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Web-Based Deployment of Single-Factor Biofeedstock Supply Chain Sensitivity Analysis Using Monte Carlo Simulation

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 59(6): 1555-1561. (doi: 10.13031/trans.59.11770) @2016
Authors:   Collin C. Craige, R. Scott Frazier, Michael D. Buser, Rodney B. Holcomb, S. Salim Hiziroglu, Raymond L. Huhnke
Keywords:   Biofeedstock, Modeling, Monte Carlo, Supply chain.

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 were compiled to create distribution functions for key system variables so that a stochastic solution could be determined using Monte Carlo simulations. The sensitivity analysis programs were written in JavaScript to facilitate online development. The sensitivity analysis results for biofeedstock transportation indicated that 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 and include the system cost distribution and system variables ranked by both sensitivity and potential cost impact. 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.

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