<abstract xmlns="http://www.w3.org/1999/xhtml">

In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of <italic>EG</italic>11 is 0.1395, the importance of <italic>EG</italic>1 is 0.1116, and the importance of <italic>EG</italic>5 is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.
</abstract>

In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of EG11 is 0.1395, the importance of EG1 is 0.1116, and the importance of EG5 is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster...

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Online veröffentlicht: 09. Dez. 2023

Seitenbereich: -

Eingereicht: 28. Dez. 2022

Akzeptiert: 19. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01414

Schlüsselwörter
Fuzzy clustering, Visualization platform, Demand indicators, Mapping relationships, Customer claims

© 2023 Shengping Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on the construction of a visualization platform for customer demand analysis based on big data technology

Online veröffentlicht: 09. Dez. 2023

Seitenbereich: -

Eingereicht: 28. Dez. 2022

Akzeptiert: 19. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01414

SchlüsselwörterFuzzy clustering, Visualization platform, Demand indicators, Mapping relationships, Customer claims

© 2023 Shengping Yan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Fuzzy clustering, Visualization platform, Demand indicators, Mapping relationships, Customer claims