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Background <p>Coffee processing, in wet processing environments, where operational challenges are prevalent, is a pivotal aspect in product quality and market value. The harvesting and processing of coffee in Sakaleshpur (Arabica) and Chikkamagaluru (Robusta) are examined in this study. It focuses on 15 batches of Robusta harvesting and three different stages of Arabica harvesting: fly picking, mid harvesting, and stripping. The analysis of process features and productivity-impacting factors is conducted with the help of statistical analysis and Process Mining (PM).</p> Findings <p>The results indicate that there is a strong negative correlation between the number of workers and the quantity of coffee picked in the process of fly picking, that is, the increased number of labour does not necessarily lead to increased output. Statistical analysis has shown that labour efficiency, weather and gender are key factors affecting the harvesting performance. Although cause-and-effect research can determine significant factors influencing operational efficiency, PM can identify variations in workflow patterns.</p> Conclusions <p>The paper demonstrates that integrating PM and statistical techniques can provide informative data on the way coffee harvesting takes place. The results highlight how important it is to manage labour effectively and take human and environmental aspects into account in order to increase productivity. This method provides a data-driven paradigm for increasing coffee processing systems' efficiency.</p>

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Enhancing labour efficiency in coffee harvesting using process mining and statistical analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月19日

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  • Sridevi Saralaya,
  • Anjali Ganesh,
  • Ravikantha Prabhu,
  • Roopesh Kumar,
  • Manoj Kumar,
  • Shweta Gakhreja,
  • Ruchi Singh Rajawat,
  • Shefali Goel

摘要

Background

Coffee processing, in wet processing environments, where operational challenges are prevalent, is a pivotal aspect in product quality and market value. The harvesting and processing of coffee in Sakaleshpur (Arabica) and Chikkamagaluru (Robusta) are examined in this study. It focuses on 15 batches of Robusta harvesting and three different stages of Arabica harvesting: fly picking, mid harvesting, and stripping. The analysis of process features and productivity-impacting factors is conducted with the help of statistical analysis and Process Mining (PM).

Findings

The results indicate that there is a strong negative correlation between the number of workers and the quantity of coffee picked in the process of fly picking, that is, the increased number of labour does not necessarily lead to increased output. Statistical analysis has shown that labour efficiency, weather and gender are key factors affecting the harvesting performance. Although cause-and-effect research can determine significant factors influencing operational efficiency, PM can identify variations in workflow patterns.

Conclusions

The paper demonstrates that integrating PM and statistical techniques can provide informative data on the way coffee harvesting takes place. The results highlight how important it is to manage labour effectively and take human and environmental aspects into account in order to increase productivity. This method provides a data-driven paradigm for increasing coffee processing systems' efficiency.