Considering the demand for rapid product turnover and increasing energy prices, it is becoming more important for the heat treating industry to pursue means of process optimization. The size (mass) and arrangement of the load during the heat treatment process are the primary factors that govern the rate at which the furnace and the parts exchange heat. The main objective of this work is to develop a predictive tool that can be used to determine load configurations for best performance, i.e., maximum number of parts per hour, with minimum energy consumption per unit mass. A numerical model has been developed at the Thermal Processing Laboratory (TPL) at McMaster University to simulate heat treatment processes in batch-type furnaces. The model has been validated by comparing numerical results with experimental data collected under laboratory and real-life conditions. Experiments have been carried out at the research facility at TPL as well as at three industrial sites. This paper presents the validation of the model as well as case studies of batch heat treatment cycles where best load configurations have been investigated.