Reducing food loss and waste can improve the efficiency of food supply chains and provide food security. Here we estimate mass flow as well as food loss and waste along the US food supply chain for 10 commodity groups and nine management pathways to provide a baseline for designing efficient strategies to reduce, recycle, and recover food loss and waste. We estimate a total food loss and waste of 335.4 million metric tonnes from the U.S. food supply chain in 2016. Water evaporation (19%), recycling (55%), and landfill, incineration, or wastewater treatment (23%) accounted for most of the loss and waste. The consumption stage accounted for 57% of the food loss and waste disposed of through landfill, incineration, or wastewater treatment. Manufacturing was the largest contributor to food loss and waste (61%) but had a high recycling rate. High demand, perishable products accounted for 67% of food waste. We suggest that funding for infrastructure and incentives for earlier food donation can promote efficiency and sustainability of the supply chain, promote FLW collection and recycling along the U.S. FSC, and improve consumer education in order to move towards a circular economy.

This article uses case studies to demonstrate the potential of smart manufacturing (SM) and Internet of Things (IoT) technologies to enhance operational performance and productivity in industry. The analysis highlights benefits such as cost reduction, production flexibility, shorter product time‐to‐market, energy/water efficiency and environmental impact reduction, and increased productivity. To illustrate the effectiveness of SM and IoT approaches, the authors sought out manufacturers currently implementing or seeking to implement SM and IoT technologies. The authors identified beer brewing (NAICS Code 312120) as a rapidly expanding industry whose members appear eager to implement SM technologies to optimize production lines by revealing bottlenecks and identifying performance‐reducing nodes. This article presents two case studies based on SM and IoT technologies in breweries. It briefly describes a systematic framework introduced elsewhere by the authors and uses it to assess the energy productivity and competitiveness of SM applications in breweries. This article addresses questions concerning the information and communications technology infrastructure needed to build smart breweries and how corporations simplify the installation and deployment of SM and IoT components. This article was originally published in the Proceedings of the 2019 ACEEE Summer Study on Energy Efficiency in Industry.

Total energy comparison (TEC), classic energy intensity (CEI), and linear regression models (LRM) are the three most common approaches used to track the energy performance of manufacturing facilities. TEC simply compares the total energy consumption from utility bills. TEC is rarely used because it does not consider the variation of any factors that may affect energy consumption. One step better, CEI considers the variation of production rates by using the ratio of annual total energy consumption over annual total production. However, CEI fundamentally assumes that energy consumption is zero if the production rate is zero. This is almost never true and can cause significant errors. Using linear regression models, LRM considers the impact of multiple variables and, therefore, most accurately tracks energy performance. Unfortunately, because of the lack of either required data or technical expertise, some facilities cannot create valid linear regression models. For these cases, CEI is the only option. The goal of this article is to develop a new methodology, modified energy intensity (MEI), which is more accurate than CEI, but requires much less data and is easier to implement than LRM. Using the underlying principle of LRM, this article first outlines the mathematical derivation of the equations for MEI, then explains them from an engineering perspective. Finally, the implementation of MEI is discussed.