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Improving the functional performances for product family by mining online reviews

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Abstract

Companies continuously perfect product performances directing at consumers’ feedback, seeking to enhance customer satisfaction and product competitiveness. To make up for the insufficiency of previous research on product family performance improvement, a method applies multiple data-mining techniques to dig out online reviews is put forward to quantify the improvement priority of each performance in the product family, so as to guide product family improvement. Web Crawler is employed to collect customer reviews of various product variants, and then natural language processing technology is utilized to identify the words expressing functional performances and customer sentiments in the reviews, where the term frequency of each performance is defined as its importance factor. The mapping model between performance specifications and module instances in the product family is established to obtain the commonality factor of each performance. Lexicon-based machine learning is exploited to analyze customers’ sentimental values for each performance specification, which is regarded as satisfaction factor. According to the importance and satisfaction of each performance, Kano coefficient is assigned to each performance by utilizing the Kano model. Finally, combined the three factors and Kano coefficient, the improvement priority of each performance specification is estimated to suggest the enterprise to plan the resource allocation for product family improvement. The feasibility of the proposed method is demonstrated by performance improvement for sweeping robot product family and comparison with traditional questionnaire method.

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Abbreviations

PF:

Product family

FP:

Functional performance

PS:

Performance specification

MI:

Module instance

NLTK:

Natural Language Toolkit

CR:

Customer requirement

TF-IDF:

Term frequency-inverse document frequency

HC:

Hierarchical clustering

P :

Product family set

P n :

The n-th type of product variant, total N types

C :

The FP set of the PF

C j :

The j-th kind of FP, total J kinds

G je :

The e-th kind of PS of j-th FP, total E kinds of PS in j-th FP

R n :

The review set of Pn

R ni :

The i-th review for Pn, total I reviews for Pn

D ni :

Structured data set Dni converted from review Rni

C nij :

The j-th kind of FP mentioned in review Rni

Y nij :

Represent whether Cj is mentioned in review Rni

S nij :

Sentiment value for Cnij in review Rni

f(C j):

The number of times that Cj appears in all collected reviews

W f(C j):

Importance factor of the Cj

W f(G je):

Importance factor of the Gje

W c(G je):

Commonality factor of the Gje

W s(G je):

Satisfaction factor of the Gje

W k(G je):

Kano coefficient of the Gje

B(G je):

Improvement priority of the Gje

B in(G je):

Improvement index of the Gje

M :

The module set of PF

M d :

The d-th kind of module, total D kinds

m dw :

The w-th kind of MI of Md, total W kinds of MI for Md

H dw :

The parameter of mdw

U(m dw):

The commonality of mdw

r :

The mdw is configured in r types of product variants

Z :

The number of MI mapped to Gje

S je :

The total sentiment value Sje for each Gje

Q :

Q Types of product variants configured with the Gje

S qi j :

The sentiment value for Gje in i-th review of product variant q

w f :

The weight of importance factor Wf(Gje)

w c :

The weight of commonality factor Wc(Gje)

w s :

The weight of satisfaction factor Ws(Gje)

f k(C j):

The selected importance score of Cj in the k-th questionnaire, total K questionnaires collected

S t :

The selected satisfaction score of Gje in the t-th questionnaire, total T questionnaires describing Gje

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Acknowledgements

The financial support of this work by the National Natural Science Foundation of China (Grant Number 51875568), and the National Natural Science Foundation of China (Grant Number 51475459), and Priority Academic Program Development of Jiangsu Education Institutions of China (PAPD) are gratefully acknowledged.

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Authors and Affiliations

  1. School of Mechatronics Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, China

    Chao He, Zhongkai Li, Dengzhuo Liu, Guangyu Zou & Shuai Wang

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  1. Chao He
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He, C., Li, Z., Liu, D. et al. Improving the functional performances for product family by mining online reviews. J Intell Manuf 34, 2809–2824 (2023). https://doi.org/10.1007/s10845-022-01961-w

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