Manufacturers Face Data Hurdles in AI Adoption

Factory floors are filled with sensors and machines, but the data they produce is often too messy to use for artificial intelligence. According to a report by GN technics/ai (en-US), the primary obstacle is not a lack of information, but the quality of that information. Decades of equipment from different vendors have created a patchwork of systems where data points are inconsistently labeled and poorly documented. This fragmentation means that even with vast amounts of raw data, it is difficult to understand what individual readings actually represent.

A second major issue involves timing. Real-time sensor streams often arrive long before quality inspection results are available. These findings might take hours or days to surface, creating a disconnect between the process data and the final outcome. Without a clear way to link these delayed quality metrics to the specific moment of production, it is challenging to train AI models that can predict or prevent defects effectively.

Old systems create data silos

Manufacturing plants rarely start from a blank slate. Most facilities have accumulated equipment over many years, leading to isolated systems that do not communicate well. Industry experts note that this results in siloed data where documentation is scarce. When machines from different manufacturers use different standards for labeling data, it becomes a complex puzzle to assemble a coherent dataset. This lack of standardization is a core barrier to deploying useful AI solutions on the factory floor.

Timing gaps limit predictive insights

Even when data is collected, the timing of that data matters. Sensor readings happen in real-time, but the results of quality checks often arrive much later. This delay makes it difficult to correlate specific machine actions with final product quality. Experts argue that simply dumping large volumes of sensor data into a storage system does not create insight. Without understanding the relationships and the precise timing of events, the data remains difficult to interpret for machine learning models.

Broader supply chain resilience matters

The focus is shifting from isolated automation to broader supply chain resilience. Under the concept of Industry 5.0, manufacturers are encouraged to look at the entire network, including suppliers and logistics. This approach requires coordinating data across multiple systems and owners. While this offers a more holistic view of production, it also adds complexity. Integrating data from external partners and internal logistics systems is a significant step up from managing data within a single plant.