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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

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?)
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