The old-school way of looking at OEE used to be a simple calculation: divide the number of minutes that production lines are down by the total time they run in an eight-hour work day. This worked fine for many manufacturing companies, but it was far from perfect. It didn’t take into account downtime caused by non-production issues like quality problems or equipment malfunctions. It also excluded labor costs from the equation — and labor is a big expense for most manufacturers!
Nowadays, OEE software is much more advanced than this outdated method. It uses real-time data collected from sensors across your entire factory floor to give you an accurate picture of what’s happening with production and where possible improvements could be made. From there, it provides recommendations based on historical data, so you know exactly what will work best before implementing them yourself (or hiring someone else). Let’s dive into some examples.
What is OEE?
OEE is a metric that measures the efficiency of a manufacturing process. It was first developed by Dr. Max Shewhart in the 1920s, and it’s used to calculate the key performance indicators (KPIs) of a manufacturing process.
OEE is calculated by taking into account three main factors: availability, performance quality, and yield rate. Availability refers to whether or not machines are working as they should be at any given moment; if they’re down for maintenance or due to issues like downtime caused by human error, this number will decrease accordingly.
Performance quality refers not only to how well a machine performs its function, but also how consistently it does so–if there are fluctuations in production output quality over time (say because one worker isn’t paying attention), then this could affect your overall OEE score as well.
Finally, yield rate represents how much usable material comes out of each unit processed through an assembly line or similar setup–if there’s too much scrap material generated during this process (for example), then again this could negatively impact your OEE score since less usable product means less productivity per hour worked than expected.
Why do you need OEE software?
You need OEE software to improve efficiency, quality and visibility in your manufacturing process. The benefits of using an OEE solution include:
- Improved efficiency: By measuring the time it takes for each part of a process to be completed (from start to finish), you can identify bottlenecks that are holding back production. For example, if it takes too long for materials or equipment to arrive at a station or there are too many steps involved in completing an order, you’ll know exactly where your issues lie.
- Improved quality: Measuring cycle time gives you insight into how well each step is performing; if one step consistently takes longer than others do, then this could indicate a problem with that particular process step’s efficiency level. With this knowledge at hand, your team will be able to identify ways in which they can improve their performance levels so as not only increase overall productivity but also ensure customer satisfaction levels remain high across all departments within an organization’s operations chain – something which will ultimately lead towards higher returns on investment too!
How does analytics improve OEE?
The key to improving OEE is a data-driven approach. Analytics can help you identify and fix bottlenecks, determine the root cause of problems, optimize your processes, and more.
Analytics also allows you to make smarter decisions about equipment maintenance and upkeep by providing insights into how much downtime an asset has experienced in the past or what its current uptime percentage is compared to other assets in your manufacturing facility.
By combining all this information with real-time production data from machines and sensors throughout your plant floor–and then analyzing it using machine learning algorithms–you’ll have all the information necessary for making smart decisions about equipment maintenance and upkeep that will keep your lines running smoothly (and profitably).
Advanced analytics in OEE software
Advanced analytics is the use of machine learning and AI to make sense of data. Advanced analytics can be used to predict future events, such as when a machine will break down or need maintenance. This allows manufacturers to better plan for equipment maintenance and minimize downtime costs. For example, if software predicts that a piece of equipment is about to fail, you can schedule maintenance before it happens so that your team isn’t scrambling at the last minute trying to figure out how they’re going to fix something without proper warning or preparation time
. However, advanced analytics is more than just predicting when equipment will fail. It can also be used to predict the outcome of various scenarios and suggest the best course of action. For example, if you’re trying to determine whether it’s better for your company to buy new equipment or repair an older model, advanced analytics can help you make that decision by comparing the costs and benefits of both options
AI and machine learning in OEE software
Machine learning is the ability of a computer to learn from data, make predictions based on past performance, and improve its output over time. Machine learning algorithms can be used to automate processes and optimize OEE reporting–for example, by analyzing a company’s historical data about individual machines or production lines in order to predict future maintenance needs for those assets.
In the same vein, the fully automated pre-roll infusion machine from Sorting Robotics exemplifies how automation can transform the industry by operating non-stop, making significantly fewer mistakes, and being far more efficient than human workers, leading to enhanced productivity and consistent quality in production.
AI refers specifically to systems that mimic human intelligence (or at least try). While it’s possible for AI systems to use machine learning algorithms on their own without any human input or supervision (this would fall under “narrow AI”), most AIs need some level of human interaction before they’re able to operate independently–and even then there will always be questions about how much autonomy we should give such creations before they become self-directed entities unto themselves!
Data-driven manufacturing with OEE software
Data-driven manufacturing is a way to make better decisions based on data. OEE software by LineView can help you make better decisions by using data from your production line.
For example, if an operator is experiencing downtime because of equipment failure or maintenance issues, OEE software can show whether the problem will affect other operators on the line as well as how long it will take for those problems to be resolved. This helps you plan ahead so that if one station goes down during peak production hours, there are other stations available with operators who have less downtime in their schedules.
Modern OEE software is capable of some pretty amazing things
OEE software is capable of some pretty amazing things. It can help you identify problems before they happen, optimize your processes, and save money. In the end, OEE software will make you a better manufacturer by improving the quality of your products and services.
If you’re not already using OEE software in your manufacturing facility, it’s time to start thinking about how this technology might benefit your business in the future.
OEE software can help you manage your business more effectively and increase profitability. The software is designed to track how much time is spent on a task, how many people are involved in that task, and whether those tasks are completed successfully. By using OEE software, manufacturers can identify problems quickly and make changes before they become major issues.
Conclusion
I hope this post has given you a better understanding of what OEE software can do. The truth is that it’s still very early in the game and there are many more innovations to come, but one thing is certain: OEE software will continue to evolve as manufacturing becomes increasingly automated, allowing us to make smarter decisions about how we operate our factories and warehouses.