Manufacturing and energy companies experience downtime for a lot of reasons. Some of these causes are obvious while others can be a tad bit difficult to understand. One thing we can be sure about, however, is that they aren’t good for business. Now although there are many things you can do to make sure downtimes are less frequent, none show the level of effectiveness that predictive maintenance does.

Predictive maintenance involves the use of special sensor devices that monitor the condition of an asset and supply data about it to you in real-time. These data can then be used to accurately predict when the asset will require maintenance and help you prevent equipment failure before it occurs.

In this article, we would like to emphasize how DT4o’s predictive maintenance services can help your business reduce downtime and increase productivity.

First, let’s look at some of the common causes of downtimes.

What are the Causes of Downtime?

As we mentioned before, downtimes can be caused due to a lot of reasons. By far, human error is the most prominent cause of downtime in the energy and manufacturing sectors. Improper maintenance of manufacturing equipment or assets can be another major cause of downtime. Furthermore, the absence of a proper plan in the event of a sudden crisis can be another thing that brings business operations to a standstill.

Other causes of Downtime include

  • Unclear Working Instructions
  • Power Outages
  • Natural Disasters

Regardless of the reasons, they have to be tackled swiftly as prolonged downtimes can affect your business’s bottom line. The best way to do so is by employing predictive maintenance services offered by DT4o.

Check Out How DT4o’S Help You with Predictive Quality Management

DT4o’s predictive maintenance solutions can be used to address several key challenges that plague the energy and manufacturing sector. For instance, organizations will always know when maintenance is actually needed, thanks to data collected through predictive quality analytics and AI. As such, businesses can save time and money by not performing unnecessary routine checks.

With DT4o’S robust predictive quality management, you can detect risks to assets early on by using computer vision and machine learning algorithms. Moreover, the AI offered by our solution makes it considerably easy to monitor risks across a wide variety of assets.

DT4os predictive maintenance can also serve as a solution to detached and varying reliability systems. It does so by facilitating an integrated closed-loop workflow that is ideal for both assets and operations.

All the Major Way by Which DT4os Predictive Maintenance Helps Reduce Downtime

DT4os robust predictive maintenance service reduces downtime in the following ways:

  • AI-Driven Asset Risk Detection.
    DT4os solution performs root cause analysis to better understand the reason for a detected deviation and provide an ideal solution for it. The platform facilitates the real-time monitoring of assets. It provides you with data that accurately depict the current condition of an asset. The data can also help you detect a possible breakdown before it even occurs.
  • Prioritized Early Warning
    Using machine learning algorithms, our solution makes it very easy to identify deviations in asset performance early on. As such, businesses can immediately assign the right individual to undertake maintenance work to reduce downtime.

  • Scalable AI
    The AI offered by DT4o can be effortlessly scaled across a wide array of assets. Moreover, the solution is capable of providing end-to-end management to end-users.

  • Identifying and Mitigating Failure
    Our solution is capable of performing failure mode and effect analysis to determine the root cause of potential failures and mitigate the risk associated with them. The solution uses information that is gathered from across the industry to accurately identify failures and prescribe solutions to effectively resolve them in no time.

You can learn more about DT4o’s predictive maintenance solution by giving us a call today.