Analytics with stochastic optimisation: experimental results of demand uncertainty in process industries

This study reports the test results of a two-stage stochastic linear programming (SLP) model with
recourse using a user-friendly generic decision support system (DSS) in a North American steel company.
This model has the flexibility to configure multiple material facilities, activities and storage
areas in a multi-period and multi-scenario environment. The value of stochastic solution (VSS) with a
real-world example has a potential benefit of US$ 24.61 million. Experiments were designed according
to the potential joint probability distribution scenarios and the magnitude of demand variability.
Overall, 144 SLP optimisation model instances were solved across four industries, namely, steel, aluminium,
polymer and pharmaceuticals. The academic contribution of this research is two-fold: first,
the potential contribution to profit in a steel company using an SLP model; and second, the optimisation
empirical experiments confirm a pattern that the VSS and expected value of perfect information
(EVPI) increase with the increase indemandvariability. This study has implications for practicing managers
seeking business solutions with prescriptive analytics using stochastic optimisation-based DSS.
This study will attract more industry attention to business solutions, and the prescriptive analytics
discipline will garner more scholarly and industry attention.