پیش‌‌پردازش تصمیم‌‌گیری چند شاخصه با استفاده از داده‌‌کاوی (مطالعه موردی: انتخاب لجستیک شخص ثالث در برون‌‌سپاری خدمات وارانتی یک شرکت تولیدی تجهیزات الکترونیکی)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار، گروه مدیریت صنعتی، دانشکده علوم اجتماعی، دانشگاه بین‌‌المللی امام خمینی (ره)، قزوین، ایران

2 دانش‌آموخته کارشناسی ارشد مدیریت صنعتی، دانشکده علوم اجتماعی، دانشگاه بین‌‌المللی امام خمینی (ره)، قزوین، ایران

چکیده

در سال‌های اخیر، خدمات پس از فروش، یکی از مهم‌‌ترین عوامل مؤثر در رضایت مشتریان بوده است. ارائه خدمات پس از فروش و وارانتی هزینه‌‌های زیادی را به تولید‌‌کننده تحمیل می‌‌کند. ازاین‌‌رو اعمال سیاست‌‌های مناسب در این خصوص، از هزینه‌‌‌های بی‌‌‌مورد جلوگیری می‌‌نماید. امروزه یکی از سیاست‌های رایج وارانتی، برون‌‌سپاری وارانتی است که به آن «وارانتی شخص ثالث» می‌‌گویند. در برون‌‌سپاری وارانتی، با مسئله مهم انتخاب بهترین پیمانکار از بین شرکت‌‌های لجستیک شخص ثالث روبرو هستیم. در این مقاله رویکرد تصمیم‌‌گیری‌‌ چندشاخصه با به‌‌‌کار‌‌بردن داده‌‌کاوی، برای انتخاب شرکت‌‌های وارانتی شخص ثالث، توسعه ‌یافته است. این روش ترکیبی، شامل خوشه‌‌بندی به‌عنوان ابزار داده‌‌کاوی و روش سوارا و ویکور به‌عنوان فنون تصمیم‌‌گیری‌‌ چند‌‌شاخصه است؛ به‌این‌ترتیب که شرکت‌های وارانتی شخص ثالث، پس از شناسایی معیار‌‌های مهم برای برون‌‌سپاری وارانتی ازنظر تولید‌‌کننده، با ابزار‌‌های داده‌‌کاوی خوشه‌‌بندی می‌‌شوند. سپس خوشه‌‌های به‌دست‌آمده با استفاده از روش ویکور رتبه‌‌بندی و بهترین خوشه انتخاب می‌گردد. برای تعیین وزن معیار‌‌های تصمیم در روش ویکور، از روش سوارا استفاده ‌شده است. روش پیشنهادی، در یک شرکت تولید‌‌کننده تجهیزات الکترونیکی مورداستفاده قرارگرفته است. به‌‌کار‌‌گیری داده‌‌کاوی قبل از اجرای تصمیم‌‌گیری، اطلاعات با‌‌ارزش پنهان در داده‌‌های تاریخی شرکت را آشکار ساخته و تصمیم‌‌گیری را بهبود بخشیده است.

کلیدواژه‌ها


عنوان مقاله [English]

Preprocessing Multiple Criteria Decision-Making Using Data Mining (Case Study: Selection of third party logistic in outsourcing warranty services of an electronic facilities company)

نویسندگان [English]

  • safar fazli 1
  • reyhaneh jamaati tafti 2
1 Associate Professor, Department of Industrial Management, Faculty of Social science, Imam Khomeini International University, Qazvin, Iran
2 MA Graduate, Department of Industrial Management, Faculty of Social science, Imam Khomeini International University, Qazvin, Iran
چکیده [English]

In recent years offering after-sales services has been one of the most important factor in achieving customer satisfaction. Providing after-sales services and warranty implies an additional cost to the manufacturer. Therefore appropriate servicing strategy prevents unnecessary costs. Nowadays one of the common warranty Polices is outsourcing services to third-party warranty providers. An important issue that we are facing with is selecting the best provider. This article have developed MADM approach by using data mining for the selection of third-party warranty providers. This integrated approach includes clustering as a data mining tool and Step-wise Weight Assessment Ratio Analysis (SWARA) and VIKOR as the two MADM tools. After identifying the features that are important for outsourcing warranty from the perspective of the manufacturer, first third-party warranty service providers are clustered using data mining tools then VIKOR technique is used to rank the obtained clusters and the best cluster is selected. SWARA technique is used to weight decision criteria in VIKOR technique. Proposed approach was used in an electronic facilities company. Using data mining before the implementation of decision-making discovered the useful information that were hidden among historical data gathered by company and improved decision making process through providing effective information.

کلیدواژه‌ها [English]

  • third party warranty
  • Data Mining
  • Multiple Attribute Decision Making (MADM)
  • SWARA
  • VIKOR
[1] Aguezzoul, A., The third party logistics selection: A review of literature. International Logistics and Supply Chain Congress, Istanbul, Turkey, Nov 2007, pp. 7-14.
[2] Aghdaie, M.H., Hashemkhani Zolfani, S. & Zavadskas, E.K., A hybrid approach for market segmentation and market segment evaluation and selection: an integration of data mining and MADM. Transformations in Business & Economics 12 (2B (29B)), 2013a, pp. 42–59.
[3] Khademolqorani, S. & Zeinal Hamadani, A., An Adjusted Decision Support System through Data Mining and Multiple Criteria DecisionMaking. Procedia-Social and Behavioral Sciences 73, 2013,  pp. 388 – 395.

