Industry 4.0 in Chile:
real cases and how to get started

What is Industry 4.0, really?

Industry 4.0 is the integration of digital technologies — IoT, Big Data, AI, cloud computing, collaborative robotics — with physical industrial processes. The result is a plant that monitors itself, optimizes itself and makes decisions in real time using real process data.

In practice, for most mid-sized Chilean plants, this comes down to three concrete steps: connect the existing equipment, historize its data, and analyze that information to make better decisions.

Real cases in Chile

CMPC Papeles — Historian migration

One of the plants of Chile's largest paper group. The challenge: replace a proprietary historization system with an open architecture, without interrupting continuous production. The result: elimination of proprietary licenses, OPC UA integration, and a solid foundation for advanced analytics. ROI in under 18 months.

AJAY SQM — Chemical plant data integration

PLCs from multiple brands (Fatek among others), LoRaWAN sensors in the field and a requirement for corporate reporting. We implemented Node-RED as middleware, InfluxDB as the Historian and Grafana + Power BI for visualization. Plant floor data reached the board of directors in real time for the first time.

Aislapol — OEE and Industry 4.0

An insulation materials manufacturing plant. A plant information system with real-time OEE, energy indicators and Power BI integration for automatic reports to management. No proprietary licenses.

Agrosuper — Industrial RFID

An asset inventory at the Rosario Plant that used to take hours with manual methods. With a handheld RFID reader and a mobile app developed by Grupo Salba, the time was cut to minutes. Full traceability and fewer losses from unlocated assets.

Common mistakes when starting I4.0

• Starting with the technology, not the problem — "I want AI" without knowing which problem to solve first
• Pilot projects with no scalability plan — the pilot works but there is no route to extend it
• Ignoring plant staff — dashboard adoption fails if operators were not part of the design
• Underestimating data quality — if the PLC data is not reliable, the dashboard will not be either
• Choosing proprietary licenses without evaluating alternatives — the long-term cost can be prohibitive

A practical roadmap to get started

Based on our experience with more than 50 projects in Latin America, this is the sequence that works:

1. Connectivity assessment — inventory of PLCs, available protocols and communication gaps
2. First data pilot — connect one critical line or machine, historize for 30 days, visualize in Grafana
3. Calculate real OEE — with real data, not estimates. The result is usually surprising
4. Identify the first improvement opportunity — act on the data before adding more instrumentation
5. Scale with the lessons from the pilot — replicate the model on other lines with adjustments