The Inaccuracies in Your OEE and How to Solve Them: A Guide for Manufacturers

Efficiency is the name of the game in today’s highly competitive manufacturing landscape. One of the commonly used metrics to gauge manufacturing effectiveness is OEE (overall equipment effectiveness). While OEE is a powerful tool in determining your production environment’s efficacy, it is not infallible, especially when it is defined by archaic production collection methods.

Many modern manufacturers may be relying on OEE calculations that are less accurate than they realize, leading to misinformed decisions and suboptimal performance. In this industry thought piece from Scytec, we will explore the common pitfalls and inaccuracies in OEE measurements and how to address them to ensure your operations run as smoothly and effectively as possible.

What is OEE Derived From?

Overall Equipment Effectiveness is a somewhat wide-ranging metric that evaluates how effective a manufacturing operation is compared to its full potential. It is composed of the following three parameters…

Availability, the ratio of actual operating time to planned production time.

Performance, the ratio of actual machine speed over planned machine speed.

Quality, the ratio of good units produced to total units produced.

OEE is calculated by Availability X Performance X Quality. An OEE score of 100% signifies perfect production, manufacturing only good parts as fast as possible, with no machine downtime.

Common Sources of Inaccuracy in OEE Calculations

Now that we have dictated what goes into OEE calculations, let’s define the repetitive occurrences that lead to false OEE reporting.

The foundation of any metric is the data upon which it is built, if the data fed into the OEE calculation is inaccurate, the result will be misleading. The common issues that plague OEE calculations and their reporting are as follows… Manual Data Entry, human error can lead to incorrect data, especially if it is done the archaic way of reporting via paper and pencil. Mistakes in recording production times, output counts, and downtimes are common this way and susceptible to bias that doesn’t come from automatic data collection. Inconsistent Data Collection Methods, different production teams or shifts may use varying methods to collect data, leading to inconsistencies.

Delayed Data Entry, at some point in our professional lives we can recall hearing “Did you remember to fill out your timecard”? When data is recorded long after the fact, details may be forgotten, misremembered or never even recorded at all. Improperly Defined Parameters, to calculate OEE accurately, you must clearly define what constitutes planned production time, ideal cycle time, and quality products. Misunderstandings or ambiguities in these definitions can skew your OEE.

Some other common OEE inaccuracies include Planned Production Time, this tenet should accommodate planned maintenance and operator breaks for the most accurate OEE. Ideal Cycle Times, this should represent the fastest possible time to produce one unit, using outdated or overly optimistic cycle times will distort the performance factor. Ignoring Minor Stoppages, minor stoppages or M0s/M1s are brief interruptions that can significantly affect productivity but are often overlooked in OEE calculations. These might include small jams, misfeeds, brief resets or just a machine pause. Machine Setup and Adjustments, the time spent on setup, adjustments, or changeovers can significantly impact availability but is sometimes not accounted for accurately. Properly categorizing this time is crucial for a true representation of machine availability.

Finally…The Lack of Real-Time Machine Monitoring, static OEE calculations, derived from periodic reports, do not capture the dynamic nature of manufacturing processes. Without real-time machine monitoring, manufacturers will miss patterns and trends that can highlight inefficiencies.

Enhancing the Accuracy of Your OEE

So how can manufacturers ensure that their OEE data is correct and working for them, making the most out of their kaizen initiatives?

Investing in an automated data collection system like Scytec DataXchange will drastically reduce human error and ensure consistent real-time data acquisition. Collecting machine information over Ethernet is the primary way to gather data that develop OEE reports, however the possibility to collect data from sensors like thermocouples, current, fill levels and more that can be wired into IIoT devices that provide accurate, up-to-the-minute information is an option as well. Manufacturers will find that implementing standardized procedures across all shifts and teams to ensure consistency and training staff to use uniform data entry processes will minimize OEE discrepancies.

Regularly reviewing your definitions of planned and ideal cycle times, as well as product quality standards, is also beneficial to providing the most accurate OEE. Adjusting these parameters is necessary to reflect the current state of your operations, equipment and final product. It is also more possible than you think to account for all shop floor downtimes. With a machine monitoring platform like Scytec DataXchange, manufacturers can ensure that all downtimes including minor stoppages and setup times are accurately recorded using detailed logs to categorize different types of downtime and then address their root causes.

Implementing a Real-Time Monitoring and Analytics System is the Continual Solution to OEE Reporting Woes

Implementing and utilizing a real-time monitoring system and advanced analytics to gain deeper insights into your operations will allow you to quickly identify and respond to issues, improving overall efficiency. OEE should be part of a broader continuous improvement strategy for manufacturers around the globe. Regularly analyzing OEE data from machine monitoring platforms like Scytec DataXchange and receiving feedback from operators, thusly implementing changes to address the identified inefficiencies, will greatly improve your shop floor performance.

While OEE is an invaluable metric for measuring manufacturing efficiency, it is only as accurate as the data and methods used to calculate it. By addressing common sources of these inaccuracies and then adopting modern day data collection and analysis techniques like a machine monitoring system, manufacturers can safeguard their OEE measurements, so they truly reflect their operational performance. This, in turn, enables more informed decision-making and drives continuous improvement across the production floor. For more information on how Scytec DataXchange can improve your shop floor experience, please click here to see how.