Comfortable automotive seat design and big data analytics: A study in thigh support

This study demonstrates how big data analytics can improve automotive seat design practices pertaining to thigh support and cushion length, a consistent customer complaint across the automotive seating industry. The method featured an analysis of survey feedback (complaint and self-reported anthropo...

Full description

Saved in:
Bibliographic Details
Published inApplied ergonomics Vol. 75; pp. 257 - 262
Main Authors Romelfanger, Megan, Kolich, Michael
Format Journal Article
LanguageEnglish
Published England Elsevier Ltd 01.02.2019
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:This study demonstrates how big data analytics can improve automotive seat design practices pertaining to thigh support and cushion length, a consistent customer complaint across the automotive seating industry. The method featured an analysis of survey feedback (complaint and self-reported anthropometry) obtained from 92,258 buyers of new vehicles in the North American market. Driver seat three dimensional scans from 139 vehicles (representing 12 manufacturers) provided metrics related to cushion length allowing for determination of the percentage of an average occupant's thigh supported by an automotive seat cushion in relation to customer complaints. The range determined to provide thigh support leading to minimal complaints for overall cushion length is 83.46%–88.49% and for cushion length to trim prominence is 73.63%–80.60%. A specific vehicle program was used to confirm the targets established using big data analytics were effective in minimizing customer issues related to thigh support and cushion length. •Big data analytics of customer data improves design insight over only anthropometry.•Length ranges to provide adequate thigh support in automotive seats are proposed.•Proposed ranges applied to vehicle with length issue reduced customer complaints.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0003-6870
1872-9126
DOI:10.1016/j.apergo.2018.08.020