12 Mar 2025
We have all been there, seeing a process and thinking, there must be a better way to do this, even achieving a better, more accurate output. It could be a software flow, a manual process or even an entire system, optimisation helps businesses in finding and implementing improvements resulting in a huge impact to the business.
Certain processes can be far too complicated when they do not need to be. This means that resources’ time is wasted leading to sub-optimal productivity within a Company. The more complicated processes are, the higher the risk of human errors and setbacks, thus holding Companies back from moving projects and innovation forward, and focusing on what really matters.
Every system has its own pace, but when inefficiencies start to negatively affect a Company, it is a good idea to pause for a moment and take a closer look at the different components and tools in place and see where optimisation can make a real change to your business. Process Optimisation can truly help business make that transformation, enabling teams to focus and spend their time and energy on what’s important.
Optimisation can bring about a number of benefits to companies and can be used across all sectors, be it public policy, governmental planning, pharmaceutical, biotechnology, transportation, mobility services, manufacturing and operations, FMCG, supply chain and logistics, healthcare, medical applications and finance, just to name a few.
To understand Optimisation one has to first understand Predictive Modelling. In Predictive Modelling, as long we know the input x, the relationship between x and y (or f(x)), we are able to predict the output, y.
You might be familiar with the below example from your school days, which illustrates the equation of a straight line; y=mx+c, where m is the gradient (or slope) and c is the intercept. In process optimisation m and c could be your process settings.
Here, by knowing x and f(x),you are able to predict the output, y.
So, taking the example above, Optimisation comes in when you need to know m and c, by knowing your input (x) and what you want to get out (y).
Therefore, starting from the desired output (y), a known variable, we need to understand the relationship between x and y, i.e. f(x), which are unknowns. We do this by utilising the data that is known by us. Therefore, Optimisation is when you find out what variables you need to deploy and in what manner, in order to get to the desired result or output.
Optimisation works by attempting various iterations or value changes in the unknowns (in this case, m and c, our process settings), and varying these until we reach what is called a zero loss (0 Loss) and hence achieve the desired output, y. In this way, we are discovering the parameters needed to get to the desired y.
Optimisation can be single-objective or it can be multi-objective, with the latter having more complexity which might make obtaining a 0 Loss very difficult. In such cases, one finds what is called the global minimum, which essentially is the closest possible to a 0 Loss scenario.
In Optimisation, a specialised algorithm is used to run the simulations, according to a set of chosen rules and weights attributed to the different rules. Let’s take for instance a multi-objective process Optimisation in a manufacturing setting. Imagine a number of different ingredients which need to be combined together, each bearing different pricing, processing time, and various constraints.
A specialised algorithm helps in determining the variables and how these are to be deployed in order to get to the desired product / output. So, this means the best possible product, manufactured within a certain time, cost and of a certain quality.
With a Random Sampling technique, when working on such large number of variables and permutations, the higher the number of samples or iteration runs, the closer you get to a 0 Loss, and therefore the more accurate the output. This however leaves the probability of finding the global minimum up to chance. With a Bayesian Optimisation technique we can reach the global minimum in a much more focused manner, taking many less iterations to do so, especially in a multi-variate scenario, making it a more preferred method for Optimisation.