How to improve production with overall equipment efficiency

 

OEE insights

• Overall equipment efficiency can help streamline and improve data analysis, processes and productivity across teams.
• Three factors comprise OEE calculations: availability, performance and quality.
• OEE data can be converted into valuable visualizations.

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Maintaining high levels of productivity is essential in modern manufacturing with domestic and global businesses competing to meet rising commercial demands.

The five-year annual compound growth rate of multifactor productivity in the U.S. manufacturing sector sits at 0.7%, with labor productivity in the manufacturing sector rising by 1.3% in recent months.

To maintain a competitive advantage, navigate recent supply chain disruptions and ensure job security for the almost 15 million people working in the U.S. manufacturing industry, many business leaders are becoming increasingly reliant on smart data analytics.

Modern manufacturers can streamline key operations by collecting, organizing and visualizing high-quality data associated with various production processes. From the optimal planning of predictive maintenance to enhancements in machine efficiencies, overall equipment efficiency (OEE) is important as a continuous form of data analytics leveraged to improve efficiency, safety and productivity outcomes in modern manufacturing.

What is OEE?

OEE is a metric used to measure the performance of manufacturing equipment and processes. OEE scores help manufacturers to identify the percentage of time their facilities can be considered productive.

Manufacturers aim to achieve an OEE score of or near 100%. This means that, accounting for factors like availability, performance and quality, observed equipment is producing goods to a high standard, quickly, with little downtime.

To best understand OEE, it’s important to define the three factors involved in OEE calculations. These include:

1. Availability: The percentage of scheduled production time in which observed tools and equipment can be considered productive. Availability values can be negatively impacted by events such as machine faults, maintenance and calibration procedures.
2. Performance: How quickly observed equipment can produce relevant goods in relation to the system’s ideal cycle time, an additional value that defines the minimum amount of time a machine should take to produce a single unit of its primary output.
3. Quality: The percentage of high-quality goods produced by a machine compared to its total output, this value helps to ensure increases in efficiency don’t reduce quality.

Multiplying availability by performance by quality returns an OEE score of between 0-100.

Using these foundational aspects of OEE, manufacturers can perform unique calculations to measure the efficiency of select tools while maintaining high standards of production quality (see Figure 1). To perform effective OEE calculations, stakeholders must understand the following formulas.

• Machine availability. To accurately calculate machine availability, stakeholders will measure the amount of time that a machine is running, then compare that value to its scheduled production time.

Availability (%) = actual working time ÷ scheduled working time

The resulting figure represents the percentage of time that a machine is in active operation.

• Production performance. Calculating and analyzing production performance data helps teams determine how efficient active machines are by comparing actual production figures to a maximum potential output.

Performance (%) = number of units produced ÷ number of units that could be produced under optimal conditions

This value reflects the production efficiency of specific pieces of manufacturing equipment.

• Product quality. Adjustments made to enhance the above two metrics can negatively impact the quality of produced goods. These impacts can be understood by measuring product quality over time.

Quality (%) = number of produced goods that meet quality standards ÷ total number of goods produced

Referencing this value helps to ensure wider improvements don’t negatively impact quality.

The benefits of measuring OEE

By analyzing how OEE scores are calculated, some of the benefits of observing OEE metrics become clear. Teams can make informed decisions about maintenance, scheduling, staffing and scaling processes, backed by easily digestible data insights, to ensure manufacturing plants make the most effective and efficient use of available resources.

Below are some more specific examples of how measuring OEE can benefit manufacturers.

Less unplanned downtime: Initial calculations of metrics like availability and performance can help managers to highlight factors that cause bottlenecks and disruptions to production efforts. Measuring these metrics over time provides teams with high-quality data used to visualize typical performance values, helping leaders define optimal times for planned downtime to reduce unexpected stoppages.

Higher product quality: As OEE scores factor product quality into total performance calculations, efforts to measure and continuously improve OEE typically help manufacturers produce higher quality products more consistently. Over time, this combats issues with defective products that could lead to complaints and costly returns, boosting customer satisfaction and profitability.

Optimized production speed: By ensuring management teams have access to accurate and easily digestible information detailing the performance and availability of specific machines, organizational plans can be adjusted to reasonably optimize total production speeds. Rather than ramping up operations based solely on demand, long-term plans can be formed around actual machine efficiencies.

Minimized resource waste: OEE calculations help managers to identify areas of waste in manufacturing processes, both in terms of material waste and wasted time. Improvements made in reference to OEE scores can reduce the number of defective products a plant produces, cutting down on raw material waste, while data-backed downtime planning can help staff make optimal use of limited time.

Improved accountability: Measuring OEE can help to improve accountability across manufacturing operations. If all managers and employees know production processes are being accurately monitored and reviewed, they’ll be less likely to cut corners or deviate from established best practices, increasing the likelihood of staff members reporting issues that could impact OEE.

Advanced OEE techniques and best practices

While OEE scores are a good indicator of a manufacturing facility’s general health, this data alone isn’t always comprehensive. A site might achieve an OEE score of close to 100 for a short time due to factors that basic OEE calculations don’t account for, providing a snapshot of the facility at optimal health that may later prove to be incorrect as more data is analyzed.

