An iterative method for forecasting most probable point of stochastic demand

The demand forecasting is essential for allproduction and non-production systems. However, nowadaysthere are only few researches on this area. Most ofresearches somehow benefited from simulation in theconditions of demand uncertainty. But this paper presentsan iterative method to find most probable stochasticdemand point with normally distributed and independentvariables of n-dimensional space and the demand space is anonlinear function. So this point is compatible with bothexternal conditions and historical data and it is the shortestdistance from origin to the approximated demand-statesurface. Another advantage of this paper is considering ndimensionaland nonlinear (nth degree) demand function.Numerical results proved this procedure is convergent andrunning time is reasonable.