Traceability Automation in Coffee Production: A Case Study on QR-Code Integration to Optimize Manual Steps

Authors

  • Mayara Eduarda Terra Querme Instituto Federal de Educação, Ciência e Tecnologia do Triângulo Mineiro (IFTM), Brazil
  • Danielli Araújo Lima Instituto Federal de Educação, Ciência e Tecnologia do Triângulo Mineiro (IFTM), Brazil https://orcid.org/0000-0003-0324-6690

DOI:

https://doi.org/10.47852/bonviewAAES32021455

Keywords:

production engineering, traceability, automation, QR-code, coffee productive chain, manufacturing paradigm, 4.0 industry

Abstract

Agriculture and coffee production are vital to Brazil’s economy, generating jobs, driving regional development, and contributing significantly to the trade balance, making Brazil a leading coffee producer and exporter. In this context, this scientific article proposes automating coffee production traceability using QR-Codes to optimize processes, enhance quality, safety, and sustainability in the supply chain under the Industry 4.0 paradigm. We employ BPMN modeling to delineate coffee production stages, leveraging QR-Codes for data collection and registration during activities like harvesting, washing, drying, processing, storage, and farm certification validation. QR-Codes streamline operations, reduce errors, and enhance traceability, involving actors ranging from harvesters to quality personnel, inspectors, and clerks. The study scrutinizes coffee transfer from rural producers to warehouses, optimizing stock and delivery management. The main results obtained were the significant reduction in the number of employees, the reduction of activities and consequently the time, finally, the assertiveness to complete the process.

 

Received: 29 July 2023 | Revised: 16 October 2023 | Accepted: 28 October 2023

 

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.


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Published

2023-11-22

How to Cite

Eduarda Terra Querme, M., & Araújo Lima, D. (2023). Traceability Automation in Coffee Production: A Case Study on QR-Code Integration to Optimize Manual Steps. Archives of Advanced Engineering Science, 1–12. https://doi.org/10.47852/bonviewAAES32021455

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Section

Articles