This paper aimed at developing a stochastic model
that is capable of forecasting production to reduce
the variance with the actual production. The model
results had a deviation of 2.34% while the
deterministic had a greater deviation at 5.44%. The
stochastic model predicted better due to its ability
to incorporate the stochastic nature of the distinct
processes of the shovel-truck system that result in
production. The variability in the shovel-truck
processes is always likely to cause much difference
in what a deterministic formula will forecast and
what will be actually achieved.