AI and ERP: Digital Revolution in Metalworking Workshops

Connecting the Workshop to the ERP: A Pragmatic Strategy for SMEs

In the metallurgy sector, many small and medium-sized enterprises (SMEs) are seeking to enhance the visibility, quality, and performance of their production by connecting their workshops to ERP systems through artificial intelligence (AI) and the Industrial Internet of Things (IIoT). Although machines operate efficiently from a technical standpoint, they often remain digitally isolated, leading to manual or delayed data collection on production, thereby limiting visibility into actual performance.

SMEs in this sector face increasing challenges, such as rising energy and raw material costs, as well as a shortage of skilled labor. Additionally, the diversity of machines complicates the management of efficient and predictable production. In this context, decisions are still too often based on experience rather than reliable and up-to-date data.

To meet market demands, which include accuracy, delivery speed, and traceability, a more precise coordination of processes, tools, and resources is necessary. However, digital transformation does not necessarily mean replacing the entire machine fleet. A pragmatic approach involves gradually modernizing existing equipment through retrofitting. This entails adding sensors and IoT gateways, allowing older machines to connect to the company's IT systems. This foundation enables the development of AI-driven production optimization, leveraging field data to enhance operational management.

Gradual Digitalization of the Workshop

AI-driven production optimization solutions offer SMEs the opportunity to connect heterogeneous machines and create a common database. This links shop floor information to ERP planning systems. The machines are connected to an IIoT platform that continuously collects key parameters such as uptime, energy consumption, speed, and tool status.

By cross-referencing this data with planning information from the ERP, it becomes possible to monitor production performance in real-time and quickly identify deviations from targets. For instance, in the event of an anomaly such as an unusual increase in energy consumption or extended cycles, alerts can be generated automatically. This allows teams to intervene swiftly before issues translate into quality defects or production interruptions.

This increased visibility makes processes more flexible, improves maintenance operation planning, and paves the way for predictive maintenance, now accessible even to medium-sized enterprises.

Sensors: The Starting Point for Optimization

The interconnection of machines, intelligent data analysis, and the power of AI do not require a complex industrial project. A modular approach allows for leveraging existing infrastructures and gradually digitizing machines. In many cases, it is sufficient to equip machines with IIoT sensors connected to the ERP system via dedicated gateways to continuously collect data on energy, vibrations, and operational status.

Even older machines without digital interfaces can be integrated using external sensors. Current, vibration, or proximity sensors, for example, can detect uptime, interruptions, or performance variations. Manual workstations can also be integrated. Operators can input production progress or report disruptions via simple interfaces, thus creating a comprehensive database linking all production activities.

Implementation typically occurs in stages: one machine or production line is connected first, and then the system is gradually extended to the entire workshop.

Thermal Monitoring: A Key Quality Factor

In precision machining, temperature control plays a crucial role. Even slight temperature variations in the coolant or the machine's environment can affect dimensional accuracy, tool lifespan, and surface quality.

Without appropriate monitoring, these variations can lead to thermal drift of the machine, uneven tool wear, or dimensional discrepancies in the produced parts. Integrating temperature sensors into cooling circuits and around machines helps mitigate these risks.

The sensors continuously measure thermal conditions and transmit data to a central platform, where it can be cross-referenced with production parameters such as cycle times or part references. When a deviation from reference values is detected, an alert can be generated before quality defects arise.

This visibility allows for the rapid identification of thermal influences on production and the implementation of corrective measures. Processes become more stable, scrap rates decrease, and tool lifespan can be extended.

Transition to a Connected Workshop

The combination of real-time data collection, IIoT technologies, and ERP systems enables the creation of a continuous information chain between planning and production. Companies thus gain a more accurate view of machine status, production volumes, and inventory.

This data can then be utilized to optimize planning, anticipate maintenance needs, or improve setup times. AI-driven production optimization solutions allow for better management of processes and enhance production reliability.

For many SMEs, the challenge is not to transform the workshop into a fully automated factory, but to gain better visibility into existing production and gradually optimize processes. By connecting machines, operators, and management systems, this synergy contributes to making the workshop more transparent, flexible, and efficient. It thus serves as a concrete lever for modernizing production and transitioning from experience-driven production to data-driven production.