Optimal production planning, sourcing, distribution and routing for complex energy intensive manufacturing companies using High Performance Computing

Energy costs are the main cost drivers for large industries like chemicals & amp; gaseous products & amp; they depend on massive economies of scale and an efficient supply chain to stay competitive. Todays approaches to supply chain optimization are based on static snap-shot data and are not suitable for real-time use in tactical demand fulfillment. Companies have yet to effectively harness the potential of powerful new computing technologies and algorithms to find solutions for energy and other costly inefficiencies persisting in the supply chain many of which must be solved in real-time. This project addresses DOEs interest in turn-key solutions advancing the use of HPC technology in manufacturing & amp; is intended to result in increased supply chain efficiency, job creation, & amp; reduced carbon emissions. We propose to develop a data-integration and supply chain optimization application that uses High Performance Computing technology to address the issue of real-time supply chain optimization for tactical use in demand fulfillment. In Phase I, we will demonstrate feasibility by developing a generic data integration and supply chain optimization framework and prove the concept on a test-bed. In Phase II, we will address the deeper technical and commercial aspects of handling data confidentiality and security and we will also harden the data-model and the solver code for success in the commercial environment. Our overall objective of the combined Phase I and Phase II projects is to bring to market a cutting-edge, HPC-based supply chain optimization application. Commercial Applications and Other Benefits: We are proposing a breakthrough approach compared to what is offered by existing Commercial Off-The-Shelf (COTS) solutions. The envisioned commercial supply chain optimization application will be used to support end-to-end decision-making from sourcing, production planning, and distribution routing for energy intensive manufacturing companies. In large energy intensive manufacturers, typically, more that 70% of costs of goods sold are directly related to energy costs and hence small percentages of costs saved results in huge dollar savings. Through previous studies and isolated implementations, it has been shown that the cost savings obtained from implementing the proposed supply chain optimization is in the range of 5% to 10%, which is a game-changer for large manufacturers. The confluence of Big Data, HPC, and Supply Chain Optimization is at the center of our innovative approach and our project will be the first one to do this. The proposed solution is also expected to result in reduced energy dependence, reduced emissions, and reduction in traffic congestion (via optimal routing of vehicles). It is intended to increase the global competitiveness of the manufacturing sector and lead to job creation in the US.