A mathematical programming approach is presented that can be used to streamline the operations and suggest design modifications that will improve the efficiency and responsiveness of the supply chain of a fine chemical industry. The mathematical model consists initially of a large system of linear algebraic equations with a significant number of degrees of freedom. The model is based on a discrete time representation to capture the dynamics of the system, and it can be utilised to simulate an existing scheduling policy and evaluate its efficiency. The utilisation of manufacturing resources can be calculated for manpower and warehouse floorspace among others. Examples are given to illustrate a methodology that can be used to improve the schedule and enhance the throughput of the operation. The model can be further utilised to anticipate the impact of introducing a new product line in the plant. In this case a number of production bottlenecks can be identified and debottlenecking projects can be suggested. Finally, by introducing a number of binary variables to represent discrete decisions a large scale MILP model has been developed. This model can be used by the production schedulers to ensure operation feasibility and to identify possible operation improvements.