Crude oil refinery preheat trains are designed to reduce energy consumption, but their operation can be hampered by fouling. Fouling behaviors vary from one refinery to the next. Effective management of preheat train operation requires inspection of historical plant performance data to determine fouling behaviors, and the exploitation of that knowledge in turn to predict future performance. Scenarios of interest can include performance based on current operating conditions, modifications such as heat exchanger retrofits, flow split control, and scheduling of cleaning actions. Historical plant monitoring data are frequently inconsistent and usually need to be subject to data reconciliation. Inadequate data reconciliation results in misleading information on fouling behavior. This article describes an approach to crude preheat train management from data reconciliation to analysis and scenario planning based around a preheat train simulator, smartPM, developed at Cambridge and IHS. The proposed methodology is illustrated through a case study that could be used as a management guideline for preheat train operations.