Artificial intelligence (AI) and its subsets are benefitting tons of fields, but you’d be hard-pressed to find one that’s taking more advantage from them than the manufacturing sector. Major companies around the world are heavily investing in machine learning (ML) solutions across their manufacturing processes and seeing impressive results.
From bringing down labor costs and reducing downtime to increasing workforce productivity and overall production speed, AI – with the help of the Industrial Internet of Things – is ushering in the era of smart manufacturing. The numbers speak for themselves; recent estimates predict that the smart manufacturing market will grow at an annual rate of 12.5% between this year and next.
It certainly makes sense. Numerous businesses are already experiencing the advantages of ML in several ways and working with QA testing services to refine what they are getting out of it. Here are some examples of current implementations.
1. General process improvement
One of the first things that come to mind when thinking about ML-based solutions is how they can serve daily processes across the entire manufacturing cycle. By using this technology, manufacturers are able to detect all kinds of issues on their routine methods of production, from bottlenecks to unprofitable production lines.
By combining machine learning tools with the Industrial Internet of Things, companies are taking a deeper look into their logistics, inventory, assets and supply chain management. This brings high-value insights that uncover potential opportunities not just in the manufacturing process but in the packaging and distribution as well.
A great example of this can be found in the German conglomerate Siemens, which has been using neural networks to monitor its steel plants in search of potential problems that might be affecting its efficiency. Through a combination of sensors installed in its equipment and with the help