<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v7i1.6074</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information</title><url>https://artdesignp.com/journal/PBES/7/1/10.26689/pbes.v7i1.6074</url><author>MaoGuo</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>7</volume><issue>1</issue><history><date date-type="pub"><published-time>2024-02-25</published-time></date></history><abstract>To effectively evaluate the fuzziness of the market environment in product planning, a customer requirements analysis method based on multiple preference information is proposed. Firstly, decision-makers use a preferred information form to evaluate the importance of each customer requirement. Secondly, a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix. The ranking vector is then calculated using row and normalization methods, and the initial importance of customer requirements is obtained by aggregating the weights of decision members. Finally, the correction coefficients of initial importance and each demand are synthesized, and the importance of customer requirements is determined through normalization. The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Wang ZQ, Chen ZS, Garg H, et al., 2022, An Integrated Quality-Function-Deployment and Stochastic-Dominance-Based Decision-Making Approach for Prioritizing Product Concept Alternatives. Complex &amp; Intelligent Systems, 8: 2541–2556. https://doi.org/10.1007/s40747-022-00681-1</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Sen K, Ghosh S, Sarkar B, 2019, Evaluation Strategy of Hydraulic Crane Through Mathematical Programming: An Automated Approach to Meet Customer Requirement. Journal of The Institution of Engineers (India): Series C, 100: 647–664. https://doi.org/10.1007/s40032-019-00523-z</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B3" content-type="article"><label>3</label><element-citation publication-type="journal"><p>Dong C, Yang Y, Chen Q, et al., 2022, A Complex Network-Based Response Method for Changes in Customer Requirements for Design Processes of Complex Mechanical Products. Expert Systems with Applications, 199: 117124. https://doi.org/10.1016/j.eswa.2022.117124</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B4" content-type="article"><label>4</label><element-citation publication-type="journal"><p>Shi Y, Peng Q, 2021, Definition of Customer Requirements in Big Data Using Word Vectors and Affinity Propagation Clustering. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 235(5): 1279–1291. https://doi.org/10.1177/0954408921100177</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B5" content-type="article"><label>5</label><element-citation publication-type="journal"><p>Wang Z, Fung RYK, Li Y-L, et al., 2018, An Integrated Decision-Making Approach for Designing and Selecting Product Concepts Based on QFD and Cumulative Prospect Theory. International Journal of Production Research, 56(5): 2003–2018. https://doi.org/10.1080/00207543.2017.1351632</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