To make the most effective use of OEE calculations, provisions must be made to review and analyze scores over time. OEE scores should be combined with wider analytical datasets to help managers identify trends in production efficiencies that best represent actual conditions. The following OEE techniques and best practices can assist manufacturers in achieving this.

Developing an organizational structure to guide the implementation of OEE practices should be the first step in their implementation. Site operators should appoint a trusted staff member to fulfill the role of OEE leader. This person will oversee the implementation of the site’s OEE practices and the communication of OEE findings, as well as delegate roles to workers.

To ensure high-quality and relevant OEE data is collected, workers with experience operating specific machines should be tasked with collecting data associated with their use. Developing an organizational structure like this improves accountability across OEE practices and reduces the likelihood of poor-quality data going undetected during collection.

Convert OEE data into visualizations

It can be hard to gain actionable insights from complex data in raw formats, especially when multiple variables are being analyzed together. To make the most effective use of OEE data, and to help managers communicate insights to wider stakeholders, efforts must be made to convert findings into concise, scannable visualizations like graphs, charts and plots.

Modern software solutions can streamline and automate this process, with specialized OEE dashboards customized to suit unique needs. OEE leaders can create real-time displays, accessible from multiple devices, programmed to convert collected OEE data into visualizations that best represent real-time figures and emerging OEE trends.

Leverage root cause analysis

OEE helps manufacturers identify losses of efficiency on a macro scale, often referred to as the six big losses. These variables can be further divided across the above-mentioned three core aspects of OEE, availability, performance and quality.

While OEE calculations can identify these losses, they don’t typically offer many details as to why they occur. For manufacturers to improve OEE, wider root cause analysis is needed.

Root cause analysis can be seen as an extension of OEE, helping staff address the specific issues that contribute to inefficiencies. Here, teams will look deeper into a specific loss to define the event, find the root cause, develop a solution and implement corrective measures.

Implement continuous improvements: One of the great benefits of OEE implementation is the ability for manufacturers to observe trends and gradual changes in production processes over time, helping teams highlight core issues that may otherwise have been overlooked. To get the most out of OEE, efforts should be made to regularly review and discuss findings with stakeholders across key departments.

Representatives from every department with a direct influence on the production process should attend regular meetings. During these sessions, OEE findings should be discussed in detail, with data visualizations and insights used to support recommendations that can be documented, presented to decision-makers and implemented as improvements.

Invest in skill development and training: Ensuring proposed improvements to production processes are implemented effectively will only be possible if investments are made into skill development and training. OEE practices can be invaluable in terms of identifying exactly where inefficiencies are present, but it will be down to the workforce to ensure efficiency improvements are made and followed.

Guided by OEE insights, plans should be enacted to continuously train machine operators, shop-floor staff and department heads in essential maintenance, scheduling and production management tasks. In addition, all members of the workforce should be made aware of OEE practices and encouraged to request skill development opportunities pertinent to their roles.

The impacts of digital transformation on OEE

Just like in many other major industries, digitization continues to transform the way workers and managers in the manufacturing sector approach critical tasks. Research shows that the adoption of digital technologies like AI, data analytics software and the industrial internet of things (IIoT) may enable manufacturing companies to boost productivity by more than 26%.

When supported by intelligent digital technologies capable of automating and streamlining the collection, analysis and communication of manufacturing data, OEE practices can be measurably optimized. Below are just some of the impacts of digital transformation on OEE.

IIoT integrations for real-time data collection: IIoT sensors can be attached directly to machines and programmed to autonomously collect operational data. This provides real-time visibility into production processes and related efficiencies, enabling staff to perform accurate, live OEE calculations on a continuous basis.

Cloud-based management and OEE dashboards: IIoT developments can be further optimized via integrations with cloud-based management tools. Real-time data collected by IIoT sensors can be sent to remote management platforms where native tools can analyze data and display high-quality insights on custom dashboards.

Automated data collection and reporting tools: Digital tools deployed to collect data and generate reports help teams to generate accurate OEE insights. Manually collected data can be error prone. For example, two employees may attach a different timeframe to the same stoppage, skewing data insights on a macro scale. Automating these processes helps to improve the quality and accuracy of OEE calculations.

Integrations between OEE and enterprise resource planning solutions: Integrating tools used to collect and analyze OEE data into wider enterprise resource planning solutions helps teams deliver actionable insights to stakeholders in key departments. Real-time information and data-backed objectives can be communicated to shop-floor workers, managers and wider stakeholders almost instantly, helping teams adjust calculated to improve OEE.

Improving efficiency with OEE scores

Establishing technologies, practices and processes well-positioned to improve productivity in manufacturing is central to success in the modern market (see Figure 2). Owners and operators of facilities must find ways to accurately analyze production data to remove bottlenecks, reduce unplanned downtime and implement corrective actions to ensure systems function optimally.

OEE scores and the insights gained through their monitoring and analysis offer manufacturers valuable opportunities to continuously improve key operations and implement effective efficiency enhancements. By understanding OEE and integrating OEE calculations into daily operations, production efficiencies in the manufacturing sector can be maximized.

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