SBIR-STTR Award

This Small Business Innovation Research (SBIR) Phase I Project seeks to develop a Network Algorithm for efficiently running large-scale network simulations to perform enterprise planning and risk analysis. Currently, supply-chain models consist of only simplistic, low-detail nodes which only approximate the facility's parameters that they represent. Because of this, it is difficult to determine the effect of operational level changes and relationships on a network-wide level. Research has shown that running a large-scale, supply chain model consisting of detailed operational models will run too slowly to perform any meaningful analysis in a timely manner. This project aims to develop a simulation methodology that meaningfully links together highly-detailed operational level models with its large network-scale model. Each operations simulation will be linked by network relationships such as supply and demand, product flows, and inventory holding centers. It is then possible to create a matrix which stores these relational parameters that minimize the computing time investment required. The broader impact/commercial potential of this project will provide organizations with a better understand of the risks they face both internally and across the entire production network. Industrial mishaps, such as the Ericsson facility fire which decimated the firm's inventory levels, have underlined the need to understand the complex inter-relationships between, as well as within, companies. A network simulator allows analysts to explicitly see how facilities are interrelated and how adverse events affect not just one facility, but the entire network. The technology has to potential to be used across the biopharmaceutical industry and both increase quality of care to the patient as well as reduce manufacturing costs by a similar amount.

This Small Business Innovation Research (SBIR) Phase II project seeks to further research and implement a Network Algorithm for efficiently running large-scale network simulations and using those simulations to perform enterprise planning and risk analysis. The company's algorithms (and associated early-release software) have been shown to run supply chain models one order of magnitude faster, with one order of magnitude more complexity, than current simulation models commonly deployed. Bioproduction Group has created a simulation methodology that meaningfully links together highly-detailed operational level models with its large network-scale model. Each operations simulation is linked by network relationships such as supply and demand, product path flows, and inventory holding centers. Bioproduction Group has received contracts with several biotech firms to implement advanced prototypes of this research in biopharmaceutical manufacturing as they come online. The goal is to use this simulator to reduce biopharmaceutical inventory levels across the industry by 10% or more, while reducing risk across the manufacturing network. If successfully deployed in a large enterprise, it is believed that this inventory reduction would have a yearly return of more than $20mm per organization. The technology has the potential to be used across the biopharmaceutical industry to increase quality of care to the patient as well as reduce manufacturing costs. These goals have significant direct flow-on savings benefits to the hundreds of thousands of patients across the entire public and private healthcare sector