Overall Equipment Efficiency (OEE), a stalwart of KPIs, has grown from the measure of a single line to a global lighthouse, revealing the overall performance of an entire manufacturing organization.

Although OEE is a stalwart of KPIs, calculating OEE has historically been challenging and time-consuming. Global OEE calculations can have quality problems that keep them from maximum accuracy. When real-time data collection isn’t available, and downtime reporting remains inaccurate, the end result is that each site calculates OEE a little differently. Global numbers can show green when too many machines are flashing red.

To be useful, global OEE must be high quality at every level. Everyone should look at the same up-to-date, accurate information from the factory floor to global operations. With this level of visibility, every level in the organization can spot problem areas and take action.

What is missing from manufacturing data?

The state of data collection and processing has not kept up with technological improvements, and this needs to change. Oftentimes, manufacturing line data has needed to be collected manually, using different methodologies for different machines and lines. Data entry into spreadsheets or databases has been too slow and error-prone.

The end result is a painstakingly calculated OEE measurement that takes too much time and too many worker hours. But Arch knows what manufacturing organizations need to make global OEE ultimately useful:

  • Data collection and processing standards across the organization
  • Implement and utilize better data collection
  • Make OEE insights available at all organizational levels

The modernization of OEE.

Manufacturing is not at the end of the automation journey. As machines get smarter and production lines get faster, it becomes more and more important for all levels of a manufacturing organization to see where improvements can happen, where failures are likely, and how productivity can be maintained or improved.

Today, there are better ways to collect and use OEE data.

Create standards within the organization.

To raise the accuracy of a truly global OEE, every line, every site, and every region must adhere to the same standards for OEE data and calculation. The organization needs a standard OEE definition before any consistency can be expected.

Using the same solution for collecting production and uptime data can be a powerful tool for enforcing an OEE standard. Consistent data collection methods and centralized storage are key to both accurate global OEE calculations and discovering line and site-specific improvements.

Require and utilize better data collection.

The current challenge is collecting all of this data, standardizing it, and analyzing it. Standardized data for downtime can help pinpoint if the causes are human, machine, or material. 

Most machines are already producing the needed data, and legacy machines can be retrofitted with data collection IoT devices. Data doesn’t need to remain trapped in machines and proprietary collection methods. The labor required for collecting machine data has gone from huge to almost zero.

Make insights available at all levels.

The primary obstacle to line-level operators is a lack of visibility—they cannot see where the problems are. With data insights available at all levels, teams and team leaders can quickly learn where they need to take action to maintain or increase performance.

From the line level to executives, better data availability leads to better insights. Providing a real-time view of OEE is one of the best ways to improve the numbers. For OEE to scale to any organization, it must unify teams at every level. Global leaders, site leaders, shift managers, line operators, and engineering teams all need access to information and functionality that serves their roles. Only if a solution actually delivers this functionality can it truly be effective at also delivering cloud-based, global, and effective OEE scoring.

The use of OEE will continue to evolve.

In the era of Industry 4.0 and smart factories, interconnected machines and data-driven automation systems have all the capabilities to collect the right data and perform the right analysis—in real time. Manufacturers need to take advantage of real-time OEE calculations that provide pinpoint accuracy and high resolution identification of problematic lines and sites.

To meet this challenge, manufacturers must implement standardized data collection processes, leverage automated monitoring systems, integrate with machine data streams, and train teams to consistently and accurately record downtime data. As data improves, OEE has greater utility.

Learn more about the ArchFX platform streamlines OEE monitoring