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Update on Systems-Based Logistics Model for Eastern Redcedar

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

Citation:  2015 ASABE Annual International Meeting  152129937.(doi:10.13031/aim.20152129937)
Authors:   Collin C Craige, Michael D Buser, R. Scott Frazier, Rodney B. Holcomb, Raymond L. Huhnke
Keywords:   Supply chain, modeling, transportation, harvesting, GIS, web-based, bioenergy, bio-products, forestry, economics.

Abstract. A body of knowledge exists for many aspects of the Eastern Redcedar commodity supply chain; however, the data available is not sufficient to fully evaluate various commercialization strategies. The ability to model a supply chain in its entirety, from identifying the facility location through the harvest, transport, processing, and refining stages is necessary to characterize the feasibility of a given strategy. To facilitate development of Eastern Redcedar commerce, a comprehensive, modular, commodity based supply chain model was developed as a computational tool for the industry. The model is web-based to provide improved accessibility and ease of use while the modular structure gives it the flexibility to evaluate niche markets. ArcGIS Online is used to perform location allocation; develop service areas, determine routes, and create biomass yield maps. This data is fed into each of the cost modules, along with additional user inputs, to estimate the cost at each stage of the supply chain. Rejection sampling is used to generate random numbers according to empirical probability distribution functions for key cost variables in Monte Carlo simulation. One-way sensitivity analysis is conducted to derive the interdependency, relative cost impact, and relative sensitivity of variables on total system cost. All results are displayed visually as interactive bar graphs, line charts, and maps. The web-based model greatly increases public access while providing a computationally efficient method of calculating supply chain costs and performing robust sensitivity analysis. The model is expected to reduce the risk associated with high level production of Eastern Redcedar commodities and provide a strong foundation for expansion to include other biomass feedstocks

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