MathIT.Factory. High tech, high impact.

The truth is that, although we feel a certain modesty when it comes to talking about the positive impact we have had on industries following the implementation of our solución MathIT.Factory, ssolution, we also feel a great sense of pride that we sincerely cannot and do not want to conceal.

The optimisation of complex manufacturing processes, with real examples in production at large customers that are generating significant savings in electricity supply or waste in manufacturing processes, and their direct impact on the profitability and sustainability of the operation, with a parallel reduction in pollution levels and carbon footprint, is providing these companies with strategic value that is achieved thanks to the application of Artificial Intelligence (AI) and Operational Research (OR) techniques designed and managed by the team of experts at oga.ai.

It is not an easy process and not all companies are ready to embark on this highly efficient path and contrary to what might be assumed, it has nothing to do with the size or resources of the companies.

The key

The key, and undoubtedly the triggering element, lies in understanding the role that data plays today in all organisations and the paradigm shift (figure 1) in which traditional business rules-based models are clearly outdated in certain phases of manufacturing processes, whether discrete or process-based.

Traditional Project programming based vs IA Project (Machine/Deep Learning)
Figure 1. Traditional Project vs IA Project (Machine/Deep Learning)

Any company dedicated to manufacturing or processing that bases its production on complex processes has in Artificial Intelligence and Operations Research clear allies in the search for efficiency and differential values with respect to its competition. Once this is understood and assumed, we can help them to start the process that will undoubtedly take them to the next level.

The process

We are talking about a sequential process (figure 2) in which, together with the business, production or digital transformation managers, we understand the internal aspects of their business and analyse the available data while complementing it with data from other contrasted sources and based on our experience.

This part of the process leads us to define a ‘GO – NO GO’ depending on the data provided and the quality of the data. In cases where the data does not allow us to work with AI, clients obtain a clear vision and a roadmap that leads them to sensorise their processes and start obtaining quality data, which is already a step forward and a positive impact in terms of their production strategies and an increase in their digital maturity that is critical to their future.

In a significant majority of cases, and given that large manufacturing companies tend to have increasingly high-tech and highly sensorised production systems with extensive IoT ecosystems, we have access to a good collection of quality and sufficiently representative historical data on their manufacturing processes, which allows us to build datasets on which we can deploy the next step, in which we begin to apply Machine Learning and Deep Learning techniques that allow us to detect the variables with a critical impact on each sub-process and the most relevant: modify those on which we can intervene in a feasible way according to the prescription of our Operational Research (OR) systems embedded in our solution, thus optimising the integral process.

With all the functional experience we have accumulated, exploring the limits of the application of these analytical techniques in the optimisation objectives of processes as complex as those of discrete or continuous manufacturing, we can undoubtedly affirm that even AI itself, with all its power of evident transformation in so many scenarios, has its limitations in these complex approaches and that only with the hybridisation of Artifitial Intelligence (AI) and Operational Research (OR) can we unlock this transforming value of the business and achieve this new paradigm.

Thus, with the implementation of this solution, in which algorithms and Artificial Intelligence and Operational Research models coexist and feed back, we define the operating models that ensure the maximisation or minimisation of the ratios that in each case the manufacturer considers most critical for the profitability and efficiency of the specific manufacturing process on which we are impacting.

This scenario of collaboration and co-creation has led us to become technological and functional partners of large multinationals that today rely on oga and its team to make a truly transformative qualitative leap and we could not be more proud and excited with this reality and with this type of advanced services that we make available to you.

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