The steps to integrate and deploy an Industry 4.0 project with AI

Define goals and roadmap

After carrying out the audit of the existing situation, it is essential to define clear objectives to guide the transformation towards Industry 4.0. This step makes it possible to establish a strategic vision aligned with the needs of the company and to structure the actions to be implemented. A precise definition of objectives facilitates the allocation of resources and guarantees an efficient deployment of technological solutions.

Objectives must be defined according to the specific challenges of the industrial site. They may concern the improvement of overall performance, the optimization of processes, the digitalization of operations or even the reduction of costs and the improvement of industrial KPIs in general. For example, a company may aim for an increase in the overall equipment efficiency rate ( OEE ), a reduction in machine downtime thanks to predictive maintenance or better energy management. The key is to identify relevant performance indicators to measure the progress made.

Once the objectives have been set , it is necessary to establish a detailed roadmap. This must specify the stages of the project, the target VSM, the future network architecture and the technologies to be integrated and the necessary resources . Effective planning is based on a division into several phases: short-term actions for rapid results, medium-term initiatives to stabilize gains and long-term projects for a complete transformation. This progressive approach makes it possible to limit risks and ensure that teams develop their skills.

Stakeholder involvement is a key success factor. Management must validate the strategy, technical teams must be involved in implementation, and operators must be trained in new tools. Regular communication and the establishment of monitoring indicators facilitate project buy-in and ensure effective management.

Network Architecture: Integration of IoT and AWS protocols

The integration of IoT protocols and Edge Gateways is essential for a connected factory, ensuring real-time data collection, processing, and analysis. MQTT, with its publish/subscribe model, is widely used to transmit OEE data to a BI system or predictive analytics platform.

An Edge Gateway acts as an intermediary between industrial equipment and the cloud. It integrates an MQTT broker to secure, filter and transmit data, thus optimizing bandwidth and reducing latency. This local pre-processing improves the reliability of analyses and allows increased responsiveness of industrial systems.

Example of a hybrid AWS architecture (Edge ↔ Cloud)

AWS offers a scalable and flexible architecture for leveraging industrial data. AWS IoT Core collects information, Amazon S3 stores data, and Amazon Kinesis processes streams in real time. Amazon SageMaker applies AI and machine learning models for predictive maintenance, while Amazon QuickSight enables visualization of industrial KPIs. This hybrid approach ensures optimized operations management, improving productivity, responsiveness, and automation of connected factories.

Source: AWS – Industrial network architecture connected to the cloud

Implement the actions

Implementing a connected factory relies on two pillars: a reliable infrastructure to interconnect equipment and intelligent applications that exploit the collected data. Depending on the needs, the company can favor a local approach (Edge Computing) or a cloud approach.

Prepare the infrastructure and interconnect the equipment

Before deploying new sensors or protocols, it is essential to prioritize access to data already existing in industrial management systems (ERP, MES, SQL, BI) . This data is often available via third-party APIs or standardized connections. Direct integration with these systems allows you to immediately exploit information that is already structured and validated, ensuring better consistency of analyses.

If the data collected is incomplete or insufficient, it becomes necessary to deploy smart sensors to enrich the measurements. These sensors will send their data via industrial protocols (MQTT, OPC-UA, Modbus), which will then be integrated into central systems via an Edge Gateway. This progressive approach avoids data redundancy and optimizes the existing infrastructure before investing in additional hardware.

Integration must therefore follow an optimization logic: first exploit existing connections (API, SQL databases, MES) and, if necessary, supplement with connected sensors to fill the gaps and ensure a global vision of industrial operations.

There are two options for data processing and storage:

• On-premises infrastructure : Ideal for sensitive data or critical response times, it requires industrial servers, a local data lake, and SQL or NoSQL databases.
• Industrial Cloud : More flexible and scalable, it is based on solutions such as AWS IoT Core, Azure IoT Hub and Google Cloud IoT, enabling centralized storage and the integration of advanced analysis tools.

The choice depends on security, connectivity and cost constraints.

Data type and transformation into usable format

Sensors collect raw data such as temperature, pressure, energy consumption or vibrations. This data must be converted into a structured format (e.g. JSON, XML, CSV) to be integrated into analysis systems.

