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

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.