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677. A Multicriteria Model for Sustainable Customer Segmentation using a Sorting Outranking Method
Invited abstract in session MA-47: MCDA applications in Engineering and Management 1, stream Multiple Criteria Decision Analysis.
Monday, 8:30-10:00Room: 50 (building: 324)
Authors (first author is the speaker)
| 1. | Concepción Maroto
|
| Applied Statistics, Operations Research and Quality, Universitat Politecnica de Valencia | |
| 2. | IVAN FELIPE BARRERA
|
| Department of Applied Statistics and Operations Research and Quality, Universitat Politècnica de València | |
| 3. | Marina Segura
|
| DEPARTMENT OF FINANCIAL AND ACTUARIAL ECONOMICS AND STATISTICS, Universidad Complutense de Madrid | |
| 4. | Baldomero Segura
|
| Economía y Ciencias Sociales, Universidad Politécnica de Valencia |
Abstract
Customer segmentation plays a key role in improving supply chain management by implementing appropriate marketing strategies. The objectives of this research are to design and validate a multicriteria model to support decision making for sustainable customer segmentation in a business to business context. First, the model based on the transactional customer behaviour is extended by a hierarchy with three main criteria: Recency, Frequency and Monetary (RFM), customer collaboration and growth rates. Customer collaboration includes quota compliance, variety of products and customer commitment to sustainability (reverse logistics and shared information). Second, the Global Local Net Flow Sorting (GLNF sorting) algorithm is implemented and validated using real company data to classify 8,157 customers of a multinational healthcare company. Third, the SILS quality indicator has been implemented and validated to assess the quality of preference-ordered customer groups. The results are also compared with an alternative model based on data mining (K-means). The multicriteria system proposed allows to segment thousands of customers in ordered categories by preferences according to company strategies. The segments generated are more homogeneous, robust and understandable by managers than those from alternative methods. These advantages represent a relevant contribution to automating supply chain management while providing detailed analysis tools for decision making.
Keywords
- Supply Chain Management
- Multi-Objective Decision Making
- OR in Sustainability
Status: accepted
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