CC-O is a metaheuristic methodology to optimize emissions reduction projects across
multiple processes, throughout your enterprise. As a strategic decision-making tool,
CC-O reduces risks and provides certainty that emissions reduction goals will be achieved.

At the factory level, when hundreds of parameters are at stake, and when emissions reduction
solutions are numerous, picking the best combination of actions can be a daunting task. But, when
taking into account your enterprise’s goals and constraints, there is always an optimum solution: the right combination of actions for a set of optimization and decision variables. Using advanced
combinatorial and probabilistic techniques, CC-O provides the ability to simulate emissions reduction scenarios and find the optimum solution to achieve emission reduction goals at the lowest possible cost.

CC-O’s optimization engine is based on metaheuristic algorithms and mathematical procedures
such as neural networks and scatter searches used to optimize functions specified by the user.
For example, CC-O can be set with decision variables such as total capital cost and emissions
reduction goal, while optimizing variables such as number of tools retrofitted to minimize upfront
investment, operational costs, or consumption of natural resources. By specifying constraints on
decision variables, the user can find the optimal combination of actions to reduce emissions at the
lowest possible cost.

Whether you like it or not, your ability to optimize solutions at the enterprise level depends on
combinatorial mathematics. Unfortunately, ‘one solution fits all’ strategies are generally not
optimal. But with CC-O, you can find the most effective solution to achieve your goals.

To learn more about how CC-O can help with your sustainability initiatives,
please request the CC-O brochure here