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Simulacion luis garciaguzman-21012011

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  • 1. Process Improvement with Discrete Event Simulation
    Luis Garcia Guzman, PhD
    Asst Research Scientist and Adjunct Professor
    Industrial and Operations Engineering
    The University of Michigan
  • 2. Biografía
    2
    Educación:
    IIS-90 (ITESM-Campus Estado de México)
    MSE and PhD (U of M) --- Industrial & Operations Engineering
    ExperienciaLaboral:
    Investigador y Profesor– Ingeniería Industrial, Universidad de Michigan
    Ingeniero en Logística, Ingeniero de Producto y de Calidad~ Duroplast (Naucalpan), AMP Industries (Michigan), Daimler Chrysler (Michigan) y GM (Michigan).
    Docencia:
    Probabilidad y Estadística
    Ingeniería Estadística
    Diseño de Experimentos
    Control de Calidad
    Simulación de EventosDiscretos
    Seis Sigma – Cursos de Green Belt y Black Belt
  • 3. Topics
    Overview of Simulation Models
    Steps in a Simulation Study
    Process Simulation Examples
    3
  • 4. What is Simulation Modeling?
    A model is an imitation of a system (or process) in real-world over time.
    A system is a collection of interrelated elements (or processes) which function cooperatively to achieve a stated objective.
    There is a measurement of performance
    Model of a system (or process) should reflect and mimic the behavior of the system (or process)
    Understanding the model implies at least some understanding of the real system
    4
  • 5. System and Model
    System Environment
    System
    Endogenous
    Exogenous
    Model
    State
    Entity, Attribute
    Activity, Event
    Model Scope
    System Boundary
  • 6. Components of a System(Example: Supermarket)
    • Customer
    • 7. Buying habits, preference
    • 8. Strolling through aisle
    • 9. # customers in each aisle
    • 10. Started/finished aisle, enter cashier queue, exit queue
    Entity
    Attribute
    Activity
    State of a system
    Event
    Endogenous/exogenous (activity, event)
  • 11. Types of Simulation Models
    Dynamic versus Static
    Stochastic versus Deterministic
    Discrete versus Continuous
    Since models mimic real-world systems, these definition apply to systems as well.
  • 12. Why Simulate?
    Typical Decision Support Problems:
    Evaluate alternative configurations of a system
    capacity, utilization, bottlenecks, scrap, etc.
    Identify the desirable/feasible configuration(s) of the system for a specified objective (optimization)
    Identify a robust strategy to achieve a specified objective for a system
    Go – No Go decisions for project management
    Evaluate the value and the risk of an asset
    8
  • 13. Ways to Study a System
    System
    Why model?
    - describe
    - explain
    - predict
    - demonstrate
    Experiment
    With the
    Actual system
    Experimentwith a model
    of the system
    Physicalmodel
    Mathematicalmodel
    Analyticalsolution
    Simulation
    9
  • 14. Experiment with Actual System
    Advantages
    Don’t have to spend time/resource to model the system
    No loss of accuracy
    Disadvantages
    May interfere with current operation, or is cost inhibitive
    May be difficult to repeat, e.g. war game
    Not possible if there is no real system yet
    10
  • 15. Analytical Methods
    Advantages
    Low requirement on modeling efforts
    Provide great insights on relationships among variables
    Answer is exact (not necessarily accurate)
    Disadvantages
    May need lots of variables or distributions
    Closed form solution may not exist or is difficult to derive
    11
  • 16. Advantages of Simulation Models
    Most complex systems cannot be accurately described by the alternatives (e.g., analytical math models)
    Allows estimating the performance of an existing system under some projected set of operating conditions
    without disrupting ongoing operations
    without committing resources for acquisition of new hardware
    Promotes the understanding of how the system works
    Test hypotheses about how or why phenomena occur
    Obtain insight about the interaction of variables
    Obtain insight about the importance of variables to performance
    Bottleneck analysis
    Control over experimental conditions
    Allows great flexibility for ‘what-if’ analysis
    Enables comparison of alternative system designs
    12
  • 17. Disadvantages of Simulation
    Simulation models can be expensive and time consuming to develop
    Lots of upfront work, e.g. input modeling, computer coding
    Requires special training, open to interpretations
    Simulation results may be difficult to interpret
    Each run produces only estimates of a model’s true characteristics for a particular set of input parameters
    Computer model may be wrong, e.g. programming bugs
    The large volume of numbers or the persuasive impact of realistic animation often creates a tendency to place greater confidence in the results than is justified
    Possibility of misinterpretation of random results
    13
  • 18. Simulation is not appropriate when…
    The problem can be solved using common sense
    The problem can be solved analytically
    It is easier to perform direct experiments
    The costs exceed possible savings
    Resources are not available
    Time is not available
    No data, not even estimates, are available
    Not enough time to verify and validate
    Managers have unreasonable expectations
    The system behavior is too complex or cannot be defined
  • 19. II. Steps in a Simulation Study
    Problem formulation
    Setting of objectives and overall project plan
    Model conceptualization
    Data collection
    Model translation
    Verified?
