Service defects identification by integrating fuzzy clustering and optimization model with quality function deployment

Abstract

Service defect is critical for the success of fresh product instant delivery (FPID) service. Therefore, the identification and prioritization of such defects is an important demand from FPID companies. Quality function deployment (QFD) can help companies identify and prioritize service defects. However, existing research based on QFD failed to consider the subjectiveness and similarity of evaluation information simultaneously while neglecting the integration of service quality characteristics and customer expectations. To fill these gaps, a novel methodology based on QFD model and optimization model is proposed. Firstly, a two-phase QFD framework for FPID service is developed to establish service quality characteristics, strengthening the relationship between customer requirements and service quality characteristics. To address the fuzziness and similarity of evaluation information, a new analysis model, named L-FCM-HOQ, by integrating Linguistic terms, Fuzzy C-Means (FCM), and House of Quality (HOQ) is proposed to determine related weights in QFD. Secondly, a decision variable, "improvement rate" is introduced to determine service defects among service quality characteristics and quantify their improvement degrees. Based on this decision variable, a multi-objective optimization model is constructed to derive optimal improvement strategies aligned with customer expectations. Finally, the proposed methodology is illustrated with a case study regarding a FPID company of China, and its efficiency and advantages are verified via comparative analysis. The proposed methodology effectively identifies service defects and assesses improvement potential, contributing to the development of FPID service quality and actionable insights to guide the FPID companyies in achieving sustainable improvement.