The Evolution of Industrial Analytics

Industrial cyber security is a maturing area where AI is being used for anomaly detection – these solutions baseline the normal industrial process/network behavior, and then look for deviations that might indicate a breach. Now that industrial control systems are being targeted maliciously, the space is developing quickly – the industrial malware events of 2017 (WannaCry and Petya) were major accelerators, and now nearly all industrial enterprises have bought a solution or have it high on their roadmaps.

Scheduling has historically been a highly-manual process for manufacturers, requiring staff to spend tedious hours making plans for materials, equipment, and labor. Novel AI solutions can integrate both internal and external data to rapidly produce optimized production plans. Manufacturers are leveraging AI-based scheduling tools to save big on production time, on-time delivery, reduced inventory, machine utilization, setup times, labor flexibility, and more.

I never thought of Hiring as a cool AI use case, but I’ve seen a few different tools on the market than leverage AI to better understand skillsets based on resumes and prior experience, in order to make recommendations about who would excel in different roles. Much of this is focused around hiring people based on specific skills/competencies for managing equipment and executing extremely specialized tasks. Some tools go as far as to normalize applicant profiles to filter out a range of biases.

Safety is a wholesome use case for AI in industrial operations. Slips, trips, falls, sprains, strains, and tears lead to untold lost hours and worker’s compensation. There are several AI solutions on the market (often including wearables) that can help industrial workers predictively avoid these kinds of injuries. Forklifts alone account for tens of thousands of serious injuries annually in the US – there are image analysis systems that can identify a person, and then shut down the forklift. Accurately identifying a human vs. another object is important to avoid the system becoming a nuisance and constantly shutting down the forklift.

Since we’re already talking about Image Analysis, let’s move along to some of its use cases in quality and reliability. Image systems can leverage AI to build a baseline of what a product should look like (especially high-volume discrete products) and then identify quality defects based on deviations from the baseline. Some systems can even watch people manually assembling discrete products to ensure people are doing their jobs optimally. Meanwhile many operators point AI camera systems at critical equipment, and the systems can learn to visually detect anomalies that indicate upcoming machine health issues (a cool take on predictive maintenance).

I’ll stop there since this isn’t an encyclopedia article… but I certainly haven’t cornered the market on industrial AI use cases, so please post with additional use cases that you think are valuable! And if any of the aforementioned categories are relevant for you, please feel free to reach out and I can share some insight, or connect you with potential partners in the space.

That all said, we need more industrial companies to adopt AI at scale. The use cases described above are not in production at most industrial companies, just at the more innovative ones. If you look at recent studies from the likes of PwC and McKinsey, most of them report that less than 25% of industrial companies are using AI at scale in production operations (many of the studies say less than 10%).

We need AI to become more prevalent within industrial operations, because we’d all obviously benefit from operations that are more reliable, sustainable, secure, predictable, diverse, safe, and of higher quality. Data acquisition hardware continues to become cheaper, while AI tools continue to become more available to non-data scientists that can focus on novel operational use cases – but the tech is the easy part… we need more believers, more cultural change, and more proof of ROI.