Enhancing labour efficiency in coffee harvesting using process mining and statistical analysis
Sridevi Saralaya, Anjali Ganesh, Ravikantha Prabhu, Roopesh Kumar, Manoj Kumar, Shweta Gakhreja, Ruchi Singh Rajawat, Shefali Goel · 2026
Read Enhancing labour efficiency in coffee harvesting using process mining and statistical analysis by Sridevi Saralaya, Anjali Ganesh, Ravikantha Prabhu, Roopesh Kumar, Manoj Kumar, Shweta Gakhreja, Ruchi Singh Rajawat, Shefali Goel (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13523206.txt.
Description
Sridevi Saralaya and co-authors apply process mining and statistics to coffee harvesting in India in a 2026 study. Operations were examined in two Karnataka districts covering Arabica and Robusta coffee, including fifteen Robusta batches and three Arabica picking stages from early fly picking to final stripping. Workflow records were mined for patterns while statistical tests weighed productivity factors.
During fly picking, adding more workers was linked to less coffee picked per effort, showing that larger crews do not guarantee higher output. Labor efficiency, weather, and gender emerged as key influences on harvesting performance. Process mining revealed workflow variations that cause-and-effect analysis alone would miss. The authors present the combined approach as a data-driven model for managing harvest labor with human and environmental factors in view.
Catalog record
| CREATOR | Sridevi Saralaya, Anjali Ganesh, Ravikantha Prabhu, Roopesh Kumar, Manoj Kumar, Shweta Gakhreja, Ruchi Singh Rajawat, Shefali Goel |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 59060 |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |