Addressing supply uncertainties using multi-period stochastic economic evaluation: A graph-theoretic aided element targeting approach

Abstract

The challenges to commercialize biomass industry includes biomass supply shortage which is dependent on geographical location and seasonality. Most of the present biomass supply chain studies had not considered incorporation of supply chain uncertainties that may lead to overestimation of financial performance. Therefore, a hybrid framework was proposed via integration of stochastic Monte Carlo Simulation model with element targeting approach (Biomass Element Life Cycle Analysis, BELCA-P-graph model) to perform scheduling and economic analysis for the biomass supply chain. The BELCA-P-graph model aimed to generate a baseline for the feedstock ratio of each input biomass. This was then input into the stochastic model, capable of estimating the financial probability of the supply chain while incorporating supply chain uncertainties (i.e., biomass element characteristics, transportation-related parameters, raw material pricing, biomass availability, market demand, and selling price of final product). Results showed that biomass shortage had decreased the mean Net Present Value (NPV) of the base case scenario (without consideration biomass supply shortage) by 1.39%–12.21%. Storage capacity consideration had decreased the mean NPV by 11.59%–12.21%. The sensitivity analysis found that syngas demand and syngas selling price uncertainty offered significant impact on the mean NPV outcome. • Integrated Monte Carlo model with BELCA-P-graph model was presented. • Six supply chain uncertainties are modelled using Monte Carlo simulation. • Biomass shortage reduced mean NPV of base case up to 12.21%. • Implementation of storage resulted in a further reduction in mean NPV.