running including Human interaction
• Automatical Data feeding (simulation and human
  actions with tracking system)

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Methodology_Steps_2

• Step 4.
• Explicitations Interviews with the VIS user
• Identification of decisions related element
  (cause, state of the world , HB
  characteristics, System performance)
• Data introduction into existing data base tables
• Step 5
• Ontology creation / updating by gathering all
  the decision making related elements and
  litterature
• Step 6
• Model building using the ontology and the
  interviews results
• Step 7
• Validation by creating agents using the HDMP
  model.

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Méthodology_overview
Visual Interactive Simulation Critical
Decision Method Explicitation interviews
Figure 1 Research methodology
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Current progress

• SC Model
• A validated model based on a real SC
• A reflexion model for HDMP modeling
• Visual Interactive Simulation
• Easy starting model used to validate the
  methodology
• User interaction Applied to  Fabrication Order 
  launching (Operational level)
• DB tables
• DB tables have not been designed for this use of
  the simulation tool (HDMP)
• ?DB Structure must be updated in order to match
  with the new ontology
• Next steps
• Run simulations and interviews
• Update the methodology (if necessary)
• Modify the VIS interface towards the planning
  tactical level
• Update existing ontology
• Model the Human Decision Making Process

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Existing SC Model
Markets
Enterprise core
Branches
Suppliers
Assemblers
Figure 2 Model of a watch making supply chain
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Human Decision Making Model
Figure 3 Reflexion model
? This model must be validated / updated
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Visual Interactive Simulation
Order quantity proposed by the management system
Assemblers loads
Order quantity introduced by the planner
Components availability
Figure 4 Example of a Visual Interactive
Simulation interface
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Ontology_Examples

• Decision making ontology (TOGA project) (1)
• Choice
• Criteria
• Alternatives
• Decision making
• Social Decision Making ontology (2)
• Humans
• Domains
• Problems
• Solutions

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Current ontology
Figure 3 Human Decision Making Process Ontology
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Conclusion

• VIS is a promising tool for HDMP elicitation.(5)
• Ontologies are helpful to list and structure HDMP
  relevant data
• But many questions must be considered
• How to ensure that human being behaves the same
  way in reality and in simulation?
• How to be sure that all the information linked to
  a HDMP has been considered?
• How to elicit links and interactions between
  ontology elements to model efficiently these
  processes?

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References

• (1).A. M. Gadomski, TOGA,  Socio-Cognitive
  Approach, White paper,
• (2).M. A. Rodriguez, Proceedings of the 40th
  Hawaii International Conference on System
  Sciences 2007
• (3).Rasmussen, Skills, rules, and knowledge -
  signals, signs, and symbols, and other
  distinctions in human-performance models. Ieee
  Transactions on Systems Man and Cybernetics,
  13(3)257-266, 1983.
• (4).Davis, Effective supply chain management.
  Sloan management review, Summer35-46, 1993
• (5). Min et Zhou, Supply chain modeling past,
  present and future. Computer and Industrial
  Engineering, 43231-249, 2002.
• (6) Robinson, Knowledge-based improvement 
  Simulation and artificial intelligence for
  identifying and improving human decision-making
  in an operations system, Journal of Operational
  Research Society (2005) 56, 912-921.