Optimization can be learned

Using AI to optimize processes. That sounds good, right? There are several ways this can be done, but before that, let's go into what exactly optimization means.

Optimize literally means to review, plan and request changes to achieve maximum efficiency and effectiveness in a process, configuration item, application, et cetera.

So it is about improving something to the best state possible. To determine what that best state is, we have to find out what the most important aspect is.

There are several ways you can optimize, and it depends entirely on the business case. In any case, optimization with AI has many advantages. Think of cost savings, more speed and more accuracy, for example. Below we explain it to you.

The first thing you probably think of when optimizing is keeping costs as low as possible. AI can take on manual and time-consuming processes, allowing your staff to focus on other important tasks. AI doesn't need a coffee break or rest, making performance much more sustainable and constant! To optimize and cut costs, it is necessary to know what the fixed costs are and what the variable costs are, and how they relate to each other.

Actually, completing a specific task as quickly as possible is related to the previous benefit about cost savings. If you produce as quickly as possible, something will take you as little time as possible, which in turn leads to the least possible cost and thus efficiency. One possibility in this is, for example, adjusting a machine faster. That way you get more output per unit of time. However, this can also have other consequences: the machine may have a shorter lifespan, which will increase other costs. So it is important to find out how one outweighs the other.

Faster production is sometimes more necessary than keeping costs as low as possible. Consider, for example, the production of corona vaccines: that has urgency right now.

An AI model gives you more information. When you have more information, you can also make more accurate predictions. For example, we are currently developing the Luistervinq, a birdhouse that can monitor sports usage in outdoor spaces based on audio devices and AI algorithms. With the Listenervinq, we take in data that we turn into information. This information allows us to make more accurate predictions for the future.

Of course, we will give you an example about how we optimize within Datacadabra. In particular, our technical team optimizes the models used for the solutions being developed, because in doing so, a balance must be achieved between two desirable but incompatible aspects. In our case, these are usually speed and structure: you want to integrate both aspects as well as possible, but must strike a balance where both aspects work optimally and the best state is achieved.