Multivariate statistical analysis methods such as principal component analysis (PCA) and partial least squares (PLS) are powerful tools for chemical process modeling and monitoring. This paper applies PCA/PLS techniques and their variants, multiway PCA/PLS and orthogonal PCA, to an industrial batch process. The study utilizes an existing large historical process data set and combines multivariate statistical methods with batch time optimization calculations to identify possibilities for process improvement. The objective is to increase throughput by shortening the batch reaction time. The batch time optimization calculations provide feasible setpoint operational suggestions while maintaining the underlying data correlation structure. A pseudo-setpoint approach is also proposed to investigate the reaction period during which the setpoint profiles remain constant. Results for an industrial reactor indicate that the batch time can be shortened by approximately 4.3%.