Top 5 Benefits of Improving OEE - Stefanini

Top 5 Benefits Of Improving OEE

OEE is an important metric that contributes to manufacturing growth. What exactly is OEE and what kinds of strategies can you use to improve it? Read our Trends blog for the answer!

As manufacturers seek to quantify and improve plant, manufacturing line, and machine-level performance, manufacturers turn to overall equipment effectiveness (OEE) for the answer. OEE is made up of lean manufacturing and total productive maintenance (TPM), which are contributing to its growth today. Manufacturers can produce the highest quality products at the lowest cost within the challenging constraints of short lead times by collecting valuable data at the machine, assembly line and plant level. OEE and comparable manufacturing metrics are also fueling the development of business intelligence software and advanced analytics, which includes the next generation of manufacturing intelligence applications.

While OEE brings a plethora of benefits to the manufacturing process, it is important to take a step back and set realistic expectations as to what this metric can and can’t do. OEE can cause companies to be less customer-centric and lean, and also slow down production. It can also mask bigger and potentially more challenging manufacturing problems if not used in the right situation. Specifically, relying too much on OEE can hide manufacturing performance gaps at the machine, production line, and plant or factory level.

So, what exactly is OEE and what kinds of strategies can you use to improve it? Read on for the answer!

What is OEE?

According to Wikipedia, overall equipment effectiveness is a measure of how well a manufacturing operation is utilized, including time, material, and facilities, when compared to its full potential during the periods when it is scheduled to run. An important component of the manufacturing process, OEE identifies the percentage of manufacturing time that is truly productive. For instance, an OEE of 100 percent means that only the good parts are produced (100 percent quality) without interruption (100 percent availability) at a quick speed (100 percent performance).

Important insights can be gained on how to systematically improve the manufacturing process by measuring OEE and the underlying losses. Specifically, this manufacturing best practice is an effective metric for benchmarking progress, identifying losses, and improving the productivity of manufacturing equipment like eliminating waste.

How does Stefanini support smart manufacturing? Learn more about our solutions here!

Benefits of OEE

According to Plastics Today, improving OEE performance can boost production quality and efficiency while contributing to increased revenues and profits. Check out the following benefits of OEE:

1.      OEE is one of the best manufacturing metrics for stabilizing the production levels of a given asset or machine. After pilot tests with specific machines are complete, consider scaling OEE across an entire production line. Comparing lines using OEE provides an accurate baseline of machines and their ability to improve.

2.      Include OEE measurements by machine in quality management reports and dashboards to gain insights into how machine yields, scrap rates and reject rates are impacting cost of quality (CoQ), and acceptance rates. OEE will contribute by stabilizing production levels by machine or asset across the shop and plant floors. It’s possible to quantify manufacturing reliability by combining OEE with other metrics. Transition from using OEE as a metric to stabilize manufacturing machinery to quantifying plant-level manufacturing reliability.

3.      The most effective OEE strategies are created to support long-term organizational or strategic goals and are focused on improving process quality and line performance. In the short term, OEE is a great stabilizing metric. Long-term goals, however, need to measure the reliability of the entire manufacturing process by tying performance back to business outcomes and return on OEE investments. The best manufacturers are using real-time monitoring to track OEE to the machine level while gaining new insights into manufacturing’s contribution to improved productivity and quality, cost reductions, and increased capacity.

4.      Quality is forced to improve once OEE is scaled to improve production-line performance. In fact, plant force processes improve when a production operation transitions from using OEE as a manufacturing stability metric to predicting manufacturing performance. This improvement is driven by the greater insights gained into how each machine’s performance on a production line can be improved with more scalable, less complex processes across the production floor.

5.      OEE performance gains are fueled by tying line performance and process quality gains to business outcomes. Tie back improvements to business gains and outcomes in order to accelerate OEE performance. Cost and revenue goals are achieved quickly when a solid connection is made between improving OEE performance and seeing improved business outcomes. Knowing how each incremental gain by machine, line, and plant reduces costs and improves revenues is key to improving OEE performance payoff.

