Five reasons for applying machine learning in manufacturing

Article by Alberto Martínez, CEO of Lantek

But… how can we do this without the costs escalating and whilst maintaining the quality of both the product and the service? The challenge depends on implementing an innovative tool: Machine Learning or the Automatic Learning of machines.

This Industry 4.0 enabler opens up a wide range of opportunities in the production chain, increasing productivity, reducing costs and gaining in efficiency using the analysis of the data that we generate and algorithms which optimize the production chain in real time. Let’s take a close look at the advantages offered by Machine Learning in advanced manufacturing:

1. Data analysis. Digitalization is no longer a matter concerning one exclusive division, it must become a strategy which can be applied in all departments. This requires computerizing and sensorizing the immense amount of data that we produce. From then on, incorporating it into all areas in order to have a vision of the whole and, thus, get the most out of it and be able to make better decisions. In this way, the machines learn and offer responses for any setting in real time. For example, finding out the status of the machines and, if necessary, redistributing manufacturing in order to avoid losing time; checking the capacity of the warehouse to satisfy the material requirements of the orders; visualizing the progress of orders in order to meet delivery times…
   
   
2. Prediction. The historical record of data allows the system to think ahead. In terms of production: to predict new orders from habitual clients, identify new consumer patterns in order to increase sales or adapt the workload to match the volume of orders. All of this could scale to other areas. In terms of the inventory, it helps us to better manage the stock, and foresee a possible shortage. This capacity to predict can be extended to other processes such as maintenance, in such a way that faults can be detected before they occur and revisions can be programmed in accordance with demand.
   
   
3. Automation. Machines learn from incidences from the real world (unscheduled stops, urgent orders, lack of workers…) From then on, many of the responses that they offer can be automated, which makes the presence of an operator at the plant no longer necessary, meaning that they can dedicate their time to other activities which create value. Additionally, the machines can be taught to identify poor quality patterns, thus reducing reworking. As we can see, automation offers great agility and speed in production, whilst people can direct their focus to other areas.
   
   
4. Prescription. This innovative tool also visualizes the capacity of the assembly line in a virtual manner. In this sense, if the system detects that the machines will not be able to handle a volume of work within the planned deadlines, it evaluates the possible delays of other orders and offers alternatives or, in some cases, automates them. It can also prescribe rates. By having all of the information in real time, the system recommends prices, taking into account the expenses (cost of the raw material, energy…) and the demand in such a way that it offers the appropriate margin.
   
   
5. Customization. Mass production has become of secondary importance. Á la carte orders are being demanded and with Automatic Learning, once again, having one person in charge of customization will not be necessary. The machines will be capable of doing it with maximum precision and with fast production. What’s more, this implies a new business model for the industry, beyond that of the manufacturing and sale of the product. That is to say, the possibility of offering services surrounding an article, such as the customization of said article.

Let us, then, support these disruptive tools, not only in order to avoid losing competitiveness, but also to position Spain as one of the reference European countries in terms of digitalization.