    Validated?
    Experimental design
    Production runs and analysis
    More runs?
    Documentation and reporting
    implementation
  • 20. Steps in a Simulation Study
    What is the problem?
    Formulation
    Is simulation appropriate?
    Define alternative systems
    Project planning
    Define Project Goal & Plan
    How?
    Data Collection
    Model Conceptualization
    An Art
    Start simple
    Then expand
    Model Translation
    No
    Verified?
    Is code OK?
    Yes
    Represents the system well?
    No
    No
    Validated?
    Yes
  • 21. Steps in A Simulation Study
    What runs to make to answer question efficiently?
    Experimental Design
    Production Runs
    & Analysis
    Estimate the
    performance measures
    Yes
    Yes
    More Runs?
    No
    Customer acceptance
    Documentation
    & Reporting
    Program and Progress
    scope of this class
    Implementation
  • 22. Process Simulation – Queuing ModelsDescribed by
    Customer Population
    Queue Channels and Phases
    Customer Arrival Process
    Service Process
    Queue Discipline
    18
  • 23. 1. Customer Service Populations
    Infinite
    Cars Passing Toll Booth
    Supermarket, Bank, Restaurant Customers
    Telephone Calls at Service Center
    Finite
    Geriatric Patients under nursing care
    TV Networks
    Students in course
    19
  • 24. 2. Queue Channels and Phases
    Servers
    Single Server (Single Channel)
    Multiple Server (Multiple Channel)
    Phases
    Single Phase (Single Service)
    Multiple Phase (Multiple Sequential Services)
    20
  • 25. 3. Customer Arrival Processes
    Constant
    Example: Scheduled Outpatient Care
    Variable Arrivals (random variable)
    Independence (between customers)
    Single Customer
    Example: Emergency Room Care
    Batches of Customers
    21
  • 26. 4. Service Process
    Constant Service Rate
    Automated Assembly Line
    Automated Car Wash
    Streaming Video Distance Learning
    Variable Service Rate (Random)
    Gasoline Station
    Shopping Center
    22
  • 27. 5. Queuing Discipline
    First Come, First Served
    Priority Customers
    Shortest Processing Time
    Reservations First
    Limited Needs
    Other
    23
  • 28. Simulation and Six Sigma
    Six sigma is a data-driven methodology for improving quality in many aspects of a company’s products and services
    Phases of six sigma methodology typically are: Define, Measure, Analyze, Improve and Control (DMAIC) for existing processes or Define, Measure, Analyze, Design, Verify (DMADV) for new processes or major changes or re-designs (Design for Six Sigma)
    Simulation is one of the available tools in a Six-Sigma initiative. Particularly within the Analyze and Improve of the DMAIC project or Analyze and Design of a DMADV project or Design and Optimize of a IDDOV project
  • 29. Simulation and Six Sigma
    Benefits of simulation in the context of six sigma:
    Considers process variances, uncertainties and interdependencies
    Easy to include and study alternative solutions
    Models can be developed without disruptions to existing processes
    Takes subjectivity and emotion out of decision making (data-driven=six sigma)
    Animation tool helps illustrate and convince others on the best solutions
    Reusable models can encourage continuous improvement
  • 30. III. Process Simulation Examples
    Process Simulation Examples
    OEM Paint Shop Operations
    OEM Work In Process Inventory (WIP) reduction
    Supply Chain Optimization
  • 31. 1. North American OEM Paint Shop
    27
    Problem Description: The paint shop assembly line at an OEM plant is complex and can be improved.
    Currently, 80% of the painted vehicle bodies are declared a success.
    Project goal: To increase the number of successfully painted vehicle bodies by:
    Decreasing system down time,
    Optimizing color sorting, and/or improving paint robot success rates.
