We have been talking for years about digital transformation, a paradigm that involves the introduction of digital tools to streamline and make processes, whether production or business, more efficient. The digital transformation however, is only the first step in a process of innovation, which then must continue steadily to continue to optimize. An approach that Regesta has termed TIR, Intelligent Transformation with Regesta, and leveraging defined enabling technologies (including cloud, IoT, IIoT) and data to bring innovation. The ultimate goal is to not only have greater control over processes and the ability to optimize them, but also to get to “predict the future.” We are of course not talking about magic, but about leveraging historical information and that acquired in real time to enable predictive maintenance scenarios so as to minimize unplanned downtime.
Intelligent transformation according to Regesta
Regesta’s TIR path is divided into six different phases, the first of which is data digitization. In this phase, Internet of Things technologies are adopted to connect machinery to the internal network and the Internet. This information then flows into what is called a “government center,” where it will be processed and stored. What kind of information? It depends, of course, on the activities and processes involved, but in general all that can be useful to better understand the whole system: the temperatures and vibrations of machine tools, the paths taken by robots and AGVs, the occupancy of warehouses, the workload of servers are just a few examples.
The second phase is data consolidation in the cloud. Information transited in the government center is uploaded to the cloud, where it is integrated with other business information, such as that from MES, CRM, and ERP platforms.
At this point, the data set is complete and you can move on to the third phase, the creation of a dashboard that provides a view across all systems, wherever you are.
This makes it possible to keep an eye on every stage of the production process, starting with orders and ending with shipment, and to be able to easily and conveniently view statistical information, which is useful for identifying bottlenecks, inefficiencies and generally those areas that can be further optimized. And here we come to the fourth phase, where additional applications can be introduced for monitoring, planning and even managing the servitization, which is becoming an increasingly important aspect of business for many companies. Offering the equipment and services as-a-service provides end users with greater cost control and a shift of investment from capex to opex, and manufacturers with a source of recurring revenue.
All the work done so far leads directly to the next step (step five), the one where the concept of prediction is introduced. Using the acquired data, AI and machine learning algorithms can indicate potential problems in advance: a machine operating outside of specifications, supply chain disruptions, positive or negative peaks in supply and demand.
This brings us to the last stage, the sixth, where all these processes are automated. All of the more repetitive and tedious tasks can be handled by machines, allowing operators to focus on more value-added work.
The concept of digital twin and the Feralpi case
The TIR process described above also makes it possible to create a digital twin (digital twin) of a machine or an entire manufacturing plant. What is this? Think of a kind of identical copy of a process, machinery or factory, fed with real data and updated in real time. A simulation? Yes, but with a difference: you are not simulating a generic piece of machinery (or system) under generic conditions, but that precise piece of machinery, under its actual operating conditions.
A practical example of the TIR process, later resulting in the implementation of a digital twin comes from Feralpi. The steel company, supported by Regesta, has fully sensorized one of its plants so it can implement a digital twin of the continuous casting used for production. The advantages? In the case of production problems, possible causes can be traced back through the production chain.
Regesta LAB accompanies you through the smart transformation process as it combines knowledge of new IoT and Big Data paradigms in the cloud with decades of solid experience in the SAP world.
Contact us to learn more about the TIR path – Smart Transformation with Regesta.
