The Law of Industrial Dynamics ensures that if a production control system can amplify then it will surely find a way of doing so despite the best efforts of production schedulers to take corrective action. In fact, practical studies show that such human intervention frequently aggravates the situation with both stock levels and order rates fluctuating alarmingly. The solution is to design an effective system via simulation. This requires the selection of the appropriate control system structure, agreement on the test cases to be used to mimic the operating environment, and finally setting the system parameters to achieve best performance for this scenario. Demonstrates a system which has three controllers utilizing sales, inventory and work in progress (WIP) data to set production order rates. The resulting decision support system (DSS) is a generic tool that can be used by production schedulers with confidence in the knowledge that the Law of Industrial Dynamics effects may be minimized. Simulation experiments can determine the best available trade-off in any particular situation such as achieving the lean logistics aim of minimum reasonable inventory (MRI) while retaining high customer service levels (CSL). The experimental facility available within the simulation model includes provision for assessing the impact of variable production lead times and information delays on system performance. Describes a specific application of the DSS and the specific improvements in a company’s performance. Places the DSS in the context of a case-based reasoning environment in which a knowledge base of system structures and their dynamic properties is achieved. Outlines the opportunity of utilizing the DSS in uncertain lead-time environments in a range of industry sectors.