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Benchmarking of TeknoSA Hard Disks Through Data Envelopment Analysis ( DEA ) a Visualization

Benchmarking of TeknoSA Hard Disks Through Data Envelopment Analysis ( DEA ) a Visualization. Sena Partal 9232 Yasin Yalçınkaya 9022. Outline. Introduction Methodologies Data Collection Data Set of April 2010 Visual Analysis of April 2010 Data Set of May 2010

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Benchmarking of TeknoSA Hard Disks Through Data Envelopment Analysis ( DEA ) a Visualization

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  1. Benchmarking of TeknoSA Hard DisksThroughData Envelopment Analysis (DEA) a Visualization Sena Partal 9232 Yasin Yalçınkaya 9022

  2. Outline • Introduction • Methodologies • Data Collection • Data Set of April 2010 • VisualAnalysis of April 2010 • Data Set of May 2010 • VisualAnalysis of May 2010 • Data Set of June 2010 • VisualAnalysisJune 2010 • Results of Analysis • Conclusion

  3. Introduction • Benchmarking study of Hard Disks of TeknoSAtofindoutefficientoneswhichprovide a pathtoreduceinventorycosts. • Twodistinguishingaspects of ourstudy • Usingrelevant data in DEA • Visualizing the efficiency scores of the products in relation to the brandsthatareincluded in inventory of TeknoSA.

  4. Introduction • What is Data EnvelopmentAnalysis (DEA) ? • A nonparametric techniquewhich can be usedtocompare a set of “decisionmakingunits” (DMUs) amongsteachotherbymeasuringefficiencyviamultipleinputsandoutputs.

  5. Introduction • Why DEA? • Not onlyhelpstoidentifythebenchmarks but also set thegoals • How ? • Bythehelp of theefficientfrontierandDMU'spositionrelativetothefrontier.

  6. Introduction • Ourstudy: • Statementsfor hard disksthatareavailableduring April, May andJune 2010 at bothTeknoSAstoresandofficialwebsite. • Benchmarking through DEA • Results of DEA visualized • Orange Software • Detectingpatterns • Derivingusefulinsights

  7. Methodologies • Data EnvelopmentAnalysis • Approachto measure efficiency of decisionmakingunits(DMUs) in comparisontoeachother • “Weights” formultipleinputsandoutputsassignedautomaticallywithin DEA • Appliedby DEA Solver

  8. Methodologies • Data Visualisation • Detecting outliers • Finding patterns • Visualizations include: • scatter plots, histograms, linearprojection

  9. The DEA Model • Data is gatheredfromTeknoSAofficialwebsite. Price Capacity DMU Weight Cycle

  10. DEA Solver

  11. Data Collection of April,2010 • Brands: • EYEQ (3) • HP (1) • IOMEGA (1) • LG (1) • PHILIPS (3) • SAMSUNG (17) • SEAGATE (4) • SMART (2) • TOSHIBA (22) • TRANSCENT (6) • TREKSTOR (5) • Total of 66 hard disksarebenchmarked in April, 2010

  12. Data Set of April,2010

  13. VisualAnalysis of April,2010

  14. VisualAnalysis of April,2010

  15. Data Collection of May,2010 • Brands: • EYEQ (3) • HP (1) • IOMEGA (1) • LG (1) • PHILIPS (3) • SAMSUNG (21) • SEAGATE (2) • SMART (3) • TOSHIBA (26) • TRANSCENT (7) • TREKSTOR (5) • Total of 73 Hard disksarebenchmarked in May,2010

  16. Data Set of May,2010

  17. VisualAnalysis of May,2010

  18. VisualAnalysis of May,2010

  19. Data Collection of June,2010 • Brands: • EYEQ (3) • HP (1) • IOMEGA (1) • LG (1) • PHILIPS (3) • SAMSUNG (20) • SEAGATE (4) • SMART (4) • TOSHIBA (27) • TRANSCENT (7) • TREKSTOR (5) • Total of 76 Hard disksarebenchmarked in June,2010

  20. Data Set of June, 2010

  21. VisualAnalysis of June,2010

  22. VisualAnalysis of June,2010

  23. Results of Analysis • EfficientProducts • %15,15 of products in April, 2010 arefoundefficientdueto Data EnvelopmentAnalysis. • %19,44 of products in May, 2010 arefoundefficientdueto Data EnvelopmentAnalysis. • % 21,05 of products in June, 2010 arefoundefficientdueto Data EnvelopmentAnalysis.

  24. Conclusions • Methodological contribution: • Byusingcolored scatter plots, DEA resultsarevisualised • AppliedContributions: • Analysis of Hard disks of TeknoSA • Comparedefficiencies of April,May, June 2010 bytheuse of DEA Solver.

  25. Thankyou !

  26. Acknowledgements • Specialthanksto; • Dr. GurdalErtek • Firdevs Ulus • DEA projectteammates

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