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<p>This study develops a fuzzy quality function deployment (QFD) framework to improve food packaging design by integrating fuzzy SWARA, fuzzy CODAS, and a proposed clustering method. Seven attributes and fifteen sub-attributes of packaging design were evaluated based on consumer importance and performance ratings. Using the proposed clustering method, four critical attributes—complete information, clear information, ease of storage, and eco-friendliness—were identified as priorities for improvement. These were further translated into fifteen technical responses through the fuzzy QFD approach. Sensitivity analysis, along with Friedman and Kendall’s W tests, confirmed the robustness and statistical significance of the results. Compared with fuzzy c-means clustering, the proposed method demonstrated higher selectivity, managerial relevance, and consistency with the importance–performance analysis principle. The findings highlight the role of technology plays in the configuration of pack size, shape, and user-friendliness to keep the product freshness, maintain shelf life of the food, and increase portability.</p>

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A Novel Fuzzy QFD Framework for Packaging Design Improvement: Integration of SWARA, CODAS, and Clustering

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Thomy Eko Saputro,
  • Agus Eko Minarno,
  • Fardini Sabilah,
  • Siti Khoiruli Ummah,
  • Arif Luqman Hakim

摘要

This study develops a fuzzy quality function deployment (QFD) framework to improve food packaging design by integrating fuzzy SWARA, fuzzy CODAS, and a proposed clustering method. Seven attributes and fifteen sub-attributes of packaging design were evaluated based on consumer importance and performance ratings. Using the proposed clustering method, four critical attributes—complete information, clear information, ease of storage, and eco-friendliness—were identified as priorities for improvement. These were further translated into fifteen technical responses through the fuzzy QFD approach. Sensitivity analysis, along with Friedman and Kendall’s W tests, confirmed the robustness and statistical significance of the results. Compared with fuzzy c-means clustering, the proposed method demonstrated higher selectivity, managerial relevance, and consistency with the importance–performance analysis principle. The findings highlight the role of technology plays in the configuration of pack size, shape, and user-friendliness to keep the product freshness, maintain shelf life of the food, and increase portability.