Either to guarantee continuity in the manufacturing chain, to accelerate processes, to make talent more profitable in a more productive way or to increase efficiency, the automation of factories implies a qualitative leap in which Artificial Intelligence and its Machine Learning variant allow us to optimize processes even more.
At a time where there’s a great pressure on costs to meet demand, robotic process automation (RPA) software is "a key driver due to its ability to improve the quality, speed and productivity of processes," the consultancy Gartner recently pointed out in a report that predicts that in 2021 revenues from this solution will reach 1.89 billion dollars worldwide, almost 20% more than in 2020, and estimates that this will continue to grow to double digits in the next two years.
Let’s take a look back and think about the number of manual processes that can be automated today in the metal and sheet metal industry. Routine activities where automation allows companies to improve their activity by reducing errors and improving the quality and agility of processes and manufacturing, subsequently allowing people to dedicate their time to operations that add greater value where automation is not able to have an impact.
The automation of these tasks has a direct impact on productivity and competitiveness. We’re not only talking about saving time, but also money in the medium and long term as, once the investment is amortized, with advanced software, the ROI accelerates.
Another report by McKinsey from over three years ago, but which contains reflections that are still valid today, highlights that in times of low productivity, automation can provide the necessary boost for economic growth, as well as help to offset the impact of the decline in people of working age. Based on their model of scenarios, automation can increase productivity growth globally by between 0.8% to 1.4% per year.
Some industries, such as the automotive one, a pioneer in Industry 4.0, have achieved a high degree of process automation, but many SMEs in the sheet metal industry are still holding onto manual processes and are yet to grasp the advantages of automation.
Up until now, sheet metal manufacturing processes have often been completed with isolated non-networked solutions, which takes time as technicians need to coordinate processes. This tedious task creates no added value and has a negative impact on productivity in the Industrial 4.0 era, which demands more flexibility and speed than ever. Subsequently, the automation of sheet metal manufacturing is essential, making the digitization and connectivity of machines and systems also essential in order to achieve better optimization of the factory.
The key here is to work hand in hand with a technological partner with experience, who is able to offer comprehensive solutions adapted to the user’s needs, intelligent solutions that can anticipate scenarios and provide quick responses. Some are implemented autonomously and in real time; others provide alternatives to professionals, becoming interpreters of this data, translating it into more advanced decisions. That is, they work with a data-driven focus.
At Lantek, we offer integrated solutions from the design and cutting process Lantek Expert (CAD/CAM), to manufacturing management (Lantek MES), including the planning of enterprise resources (ERP). These software programs are interconnected which makes it possible to automate many processes, making production more streamlined.
But automation is not only in the physical world, but also in the virtual one. At Lantek, we also work in the Cloud environment under the SaaS model, where the user uses the software on demand via a subscription payment model that is updated automatically. The data generated by each plant according to its own KPIs is processed in the cloud using Lantek Analytics to give real-time and automatic responses, both predictive and prospective.
Ultimately, the automation of processes in sheet metal plants adds productivity to the entire value chain, which today is essential in order to manufacture using competitive margins and prices.
.png)
.png)
