Managing natural resources for processing requires collaboration between multiple stakeholders and decision makers who do not share the same objectives or planning processes. Resource management also involves other challenges, including the need for significant long-term investment, the substantial quantities of raw materials and products involved, and the fact that processes are often based on uncertain information and data. UL researchers are developing software systems that coordinate activities across entire industries and process and analyze vast amounts of data in order to optimize supply chains from raw materials to customers. Interconnecting all of a company’s operations facilitates decision making and improves profitability.

The remarkable progress in computer science in recent years has now made it possible to use computers to model and simulate highly complex phenomena. Algorithms can be optimized to obtain stable and highly precise formulations. Given the complex problems many industries face, digital simulations are often the only way to get a better handle on manufacturing processes with a view to producing high quality, value-added products. This is a good example of the contributions mathematics can make to applied science.

Process optimization starts with observation—extracting reliable data from cause-and-effect relationships between variables in order to develop virtual models used to understand and predict a system’s complex behaviours. In the optimization phase, these results serve as a basis for continuously modifying the process to maximize production and product quality while reducing costs and energy consumption.