Engineering Strategies to Improve OEE

Improving overall equipment effectiveness (OEE) requires a holistic analysis of the system. Patti Engineering is often approached to address issues pertaining to OEE: quality issues, disappointing throughput performance, or problematic downtime issues. The common thread connecting these concerns is that the real-world output of the system isn’t anything close to its expected capabilities. By reviewing each ratio in the OEE product, this article discusses actionable ways to improve each contributing component of OEE.

Overall Equipment Effectiveness (OEE) = Availability x Performance x Quality

OEE: when written as a mathematical expression, the concept feels abstract in nature, out of touch with those who are in the midst of struggling with real-world manufacturing production shortfalls. More often than not, a manager or engineer is perplexed, unable to pin down the underlying reasons for the gap in their expected equipment capabilities (its expected OEE), and the actual amount of quality parts produced on a regular basis. 

However, by reviewing each of the rates that are part of the OEE expression, useful insights can be gained to find and resolve these issues and bottlenecks, leading to better consistency of throughput and high-quality results. 

Mathematically, OEE is expressed as a percentage, and it is the product of three ratios. The higher each contributing ratio is, the higher the overall OEE.

OEE = Availability x Performance x Quality

OEE =   ^{Total Time Planned – Lost Time}_{Total Time Planned}   X  ^{Actual Part Output}_{Possible Part Output}   X  ^{Flawless Parts}_{Actual Parts}   X   100

A closer look at reach ratio and the factors that may influence each quantity can shed light into process improvements.

Availability

Availability =   ^{Total Time Available – (Startup + Shutdown time)}_{Total Time Available}

Increases in the rate of availability can come from several wide-ranging improvements centered around reducing downtime. Consider the following:

System Diagnostics

Because downtime duration is deeply tied to overall machine availability, it is important to streamline the process of returning a system to full operation in the event that it goes down. This task can be accomplished by including better system diagnostics to be able to quickly identify the reason for a downtime event, and therefore ensuring a quick resolution. 

Patti Engineering frequently gets involved in providing better system diagnostics for its clients with the goal of minimizing the duration of a downtime event. This task is accomplished by providing easily digestible and readable information about the reason why the system went down. This allows an operator or maintenance person to fix the problem and quickly bring the system back up. Engineers accomplish this by adding better alarming as well as overhead information screens and other visual indicators with information about the current state of operation.  In addition, push notifications can be set up for managers or other personnel to receive notices via text and/or email notifications. 

Legacy Controls System Unreliability

Aging controls systems bring with them an array of challenges discussed in an earlier blog, including frequent, difficult to diagnose downtime events. These downtime events can be intermittent in nature and can originate from a variety of aging system components. By upgrading the controls system, the overall system reliability is improved leading to less downtime.

In a recent OEE-related project completed by Patti Engineering, a combination of aging hardware along with software database communication inefficiencies were found to be driving issues slowing down throughput. This project is discussed in further detail here. 

Robotics to Address Labor Shortages

In an article featured in the May 2024 publication of Packaging Technology Today, Patti Engineering describes the severity of the labor shortages across all manufacturing sectors. These shortages are pervasive and projected to increase over the next several years. When manufacturing roles can’t be filled, it heavily impacts system availability. However, today’s robots can complete more complex tasks than ever before, often with impressive ROI numbers, positioning them as an effective means of addressing labor shortages. Our recent eBook, Common Considerations Integrating Robotics in Manufacturing, provides newcomers to robotics with actionable in-depth information to get started.

Streamline System Start-up with Well-Designed Controls System Software 

A recent project completed by Patti Engineering involved rewriting the controls software to streamline the process of starting up a line. The original software had not been designed for ease of use and required operators to individually tend every machine on the line during start up. This unnecessary, time consuming process stemmed from poorly designed code. By rewriting it, Patti Engineering was able to reduce the total duration of a downtime event by eliminating the extra 15 minutes associated with the original poor code structure, thus increasing machine availability.

