Reinforcement Learning system to capture value from Brazilian post-harvest offers

Abstract

This study assesses the value capture of a result-oriented Product-Service System offer that constitutes a post-harvest solution. Applying the reinforcement learning reward system and general linear models, we identified the Brazilian farmer's propensities to choose different products and services from the proposed system. Reinforcement learning enables one to understand the choice process by rewarding the attributes selected and applying penalties to those not chosen. Regarding product options, farmers' most valued attributes were extended capacity, fixed installation, automatic dryer, and CO2 emission control, considering the investigated system. Regarding service options, the farmers opted for maintenance plans, performance reports, no photovoltaic energy, and purchase over the rental modality. These results assist managers through a reward learning system that constantly updates the value assigned by farmers to product and service attributes. They allow real-time visualization of changes in farmers' preferences regarding the product-service system configurations.