[4] Asgharizadeh, E., Bitaraf, A., & Nazabadi, M.R., Services and warranty outsourcing model by MCDM. Journal of executive management 3(5), 2011, pp. 13-28.

[5] McGinnis, M.A., Kochunny, C.M., Ackerman, K.B., Third-party logistics choice. The International Journal of Logistics Management 6 (2), 1995, pp. 93–102.
[6] Meade, L., Sarkis, J., A conceptual model for selecting andevaluating third-party reverse logistics providers. Supply Chain Management: An International Journal 7 (5), 2002, pp. 283–295.
[7] Bottani, E., Rizzi, A., A fuzzy TOPSIS methodology tosupport outsourcing of logistics services. Supply Chain Management: An International Journal 11 (4), 2006, pp. 294–308.
[8] Qureshi, M. N., Kumar, D., & Kumar, P., An integrated model to identify and classify the key criteria and their role in the assessment of 3PL service providers. Asia Pacific Journal of Marketing and Logistics 20, 2008, pp. 227–249.
[9] Liu, H. T., & Wang, W. K., An integrated fuzzy approach for providerevaluation and selection in the third-party logistics. Expert Systems with Applications 36, 2009, pp. 4387–4398.
[10] Ho, W., He, T., Lee, C. & Emrouznejad, A., Strategic logistics outsourcing: An integrated QFD and fuzzy AHP approach. Expert Systems with Applications 39, 2012, pp. 10841-10850.
[11] Aguezzoul, A., Third-Party Logistics Selection Problem: ALiterature Review on Criteria and Methods. Omega 49, 2014, pp. 69–78.
[12] Senthil, S., Srirangacharyulu, B. & Ramesh, A., A robust hybrid multi-criteria decision making methodology forcontractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications 41, 2014, pp. 50-58.
[13] Tavana, M., Zareinejad, M., Di Caprio, D. & Kaviani, M. A., An Integrated Intuitionistic Fuzzy AHP and SWOTMethod for Outsourcing Reverse Logistics. Applied Soft Computing 40, 2016, pp. 544–557.
[14] Bagherinejad, J., & Sadegh Amalnik, M., A model to select the most suitable option of third party logistics companies in Iran. Journal of supply chain management 14(36), 2012, pp. 4-19.
[15] Rad, A., Naderi, B. & Soltani, M., Clustering and ranking university majors using data mining and AHP algorithms: A case study in Iran. Expert Systems with Applications 38(1), 2011, pp. 755–763.
[16] Peng, Y., Zhang, Y., Tang, Y. & Li. S., An incident information management framework based on data integration, data mining, and multi-criteria decision making. Decision Support Systems 51(2), 2011, pp. 316–327.
[17] Khalili-Damghania, K., Sadi-Nezhadb, S., Lotfic, F.H. & Tavanad, M., A hybrid fuzzy rule-based multi-criteria framework for sustainable project portfolio selection. Information Sciences 220, 2013, pp. 442–462.
[18] Azar, A., Mahdavi Rad, A. R. & Musakhani, M., hybrid model of data mining and multi-criteria decision making(Case Study: Subsidies Database of Statistical Center of Iran). Journal of operational research in its application 1(44), 2015, pp. 95–111.
[19] Shahin, A., Saleh Zade, R. & Ghandehari, M., Proposing an Integrated Model of Clustring, AHP and kano Approaches for Service Recommendation With a Case study in Saman bank of Qom. Journal of management researches in Iran 1(16), 2012, pp. 73–91.
[20] Bashiri Mousavi, A. R., Afsar, A. & Mahjubifard, A., Customer value analysis in bank with data mining technique and fuzzy analytic hierarchy process. Journal of management researches in Iran 1(19), 2015, pp. 23–43.
[21] Kersuliene, V., Zavadskas, E. K. & Turskis, Z., Selection of rational dispute resolution method by applying new step_wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management 11(2), 2010, pp. 243–258.
[22] Stanujkic, D., Karabasevic, D. & Zavadskas, E. K., Framework for the Selection of a packaging design based on the SWARA method. Inzinerine Ekonomika – Engineering Economics 26(2), 2015, pp. 181- 187.
[23] Opricovic, S. & Tzeng, G. H., Multi-criteria planning of post earthquake sustainable reconstruction. Computer-Aided Civil and Infrastructure Engineering 17, 2002, pp. 211–220.
[24] Tavakkoli Moghaddam, R., Najafi, E. & Yazdani, M., Project Manager Selection by using a Fuzzy Hybrid Delphi-VIKOR approach. Journal of management researches in Iran 4(16), 2013, pp. 19–44.
[25] Punj, G., & Stewart, D.W., Cluster analysis in Marketing Research: Review and Suggestions for Application. Journal of Marketing Research 20, 1983, pp. 134–148.