AI or Failure: The Future of Factories Requires Immediate Action!

AI or Failure: The Future of Factories Requires Immediate Action!

In the current economic climate, there is no time for hesitation. Those who fail to act now will fall behind, and in this industrial competition, falling behind means complete exclusion. Artificial intelligence (AI), data collection, instant processing, and intervention these are the tools without which the factories of the future cannot operate. And not ten years from now immediately!

Many companies have already realized this, while others still cling to outdated methods. But this is no longer the past! This is the new world order, where those who do not implement AI and do not prioritize development will quickly fall into a competitive disadvantage.

AI and Maintenance: The Key to Quality and Competitiveness

In the field of maintenance, this is particularly critical. The era of outdated reactive and scheduled maintenance strategies is over. AI-based predictive maintenance is not an option but an essential tool! Companies that ignore this will soon experience:

·       A drastic reduction in unplanned downtime.

·       Significant decreases in maintenance costs by intervening only when and where it is truly necessary.

·       A minimization of human errors, as AI analyzes data and makes the best decisions.

·       Increased production efficiency, reduced scrap rates, and improved quality.

·       A substantial reduction in customer complaints since defects are detected before reaching the customer.

Reactive maintenance or running equipment until failure means using machinery until it breaks down, then repairing or replacing it. While this strategy may seem cheaper in the short term, it can lead to unpredictable downtimes and higher repair costs. Unexpected failures cause massive production losses and additional expenses, especially if critical parts are not immediately available.

In contrast, predictive maintenance evaluates the condition of equipment based on certain parameters, providing an estimate of how long it can operate without failure. Properly applied, this method not only prevents machine downtime but can also identify and predict failing components. It allows companies to foresee when a failure will occur, its severity, and which parts need replacement.

AI, with accurate and continuous data collection and analysis, eliminates the disadvantages of reactive maintenance. AI-supported predictive maintenance enables targeted and planned interventions before failures occur. Additionally, it helps prevent secondary and tertiary damages, severe equipment failures, and unexpected downtime, all with minimal and strategically scheduled stoppages.

AI not only enhances productivity but also directly contributes to quality assurance. Automated data processing and real-time analysis allow immediate corrections in the production process, preventing scrap and quality issues before they arise.

Example: On an automotive production line, AI analyzes assembly processes and component fitments in real-time. If a robotic arm exhibits even a slight deviation from a predefined reference value, AI instantly detects it, alerts the system, and automatically corrects the issue before the final product is faulty. As a result:

·       Scrap rates decrease by 20%.

·       Customer complaints are cut in half.

·       Quality control costs drop significantly.

AI in Complaint Handling

If a defect-related complaint arises from a customer, AI can trace the manufacturing data to identify which shift, operator, and conditions were involved in assembling the faulty part. AI assists in:

1.     Identifying affected products AI reviews all identical products manufactured during the relevant period and identifies those assembled under similar parameters.

2.     Root cause analysis AI compares production data from defective and non-defective units to determine if the issue stems from assembly torque, component fitment, or other factors.

3.     Immediate intervention AI automatically alerts production management and maintenance teams to address the problem.

4.     Strengthening quality control AI highlights potentially affected products in future production cycles and recommends targeted quality checks.

5.     Operator training AI helps develop precise training materials for operators to prevent recurring errors.

Illustration: An AI-based system immediately detects if an operator tightens a screw with incorrect torque and alerts them. If a defective product still leaves the factory, AI can quickly track back the assembly steps, identify affected items, and implement quality improvement measures.

AI and Cybersecurity: Are Your Data Secure?

AI processes vast amounts of data, which can be sensitive for businesses. Therefore, key security measures include:

·       Encrypted data management To protect AI-generated and processed data, encrypted management must be implemented. Companies like Tesla and Siemens have already introduced encryption protocols to secure sensitive industrial IoT data from unauthorized access.

·       Strict access control To prevent misuse of AI systems, access rights must be rigorously managed. Only authorized professionals should have access to maintenance data and decision-support systems. In 2021, a major automotive supplier suffered severe downtime because a low-level employee accidentally misconfigured an AI system, leading to multi-million-euro losses.

·       Continuous monitoring and intrusion prevention AI-driven systems must be constantly monitored to prevent cyberattacks and data breaches. The infamous Stuxnet malware demonstrated how industrial systems can be infiltrated and disrupted. To prevent such attacks, companies now employ advanced SIEM (Security Information and Event Management) systems that detect and mitigate suspicious activities instantly.

AI and Responsibility: Who Is to Blame When Something Goes Wrong?

    The use of artificial intelligence raises the issue of responsibility. If a maintenance worker follows AI-generated instructions but an accident or complaint still occurs, who is accountable? This is a crucial question since regulations in manufacturing and maintenance are not yet entirely clear.

Key considerations:

1.     AI as a decision-support tool AI does not make autonomous decisions but analyzes data and provides recommendations. The final responsibility always lies with human experts.

2.     Maintenance personnel expertise This is why skilled professionals, rather than underqualified operators, are employed for maintenance. They must assess AI-generated information and make informed decisions.

3.     Regulatory background Currently, many countries lack clear legislation on liability in AI-assisted decision-making for operators or management. However, legal trends indicate a shift toward emphasizing human responsibility alongside AI’s role as a supportive tool.

AI or Failure! Minimal Cost, Maximum Profit!

The world does not wait! AI is already available, and companies that fail to act in time will inevitably fall behind. AI-based maintenance and production are not luxuries but essential tools for cost efficiency and production stability. The investment required for AI implementation is minimal compared to overall production costs, while delivering substantial savings and competitive advantages.

Many industry giants have already successfully piloted AI-driven maintenance systems, including:

·       BMW and Audi—Minimized production line failures using AI-driven machine vision and predictive maintenance.

·       General Electric (GE)—Achieved significant cost reductions and uptime improvements through predictive maintenance of industrial turbines and energy systems.

·       Boeing—Optimized aircraft component lifespan and reduced maintenance downtimes with AI-supported equipment analysis.

AI adoption is best initiated in smaller factories or test plants, such as:

·       Automotive suppliers, where production processes are highly sensitive to machine downtimes.

·       Food processing plants, where AI helps maintain strict hygiene and quality standards.

·       Chemical and pharmaceutical industries, where predictive maintenance is crucial for safety and productivity.

Optimizing maintenance expenses, reducing scrap rates, and eliminating unexpected downtimes provide a rapid return on investment. The real question is not whether AI is worth it, but when competitors will overtake you with its help. Act now, or watch others leave you behind!

Best regards, LBMM Team