Forecasting workloads in multi-step, multi-route business processes

This paper presents a technique developed to forecast workloads in a business process. Business processes such as the process of engaging on a service contract consist of multiple steps that are not necessarily sequential. There can also be multiple routes that work can take in transition. In order to forecast workloads at different steps of such business processes, one needs to predict dynamic movements of process instances within the system as well as the arrival of new instances from outside. By analyzing transition log data, we construct a Markov chain, which models the movement of process instances across different steps of the business process. Our approach takes into account the fact that an instance's prior trajectory may affect its future transitions. Via numerical studies, we demonstrate the overall performance of the proposed forecasting method. We also investigate how the performance of the forecasting method changes as various characteristics of the business process change. The proposed technique is general, and can be applied to a large class of business processes.