Easing into AI and Machine Learning

Contextual insights from real-time shop floor data can optimize production scheduling, predict defects, spot bottlenecks, reduce waste and more. Machines can analyze sensor inputs to self-diagnose maintenance needs before breakdowns occur. Computer vision can catch product flaws early. 

Easing into this approach, manufacturers can begin testing edge AI/ML capabilities by identifying an entry point through use cases that rely on clean, structured data sources, and allowing complexity to grow over time. This may include monitoring vibration sensors on critical motors to predict maintenance needs days or weeks out. Alternatively, it could be used for scanning barcodes or serial numbers as products move through the line, offering more input data to optimize scheduling around volatile demand.

As generative AI technologies and the supporting infrastructure mature, manufacturers can work towards ingesting unstructured data from images, video, and audio to expand use cases. Most immediately, giving better vision to machines can speed up quality inspection for defects or production errors, while identifying issues that are difficult for humans to consistently catch. In the future, speech recognition could capture machine tones signaling issues. Over time, this additional data richness improves model accuracy and ultimately highlights what processes are ripe for transformation.

Industry 4.0 at the Edge in the Real World

Current manufacturing and industrial business processes like predictive maintenance, quality control, and production monitoring are propelling more on-device ML applications. With the rise of interconnected devices and available data in Industry 4.0 through embedded sensors along the production line, manufacturers can leverage AI/ML models at the edge to unlock major operational improvements. Here’s what that looks like in practice:

Predictive maintenance: Historical sensor data feed models to determine normal vs. abnormal conditions in which abnormalities may indicate impending breakdowns. Predictive models will improve over time, and more accurate foresights will minimize disruptions. Monitoring critical equipment over time to allow training on new data enables AI-powered predictive maintenance to increase equipment uptime by up to 20%. Reductions in breakdowns could be up to 70%. These initial use cases have the potential to reduce overall maintenance costs by five to 10% and maintenance planning time by 20 to 50%.

Inspection and quality control: Computer vision can better detect defects invisible to humans – microscopic cracks, misalignments and counting components. Edge ML enables quicker inference times over cloud analysis – essential for production lines outputting thousands of units per hour –  and models can be trained to quality standards using images of historical rejects vs. approved products. Real-time feedback during production fixes processes before more waste occurs. According to McKinsey, AI-based visual inspection can reduce defects by up to 90% over manual methods.

Production monitoring: Most production teams can’t afford latency of the cloud due to the waiting time to understand where bottlenecks are starting to creep up. Shop floor connectivity provides data to optimize manufacturing operations. Machine effectiveness can increase productivity by an estimated 25% with AI monitoring models continuously analyzing cycle times, throughput and yield analytics to spot bottlenecks and inefficiencies. This allows quicker adjustments to maximize productivity overall.

On the Edge of Smart Manufacturing

The AI/ML transformation of manufacturing is inevitable. As this technology permeates operations, the benefits will extend beyond factory walls to supply chains, inventory management, product development and more.