We investigate the performance of workload rules used to support customer order acceptance decisions in the hierarchical production control structure of a batch chemical plant. Customer order acceptance decisions need to be made at a point in time when no detailed information is available about the actual shop floor status during execution of the order. These decisions need therefore be based on aggregate models of the shop floor, which predict the feasibility of completing the customer order in time. In practice, workload rules are commonly used to estimate the availability of sufficient capacity to complete a set of orders in a given planning period. Actual observations in a batch chemical manufacturing plant show that the set of orders accepted needs to be reconsidered later, because the schedule turns out to be infeasible. Analysis of the planning processes used at the plant shows that workload rules can yields reliable results, however at the expense of a rather low capacity utilization. In practice this is often unacceptable. Since, solving a detailed scheduling problem is not feasible at this stage, this creates a dilemma that only can be solved if we can find more detailed aggregate models than workload rules can provide.