Simulacion luis garciaguzman-21012011

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Simulacion luis garciaguzman-21012011

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

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