Example of JSON format for OEE (Overall Equipment Effectiveness) data collected in real time from a production line:

{
 "timestamp": "2025-03-01T14:15:00Z",
 "machine_id": "MACH-07",
 "shift_id": "SHIFT-B",
 "operator": "John Doe",
 "oee_metrics": {
 "availability": {
 "runtime": 3200,
 "planned_downtime": 600,
 "unplanned_downtime": 200,
 "availability_ratio": 0.88
 },
 "performance": {
 "actual_output": 480,
 "ideal_output": 550,
 "cycle_time": 7.5,
 "performance_ratio": 0.87
 },
 "quality": {
 "total_produced": 480,
 "good_units": 460,
 "defective_units": 20,
 "quality_ratio": 0.96
 }
 },
 "oee_score": 0.73,
 "unit": "%",
 "alerts": [
 {
 "type": "unplanned_downtime",
 "duration": 120,
 "cause": "Motor Overheat",
 "severity": "high"
 },
 {
 "type": "quality_issue",
 "affected_units": 5,
 "cause": "Misalignment",
 "severity": "medium"
 }
 ]
 }

Transfer, storage and processing of data

The data collected by sensors and automation systems are transmitted via an Edge Gateway, depending on the monitoring and analysis needs. For real-time alerts, they are sent immediately via MQTT or WebSockets, while historical analyses and reports use protocols such as OPC-UA, REST API or are stored in SQL/NoSQL databases. Before being sent to the cloud, local filtering and aggregation optimize bandwidth and avoid unnecessary transfers.

Exploitation of industrial data

Once the data has been collected and centralized, the key step is to use it to improve industrial performance, optimize maintenance, and strengthen decision-making. This phase is based on three main axes: real-time analysis, data enhancement using BI and AI tools, and automation of actions based on the insights generated.

Data Valorization with BI and AI

Historical data can be leveraged through Business Intelligence (BI) tools such as Power BI, Amazon QuickSight, or Tableau. These platforms enable you to visualize trends, detect performance gaps, and compare industrial KPIs.

Data exploitation should not be limited to analysis, but lead to automated actions to improve productivity. An alert management system makes it possible to anticipate breakdowns and trigger interventions before a problem occurs.

Artificial Intelligence (AI) and Machine Learning (ML) play a key role in process optimization. With platforms like Amazon SageMaker, Azure ML or Google AI, it is possible to:

• Plan production in real time and manage your supply chain with AI
• Predict failures and improve preventive maintenance.
• Optimize production parameters by automatically adjusting equipment settings.
• Detect quality anomalies using automated inspection algorithms.

Prepare local AI infrastructure for inference and predictive maintenance

Integrating AI models on-premises helps reduce latency and process data directly on-premises without relying on the cloud. This is particularly useful for predictive maintenance, where a model can analyze equipment trends and anticipate failures.

For this, a suitable hardware infrastructure is necessary:

• AI servers with GPU or TPU (eg NVIDIA Jetson, Dell Edge Servers) to run models in real time.
• Smart sensors with embedded AI to detect anomalies directly on the machine.
• Edge AI Frameworks like TensorFlow Lite, OpenVINO to run AI models locally.

Leveraging machine vision and advanced analytics in real time

The integration of smart cameras and AI models in machine vision is revolutionizing quality control, workstation optimization, and root cause identification in production. Unlike traditional methods requiring human intervention, these solutions provide continuous monitoring and advanced analytics without interruption. They complement IoT sensors by providing a more detailed vision and cross-referencing information with other industrial data.

Real-time monitoring automates quality control by immediately identifying product defects, monitors workstation ergonomics to prevent musculoskeletal disorders, and improves OEE by accurately detecting micro-stoppages and production deviations. By correlating images with sensor signals, it becomes possible to identify the root causes of manufacturing failures and anomalies more quickly.

High-capacity storage is essential to preserve video streams and analytics. Local processing is often preferred to avoid massive data transfer to the cloud and ensure the confidentiality of sensitive information.

Custom AI Application Development

The connected factory can leverage AI beyond predictive maintenance, developing smart industrial applications tailored to specific production needs.

Some possible use cases:

• Quality defect detection with an AI vision model integrated into an industrial camera.
• Optimization of machine parameters via an adaptive learning algorithm.
• Predicting demand and adjusting resources based on production trends .

These applications can be developed on open-source frameworks like TensorFlow, PyTorch and integrated into local or cloud infrastructures depending on performance and security constraints.

Creating AI agents for industry

Industrial AI agents help optimize production , anticipate failures, and automate operations management. They analyze data in real time, adjust machine parameters, and improve product quality using advanced AI models. They can be deployed via SaaS cloud solutions, where you simply authenticate, create flows, and test a bot via integrated applications, or locally, using open-source models and specific multi-level prompts for personalized and secure control.

Conclusion

Industry 4.0 is transforming production by connecting equipment, data and artificial intelligence. A phased approach starts with an audit to identify possible improvements, followed by the integration of sensors, IoT protocols and management systems. Leveraging data via BI and AI helps optimise production, anticipate breakdowns and reduce costs. Cloud solutions offer rapid implementation, while local infrastructures ensure better control of data. By combining IoT, AI and automation, companies gain agility and efficiency while securing their operations.