Services
Process & Parameter Optimisation
Achieving Desired Outcomes by Determining the Optimum Parameters
Optimisation applies to all industries. If you know what you would like to achieve but are unsure of which parameters to change, how and when, that is where Funis Consulting can help you. Across sectors, manufacturers and operators face variations of the same challenges, whether that’s tight margins, high energy costs, supply chain uncertainty, labour shortages, tight timelines, or constant pressure for quality and consistency. Despite widespread automation, many operations still rely on reactive decisions and trial-and-error adjustments. Valuable experience remains essential, but in today's fast changing world the tools exist to move from intuition to informed decision-taking.
-- Prediction versus Optimisation diagram --
Any optimisation tool starts with a robust predictive model, whether that’s data-driven or based on first-principles. With such models you can test ideas in a virtual environment before changing anything in real life. Digital models mimic the behaviour of your process or system, allowing you to explore “what-if” scenarios, identify constraints, forecast outcomes and design improvements safely and efficiently.
Optimisation goes a step further and finds what are the optimum input parameters for your system so that you can achieve the desired outcome. This can be done either in two ways. The first is to carry out hundreds or thousands of “what-if” simulations and then analysing all of the resulting data. This can be used to not only determine the optimal parameter values, but also determine the sensitivity of your system to specific inputs. The second is to use machine-learning algorithms, such as gradient descent or Bayesian techniques, to quickly home onto the optimal parameter settings.
-- Rapid convergence using Bayesian Optimisation --
Optimisation can be single-objective or multi-objective, meaning you can optimise your parameters for a single outcome (e.g. lowest cost) or multi-objective (e.g. lowest cost and shortest time). Multi-objective optimisation can be tricky, as the objectives can be conflicting (e.g. a lower cost means a longer production time and vice-versa). Multiple models can also be linked to capture interactions across processes. This is where Funis Consulting comes in, advising on how to approach such optimisation problems and testing out various possibilities before communicating all to you to find a solution that works best for you.
This work requires more than code. It demands a deep understanding of how physical and chemical processes behave, and how to translate that knowledge into models that work in practice. In fact, we work alongside domain expertise for most of such projects as we believe that a joint effort is required to fully understand a system in a holistic way.