Data-Driven Predictive Maintenance 

There are a variety of artificial intelligence subscription-based tools for monitoring system performance and predicting maintenance needs. They typically use machine learning methodologies to model a system and then use that model to detect maintenance-related anomalies. 

As an example, FANUC’s Zero Downtime (ZDT) predictive maintenance software is a cloud-based solution that monitors robotics equipment to predict failures. It collects real-time data from connected robots, including motor performance, sensor readings, temperature, and cycle time. This data is stored in FANUC’s Analytics Center for modeling and analysis. Machine learning algorithms process the data to identify patterns and deviations, enabling predictions about maintenance needs or potential failures. Customers receive this information for proactive maintenance, leading them closer to the “zero downtime” goal.

Performance 

Performance =   ^{Actual Part Output}_{Possible Part Output}

The performance portion of the OEE product addresses part cycle time. Consider the following for improving performance:

Robotic Path Optimization

Consider reviewing the motion path taken by any robotics in the system. It’s common for the programmed path to lack optimization, leading to unnecessary wasted motion and therefore excess time. FANUC’s Roboguide is a helpful tool to analyze and optimize a robot’s movement path. As a result, not only will cycle time be more favorable, but long-term stress on the robot is also likely to decrease as it will only move as much as necessary to complete the task.

Sluggish Legacy Controls Systems

A legacy PLC or other controls system may contribute to increased cycle time in a variety of ways, including the overall processing rate. Since the PLC must read Inputs, process them, and then drive resulting outputs, a more powerful system with a faster processor and more memory, will do this work in less time. A controls upgrade may provide significant improvements in cycle time, along with other system improvements, as detailed in an earlier blog.

Line Layout

Sometimes a careful look at the overall flow of a line can yield simple improvements to streamline production. As an example, ensuring all parts and tools needed for the task are within close proximity to those who use them can have a meaningful positive impact on overall performance.

Minimizing Computation Time for Processing Intensive Algorithms

Systems that rely on a significant amount of computation (such as vision processing, and line tracking algorithms) have the potential to require a non-trivial amount of time to complete. In broad strokes, selecting the device with the fastest processing speed will likely be the best design choice. However, communication latencies between devices must also be considered if processing is done non-locally. When truly intensive processing is required, an external dedicated vision processing unit may provide best outcomes for cycle time impact.  

A recent project by Patti Engineering described at length in the upcoming May 2024 publication of Packaging Technology Today is an example where the robot’s processor was sufficient to perform the line tracking computation without impacting cycle time. In this project, the robotic system used line tracking techniques to follow a target part along a moving conveyor. The robotic system’s PLC was responsible for selecting target parts on a moving conveyor. However, once chosen, the PLC notified the picking robot of the upcoming target part, and the encoder’s output was directly connected to the robot. The robot’s own processor was sufficient to perform the line tracking algorithm, freeing the PLC’s processor for other tasks and avoiding latency communication issues.  

In another recent project involving 3D vision processing for random bin picking, Patti Engineering had to be very cognizant of the time required to complete the vision processing. It was determined that a dedicated processing unit external to the system would provide the least cycle time impact, rather than relying on the processor within the robot. 

Quality

Quality =   ^{Flawless Parts}_{Actual Parts}

The quality portion of the OEE product considers the number of correctly made parts in relation to the total number of parts, including those that do not meet quality standards. Patti Engineering has found that many quality issues can be handled with vision inspection systems. These systems can often be placed in a non-bottleneck location of the assembly line, leading to quality gains without a cycle time cost. 

Vision Inspection Systems

Patti Engineering frequently becomes involved in quality improvement projects by designing and implementing vision inspection systems. Smart cameras are used to process images to look for the presence or absence of a part or feature. Sometimes a particular measurement is taken and other times pattern recognition is used to detect anomalies, such as scratches. 

