Understanding, Accuracy and Trust: The Three Pillars of Automated OEE

Last month in ‘What is OEE? Understanding and Calculating Overall Equipment Effectiveness’ we discussed industry standards for calculating and monitoring your production efficiency. This covered the ‘understanding’ pillar of automated OEE, and is accessible here.

But before you move onto implementing OEE measurements into your organisation, it’s important to ensure that you’re covering two important concepts:

1. Ensuring that your manufacturing data is accurate.

2. Ensuring that you and your staff have trust in your data collection approach.


Concerns around accuracy are natural when first implementing an automated OEE system. In fact, for most manufacturers, it will seem as though they are performing worse after the switch. Let’s look at why this is.

Manual paper-based system: Relies on people to record and capture information from production machinery into paper-based forms. Manufacturing issues, such as shortstops and changeover times are often inaccurately entered, or not entered at all if they are under 5 minutes in duration.

Automatic OEE system: Captures ALL information from your production line. Accurately tracks shortstops to the second and records all changeover and setup times.

There are a few different components that affect the accuracy of your data collection approach:


One example of this is that suddenly shifts might appear to be starting later than scheduled. But before you confront your operators on their timeliness, you should look to how your new automated OEE system works.

Just because an operator arrives at 5:55am and clocks on at 6am, doesn’t mean that the shift on the machine starts at 6am.

An automatic OEE system will report quite precisely the times that shifts start, which can be confronting at first, when it becomes clear that very little happens in the first 15 minutes of the day! Counting & Data: An automated OEE system will also swiftly reveal inconsistencies or errors in your production processes, which can be particularly noticeable with machine signals like counters. An automated OEE system depends on having clean, accurate signals at all times. This may include “scale factors”, where a box is counted, and is multiplied by a “scale” (e.g. the number of units in the box). If the counter isn’t well maintained, or the product data contains inaccuracies (such as an incorrect number of units per box), then there will be issues with the data collected.

One of the things to look for in an OEE system is a highly visible dashboard, as well as an alerting feature that will prompt operators of problems as soon as they occur. This will ensure that your production facility has a timely call to action and increase the efficiency of your operations.


Trust is an important concept when measuring your OEE, and other manufacturing metrics. This is because the information provided from your automated OEE system should be seen by everyone in the organisation as the single source of truth.

A sure fire way to have trust issues is to ask someone to be accountable for a metric that they have limited control over. The right idea is to ensure that the right metrics are used for the relevant audience. This means using metrics where the people being measured have control over the tasks at hand and can actually impact the outcome in the right direction. There are several flavours of OEE available (Please note: different industries may refer to OEE by another name, each should still address the same data), and each is intended for a specific audience:

OEEc (Capital Time) This calculates OEE using capital time, which is all time available 24/7. This form of OEE calculation would be particularly relevant to a business owner who wants to measure the effectiveness of their plant and equipment against at time scale of 24/7.

OEEs (Shift Time) This calculates OEE only using the time your machinery is crewed by operators, measured as the duration from shift start to shift end. This would be relevant to a plant manager, who is responsible for efficiency during staffed hours, but is not accountable for weekend and other times when the production facility is not in operation.

OEEo (Open Time)

This calculates OEE by planned production time. This calculation takes into account all the time the machinery is planned to be in operation, including the time the machinery will spend in setup time. This is a useful metric for Shift Supervisors as it provides them with visibility on the efficiency of their setup times, as well as an overview of their overall shift performance.

OEEp (Production Time) This calculates OEE by production time. This means the calculation only takes into account the time when the machinery could actually be producing, excluding things such as setup and planned downtime. This would be more relevant to operators who are responsible for maximising runtime, but should not be held accountable for the high number of setups that scheduling require them to complete.

Incorporating all three pillars of OEE will ensure that you’re offering the right metrics to the right people at the right time. If you’d like to learn more about the different metrics that can be used to calculate OEE, and how to measure these, click here to access the OFS OEE white paper.

Stay tuned for the next piece in this series which will delve further into each OEE metric and discuss how organisations can interpret their OEE metrics to affect change.


To learn more about how OFS can increase the trust and accuracy of your data collection, click here to contact our sales team for a free demonstration of our software.


The Author

Shoni Even-Chaim is the Founder of Operations Feedback Systems. Mr Even-Chaim is driven by his vision of a world that produces the things it needs in the most efficient and most sustainable manner, and by his firm conviction that the operators who make the products always know a smarter way. He started Operations Feedback Systems with one goal in mind: to develop innovative technology that uncovers these ideas and empowers the operators who have them, resulting in products that are better made, with less effort and less waste.