Using OEE Data to Improve Manufacturing Operations

Summary

As manufacturers continue to tackle the challenges of the modern operating environment, more of them are turning to data and technology to solve current problems and plan for the future.

A shortage of labor, the ongoing supply constraints, and high demand for products are pushing manufacturers to rethink how they run, monitor, and optimize their production environments. A place where seat-of-the-pants decisions and guesswork don’t cut it, modern manufacturing requires a data-centric approach that helps companies identify problems, leverage opportunities, and pivot accordingly.

Checking all these boxes in the fast-paced manufacturing environment isn’t easy, but technology has advanced to the point where it can help companies improve operations without having to add more labor, equipment, and production lines. Under constant pressure to do more with less—and with less downtime—companies are using data analytics to get more productivity out of their existing facilities in a safe and predictable manner.

Of course, continuous manufacturing process improvement requires access to all data, not all of which is easy to capture, aggregate, or make actionable. Without this data, process inefficiencies, unexpected machine downtimes, and production losses emerge. By monitoring overall equipment effectiveness (OEE) and production lines, companies get all the information they need to be able to readily identify bottlenecks and points of failure, and to ensure process optimization across all production lines.

What Is OEE?

OEE is a means to measure manufacturing productivity. It helps to identify the manufacturing time that is truly productive and gives the information a manufacturer needs to be able to readily identify bottlenecks, points of failure, and areas of opportunity. With these data points at their fingertips, companies can optimize processes to meet the demands of today’s operational environment.

A common metric used by manufacturers, OEE scales from zero to a 100% but is more than just one single number. Three different factors go into it: performance, availability, and quality. Together, these factors make up a manufacturer’s OEE value. Leaders in their fields tend to run in the 85% range, while companies that are playing catchup usually have OEEs that range from 30% to 60%.

At its simplest, OEE measures manufacturing productivity and tells companies just how effectively they are (or aren’t) utilizing their equipment. For organizations that invest much of their capital in equipment—and then don’t use those assets to produce revenues or experience a lot of downtime on that equipment—low OEEs are fairly common.

Keep the machines running

When equipment goes down on a production line, everything from inventory levels to supply chain management to customer service can be negatively impacted. Employees are left idle while the machines are fixed, throughput can come to a halt, and performance targets are missed. By Deloitte’s estimates, manufacturing downtime costs companies about $50 billion annually, while poor maintenance strategies may hinder production capability by anywhere from 5% to 20% for a single plant.

Learning the cause of the downtime is the first step in reducing these numbers and keeping the machines running, and it starts with knowing the OEE of those assets. In most cases, the root of the problem is an electrical issue, a mechanical malfunction, or operator error. If it’s an electrical issue created by an excessive number of loose wires in the machinery, for instance, then better training of electrical department employees might be in order.

With accurate insights into why the machines are failing (AKA fault metrics), companies can readily address the issues and minimize overall downtime. The problem is that not all companies have visibility into those fault metrics. Even for those that do, their OEE insight might be focused solely on the number, without really understanding why it continues to fall or what to do about it. Not much can be done with the OEE number without awareness of the areas that are in need of improvement.


Equipped with the right data collection and assessment tools, however, the same company gains high levels of visibility over performance problems, bottlenecks, throughput constraints, and other challenges. Using the data, manufacturers can effectively tackle these issues and increase their productivity and uptime. This, in turn, will result in a higher OEE number.

Using data to optimize productivity

A leading producer of sensors and sensor solutions for industrial automation applications, SICK makes sensors that are used on the shop floor to collect and analyze the data that manufacturers need to be able to run their operations at optimal levels. Using key data points like running status, product count, and quality counts, SICK’s solutions encapsulate the insights in a software analytics platform that provides OEE calculations.

Manufacturers use the platform to see how well their lines are performing. With a few sensors and communication enabled by OEE analytics, manufacturers gain access to real-time and historical insights into meaningful KPIs that they can use to stabilize and/or optimize productivity.

SICK offers both a standard Package Analytics Platform and a Rapid Deployment Kit (RDK). Using either of these options, manufacturers can start with just one machine and then scale up to an entire production, packaging, or other line.

Companies can start small and zoom into the area of initial concern, knowing that the solution can then be expanded to other areas of the facility. By beginning at one end of the plant and making their way across the facility, companies can improve operations, performance, and quality as they go.

