Sara Hernández García

Today, what we know as the Fourth Industrial Revolution refers to the profound changes we are currently experiencing; changes that began to take shape at the end of the 20th century. This term, which has gained strength over the years, reminds us in simple terms that each era is marked by major transformations, usually linked to technology. In this case, the technology that is most defining this stage is artificial intelligence (AI), closely linked to the data explosion, the accelerated growth in the generation and storage of information that enables the analysis of large volumes of data. The role of AI in industrial transformation Over the past decades, we have gradually integrated various operational tools into production processes. This has made it possible to collect massive amounts of data from machines, sensors, management systems, production lines, and even customer behaviour. All this information has been stored thanks to the capabilities offered by the cloud, turning it into enormous data warehouses that can now be processed with a power that would have been unthinkable just a few years ago. And it is precisely this ability to store and process vast amounts of data that has paved the way for today’s AI. Without this technological foundation, AI could not have developed as we know it, since its effectiveness depends directly on the availability of data and the ability to analyse it at scale. Practical applications of AI in industry In this context, a common question arises across the industrial sector: how can all this potential be brought to life in factories? How can AI be leveraged in production processes that generate millions of data points but in many cases are not being used efficiently? The truth is, there are numerous use cases that can be applied to almost any business. The key lies in identifying those that truly add value and are capable of transforming day-to-day operations: Here are some relevant examples: 1. Predictive maintenance Machinery constantly generates data such as energy consumption, downtimes, temperature, pressure or humidity, and thanks to AI, it is now possible to analyse these patterns and detect potential failures days or even weeks in advance. This brings several advantages: Fewer unexpected breakdowns. Reduced repair costs. Longer machinery lifespan. Much more efficient intervention planning. 2. Intelligent inventory management Inventory management also generates a vast amount of data such as stock levels, consumption frequency, restocking times, or supplier behaviour, and through advanced analytics, AI can interpret this data to optimise inventory and significantly reduce costs. This enables: Fewer stockouts. More accurate and cost-effective restocking. Less tied-up capital. 3. Demand forecasting When it comes to demand forecasting, historical sales data, market trends, seasonality and customer behaviour become a key source of insight. Using advanced analytical models, AI can anticipate future demand and help plan production more accurately. This leads to several key benefits: Production aligned with actual demand. Lower excess costs and waste. Better responsiveness to demand spikes or drops. 4. Purchase order recommendations In the case of purchase order recommendations, AI analyses historical consumption data, market trends and supplier availability to suggest what products to buy and when. This enables purchasing optimisation, cost reduction, and a more efficient supply chain. Its applicability results in: Avoidance of unnecessary or late purchases. Improved supplier relationships. Lower costs and greater procurement efficiency. 5. Transport route recommendations In transport route optimisation, AI analyses multiple variables such as traffic, weather, vehicle load and operating costs to recommend the most efficient and cost-effective routes. This helps reduce delivery times, save logistics costs, and improve punctuality. The benefits include: Optimised delivery times. Lower emissions and fuel consumption. Reduced logistics costs. Conclusion The emergence of AI in production processes is not just a technological evolution, it represents a profound shift in how companies approach both their day-to-day challenges and strategic goals. Beyond efficiency and cost optimisation, AI opens the door to a new mindset built on foresight, constant adaptation and continuous improvement. It invites us to rethink decision-making, drawing on rigorous data analysis and the ability to anticipate future scenario, enabling businesses to respond to difficulties, stay ahead of them, and turn them into opportunities for growth. Connectivity & IoT AI & Data Factory floor inference: how AI transforms manufacturing in real time September 25, 2024