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Simulation of End-of-Life Computer Recovery Operations

Simulation of End-of-Life Computer Recovery Operations. Design Team Jordan Akselrad, John Marshall Mikayla Shorrock, Nestor Velilla Nicolas Yunis. Project Advisor Prof. James Benneyan. Project Sponsor Prof. Sagar Kamarthi. Background Information.

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Simulation of End-of-Life Computer Recovery Operations

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  1. Simulation of End-of-Life Computer Recovery Operations Design Team Jordan Akselrad, John Marshall Mikayla Shorrock, Nestor Velilla Nicolas Yunis Project Advisor Prof. James Benneyan Project Sponsor Prof. Sagar Kamarthi

  2. Background Information Research project by sponsor, Professor Kamarthi Sensors are being developed for computer components Sensor Embedded Computers (SEC) Sensors closely estimate remaining useful life Product Recovery Facilities (PRF) exist that refurbish computers Ongoing research to determine sensors’ impact on entire reclamation process B A C K G R O U N D

  3. Project Scope Determine the effect expected component life information has on a Product Recovery Facility S C O P E

  4. Project Goal Develop a simulation tool which models Product Recovery Facilities Comparative model analysis Apply optimization techniques across simulation model Determine if sensors improve the cost effectiveness of computer recovery operations S C O P E

  5. Refurbishing Process S C O P E

  6. Design Concepts Considered ARENA Complex logic needs to be implemented Excel Interface Amount of data is overwhelming to user Event Based Simulation Unnecessary due to lack of queuing S I M U L A T I O N

  7. Simulation Design Custom user interface C# / .Net backend Serves as window into simulation Assists in debugging model Rapid development, run anywhere Human Factors Considerations Simple Interface with powerful capabilities Easy to run large scale experiments Data easily importable / exportable Built in graphing for real-time analysis S I M U L A T I O N

  8. Price Generation Arbitrary computer configurations Each price contributor given a weight to influence score Weights solved to maximize price vs. score correlation Generated equation used to price dynamically S I M U L A T I O N

  9. Simulation Demo S I M U L A T I O N

  10. Sensor Times Benefit Minutes per Component Sensors No Sensors A N A L Y S I S

  11. Profit Contributors A N A L Y S I S

  12. Design of Experiments 2 level, 10 Factor Experiment 1024 Combinations, 15 Runs each Output for 3 performance objectives Profit, Waste, Reliability Minitab used for analysis Variable interactions examined Approximation equations developed Efficient set extracted A N A L Y S I S

  13. Interaction of Profit Factors Purchasing Costs Purchasing costs have the greatest effect on profit A N A L Y S I S

  14. Reliability Analysis Warranty Failure vs Sensor Error Percent of Components Failed Warranty Sensor Error in Months Without sensors 23% failure rate Failure rate increasing with sensor error A N A L Y S I S

  15. Estimating Life: Without Sensors • Dispose if probability component working in one year is less than tolerance • Optimal tolerance 54% Profit vs Tolerance O P T I M I Z A T I O N

  16. Estimating Life: With Sensors • Expected life reported with mean at failure date • Sensor error is in months of deviation from mean, default 6 • Sensor reading is corrected to prevent warranty failures Profit vs Sensor Correction Optimal profit at 1 deviation of correction Percent Profit Correction of Sensor O P T I M I Z A T I O N

  17. Maximize Profit Minimize Waste Maximize Reliability Multi-Criteria Optimization • Surface is the efficient solution front • Efficient implies non-dominated trade-off between values O P T I M I Z A T I O N

  18. Conclusions Fully developed simulation tool Easy to use Exceeds research needs Preliminary Analysis Performed Without sensors refurbishment is infeasible 23% failure rate With sensors 21% reduction in time spent per component 22% reduction in processing cost per component Sensors strongly recommended Overall profit increase 48% Customer failure rate 3% C O N C L U S I O N S

  19. Future Considerations Improve MTBF data accuracy Research shows MTBF specified by manufacturer is unreliable Ideas to enhance accuracy Facilities record component failure rates Sensors report failure time to manufacturer Integration into facility Simulator used as a prediction engine C O N C L U S I O N S

  20. Questions Thank you

  21. Waste Analysis Sensor Error vs Working Disposals Percent of Working Components Disposed Deviation of Sensor Error in Months Working components disposed increases with sensor error Without sensors 11% of disposed components are working A N A L Y S I S

  22. Sensor Cost Benefit Dollars per Component No Sensors Sensors A N A L Y S I S

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