The concept of Overall Equipment Effectiveness (OEE), a key manufacturing measurement for decades, has gained new momentum as plants are going digital. Processing plant managers and researchers have touted the method, today enhanced by AI, as a solution for improving productivity and cutting costs.

As plant assets increase in cost and complexity along with digital transformation, OEE is reemerging as an important measurement for gaining a quick snapshot of the plant’s efficiency and for measuring its improvement.

Leveraging Real Time Data for Assessing Process Plant OEE

What’s Behind the Letters O.E. and E.: Making the Right Calculation

At the heart of it, OEE is the actual output divided by the theoretical maximum output, in percentage form.

How do we calculate the maximum theoretical output? If we multiply the maximum theoretical output per hour by the number of hours a plant is expected to be active (in theory 24 hours), this is the maximum theoretical output. However, if quality is not taken into consideration, then you could — in theory — achieve the ideal maximum output with extremely low quality. So the whole thing needs to be multiplied by a factor that represents the quality of the output. The widely accepted OEE calculation = Availability × Performance × Quality.

To be more specific: 100 x Actual (Availability x Performance x Quality) / Theoretical Maximum (Availability x Performance x Quality)

In the context of process industries, these metrics should be adapted as follows:​

Availability calculates if the plant or equipment was actually running, if there was downtime – whether for maintenance or due to breakdowns, and at what time such variables occurred.

Availability formula: (actual running time/time available for running) x 100

Performance calculates the “actual equipment performance” to “nameplate (or, maximum) performance,” while considering the different levels of nameplate performance among the various pieces of equipment along the processing chain.

Performance formula: (the actual hourly performance rate/average of top 6 hourly rates over time) x 100

Quality is perhaps the most complicated metric because in process manufacturing, there are often not discrete products that can be measured and tested for quality; indeed most of the ‘products’ throughout the process are intermediates. While this calculation needs to be tailored to every plant, it must include the various components that affect final quality output. The idea is to ensure that values remain within a certain metric range. In one example, researchers from MESA International, a global not-for-profit industry association determined for a mining operation that Quality = CrusherGrind x PrimaryMillGring x FloatSG x MassPull x ReagentDosages; as you can see it’s completely industry dependent.

Constant, Online Updates to Increase the Bottom Line

Precognize’s SAM GUARD software provides the information that makes the OEE calculation as accurate — and therefore as useful — as possible. It allows you to get a trackable, real-time overview of plant operations. With precise insights from SAM GUARD into availability, performance and quality of the output of a process, you can understand how well you’re meeting your production expectations. The information, moreover, allows you to predict and strategize your capabilities and needs for the future.

Beyond the standard measurements like current, speed, weight and temperature, that can be used to calculate availability and production levels SAM GUARD’s AI-powered virtual sensors identify the quality level of intermediates and outputs in real time, enabling OEE to be measured at any given moment, instead of waiting for timed lab tests.

The constant flow of accurate and updated data into SAM GUARD enables it to issue relevant alerts at the moment deviations occur; thus it can easily point to any issue in the plant. As a result, plant employees can quickly zero in on any problem that is affecting availability, performance or quality, which would in turn affect OEE. Once a problem is identified, managers can change, or event halt, a process if necessary. While halting a production line may seem counterintuitive to increasing OEE, in reality the faster the problem is identified and remediated, the more the OEE can be kept at high levels.

OEE has come a long way since it was first introduced by Japanese manufacturing industry pioneer Seiichi Nakajima in the 1960s, as a means to assess manufacturing operation effectiveness. While data was once collected manually and typically focused on a single piece of equipment, more modern OEE calculations factor in the output of various machines along the processing chain and today are optimized by solutions like Precognize’s SAM GUARD, which follow machinery and processes in real time, across the entire plant.

By identifying inefficiencies, Precognize’s OEE software can help improve all of the plant’s aspects that affect OEE. While its applicability is unique to every situation, it can help you come closer to achieving zero delays, breakdowns, and impediments to production.

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