AI in Industry: Toward smarter, more efficient factories

Since the First Industrial Revolution, each wave of innovation has transformed the world of manufacturing. From steam engines to automation, from assembly lines to robotics, the industry has continually reinvented itself to improve productivity, precision, and efficiency. This article explores the emergence of artificial intelligence within the framework of Industry 4.0.

Thanks to increasingly powerful algorithms and the use of real-time data, AI is disrupting production processes, redefining the roles of operators, and paving the way for new industrial models.

From automation to Artificial Intelligence

The industry has undergone several major transformations. In the 1970s, automation made it possible to standardize repetitive and strenuous tasks, delegating them to programmable machines (also known as Programmable Logic Controllers, or PLCs). In the 2000s, the introduction of sensors, industrial networks, and cloud computing marked the beginning of Industry 4.0, a connected industry where data became a critical resource.

It is within this context that AI is making its entry into the industrial world. Unlike traditional rule-based systems, artificial intelligence does not simply execute predefined instructions: it learns, analyzes, and predicts. It becomes a decision-support tool, capable of suggesting solutions, and in some cases, even implementing them automatically.

This evolution has been made possible by the growth of the Industrial Internet of Things (IIoT), which enables the collection and centralization of massive amounts of data from production lines, equipment, operators, and control systems. AI leverages this wealth of data to train its models and deliver actionable insights aimed at optimizing industrial performance and enhancing safety.

Tangible benefits across the entire production chain

Artificial intelligence is not limited to one specific task. It plays a role at every stage of the production process (from planning to delivery) including manufacturing, quality control, and maintenance. Here are some common benefits of AI in industry:

• Process Optimization
  By analyzing both historical and real-time equipment data, AI algorithms can detect anomalies, predict failures, and suggest corrective actions. AI can also optimize production sequences by factoring in dozens of variables, such as machine availability, production rate, scrap rate, and energy consumption, far beyond the capabilities of traditional systems, especially in real time.
• Intelligent Automation
  Repetitive task automation is enhanced through AI. Machines can now adjust their actions dynamically based on their environment, thanks to computer vision and machine learning. This paves the way for more flexible, adaptive production.
• Quality Improvement
  In manufacturing, quality control is a critical step. With AI, vision systems can inspect every single part produced, detecting defects that are either invisible to the human eye or that would take much longer for an operator to identify. This constant monitoring not only reduces scrap rates, but also helps quickly pinpoint root causes of defects and enables real-time machine adjustment.
• Assisted Decision-Making
  Industrial dashboards that display real-time data can be significantly enhanced with AI. Supervisors gain access to intelligent alerts, predictive recommendations, and even simulated scenarios to help select the best course of action. This transforms the control room into a truly intelligent command center.

Case study: Predictive maintenance on a bottling line

On a bottling line, machine data analysis enabled the anticipation of certain technical failures. Information such as vibrations, temperature, energy consumption, and production speed was used to better understand equipment performance and detect early signs of wear.

Once this data was processed and made actionable, a relevant machine learning model was developed to more accurately estimate the remaining lifespan of machines and the likely causes of failure. The results were then integrated into the supervision system to allow for prompt handling of future malfunctions.

For example, it became possible to identify that a component of a filler was showing signs of fatigue, with an estimated two weeks remaining before failure. Similarly, the condition of a labeler on the line could be classified as critical, with only three days left due to motor overheating. This approach enables timely interventions, reduces costs associated with unexpected production stoppages, and allows for more efficient maintenance scheduling.

Challenges to overcome

While the benefits of AI appear promising, several obstacles must be addressed to ensure successful implementation:

• Data quality: AI performs well only if input data is reliable, cleaned, and often labeled. However, many factories have heterogeneous systems with incomplete data. Digitally transforming the existing infrastructure is a major initial undertaking.
• Skills and change management: It is crucial to train operators on these new tools and foster a culture of experimentation. Humans remain at the core of the process—AI should act as an assistant, not a replacement.
• Security and governance: Intelligent systems can pose cybersecurity risks if not properly protected. Additionally, decisions made by AI must be explainable to enable users to make informed choices.
• Initial investment: Although AI strategies typically yield rapid returns, their implementation often requires significant upfront investment.

Conclusion

Artificial intelligence is establishing itself as a major driver of transformation in the industry. By combining the power of data with advanced analytical capabilities, it enables industries to meet increasing demands for performance. In modern production environments, AI plays a crucial role: it anticipates, learns, and recommends. Its impact is especially visible in control rooms and supervision systems. Where operators once had to process vast amounts of information, AI now acts as a true assistant capable of detecting deviations, simulating scenarios, and proposing decisions, all while adapting to real-time context.

At Motilde, by implementing efficient industrial data management, we harness artificial intelligence to support operators, turning their control rooms into genuine augmented decision centers.

The AI in Control Rooms Guide

Discover how AI can transform your operations and optimize the supervision of your processes.

Access the free guide