Simulation of End-of-Life Computer Recovery Operations

Design TeamJordan Akselrad, John MarshallMikayla Shorrock, Nestor Velilla

Nicolas Yunis

Project AdvisorProf. James Benneyan

Project SponsorProf. Sagar Kamarthi

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

Project Scope Determine the effect expected component life information has on a Product

Recovery Facility

S C O P E

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

Refurbishing Process

S C O P E

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

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

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

Comp (Ci) Weight (Wi)

CPU 215.4

Cores 817.4

Memory 0.4

HD 4.1

Gfx 116.3

Score vs Price

y = 5.6625x + 1137.3R2 = 0.8065

0

1000

2000

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0 100 200 300 400 500 600 700 800

Price

Sco

re

Simulation Demo

S I M U L A T I O N

Sensor Times Benefit

SensorsNo Sensors

Min

ute

s p

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A N A L Y S I S

Profit Contributors

A N A L Y S I S

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

Purchasing Costs

Interaction of Profit Factors

A N A L Y S I S

Purchasing costs have the greatest effect on profit

Reliability Analysis

Per

cen

t o

f C

om

po

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ts F

aile

d

War

ran

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Sensor Error in Months

Warranty Failure vs Sensor Error

A N A L Y S I S

Without sensors 23% failure rate

Failure rate increasing with sensor error

Estimating Life: Without Sensors

O P T I M I Z A T I O N

Dispose if probability component working in one year is less than tolerance

Profit vs Tolerance

Optimal tolerance 54%

Estimating Life: With Sensors

Per

cen

t P

rofi

t

Correction of Sensor

Profit vs Sensor Correction

O P T I M I Z A T I O N

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

Optimal profit at 1 deviation of correction

Maximize Profit Minimize Waste Maximize Reliability

Multi-Criteria Optimization

O P T I M I Z A T I O N

Surface is the efficient solution front

Efficient implies non-dominated trade-off between values

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

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

Questions

Thank you

Waste AnalysisSensor Error vs Working Disposals

Deviation of Sensor Error in Months

Per

cen

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f W

ork

ing

Co

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Dis

po

sed

A N A L Y S I S

Without sensors 11% of disposed

components are working

Working components disposed

increases with sensor error

Sensor Cost Benefit

SensorsNo Sensors

Do

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rs p

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A N A L Y S I S