ASABE Technical Library - Abstract

Abstract. There is a high degree of uncertainty in biofeedstock supply chains that is not easily quantified in current modeling systems. Monte Carlo and one-way sensitivity analysis can be used to quantify supply chain uncertainty and identify critical cost factors. To improve current supply chain modeling capabilities, Monte Carlo and one-way sensitivity analysis were incorporated into an online, modular, commodity based supply chain model. Empirical data was compiled to create distribution functions for key system variables such as fuel, truck, trailer, and tire costs to generate stochastic cost solutions using Monte Carlo simulations. The sensitivity analysis programs were written in JavaScript to facilitate online development. Sensitivity analysis results for biofeedstock transportation were used to rank cost variables by total cost impact and overall system sensitivity to variable changes. The results indicated the system was most sensitive to changes in fuel cost while truck weight had the highest potential cost impact. Minimum, maximum, average, and quartile cost estimates were calculated from the Monte Carlo simulation. Analysis results are displayed graphically in the model and are interactive. The inclusion of robust sensitivity analysis techniques in a web-based supply chain modeling system is an improvement over current systems. Additional value is provided to users for better quantitative analysis of biofeedstock supply chains.