In the process industry, various types of degradation occur in processing plants, resulting in significant economic losses. Modeling of degradation is important because it provides quantitative insights for consideration of degradation impacts in the operations of process manufacturing. This paper studies batch processes that show a periodic pattern for the evolution of degradation. A new data structure, the campaign, is applied for data-driven modeling of the periodic batch-to-batch evolution of degradation using a new multiway partial least squares approach, and it is further employed to predict the evolution of degradation in a series of batch runs. The proposed approach is illustrated and applied in a comprehensive industrial case study. The example illustrates the efficacy of the proposed model and presents a fair potential for applications of degradation prediction. (C) 2019 The Authors. Published by Elsevier Ltd.