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Successfully Integrating OEE into Operations

Manufacturers stay strong by maintaining revenue growth and controlling costs. The essence of OEE is knowing how effective each machine is in meeting production goals by tracking performance, availability, and quality.

Plastics Today outlines several lessons manufacturers have learned by integrating OEE into their daily operations:

  • Use OEE to reduce downtime losses: stabilize all machines on the shop floor during the pilot phase. OEE makes an immediate impact on manufacturing performance by tracking and analyzing, in real-time, if a certain machine on the shop floor is about to break, factoring in equipment setup times and both unplanned and planned downtimes. Examine how an initial OEE pilot can capture and provide data regarding production machines that need immediate attention to reduce and eliminate downtime losses.
  • Aim to create trusted, scalable datasets: these datasets should accurately reflect 100 percent of all states of operation and machine activity. However, work to ensure that your OEE measurements do not become too biased or politicized as OEE has started to be included in quality, manufacturing, and production teams’ compensation and bonus plans. While it’s important to achieve high OEE scores, it is far more crucial to have a trustworthy, credible process for arriving at OEE that scales across the company. To ensure the data produced is trustworthy and accurate, consider de-linking OEE from salary increases and bonuses.
  •  When comparing aggregate OEE metrics between production lines, count the individual machine first-pass yields, run times, and scrap rates. More and more manufacturers are using OEE to compare production line performance, with many using aggregate product line metrics at the top of their dashboards and scorecards, and also have drill-down metrics to the machine level all on one screen. Enabling quality management and production teams to drill down into OEE calculations and seeing the mix of efficiency, availability, and quality metrics is invaluable. Drill-down data helps to troubleshoot individual problems quickly that may be hidden behind a single aggregate OEE metric. Accuracy is not assured by comparing machines with two identical OEEs. One could have 90% x 70% x 80% and the second could have 70% x 90% x 80%. Both have the same OEE, yet one machine is not as efficient (70%) while the second has limited availability (70%). When comparing production lines and entire plants, the same logic holds.
  • Factor out equipment setup times from OEE measurements. Reducing overall equipment setup times has an immediate impact on availability, further artificially inflating OEE performance. It’s a good idea to also factor equipment setup times into any process re-engineering projects across the shop floor since these often skew value stream-based production scenario calculations. Initiating a time series analysis of setup times along with OEE measurements provides insights into how availability can be improved quickly.
  • Consolidate OEE measurements on a single dashboard, enabling real-time monitoring to accelerate production line, machine, and plant performance gains. Each machine operates at a unique rhythm with variations in the key components of performance, availability, and quality. With real-time data from every machine in a production line, heads-up displays are being used across production floors today.

The first factor that drives the majority of manufacturers to adopt OEE is stabilizing machinery performance. As production lines and individual machines stabilize, OEE reflects manufacturing reliability.

While OEE improves many areas of manufacturing performance, it is not meant to be used as a single, end-all metric of manufacturing performance. Instead, group OEE into a dashboard of metrics that expand visibility into performance, availability, and quality at a deeper dimension than the structure of the OEE metrics allows on its own. When taken in the context of overall manufacturing performance, OEE has the potential to revolutionize production operations.

At the end of the day, OEE delivers insights at the production line, machine, and plant level that have not previously been available to many manufacturers. Knowing how performance, quality, and availability impact the most financially important areas of their business can transform how manufacturers meet and exceed customer-driven goals for their business.

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Smart Manufacturing with Stefanini

Smart factories today require more than robots and automation. Disruptive technologies like IoT transformed manufacturing into Industry 4.0. Now smart manufactures must capitalize on their IoT data for process insight and prescriptive analytics. At Stefanini, we’re on the forefront of these digital tools to ensure our clients are leveraging them for productivity and profitability.

Our industrial IT services include MES, PIMS, LIMS, and customized or off-the-shelf software. We also implement design thinking, innovation frameworks, and autonomous decision-making for digital transformation. Chat with an expert today to learn more!

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