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Brennan Aircraft Division (BAD) Case Study. By Elena White, Luigi DeAngelis & John Ramos. Overview of Presentation. Executive Summary Data Analysis Basis of Simulation Conclusion. Executive Summary. BAD operates large number of plotting machines

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Brennan Aircraft Division (BAD) Case Study

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Brennan aircraft division bad case study

Brennan Aircraft Division (BAD)Case Study

By

Elena White, Luigi DeAngelis &

John Ramos


Overview of presentation

Overview of Presentation

  • Executive Summary

  • Data Analysis

  • Basis of Simulation

  • Conclusion


Executive summary

Executive Summary

  • BAD operates large number of plotting machines

    • Consist of minicomputer system that directs 4 pens to move until desired figure is drawn

    • Connected to a 4 – by-5 foot table with series of ink pens suspended above it

    • Very reliable with exception of ink pens clogging, jamming, rendering plotter unusable


Executive summary cont

Executive Summary Cont…

  • BAD replaces ink pen upon failure of each

  • Alternative repair by service manager

    • Replace all 4 ink pens upon one failure

    • Ideally reducing the frequency of failures


Data analysis

Data Analysis

  • The following data was provided by the case study:

    • Total cost of downtime $50/hr

    • Replacement time of 1 pen = 1 hr/pen

    • Replacement time of 4 pens 2 hr/set

    • Cost of each pen $8/pen


Data analysis cont

Data Analysis Cont…

  • Probability Distribution Between Failures

    (each pen replaced as it fails)


Data analysis cont1

Data Analysis Cont…

  • Probability Distribution Between Failures

    (4 pens replaced as 1 fails)


Data analysis cont2

Data Analysis Cont…

  • Additional data (assumptions used in simulation to establish year utilization)


Basis of simulation

Basis of Simulation

  • Simulated Brennan’s problem for two options

    • Case 1 : Replace ink pen as it fails

    • Case 2 : Replace all four ink pens as one fail

  • Used “Next Event Increment Model” approach to carry out the simulation

  • Split runs in “Year (of 2500 hrs each)” this helps in results analysis

    • Each run is arrested when “close enough” to 2500 hrs. A “While-cycle” would have been best approach. A spreadsheet works well as analysis is simple

  • Used VLOOKUP to instantaneously look-up probability tables and determine hours between plotter failures


Basis of simulation cont

Basis of Simulation Cont…

  • Computed total time adding downtime to TBF computed from Probability Distribution.

  • Derived total cost of each failure

    • Cost of 1 pen plus cost of One hour of downtime (case 1) = 58 $

    • Cost of 4 pens plus cost of Two hour of downtime (case 1) = 132 $

    • Computed failures for the equivalent of 1 plotter year. Run repeated 5 times (reasonable life-cycle for a plotter).


Simulation results

Simulation: Results

Case 2 is the most convenient choice evaluated as an average on a 5-Year simulation.


Analytical results

Analytical Results

  • A different approach has been followed based on analytical considerations.

  • The Mean for each distribution has been calculated, i.e. MTBF.

  • We calculated number of failures X year as:

    • Numb. Fail. X Year= 2500 / (MTBF + MT)

  • We calculated costs in 1 Year as:

    • 1 Y Cost = [Numb. of Fail. X Year] * [Repairing Costs]

    • NOTE: Tot. Cost = [1 Y Cost] * [N Year]


  • Analytical solution results

    Analytical Solution: Results

    Best Choiche is again Case 2.

    Note how close Analytical and Simulated results are evaluated as an average on a 5 Y time frame.


    Conclusion

    Conclusion

    • Based on the results achieved with the .xls simulation we observed the progression of costs and maintenance times

    • Determined that in Case 2, replacement of all 4 pens upon one failed pen, will minimize maintenance costs for BAD

    • Analytical results reinforce our simulated study that Case 2 is indeed the best policy to implement. (or viceversa?)


    Any questions

    Any Questions?


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