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Barriers and enablers to food mass customization

Author

Listed:
  • Luiz Philipi Calegari
  • Marianne Costa Avalone
  • Diego Castro Fettermann

Abstract

Purpose - This study is to propose a procedure to support decisions on which enablers should be employed to minimize the impact of barriers to implementing mass customization strategies in food companies. Design/methodology/approach - Through interpretive structural modeling, the authors analyzed the relationships between barriers. Then, with an approach similar to the quality function deployment technique, commonly used in general product and process development, the authors clarified the relationships between barriers and enablers. Findings - The results revealed 19 barriers and 17 enablers for implementing food mass customization. The analysis indicates that most of the barriers (16) present strong associations with each other. The barrier “products with non-customizable features” depends on the whole chain of associations and causes a minor impact on the other barriers. In turn, the barrier “ingredient incompatibility” causes impact over the whole chain, and its dependence on other barriers is very low. Research limitations/implications - The results were tested in a single Brazilian company in the food sector. Practical implications - The findings can allow food manufacturing companies to focus their efforts on the improvement of enabling technologies, such as smart packaging, Internet of Things and additive manufacture. Social implications - This study would help food companies to improve their business and provide better products to society. Originality/value - There are few recommendations in the literature to how to implement mass customization strategy in companies from the food sector. This study fills in this gap presenting a procedure to guide managerial staff to develop this promising approach for food companies.

Suggested Citation

  • Luiz Philipi Calegari & Marianne Costa Avalone & Diego Castro Fettermann, 2020. "Barriers and enablers to food mass customization," Journal of Agribusiness in Developing and Emerging Economies, Emerald Group Publishing Limited, vol. 10(4), pages 403-428, April.
  • Handle: RePEc:eme:jadeep:jadee-08-2019-0116
    DOI: 10.1108/JADEE-08-2019-0116
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    Cited by:

    1. Kabadurmus, Ozgur & Kayikci, Yaşanur & Demir, Sercan & Koc, Basar, 2023. "A data-driven decision support system with smart packaging in grocery store supply chains during outbreaks," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).

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