Assessment of logistics service quality based on the application of fuzzy methods modeling

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Improving the logistics service quality (LSQ) requires its assessment to identify appropriate reserves, which actualizes the scientific task of improving the appropriate methodological support for LSQ assessment. The purpose of this paper is to develop a model for assessing the quality of logistics services based on a specified list of criteria, their grouping, and the application of the mathematical apparatus of the fuzzy sets theory.
The study substantiates the expediency of using the fuzzy set method to assess the quality of logistics service and builds an LSQ assessment model that includes 12 criteria grouped into four groups: company reputation, product availability/quality, reliability/flexibility, and consumer service.
As a result of assessing the quality of logistics service, an integral indicator was obtained, which made it possible to determine the evaluations of its components: product availability/quality is rated high; reliability/flexibility – average; consumer service – good; and company reputation – poor. The obtained results indicate that such an aspect of logistics service quality assessment as company reputation needs particular attention, which confirms the modern trend of prioritizing the perception of the quality of logistics service, personal service/contact, and empathy by customers. Therefore, customers’ perception of the quality of logistics service becomes a decisive factor in the competitive struggle in the logistics services market. Moreover, it is a bottleneck in the process of increasing LSQ, which requires further research to develop appropriate management mechanisms.

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    • Figure 1. Techniques and methods of assessing the logistics service quality
    • Figure 2. Model for evaluating the logistics service quality of logistics operators/providers
    • Figure 3. Intermediate parameters (A, B, C, D) and the integral output indicator of the logistics service quality
    • Figure 4. Obtaining the intermediate parameter company reputation (A1) in the form of the membership function editor
    • Figure 5. Fragment of the rule base for obtaining the intermediate parameter company reputation
    • Figure 6. Assessment model of the logistic service quality
    • Figure 7. Integral indicator of the logistics service quality (S) in the form of membership functions of the fuzzy output system
    • Figure 8. Implemented data defuzzification to the final level of the developed fuzzy model – an integral indicator of the quality of logistics service for Company No. 1
    • Figure 9. Defuzzification of the average values of intermediate modules to the final level of the developed fuzzy model
    • Figure A1. Realized defuzzification of data to the final level of the developed fuzzy model – an integral indicator of the quality of logistics service for Company No. 2
    • Figure A2. Realized defuzzification of data to the final level of the developed fuzzy model – an integral indicator of the quality of logistics service for Company No. 3
    • Table 1. Criteria for evaluating the logistics service quality
    • Table 2. Component assessments of the logistics service quality
    • Table 3. General evaluation of the logistics service quality for the developed model
    • Table 4. Generalized input, intermediate parameters, and evaluation results of the integral indicator of the logistics service quality
    • Table 5. Average integral indicator of the logistics service quality
    • Table A1. Indicators for evaluating company reputation (A)
    • Table A2. Service/product availability/quality assessment indicators (В)
    • Table A3. Indicators of reliability/flexibility (C)
    • Table A4. Indicators for evaluating the level of consumer services (D)
    • Table A5. Rules for forming an assessment of company reputation (А)
    • Table A6. Rules for forming an assessment of product availability/quality (В)
    • Table A7. Rules for forming reliability/flexibility assessment (C)
    • Table A8. Rules for forming a consumer service assessment (D)
    • Table A9. Rules for forming an assessment of the quality of logistics service of logistics operators/providers (S)
    • Conceptualization
      Tetiana Kolodizieva, Kateryna Melnykova
    • Data curation
      Tetiana Kolodizieva, Elina Zhelezniakova
    • Investigation
      Tetiana Kolodizieva, Viktoriia Pysmak
    • Methodology
      Tetiana Kolodizieva, Elina Zhelezniakova
    • Supervision
      Tetiana Kolodizieva, Oleh Kolodiziev
    • Visualization
      Tetiana Kolodizieva, Elina Zhelezniakova
    • Formal Analysis
      Elina Zhelezniakova, Viktoriia Pysmak
    • Funding acquisition
      Elina Zhelezniakova, Oleh Kolodiziev
    • Software
      Elina Zhelezniakova, Kateryna Melnykova
    • Resources
      Kateryna Melnykova, Oleh Kolodiziev
    • Validation
      Kateryna Melnykova, Viktoriia Pysmak
    • Writing – original draft
      Kateryna Melnykova, Viktoriia Pysmak
    • Project administration
      Viktoriia Pysmak, Oleh Kolodiziev
    • Writing – review & editing
      Viktoriia Pysmak, Oleh Kolodiziev