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Gürdal Ertek ertekg @ sabanciuniv .edu

Analytical Benchmarking Meets Data Mining : The SmartDEA Framework, SmartDEA Software, and Case Studies for Industry. Gürdal Ertek ertekg @ sabanciuniv .edu. Invited Seminar at A*Star SIMTECH, Singapore, August 2, 2013, Friday. Istanbul , Turke y. Singapore.

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Gürdal Ertek ertekg @ sabanciuniv .edu

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  1. Analytical Benchmarking Meets Data Mining:The SmartDEA Framework, SmartDEA Software, and Case Studies for Industry Gürdal Ertek ertekg@sabanciuniv.edu Invited Seminar at A*Star SIMTECH, Singapore, August 2, 2013, Friday

  2. Istanbul, Turkey Singapore

  3. Young, high-profile privateUniversity • Outskirts of Istanbul, Turkey • First students accepted in 1999 www.sabanciuniv.edu

  4. EstablishedbytheSabanciFoundation www.sabancivakfi.org

  5. Sabancı Group www.sabanci.com

  6. Sabancı Family: Sakıp Sabancı, Güler Sabancı, 200+

  7. ~3000 undergrad& ~500 gradstudents

  8. HighestresearchincomeperfacultymemberamongTurkishuniversitiesHighestresearchincomeperfacultymemberamongTurkishuniversities

  9. Young, high-profile privateUniversity • EstablishedbytheSabanciFoundation • Sabancı Group • Sabancı Family: Sakıp Sabancı, Güler Sabancı, 200+ • First students accepted in 1999 • ~3000 undergrad& ~500 gradstudents • Highestresearchincomeperfacultymember

  10. Dr. Gürdal Ertek • AssistantProfessorat Sabancı University, Istanbul, Turkey, since 2002 • Ph.D. from School of Industrial and Systems Engineering @Georgia Institute of Technology, Atlanta, GA, USA • Research areas include • warehousing & material handling • data visualization & data mining

  11. Analytical Benchmarking Meets Data Mining:The SmartDEA Framework, SmartDEA Software, and Case Studies for Industry Gürdal Ertek ertekg@sabanciuniv.edu Invited Seminar at A*Star SIMTECH, Singapore, August 2, 2013, Friday

  12. Motivation • Analytical Benchmarking • application of mathematics and computation based methods for benchmarking a group of entities • aims at developing objective and automated methods of benchmarking. • Overwhelmingmajority of literaturefocuses on • developing new benchmarking methodologies • An important aspect forgotten: • post-analysis of the benchmarking results

  13. Motivation • Data Mining • growingfield of computerscience • aimsat discoveringthehiddenpatternsandcomingupwithactionableinsights. • Overwhelmingmajority of literaturefocuses on • developingmoreefficientandeffectivecomputationalalgorithms. • Important aspects not drawing deserved attention: • the quest for practical actionable knowledge • data mining can be used for post-analysis of results of other methodologies & algorithms

  14. ThisSeminar • Goals • SmartDEA Solver framework for integrating analytical benchmarking with data mining • How DEA results should be structured • Meaningful interpretation of DEA results • Casestudyapplications • Automotive • Wind energy • Apparel retail

  15. ResearchQuestions • How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? (SmartDEA) • How can DEA & information visualization be used together? (Case Study 1) • Which visualization techniques are appropriate for analyzing DEA results? (Case Study 2) • How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results? (Case Study 3)

  16. Presentation Contents • Background on Data Envelopment Analysis (DEA) • SmartDEA framework • Case Studies • Automotive • Wind Energy • Apparel Retail

  17. Background

  18. Sample DEA Analysis

  19. Data Envelopment Analysis (DEA) Data • Entities = DMUs (n DMUs) • Comparison of DMUs • Inputs and outputs (m inputs, s outputs) Results • Efficiency score between 0 and 1 • Reference sets • Projections

  20. Basic DEA Models • Maximize the ratio : for each DMU0

  21. Basic DEA Models • CRR-Input model • CRR-Output model

  22. Basic DEA Models • BCC-Input model • BCC-Output model

  23. Basic DEA Models

  24. Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA Framework Alp ErenAkcay, Gürdal Ertek, GulcinBuyukozkan GurdalErtek ertekg@sabanciuniv.edu

  25. ResearchQuestions • How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? (SmartDEA) • How can DEA & information visualization be used together? • Which visualization techniques are appropriate for analyzing DEA results? • How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results?

  26. Goal • To build a framework for making analytical benchmarking and performance evaluations • To design and develop a convenient DEA software, SmartDEA

  27. Contribution • To develop a general framework • To help DEA analysts to generate important and interesting insights systematically • To integrate the results for information visualization techniques

  28. Framework • Integration of DEA results with data mining and information visualization

  29. Proposed framework • integrates data mining and information visualization with DEA, • generates clean data for mining (data auditing at the DEA modeling stage), • allows the incorporation of “other data” into the process, • can accommodate multiple DEA models within same analysis.

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  39. SmartDEA: the developed software

  40. Modeling Process • C# language • Results in file format of MS Excel • Imported data requires a certain format

  41. Modeling Process • 1- Importing Excel File: • Data requires a certain format

  42. Modeling Process • 2- Selecting the spreadsheet:

  43. Modeling Process • 3- Constructing the model:

  44. Modeling Process • 4-Selecting the DEA Model:

  45. Modeling Process • 5- Solving and generating the solution file:

  46. Case Study 1:Integrating DEA with Information Visualizationfor Benchmarking Dealers in theAutomotive Industry Dr. Gürdal Ertek, Tuna Çaprak ertekg@sabanciuniv.edu

  47. ResearchQuestions • How can Data Envelopment Analysis (DEA) results be structured such that they can be analyzed using readily available data mining techniques and software tools? • How can DEA & information visualization be used together? (Case Study 1) • Which visualization techniques are appropriate for analyzing DEA results? • How can DEA and data mining be integrated with the results of other data mining techniques, specifically association mining results?

  48. A New Approach for Benchmarking and Managing TOFAŞ Dealers Tuna Çaprak Leaders for Industry Program ’07-’08, Sabancı University GürdalErtek, Ph.D. Faculty of Engineering and Natural Sciences, Sabancı University

  49. A New Approach for Benchmarking and Managing TOFAŞ Dealers

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