Industrial Glossary

The term Big Data refers to data sets that are so large, so rapidly generated and so varied that they overwhelm the capabilities of traditional data management tools and applications. It is the very essence of Industrial Digitization and the raw material for’Artificial Intelligence (AI).

In-depth definition : Big Data is not only defined by its volume, but also by the 3 V that describe its challenges and opportunities:

1. Volume : The massive amount of data generated. In industry, this represents terabytes of data collected continuously by thousands of sensors. IoT (Internet of Things), systems MES (Manufacturing Execution System), and ERP (Enterprise Resource Planning).
2. Velocity : The speed with which data is generated, collected and, above all, processed. Real-time (or near-real-time) analysis is crucial for the Predictive Maintenance and process optimization Series Production.
3. Variety : The diversity of data formats and sources: structured data (ERP databases), semi-structured data (XML files), and unstructured data (videos, images, etc.). Machine Vision, text files, raw sensor data).

The challenge of Big Data, solved by Machine Learning and analysis, is to transform this raw mass of data into actionable information for the’Operational Excellence.

The Role of Big Data in Industrial Performance

For SMEs and Industrial Engineering, the exploitation of Big Data is a lever for transforming the Industrial Performance :

1. Predictive and prescriptive analysis : Big Data is the fuel of Predictive Maintenance. The analysis of complex patterns in historical data enables algorithms to predict machine failures with unparalleled accuracy, optimizing the TRS (Taux de Rendement Synthétique). It can also be used to identify the optimum conditions (temperature, speed) for achieving the best level of Quality.
2. Process Optimization : By analyzing flow data, you can see exactly where waste is occurring (Muda), dynamically locate Bottlenecks and assess the real impact of’Continuous Improvement.
3. Traceability and Quality : Big Data enables traceability by linking equipment data (via the’IoT and the MY), the batch of raw materials (via the’ERP and the Bill of materials - BOM), and quality control results (Machine Vision), ensuring full compliance with Manufacturing file.
4. Advanced simulation : Massive data are used to build and calibrate Digital Twin, which simulates the impact of changes on the production system before their physical implementation.

Challenges for SMEs and Industrial Engineering Intervention

The adoption of Big Data is not without obstacles, especially for companies with disparate IT systems:

• Source integration : The main challenge is to collect and unify data from heterogeneous systems (PLCs), MY, ERP) in a centralized, structured platform.
• Security and Governance : Manage access, confidentiality and security of these volumes of information, especially with the interconnection of systems (OT and IT).
• Analytical skills : Have the skills in-house to develop models of’IA and Machine Learning and transform raw data into useful information.

SXE Consulting helps its customers navigate this challenge by defining a clear data strategy: which data to collect (IoT), how to store them (Cloud or Edge architecture) and which priority analyses to implement to obtain a rapid return on investment (e.g.: target the 20% of data that will solve 80% of the problems of TRS).

In conclusion, the Big Data is the basis on which the’Industry 4.0 builds its capacity for intelligence, autonomy and performance. Good management is the key to transforming digital potential into sustainable competitive advantage.