The OEE Rapid Deployment Kits combine software and hardware to help companies quickly begin using production data to drive better business decisions. The solution’s OEE analytics and product lifecycle management (PLM) tools have been preconfigured and enable fast, easy commissioning and installation. They help companies drive improvement by providing a better understanding of production losses and reduce seemingly complex production problems to simple, accessible information that manufacturers can use to improve efficiency and lower operating expenses.

Key metrics that the solution monitors include availability, shift duration, elapsed shift time, machine/system uptime, performance, expected and predicted outputs, actual outputs, and quality output. Using real-time monitoring of these and other critical OEE metrics, companies gain a better understanding of overall system behavior and trends, identify target areas for improvement, and make better, datadriven business decisions.

Equipped with these valuable insights, companies can reallocate labor to other value-added tasks, give their customer service and sales teams more accurate information, define roles for who can access the data, increase their sales revenues, and gain an edge on their competitors. They can also take a more proactive approach to machine maintenance and stability, which in turn supports higher equipment uptime.

Manufacturers are under pressure to produce more while also keeping systems running until they fail, but this is a flawed approach. One line that goes down can throw off a whole production schedule, which then pushes everyone into disaster recovery or reactive maintenance mode.

Don’t just go with your gut feeling

With an analytics-based OEE solution in place, manufacturers know how well they’re performing on all three metrics—availability, performance, and quality—and have an overarching view of how well they’re doing.

Based on that data, companies get a grade rating as they make improvements like mechanical adjustments or operational changes to standard operating procedures (SOPs). Then, they can use their OEE numbers to assess what is or isn’t working and make further adjustments as needed.

This data-based approach is much more effective than the “gut feeling” strategies that many manufacturers rely on to keep their lines up and running. Rather than just guessing at what might work, they can use the data to test out various continuous improvement initiatives on their lines and immediately see how those shifts impact performance.

Large food manufacturer runs smarter with OEE

When one high-volume producer and distributor of various local and authentic pre-packaged gourmet foods across 37 countries noticed discrepancies in the volume between shifts, it realized that it needed a quick way to identify production metrics and bottlenecks. Historically, it conducted manual process counts of the production lines between shifts—an approach that created numerous operational inefficiencies.

For example, manual entry errors in production quantities led to miscounts and inaccurate paperwork. These, in turn, decreased actual production number accuracy. The manufacturer also couldn’t properly monitor inventory and production or predict production numbers with a high degree of accuracy. This resulted in over- and under- producing efforts, both of which impacted its profitability.

Combined, these inefficiencies were affecting the company’s daily production and impeding its ability to meet growing customer demand. Working with SICK, the manufacturer began implementing OEE concepts. It upgraded its production line and implemented a data analytics solution, the latter of which would enable better access to data and insights into how to best reduce risks and challenges.

Using SICK’s Rapid Deployment Kit, the food manufacturer gained a better understanding of production losses. The encoder ensures that the machine is running and sends a signal to the SIG200 to confirm this. Then, the product is counted using presence sensors as it runs down multiple production lines simultaneously. Finally, the machine captures product data plus any missing pieces or flaws in the production line and sends that information up to the SIG200 for review.

Once the goods pass quality inspection, the finished products are stacked for processing and shipping. With this solution in place, the manufacturer now has a baseline and can track real-time and historical OEE metrics over time, leading to better overall machine availability and a higher quality product to offer its customers.

Manufacturers do more with less

A key metric for measuring manufacturing productivity, OEE helps companies identify losses, measure progress, eliminate waste, and improve manufacturing equipment productivity. And while the OEE metric itself has been around and in use for some time now, for the most part companies have been tracking it on paper and Excel spreadsheets. Both approaches are prone to data entry errors in an environment where three-second “micro stops” that repeat themselves may significantly impede overall performance.

Using sensors, data, analytics, and a user-friendly dashboard, SICK makes it easy for manufacturers to measure OEE, address issues, leverage opportunities, and implement a culture of continuous improvement. These are key wins in a business environment where all companies are under pressure to maximize throughput and do more with less, and shutting down a line to install new software or hardware on it isn’t a workable option.

The RDK can be deployed even while the line is running, which is important for manufacturers that don’t have the luxury of shutting down their operations. With RDK, the system is up and running, and gathering the data quickly. This immediately shows exactly why those micro stops or other issues are occurring so manufacturers can take the steps necessary to improve OEE across all three metrics—availability, performance, and quality

<sub>Images courtesy of SICK

This feature comes from the ebook AUTOMATION 2023 Volume 3: IIoT & Industry 4.0.</sub>