How Data and AI are Minimizing Manufacturing Machine Downtime

How Data and AI are Minimizing Manufacturing Machine Downtime

Taking a data-first approach to manufacturing processes across the entire supply chain can be very beneficial to organizations – especially when manufacturers face enormous pressure. Any action a company can take to reduce downtime and eliminate delays in operations may be an intelligent approach.

One of the largest culprits of delayed operations is machine downtime and machine failure. However, a data-first approach using artificial intelligence (AI) can help companies get ahead of machine issues and remove them as a problem altogether. The best way to keep a machine functional and avoid aggravation across the workforce is to stay on top of machine levels and data related to a specific piece of equipment. Avoiding doing so will result in more stoppages and halts in operations. This piece will dive into what this looks like and how AI can be your operations’ most valuable asset.
There are countless nuggets of data spread out and scattered across numerous structured and unstructured data sources within a company. Enterprise search technology powered by AI connects all scattered data and puts it into a central knowledge management system. This includes company files, videos, text documents, images of machine parts, blueprints, and more. Once everything is connected, it is easy for workers to search and see a 360-degree view of different machinery and its parts.

Being Proactive with Predictive Maintenance

Predictive maintenance uses data-driven AI technology to help companies see the conditions of their machinery. This type of approach to maintenance can alert management or the proper maintenance team when failures are likely to happen and even when a fix is due to be made. The end goal of predictive maintenance is the ability for corporations to key in on possible issues well before they become actual issues.  
A typical machine check is done on a routine, and scheduled times are involved. For example, an inspection is made every Friday or every other month. While routine and scheduled checks are ordinary, they leave room for error. An equipment assessment once a week leaves seven days for something to go wrong. The best approaches to predictive maintenance utilize sensors that can automatically monitor aspects of the machine; temperature levels, pressure, and humidity. With sensors in place, alerts are made when deviations from the optimal or standard levels occur. Rather than realizing a deviation that may cause a machine to fail after the fact, the maintenance team is in a position to inspect right away, directly impacting machine failures, downfalls, and downtime.   

Seeing all your Data in a 360-Degree View

With prominent data and information existing in scattered documents, manuals, blueprints, and now coming from sensors, the connection of data sources is all the more critical. Workers can access any topic they need intelligence on in seconds, and all the intelligence is enriched with data from all sources. When it comes to machine maintenance, the subject of interest is typically a type of machine or a component of that machine. Other departments are consistently generating data, and having access to real-time information does wonders for maintenance teams on the ground.
If a worker is performing a technical fix on an air pump, they should be equipped with any piece of knowledge on the make and model of the air pump. An example of predictive maintenance combined with a 360-degree view of corporate information is as simple as a maintenance team being alerted of a deviation in temperature, going to the location to inspect it, and accessing all information needed to perform the fix.

AI has already crawled and indexed information from their company data sources, so searching for “air pump” or “how to fix air pump model Z” will shoot back relevant knowledge to the worker in seconds. Innovative technology applications can significantly cut down labor time by enabling workers not to perform specialized fixes from memory but with all the knowledge at their fingertips imaginable.  

Where Exactly do Digital Twins fit in, and What Should you Know?

Per Gartner, A digital twin is a digital representation of a real-world entity or system. The implementation of a digital twin is an encapsulated software object or model that mirrors a unique physical object, process, organization, person, or other abstraction. Data from multiple digital twins can be aggregated for a composite view across a number of real-world entities, such as a power plant or a city, and their related processes.

Using digital twins, the maintenance team can view the machine in question and analyze the problem digitally before diving into the fix. The worker can see the full scope of the machine on their tablet or computer, gathering information and insights on how they should approach the machine defect.

Overall, the data collected from sensors, company documentation, and digital twins can be the backbone of a company’s predictive maintenance game plan. Intelligent insights that stem from the connection of data sources can keep machines running and production processes fully on track. It’s time to start taking a data-first approach to your maintenance.