  This study proposes a simulation framework of procurement operations in the
container logistics industry that can support the development of dynamic
procurement strategies. The idea is inspired by the success of Passenger
Origin-Destination Simulator (PODS) in the field of airline revenue management.
By and large, research in procurement has focused on the optimisation of
purchasing decisions, i.e., when-to-order and supplier selection, but a
principled approach to procurement operations is lacking. We fill this gap by
developing a probabilistic model of a procurement system. A discrete-event
simulation logic is used to drive the evolution of the system. In a small case
study, we use the simulation to deliver insights by comparing different
supplier selection policies in a dynamic spot market environment. Policies
based on contextual multi-armed bandits are seen to be robust to limited access
to the information that determines the distribution of the outcome. This paper
provides a pool of modelling ideas for simulation and observational studies.
Moreover, the probabilistic formulation paves the way for advanced machine
learning techniques and data-driven optimisation in procurement.
