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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

Benchmarking of TeknoSA Hard DisksThroughData Envelopment Analysis (DEA) a Visualization

Sena Partal 9232

Yasin Yalçınkaya 9022

outline
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
introduction
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.
introduction1
Introduction
  • What is Data EnvelopmentAnalysis (DEA) ?
    • A nonparametric techniquewhich can be usedtocompare a set of “decisionmakingunits” (DMUs) amongsteachotherbymeasuringefficiencyviamultipleinputsandoutputs.
introduction2
Introduction
  • Why DEA?
    • Not onlyhelpstoidentifythebenchmarks but also set thegoals
  • How ?
    • Bythehelp of theefficientfrontierandDMU'spositionrelativetothefrontier.
introduction3
Introduction
  • Ourstudy:
    • Statementsfor hard disksthatareavailableduring April, May andJune 2010 at bothTeknoSAstoresandofficialwebsite.
    • Benchmarking through DEA
    • Results of DEA visualized
      • Orange Software
      • Detectingpatterns
      • Derivingusefulinsights
methodologies
Methodologies
  • Data EnvelopmentAnalysis
    • Approachto measure efficiency of decisionmakingunits(DMUs) in comparisontoeachother
    • “Weights” formultipleinputsandoutputsassignedautomaticallywithin DEA
    • Appliedby DEA Solver
methodologies1
Methodologies
  • Data Visualisation
    • Detecting outliers
    • Finding patterns
    • Visualizations include:
      • scatter plots, histograms, linearprojection
the dea model
The DEA Model
  • Data is gatheredfromTeknoSAofficialwebsite.

Price

Capacity

DMU

Weight

Cycle

data collection of april 2010
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
data collection of may 2010
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
data collection of june 2010
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
results of analysis
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.
conclusions
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.
acknowledgements
Acknowledgements
  • Specialthanksto;
    • Dr. GurdalErtek
    • Firdevs Ulus
    • DEA projectteammates