How SCADA and Analytics Software Improve OEE

With all the changes impacting manufacturing — from both a business and technology perspective — over the last few decades, companies are looking at new strategies to drive operational excellence. For example, we see original equipment manufacturers (OEMs) seeking ways to build more insightful, intuitive machines to help their customers improve operations. Simultaneously, end-user manufacturers are looking for technologies to improve operations on the factory floor to drive efficiency, reduce waste and upskill their personnel. 

The approach both OEMs and end-user manufacturers are using involves using technology to replace manual practices and uncover new operational insights.

Targeting OEE

One of the primary goals of industrial digital transformation is to improve overall equipment effectiveness (OEE), a metric used to measure the performance of manufacturing processes and individual machines. OEE remains a popular metric because it offers insight into operations and helps teams quickly set priorities for improvement. 

OEE measurements are calculated based on availability, performance and quality. If one is deficient, the others alone cannot sustain high OEE. As a result, teams need constant visibility into all three metrics — in real time — to effectively monitor and maintain peak operations. 

The problem is that the data necessary to deliver visibility into availability, performance and quality is often siloed. Whether it is trapped in disparate programmable logic controllers (PLCs) with complex data accessibility requirements, or scattered among reams of paper records, much of the valuable data necessary to improve operations is often hard to retrieve, as well as time-consuming and difficult to analyze. 

Considering this reality, the first and most important step to driving improved OEE through digital transformation is to improve visibility and analysis across operations. Both OEMs and manufacturing organizations are accomplishing this goal using two key tools: supervisory control and data acquisition (SCADA) software coupled with data production and analytics software integrated with SCADA.

This is why it is important to select SCADA and analytics software designed to handle data from sensors to the edge and even into the cloud. 

At the unit level, operators need to clearly see how their machine is performing relative to organizational goals. They should also have the ability to record and analyze downtime, understand production status and discover quality issues and their causes. For this to happen, operational data must be seamlessly delivered to analytics software that tracks and trends performance while enabling management to monitor operations at both the production line and facility levels to see overall trends. This is critical to turning data into actionable insights in a way that can improve operations.

Assessing equipment availability

Unplanned outages are, of course, one of the biggest detriments to efficient manufacturing operations. If critical equipment is not running, product is not being made and the organization is losing money. In traditional operations, when equipment stopped running, the best teams would keep careful paper records of when the downtime happened, any information they had regarding the cause and how long it took to bring the machine back to full functionality.

These records were important, but they were also highly fallible. Response times were limited by how long it took to write the report and how quickly it was delivered to a person who could analyze the data and initiate action to remedy the problem. If the organization wanted to monitor problem machines or identify patterns of failure, those same reports needed to be added to a system where someone could manually examine the data and produce insights. 

In many cases, by the time analysis was done, the information was stale and the situation on the factory floor had changed.

Moreover, because the entire reporting system relied on manual recording, it was prone to error. Hard-to-read reports might be transcribed incorrectly. Busy operators might never get around to writing reports. As a result, downtime data was often neither timely nor reliable.

The company transitioned to using integrated SCADA and analysis software to gain instant access to these critical data points. Today, they use a combination of out-of-the-box and custom KPIs around productivity and OEE dashboards to track, trend and assess performance, allowing them to make the operational and maintenance changes necessary to eliminate unplanned downtime.

Quantifying quality

Quality is another area where limited visibility can dramatically impact OEE and operational excellence. If identifying quality issues requires a manual task of pulling failed products off the line and then assessing afterward how many items were scrap, a manufacturer gains little insight into what is happening in production. 

Examining a collection of scrap products makes it hard to identify root causes. As a result, analysts lose a lot of context, such as process and operating conditions, equipment failures, etc., that are available during production. 

When teams operate using a SCADA system integrated with analytics software, they can instantly identify quality failures, recording not only the failure itself, but also the unique processing conditions at the time of production. Armed with this knowledge, the organization can more easily identify exactly what is causing quality issues and intervene quickly.

At a valve manufacturing company we worked with, management wanted to limit scrap due to product not meeting quality standards. Even with their meticulous records, however, the team found it could not intervene fast enough to reduce waste at a significant level.

After implementing a SCADA system with integrated analysis software, the team can now instantly record scrap valves and include information for why operators scrapped the product. That information is available in real time to track how specific machines and units are impacting quality.

Promoting performance

OEMs want to provide — and manufacturers want to use — machines that display the necessary information to help them understand operational challenges. At the most basic level, that information helps them see how many products are going through the line compared to expectations. Armed with that data, they can identify when machine setups need to be changed or operators need to modify their processes.

However, if data is trapped in a complex system and cannot be accessed until days or weeks after operations are complete, it is challenging, if not impossible, to turn those insights into meaningful action on the factory floor. 

To overcome this challenge, as with the examples noted above, OEMs and end users are implementing SCADA and integrated analytics software. Here, these tools provide visualization local to the machine, helping teams see real-time production and company goals on intuitive dashboards so they can make the decisions necessary to eliminate bottlenecks and improve throughput. Those same insights are also consumed by the analytics software to help identify larger organizational trends which can be targeted for improvements, such as differences in shift performance, historically underperforming equipment and feedstock issues.

Another manufacturer we work with was struggling to identify how many overall units were produced by one line each day, as well as how many of those units were scrapped and why. To get this information, operators would dig into the PLC software at the end of the week to try to identify production numbers and stoppages. The work was manual, complicated and time-consuming, and insights came too late to be applied to real-time production. 

When the team implemented SCADA with an HMI, operators could see in real time how many units went through the system and how many were scrapped. They could also compare these numbers against company metrics on an intuitive dashboard to determine how close they were to daily goals and quickly recognize when corrective action was required.

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