This thesis considers the problem of how to design an industrial network to reduce cost, increase efficiency, and reduce environmental burdens. A recent approach is further developed that uses analogies with biological food webs to guide industry design. Studying ecological food webs shows that among the metrics in use, critical quantities of interest for industry design include the internal cycling of energy, the ratio of producers to consumers, and the ratio of efficiency to redundancy in the network. Metrics that are calculated using flow based information are also introduced for use in industry, a significant step forward for bio-inspired network design. A comprehensive data set of proposed, operational, and failed eco-industrial parks is compiled for use with structural food web analyses. A data set of biological food webs is also assembled to calculate sustainable benchmark values used as goals for the industrial designs. This research an essential difficulty in bio-inspired design approaches by quantitatively analyzing components of food web design by reconstructing found relationships from science and engineering 1st principles, specifically using thermodynamic 1st law efficiency. Results from this work have the potential to provide industry-wide cost savings, increase efficiency, and reduce environmental burdens through a reduction in raw material consumption and waste disposal. The results also support the view that financial competitiveness and sustainability need not be mutually exclusive: using food web network patterns embodying both economically and environmentally desirable properties, biologically redesigned industrial networks can ease both environmental and economic burdens.