A multi-method simulation model to investigate the impact of sunflower seed segregation on silos.

The South African sunflower industry is considering transferring to a quality-based marketing system driven by an incentive. However, the ability of silos to offer necessary segregation services is critical in such a transition (Baker et al., 1997) and the silo industry is concerned about the negative impact segregation could have on operations and finances. This paper proposes a multi-method simulation approach to quantify the impact of quality-based segregation and sunflower farmer response to the incentive on silo bin utilisation and the ability of the silo to store contents of arriving trucks (service level). A combination of agent-based simulation, discrete event simulation and Bayesian network sampling is used to capture system behaviour where data is scarce. Therefore, in this study, a mixed methods ABM and DES model is implemented in a new environment: a grade-based segregation problem in the South African silo industry. Several scenarios are modelled to cross-validate methods and to tease out the impact of farmer response on the results. The model is applied to a case study silo complex to test the concept. Results obtained for the case study silo show a significant negative impact on costs due to lower service levels and bin utilisation, incurring relocation and opportunity costs. Overall, this study highlights that it is necessary to consider the impact that sunflower segregation could have on each unique silo complex and provides a method to quantify the stated impact. • A model is developed to analyse the impact of quality-based sunflower segregation on a silo. • An agent-based model is used to solve a silo segregation problem within a new environment: the South African context. • An expert-driven Bayesian network is used to overcome a data scarcity constraint. • For a case study silo, a 6%–11% reduction in financial performance was found relative to the base case. [ABSTRACT FROM AUTHOR]