Global supply chains will be under a great deal of strain in the next 12 months. Nine in 10 manufacturing leaders anticipate more frequent disruptions, according to Augury’s latest State of Production Health Report, which surveyed 700 global leaders from across the industry to get their views on manufacturing, artificial intelligence and their key priorities. With these issues top of mind in 2024, manufacturers must get innovative and figure out how to optimize supply chain management.

Even though respondents ranked supply chain as a top production challenge (25%) and a notable AI use case (43%), it still remains the lowest-ranked objective when thinking about deploying AI. It’s possible that these conflicting stats reveal a hard truth: Manufacturers don’t believe supply chain issues can be solved by AI alone.

This belief could be tied to the fact that various supply chain aspects, like third-party suppliers and transportation providers, are out of the manufacturer's direct control. Instead of focusing on these aspects, manufacturers must address what they can control: what’s happening on factory floors. In doing so, they start early in the supply chain and completely revamp their supply chain management strategies.

Production is a leading indicator of supply, and major companies are now looking at machine maintenance as a crucial part of the supply chain. Therefore, when manufacturers turn to AI to increase reliability and predictability, the positive impact ripples throughout the supply chain. Manufacturers can achieve this by focusing on machine health, process health and innovative integrations. 

Catching What Humans Can’t

For manufacturers, focusing on machine health allows them to drive true, tangible productivity improvements. This is done through the continuous monitoring of assets, which, driven by AI-powered insights, ensures that machines are running properly and eliminates unnecessary downtime by catching issues before they occur, including those that a human might have missed entirely.

For example, a slight vibrational change in a motor is a critical issue that eventually leads to failure, and is typically overlooked or missed by humans in the early stages. But AI-driven, real-time machine health alerts will flag the change in vibration and likely causes before they become an issue, so the factory can avoid a catastrophic downtime event and save millions of pounds of products.

Manufacturers are catching on to these benefits, which may be why, in 2024, two times as many respondents (36%) noted that they can now quantify benefits for machine health and downtime, compared to 16% in 2023. We can see that the industry is getting better at utilizing AI and noticing a positive return on investment, and it’s only up from here.

Decreasing Production Loss

Process health relies on machine-learning algorithms and mixes them with a wealth of expertise from production lines to identify the root cause of problems. But these AI-powered systems can also provide manufacturers with real solutions for fixing inefficiencies, preventing production loss and realizing greater productivity from machines and processes. Similar to machine health, process health presents a clear benefit to the supply chain by helping manufacturers realize the full potential of their production lines, so they can reduce waste and increase the number of high-quality products leaving their factories each day.

The data also shows that 35% of manufacturers say they’re using AI for process health and maximizing yield and capacity, with growing success in these areas. In 2024, three times as many manufacturers report they can measure AI’s impact on process health and maximizing yield and capacity, compared to 2023 (to 40% from 13%).

AI-driven process health holds many additional benefits for those working on the floor. AI frees those workers from tedious, time-consuming tasks and gives them bandwidth to focus on more strategic initiatives, such as further optimizing supply chain management strategies with cutting-edge tech integrations.

The Importance of PaaS

AI-driven parts-as-a-service (PaaS) applications are proving to be a breakthrough way by which manufacturers can improve supply chain management. PaaS tools get machine parts into the hands of manufacturers when they need them.

An effective way to up-level a PaaS system is to integrate it with machine health. When machine health insights predict a machine failure, PaaS identifies which parts need replacing, then automates their acquisition. By replacing machine parts more proactively and cost-effectively, delays are minimized, and manufacturers are able to optimize their spare parts management. 

It’s clear that any effort to enhance the supply chain must start on the factory floor. All manufacturers need to ensure that they’re consistently working to improve their operations by focusing on machine health, process health and spare parts management. 

For those who aren’t sure how to get started, the best way is to work with a trusted partner, one that deeply understands your key objectives and has the technology required to meet them. 

Brian Fitzgerald is a growth strategist at Augury.