Big Data Analytics plays a crucial role in Industry 4.0 by offering tools to improve the decision-making process. These tools comprise data management infrastructures and analytical methods. Among the economic sectors, the chemical process industry already holds mature data management structures but poorly explored analytical tools. In this sense, this work proposes an online analytical tool that can deal with Big Data to be used for identifying abnormal operations in chemical processes. It deals with a modified dynamic sensitivity matrix (DSM) and Gram−Schmidt orthogonalization (GSO) to prioritize process variables under abnormal behavior and scaling the impact they have on plant performance. In order to evaluate the effectiveness of the proposed method, the synthesis of n-propyl-propionate in a challenging simulated moving-bed reactor (SMBR) process is the object of this study. Simulations are carried out within a software-in-the-loop mode through the integration between Matlab and gPROMs. The results show that the proposed algorithm correctly prioritized the variables concerning their impact on the process performance during abnormal behavior. The capabilities of the proposed method were tested in two campaigns: considering abnormalities in two and three operating variables, respectively. In both, the method correctly prioritized their correction order based on their impact on the process performance. Finally, a "what-if" scenario shows that choosing the correction order at random leads to abnormal behavior for longer periods of time.