The manufacturing sector has been one of the most significant energy consuming and CO2 emitting sectors over the past three decades. Therefore, it is important to have energy flow models of manufacturing processes to identify efficiency improvement and waste heat recovery opportunities with very specific targets. One of the best methods to pinpoint such locations is by referring to manufacturing process step models. Such models can be created on a large scale per industry and on small scale per process within an industry branch. Industry level modeling provides energy saving contingency by large magnitude where the impact on the economy and environment can be observed more visibly especially when sequential trends are identified. The methodology, reviewed in this paper, describes how to model energy flows on an industry scale by deriving the connection between key energy conversion activities on the one hand - including energy purchased/sold and onsite electricity generated, and the key end uses on the other hand. Industry-level energy flow model, referred to in this work, does not assume any specific system. That feature makes it scalable and allows analysis of manufacturing at various levels starting with single processes and finishing at sector and national economy levels. A combined analysis of the procedure is described in detail for creating process step models of manufacturing processes. The availability of U.S. national databases to calibrate these models is discussed and the challenges that arise in finding and using such databases are identified and analysed. In addition to addressing generic data issues associated with energy flow models, the paper also focuses on energy and material models and presents several examples of energy-intensive manufacturing processes, producing industrial gases, textiles, pulp and paper. Finally, importance of water use in onsite energy conversion and process end uses in various industries is addressed along with the sources of where this important data can be found to complete the energy flow modeling.