## 1 Introduction
Additionally, some studies use simulation to outline different what-if scenarios, e.g., for farmer decision-making on crop choice, fertilizer, and pesticide usage [39] or to identify global change impacts on farmland abandonment and test policy and management options [40]. In 2020, Brazil's agribusiness sector played a significant role, accounting for a substantial 26.7% of the country's Gross Domestic Product (GDP). Within this sector, the farming segment alone contributed 7% to the nation's overall economic output. Notably, soybean production has taken center stage in the modernization of Brazilian agriculture over recent decades [41]. Back in 2007, the Brazilian Ministry of Agriculture, Livestock, and Supply (MAPA) established the standard governing the classification of soybean seeds [42]. This classification process precision is of the utmost importance, as it serves a dual purpose. Firstly, it ensures that soybean products maintain the highest quality standards. Secondly, it plays a crucial role in meeting food safety regulations [43]. These regulations are imperative for upholding product integrity and safeguarding consumer health. As a result, this task necessitates a comprehensive and dedicated approach to guarantee the accurate pricing of the highest-quality soybean seeds. While a cooperative operates as a non-profit organization, its foremost objective remains the equitable distribution of benefits among its members, thus underscoring the importance of achieving operational excellence [44]. Agro-industrial cooperatives confront notable challenges in managing their operations. Firstly, resource constraints often curtail their capacity to invest in advanced inventory management systems or expand their workforce, leading to member dissatisfaction stemming from issues like stockouts, overstocking, and delayed service. These issues directly impact the cooperative's competitiveness, resulting in financial inefficiencies and a diminished ability to attract new members. Secondly, the unpredictable nature of agribusiness, influenced by factors such as seasonal demand, climatic variability, or inefficient crop management practices, creates demand spikes that strain operations, leading to prolonged lead times and customer waiting queues. This, in turn, curtails the cooperative's bargaining power. Consequently, it becomes imperative to embrace new technologies that streamline manual processes, thus reducing cycle times and enabling the cooperative to effectively address these challenges. Despite the wide application opportunities for simulation and machine learning in agriculture, no papers were found regarding the usage of simulation to generate data regarding process performance to input a machine learning technique [45]. In addition, no paper considered using machine learning outputs for investment in process improvement. The present paper aims to present a novel approach combining computational simulation and machine learning techniques to enhance decision-making for agribusiness logistics. To forecast the average lead time and waiting queue time to help decision-makers determine the best number of workers to be hired and machines to be installed for the soybean seed classification process. To the best of the authors' knowledge, this study represents the first endeavor to employ such an approach for assisting in decision-making. In particular, the case displays a method to determine the best configuration for the process, i.e., how many machines to implement and how many workers to hire. The paper is structured as follows: Section 2 presents the soybean injury classification process and describes the applied methodology. Section 3 shows the results of the conducted case study. Section 4 concludes the paper.