Towards a simulation-based approach for enhancing smallholder business models

Smallholder farms face increasing challenges in strategic decision-making due to economic, environmental, and organizational uncertainties. While frameworks such as the Business Model Canvas (BMC) support structured representation of strategic knowledge, they remain qualitative and do not enable quantitative evaluation of operational feasibility. This study proposes an information-processing framework to bridge this gap by linking strategic knowledge with executable process models and simulation-based analysis. The proposed framework transforms structured business model inputs into formal process representations using Business Process Model and Notation (BPMN). A generative artificial intelligence (GAI) component assists in extracting and structuring process-relevant information from textual inputs. The resulting BPMN models are executed within a discrete-event simulation (DES) environment to evaluate key performance indicators (KPIs), including implementation time, cost, and resource utilization. The approach is applied to three strategic orientations: sustainable, technological, and cooperative business models. Simulation results reveal distinct operational trade-offs across strategies. Technological models achieve the shortest implementation time and lowest resource requirements, while cooperative models minimize total cost but introduce longer execution times due to coordination complexity. Sustainable models provide a balanced configuration across time, cost, and resource dimensions. Task-level analysis further identifies bottlenecks and dominant activities responsible for performance differences, highlighting the value of simulation in uncovering process-level dynamics. The framework demonstrates how structured strategic knowledge can be transformed into executable models to support quantitative, simulation-based evaluation of agricultural strategies. By integrating AI-assisted transformation, formal process modeling, and performance analysis, the approach enhances decision-support capabilities in agricultural information systems and enables more informed, transparent, and resilient planning for smallholder farming systems.