The performance of business processes such as order fulfillment, is measured and monitored in terms of Key Performance Indicators (KPIs) measured at periodic time intervals often hourly, daily, or weekly, depending on the business process. A business process is enabled by a set of IT software systems, infrastructure components, and external resources. Often, the impact of an anomalous event, such as a failure of an underlying IT system or poor weather conditions, on the business KPIs is unknown. Lack of knowledge as regards the impact of anomalous events leads to delays in handling these events. Providing a prior assessment of the impact can help prioritize the corrective actions and minimize further effect. In this work, we present a system that first uses periodic measurements of the business KPIs as a time series to correlate and identify the events impacting the KPIs, and forecasts the impact of anomalous events on the process performance. Our approach considers various types of process tasks that characterise a business process execution. We present experiments on synthetic and real-world datasets to validate the effectiveness of our approach in identifying and forecasting the impact of anomalous events on Business KPIs. We have deployed this functional component as a part of an IT Operations platform.