  • 32. Plant Layout
    28
  • 33. NA OEM Paint Shop
    29
    Process improvement opportunities:
    System down time - paint machine color cartridge replacement process
    Machine operating speed, machine age, and total machine operating time.
    wait time between locations.
    Approach:
    First, a model of the actual system was constructed.
    Then the model was verified and validated.
    Alternative configurations developed and tested to find best solution
    Results
    Recommend layout solution, increased the yield from 80% to 90%
    Reduced downtime costs by $2,700 per day  
  • 34. 2. OEM WIP Reduction
    30
    Problem Description: Excessive WIP in the Assembly Area
    Project Goal: to decrease excess WIP in the workshop.
    Process Improvement Opportunities:
    large lot sizes
    long set-up times
    long lead times
    Ineffective production scheduling
    Breakdowns of machines
    Non-value-adding activities of
    Operators
  • 35. OEM WIP Reduction
    31
    Approach:
    First, a model of the actual system was constructed.
    The reasons for excess WIP in the workshop were analyzed and identified.
    Then the model was verified and validated.
    After that, the problem solving approach was developed. By testing the results of changes on variables, the minimum stock level was reached.
  • 36. OEM WIP Reduction
    32
    Recommendations: The proposal for decreasing WIP were divided into two groups:
    Scheduling:
    creating lot sizing methods
    material pulling to the system (the number of pieces going into the systems should be equal to the required number of output)
    lead time monitoring and lead time reduction through waste elimination
    machine-operator assignments done according to priority of jobs
    increasing the number of multi-process material handling operators
    Technological:
    reduction of set up times
    methodical improvements
    automation of machines where possible
    layout optimization
    the preventative and productive maintenance
  • 37. OEM WIP Reduction
    33
    Results:
    There was a 48% reduction on the average WIP in the assembly floor
    As a result of the improvements in WIP the cost of material was reduced by the same amount.
    There was a 14% improvement by implementing only the scheduling rules.
  • 38. 3. Supply Chain Optimization
    34
    Problem Description: Excessive lead time for the distribution of confectionary products in India
    Project Goal: to cut the lead time from factories to retail depots. Determine the optimal amount of trucks to be utilized to minimize lead time at a reasonable cost.
    Approach:
    First, a model of the existing supply chain.
    Then the model was verified and validated.
    After that, alternative supply chain model was built and simulated to compare with initial model.
  • 39. Supply Chain Optimization
    35
    This model is based around a central warehouse used for storage and as a distribution point for some routes.
    Existing Supply Chain
  • 40. Existing Supply Chain
    36
  • 41. Proposed Supply Chain
    37
    Products are shipped directly from the factories to the individual depots
    much of the burden is shifted to the factories
    increase in the number of trucks required to meet demand. higher cost, however, cost savings occur due to the lack of maintenance of a larger distribution center and reduction in lead time.
  • 42. Supply Chain Optimization
    38
  • 43. Supply Chain Optimization
    39
    Results:
    The proposed supply chain cuts costs by 50%
    Lead time would be reduced by almost six times.
  • 44. Logistics – Energy Services Company
    40
    Problem: High level of maintenance costs at local maintenance centers (26 locations around the world)
    Long delays in completing maintenance jobs
    Goal of simulation: Study the effects of maintaining a single global maintenance center where experts can perform the job more quickly and cost effectively.
  • 45. 41
  • 46. Initial Results
    42
    Results:
    The proposed model could cut maintenance costs by 20%
    Increase the service level (e.g. probability of having available tools at the oil rigs from 70% to 85%)
    Lead time could be reduced by almost 30%.
  • 47. Call Center Evaluation
    43
    Comparison of 2 different layouts:
    Current layout
    Planned improvement to a cell fashion layout
    Results:
    Reduction of number of lost calls
    Reduction of average holding time
    Reduction of maximum hold time
  • 48. 44
    Summary
    A simulation model is an imitation of a system (or process) in real-world over time.
    Simulation can be a useful tool in decision making
    Allows great flexibility for ‘what-if’ analysis
    Enables comparison of alternative system designs
    Simulation models are “run” rather than solved
    Assumptions of model should be validated based on model characteristics and behavior
    Simulation applications are vast particularly in manufacturing and transactional processes
  • 49. 45
    ¿Preguntas?
    ¡Muchas
    Gracias!