A guide to industrial automation, data infrastructure and AI implementation for manufacturing excellence
In collaboration with Baseline X Centris Technologies
Your competitors' deadlines are getting shorter. Your margins are eroding. Your aging equipment is causing more and more downtime. The smart factory is no longer a futuristic scenario: it has become a question of short-term competitiveness. Yet, the most common mistake is to dive into artificial intelligence (AI) before even setting up automated data acquisition, and that's where most projects derail. This guide covers the following topics:
- The strategic value of structured data collection
- Understanding the ISA-95 hierarchy: where does your data infrastructure stand?
- AI in action: concrete value-creating examples
- Use case 1: Smart planning in a merger context
- Use case 2: Predictive maintenance on a bottling line
- Use case 3: Performance optimization on a food processing line
- Take action: build the foundations of your smart factory today
1. The strategic value of structured data collection
Capturing manufacturing data isn't just about plugging in sensors. It's about converting your equipment's electrical signals into actionable business intelligence. A well-designed infrastructure allows you to:
- Measure real OEE (Overall Equipment Effectiveness): No more paper approximations; get an exact view of availability, performance and quality.
- Spot invisible losses: The 30-second micro-stoppages that no one notices often end up representing the largest loss item at the end of the year.
- Detect bottlenecks: Precisely identify which machine is limiting your factory's overall pace.
- Prepare, standardize and transform data for AI: AI models require structured, time-stamped historical data to predict failures or optimize scheduling.
2. Understanding the ISA-95 hierarchy: the master plan
To build a smart factory, we use the international ISA-95 standard. This standard organizes industrial information systems in layers, so that your shop floor equipment communicates effectively with your management software (offices). Discover how Centris Technologies approaches industrial automation.
The five levels of integration (ISA-95)
| Level | Name | Description |
|---|---|---|
| Level 0 | The physical process | Machines, motors, conveyors and robots. |
| Level 1 | Sensors and actuators | The devices (e.g. PLCs/controllers) that measure temperature, pressure or speed and act on the equipment. |
| Level 2 | Control and supervision (SCADA) | The system that retrieves data from controllers and gives operators a real-time view of production. |
| Level 3 | Manufacturing execution systems (MES) | The software layer that manages work orders, product genealogy and quality management. |
| Level 4 | Enterprise resource planning (ERP) | The overall management of the company: accounting, purchasing, sales. |
The ISA-95 model structures the flow of information between the shop floor and management systems in order to transform raw data into data usable by artificial intelligence. In a manufacturing environment, AI performance doesn't depend on the volume of data collected, but on its consistency, contextualization and traceability from one layer of the company to another.
Between levels 0 and 2, sensors, controllers and SCADA systems measure and record physical reality. The data is normalized, time-stamped and historized, but it remains limited to a technical reading of the equipment. At this stage, its value for AI remains limited, since the business context is absent.
Level 3, driven by the MES, is decisive. It links production data to manufacturing orders, lots, recipes, products, operators and quality events. This contextualization moves technical signals to the status of structured, comparable and historized industrial information, directly usable by AI models for predictive maintenance, non-quality forecasting or process optimization.
Level 4, via the ERP, closes the chain by adding the economic and strategic dimension. Industrial performance can then be linked to costs, inventories and customer commitments, which allows AI to support decision-making at the company level.
Once this data is structured and contextualized by the ISA-95 framework, it becomes fertile ground for advanced applications, particularly the concrete use cases of artificial intelligence in the manufacturing environment.
3. AI in action: concrete examples
Data, in itself, doesn't create value. What creates value is how it's used.
As soon as your data is accessible, AI can detect patterns, anticipate failures, optimize parameters in real time, on concrete challenges. Baseline's AI expertise combined with a robust data infrastructure delivers transformative results. Here are three examples where it produced measurable results.
4. Take action
Don't let your data sleep in your controllers. The transformation toward Industry 4.0 is a journey, and every journey begins with solid foundations.
- 1 Assess your current connectivity (Level 1 and 2).
- 2 Centralize your data in a structured system.
- 3 Analyze to identify your quick wins.
- 4 Optimize with AI to achieve operational excellence.
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