  Supply chain optimization schemes have more often than not underplayed the
role of inherent stochastic fluctuations in the associated variables. The
present article focuses on the associated reengagement and correlated
renormalization of supply chain predictions now with the inclusion of
stochasticity induced fluctuations in the structure. With a processing
production plant in mind that involves stochastically varying production and
transportation costs both from the site to the plant as well as from the plant
to the customer base, this article proves that the producer may benefit through
better outlay in the form of higher sale prices with lowered optimized
production costs only through a suitable selective choice of producers whose
production cost probability density function abides a Pareto distribution.
Lower the Pareto exponent, better is the supply chain prediction for cost
optimization. On the other hand, other symmetric (normal) and asymmetric
(lognormal) distributions lead to upscaled costs both in terms of inlays and
outlays. While this is an averaged out statistics over large time regimes,
transient features may still affect such probabilistic predictions and offset
results. The predictions are shown to be in good harmony with model results
shown.
