A key paradigm shift resulting from the intersection of the information technology (IT) and utility sectors is the availability of real-time data regarding energy use across different industries. Historically, ascertaining the energy costs across the value chain of a given product or service was a laborious and expensive task, requiring many months of data collection; several proxies or approximations for cases where measured data might not be cost-effectively available; and even then, the resulting energy footprint could have significant uncertainty based on time-of-measurement, geographic diversity of manufacturing sites, etc. As dynamic energy pricing begins to take hold and environmental externalities begin to be priced into existing cost structures, the ability to optimize a given value chain for minimal energy use becomes increasingly attractive. In this paper, we discuss an approach for leveraging dynamically available data alongside historical n-tier supply chain models to avail the ability for such optimization. The approach is illustrated for the case study of a computer manufacturer, where we find that metering electricity use at a small subset of sites can allow for a reasonable estimate of the total energy use across the supply chain.

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