In this article we study the feasibility of the Ant Colony Optimisation (ACO) algorithm for finding optimal Kanban allocations in Kanban systems represented by Stochastic Petri Net (SPN) models. Like other optimisation algorithms inspired by nature, such as Simulated Annealing/Genetic Algorithms, the ACO algorithm contains a large number of adjustable parameters. Thus we study the influence of the parameters on performance of ACO on the Kanban allocation problem, and identify the most important parameters.