Being agile in decision making and in the implementation of changes is essential for a company to be a leader in its sector, and this is where agile methodologies take on great importance. Decisions must be oriented towards continuous improvement, both in terms of cost reduction or increased efficiency, as well as innovation and the creation of new products to meet new market demand.
The agile methodologies take on a new meaning and dimension when the foundations of IoT are applied. The paradigm of continuous improvement is currently being imposed, the essence of which is that if you want to improve a magnitude of a parameter you must first measure it, i.e. you cannot improve what you cannot measure.
The Industry 4.0 is a hyper-connected and sensorised industry where decisions can be made quickly. There are many questions that can arise:
- What happens when you have a lot of variables, measurements or history of these measurements?
- How can we find patterns, deviations, correlations between these parameters?
- How to measure quantities of different nature, with different protocols, on different machinery and in different locations?
- How to visualise, interpret and analyse business variables with mechanical or social variables?
- How do you make important decisions quickly based on what is happening in your production?
- What if you also want to know what is happening in the market to make that decision?
Nowadays, it is necessary to have mechanisms to help in this process of measurement and correlation. New technologies allow the replication and implementation of sensors with admissible costs on a massive scale and the capture of information from different machines, tools and systems.
Today, we have the capacity and the technology to measure a wide range of variables and parameters.
Not only is it possible to measure these quantities, but we can also collect them digitally, process them automatically, translate them into a common language and transmit them securely.
Elliot Cloud acts as the brain of the system, as an umbrella that collects this data, and is able to correlate and determine the degree of ascendancy that some parameters have over others, as well as storing and displaying the historical data in an agile way and analysing them by means of novel techniques of Big Dataanalytical, machine learning and artificial intelligence.
Thanks to these techniques, it will be possible to detect characteristics such as the seasonality of occurrences and events, and it will also be possible to define alerts that will trigger orders to activate mechanisms and protocols for action, among others.