Quick Service Restaurant industry is a massive sector which has a huge and ever increasing share in the global food market. Efficient management of resources is crucial to provide service optimization and to avoid massive amount of wastes in such a huge domain. This requires fully automated intelligence by using the power of the Internet of Things (IoT), instead of human based methods that are inefficient and prone to errors. In an IoT platform, edge computing is a vital technology to provide low latency, less redundancy, resource utilization, extra security and real time decisions. In this paper, an edge oriented IoT architecture for Quick Service Restaurants is proposed. In the proposed architecture, data is collected from a variety of wireless sensor nodes and data sources and processed at the edge to make predictions, to create timely and meaningful alerts and to make some intelligent decisions with an aim of waste management and reduction. For this purpose, it is mainly focused on anomaly/outlier detection and production service level estimation by incorporating lightweight clustering and classification techniques. Several experiments are performed in a real restaurant environment and the results show that the IoT based automation system is capable of correctly deciding on (in advance) production level, as well as triggering alerts in case of any kind of anomalous waste conditions. (C) 2020 Elsevier Ltd. All rights reserved.