Why is OEE Important to Can Manufacturing?
OEE, or Overall Equipment Effectiveness, is critical in the can manufacturing process because the speed at which cans are produced makes every second of uptime important. OEE helps verify the effectiveness of the changes being made to optimize production.
OEE, the gold standard for measuring productivity, is the percentage of manufacturing time that is truly productive. This number can grade a machine, line, product, shift, plant, or company. OEE helps identify where problems are located, so action can be taken to improve production. It does not identify the exact problem.
In general, OEE is a simple metric that alone can be used as a benchmarking tool for comparing similar machines and lines. For it to be helpful for continuous improvement, OEE needs to be supported by much more related information that is all sewn together to allow exploration properly and promote process understanding.
What is OEE?
OEE measures how well a manufacturing operation is utilized compared to its full potential during the periods when it is scheduled to run.
It identifies the percentage of manufacturing time that is truly productive.
An OEE of 100% means that only good parts are produced at the maximum speed and without interruption.
The three measurements that makeup OEE include:
- Quality of 100% means only good parts are made; there are no defects.
- Performance of 100% means it is running as fast as possible when the process is running.
- Availability of 100% means the process is always running during planned production time.
OEE Is Hard To Make Meaningful
OEE is not hard to figure; the complex part is bringing together the data to use in the OEE calculation.
How to calculate OEE is a loaded question. On the surface, it is simple:
|total run time divided by the total planned production time of the asset
|ideal time needed to produce the parts divided by the total running time of the process
|the number of usable units produced divided by the total units started
The following are some of the reasons why OEE is difficult to make meaningful and aggregate:
- Availability relies on Planned Production Time.
- Scheduling/tracking planned vs unplanned activities has proven difficult to do well. Unless the plant runs 24×7, doing this well is critical.
- Availability relies on Run Time.
- Properly accounting for and segmenting Stop Time (i.e., downtime) is critical to understand why Availability is low. It should be tracked automatically from machines and allow human clarifications.
- Performance relies on Ideal Cycle Time.
- It can vary by the product being produced. If this number is not consistently defined and updated, OEE is meaningless.
- Performance relies on Total Count.
- These numbers can be inaccurate if products can be removed without visibility within the ‘OEE system.’
- Understanding slow running states is important to understand why Performance is low.
- Tracking this ‘production velocity’ can be difficult and requires dense sensor deployments.
- Quality is based on Good Parts.
- Reject reasons codes are important for understanding why Quality is low. Individual products can have multiple reject reasons (i.e., defects) and it is important to quantify them, but Quality has to understand the number of products scrapped (spoilage). Care must be taken not to assume defects = spoilage.
- Quality can be tracked or calculated.
- Is spoilage tracked or simply calculated as the difference between infeed and discharge? How much error can be introduced with this method?
- OEE for a single machine can play by different rules than OEE for a multi-machine work cell or an entire production line.
- The production line’s OEE might be derived from a combination of data from the first and last machine. It might inherit its OEE from the line’s bottleneck. The rules might be different if the process supports rolling changeovers vs line clearances. Categorizing blocked/starved or stand-by events can be different.
- For an individual machine, stand-by states should not negatively impact Availability (it is not the machine’s issue that it cannot run). For a line, you might need to search upstream and downstream from the bottleneck to determine the root cause of its stand-by state.
Numerous questions surround the methodology used to determine OEE. While this is merely a computation, there are variables involved in determining what should be included in the calculation.
When examining OEE initiatives, the can making industry must address the following fundamental questions:
- How do we handle interruptions?
- How to handle slow startup or running slowly?
- How is Ideal Cycle Time calculated? Is it calculated per product?
- Do you figure the following in planned production time: lunch break, planned maintenance, unavailable parts, human error, end of shift, or acts of God.
- What is the number of scrap? Do defects always equal scrap?
- How do you calculate good parts? Does the system have visibility into all removed products from the line?
- How many parts can I make in an ideal situation?
- What is the total running time?
- How are blocked/starved or stand-by events handled?
- How are reject reason codes used?
- Is OEE calculated differently for different machines? Different lines?
- Is Quality tracked or calculated?
- How to handle manual stops that require consistent operator feedback?
- What approach should be used when figuring planned production time (e.g., Scheduled approach, Machine status approach, or Hybrid?
- How to count cans on wide conveyors? Are special sensors required?
We want to get OEE right for the Can Making industry!
OEE is calculated with different types of data between industries, software products, companies, and even within companies. How do you calculate OEE? How do you think it should be calculated? We want to know!
We are creating an OEE standard for the Can Making industry after hearing from people in the industry. We would like to hear:
- What is your OEE calculation?
- How do you use your OEE numbers?
- What works about your OEE?
- What challenges do you face with your OEE?
- Would you like to be part of our OEE Committee and attend a panel discussion?
Have questions on OEE? We are here to help. Contact us at email@example.com.