A recent project that falls into this category addressed an automotive-related quality issue. The client was dealing with a costly quality issue originating deep within an assembly line that was impacting their customer-facing brand reputation. The problem was that a particular part of an engine assembly was occasionally placed incorrectly – in an inverted orientation. Prior to Patti Engineering’s involvement, the issue was subtle enough that it went undetected until the new vehicle owner experienced engine failure shortly after its purchase leading to warranty repairs and an unhappy customer. To address the issue, Patti Engineering devised a vision inspection system situated directly along the assembly line and positioned in a non-bottleneck location. The system uses imaging to verify the correct orientation of this particular part. Each engine assembly includes an RFID tag. When an engine fails this inspection, it is noted accordingly on its RFID tag. Then, further down the line, the RFID tags are read and those with failed engines are directed elsewhere for rework. Ultimately, the inspection system resolved a costly quality issue that also impacted the client’s reputation. 

Eliminating Rework with Robotics

Sometimes significant quality and cycle time improvements can be had by implementing a robot for tasks requiring high accuracy and consistency. As an example, robots are excellent at completing painting tasks. 

Patti Engineering recently worked with a client struggling with a paint quality issue. The painted part had a very high-quality requirement associated with it. Prior to Patti Engineering’s involvement, the part required several passes of paint rework to produce this high-quality finish. By looking at a robotic painting solution, virtually all rework passes can be eliminated as the robotic painting job met the quality requirement on the first pass nearly every time. 

Tools for Analyzing OEE Problems

Determining the true underlying causes of OEE-related issues frequently requires a deep dive into the system as a whole, including obtaining diagnostics data from different sources along the production line. Patti Engineering uses a variety of tools to help clients find the specific issues driving down their OEE. The process often begins with an engineering study. 

An Engineering Study to Analyze the System and its Issues

Patti Engineering is often called upon to evaluate process flow to address OEE-related problems. These include addressing part quality, process inefficiencies, unacceptable rates of machine downtime, and other bottlenecks reducing production of quality parts. An engineering study will include data-driven suggestions for process improvement.

Arduino for Data Collection and Connectivity of Legacy PLCs

One of Patti Engineering’s most recent tools supporting diagnostics involves a partnership with Arduino. The Industrial Arduino PLC can be used to connect directly to a legacy system obtaining and processing data directly from the machine. The Arduino system bypasses older PLCs that were never setup for data collection and analysis. As a result, diagnostics data can be retrieved from a machine without having to modify the system.

Ignition for Consistent Data Collection and Process Optimization

For a more consistent means of sending data to a cloud-based data collection and analysis center, investing in an Ignition-based system may be helpful. Once in place, an Ignition-based system is particularly suited to data-related tasks. During regular uptime operation, Ignition makes it easier to extract data out of the PLC, perform edge-based processing on it, display relevant information on an edge device, and send the data to a cloud-based analytics system.  Additionally, during a downtime event, an Ignition-based system streamlines the process of obtaining the diagnostics information required to identify the source of the downtime, and therefore the fastest way to bring the system back up again.

Building a Digital Twin of the System 

In a recent Patti Engineering project, engineers were tasked with finding the underlying reasons for poor system performance – poor OEE. Because the issues were subtle in nature, they built a digital twin of their clients’ system using Siemens Plant Simulation and Process Simulation software. Once the digital twin was built, they used actual data from their client’s process to tune the twin in such a way that it operated in the same manner as the real system. Once the digital twin was consistently mirroring its real-world counterpart’s behavior, Patti Engineering was able to use it to devise several options for improving throughput. A few of the resulting suggestions included small program changes, as well as changing a particular robot fixture. These weren’t readily obvious system tweaks at the beginning of the project, however, the outcome showed a 25 percent increase in throughput.

Obtain a Fresh Perspective

The Patti Engineering team has the broad-ranging skill sets as well as the experience to help manufacturers identify and resolve production-related issues. Consider reaching out to our team so that we can help you address problems related to throughout, quality, and equipment availability, leading to stronger OEE which results in improved profit.