PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 2, 2026Advances in Mechanical Engineering0 citationsOpen Access

A product state expansion method integrating TRIZ and extenics for dynamic market adaptation

View Full Paper
JZJinpu ZhangLLLimeng LiuGCGuozhong Cao

Key Points

  • This research aims to improve product adaptability by integrating TRIZ and Extenics to better meet diverse user needs.
  • Identified need elements using TRIZ's evolutionary trends.
  • Constructed a structured need model with Extenics tools.
  • Developed an extension-driven methodology for need elements expansion.
  • Integrated TRIZ’s Substance-Field Model and 76 Standard Solutions for design improvements.
  • Validated the method through a boom sprayer design case.
  • The integration of TRIZ and Extenics increases the capacity to respond to dynamic market needs.
  • The method proposes systematic improvements for existing products based on novel scenarios.

Abstract

As user needs become increasingly diverse, product states must evolve to keep pace with these changes to help companies maintain competitiveness in the market. However, predicting heterogeneous needs and adapting existing products to emerging scenarios remain challenging. This study employs TRIZ’s evolutionary trends of needs to identify need elements, constructs a structured need model using Extenics tools, and develops an extension-driven methodology for expanding need elements. Furthermore, by integrating TRIZ’s Substance-Field Model and 76 Standard Solutions, this study proposes systematic design improvements for existing products to follow the novel need scenarios. A boom sprayer design case is presented to validate the method. The results indicate that the combination of TRIZ and Extenics enhances the ability of products to meet dynamic market needs. Provided theoretical and practical frameworks for product innovation.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6980ffc6c1c9540dea812947https://doi.org/10.1177/16878132261416842
Ask AI
Helpful
Bookmark
Share
View Full Paper