Book excerpt: Between 2008 and 2035 global energy demand is expected to grow by 53%. While most industry-level analyses of manufacturing in the United States (U.S.) have traditionally focused on high energy consumers such as the petroleum, chemical, paper, primary metal, and food sectors, the remaining sectors account for the majority of establishments in the U.S. Specifically, of the establishments participating in the Energy Information Administration's Manufacturing Energy Consumption Survey in 2006, the non-energy intensive" sectors still consumed 4*10^9 GJ of energy, i.e., one-quarter of the energy consumed by the manufacturing sectors, which is enough to power 98 million homes for a year. The increasing use of renewable energy sources and the introduction of energy-efficient technologies in manufacturing operations support the advancement towards a cleaner future, but having a good understanding of how the systems and processes function can reduce the environmental burden even further. To facilitate this, methods are developed to model the energy of manufacturing across three hierarchical levels: production equipment, factory operations, and industry; these methods are used to accurately assess the current state and provide effective recommendations to further reduce energy consumption. First, the energy consumption of production equipment is characterized to provide machine operators and product designers with viable methods to estimate the environmental impact of the manufacturing phase of a product. The energy model of production equipment is tested and found to have an average accuracy of 97% for a product requiring machining with a variable material removal rate profile. However, changing the use of production equipment alone will not result in an optimal solution since machines are part of a larger system. Which machines to use, how to schedule production runs while accounting for idle time, the design of the factory layout to facilitate production, and even the machining parameters -- these decisions affect how much energy is utilized during production. Therefore, at the facility level a methodology is presented for implementing priority queuing while accounting for a high product mix in a discrete event simulation environment. A baseline case is presented and alternative factory designs are suggested, which lead to energy savings of approximately 9%. At the industry level, the majority of energy consumption for manufacturing facilities is utilized for machine drive, process heating, and HVAC. Numerous studies have characterized the energy of manufacturing processes and HVAC equipment, but energy data is often limited for a facility in its entirety since manufacturing companies often lack the appropriate sensors to track it and are hesitant to release this information for confidentiality purposes. Without detailed information about the use of energy in manufacturing sites, the scope of factory studies cannot be adequately defined. Therefore, the breakdown of energy consumption of sectors with discrete production is presented, as well as a case study assessing the electrical energy consumption, greenhouse gas emissions, their associated costs, and labor costs for selected sites in the United States, Japan, Germany, China, and India. By presenting energy models and assessments of production equipment, factory operations, and industry, this dissertation provides a comprehensive assessment of energy trends in manufacturing and recommends methods that can be used beyond these case studies and industries to reduce consumption and contribute to an energy-efficient future.