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Is It Possible to Reach 100% on Overall Equipment Effectiveness? (OEE)

Overall Equipment Effectiveness
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In order to answer this question, we must first understand two concepts – Overall Equipment Effectiveness and the “six main losses” within the OEE concept. 

Without having a deep understanding of these concepts, we cannot hope to determine the key performance indicators within our facilities accurately. And without determining our KPIs, we cannot run an efficient operation.

With that said, we are going to explain what OEE is, why it is the primary KPI for any facility, and why understanding it can help us form maintenance strategies that will give us that elusive 100% on the efficiency scale. 

What Is OEE?

Overall Equipment Effectiveness is the main concept within lean manufacturing and the main key performance indicator of a facility’s production line. 

Basically, OEE indicates how effective our production lines are at churning out products, from resource allocation to handling and shipping. It helps us understand and realize the true potential of our factories and lets us capitalize on that potential.

By calculating our OEE, we can pinpoint where bottlenecks occur in our production lines exactly, and identify the root causes behind these bottlenecks, ultimately allowing us to resolve them to achieve maximum efficiency. 

Key Concepts within OEE

In order to calculate our facility’s OEE, we first must understand the three key concepts of OEE: Availability, Performance, and Quality. These three concepts are used to determine the “six main losses” that occur in every factory and help us overcome them.

Availability

Availability within Overall Equipment Effectiveness is the indicator of how much time our production time spends actually running. Two of the six main losses described here are unplanned and planned stops.

Unplanned stops, as their name suggests, are the stoppages in production caused by unplanned downtime. Basically, if the machine breaks, we have to stop and fix it, or we can’t continue making our products.

Needless to say, this is the worst type of downtime, as it causes massive backlash throughout the production line, causing a potential loss of revenue amounting to $532.000 per every hour the production line is down. 

These are massive losses, and any factory worth its salt will work hard to prevent such losses.

This leads us to the second main loss factor – planned downtime. Planned downtime is much more manageable overall and causes much less damage in the long run. 

However, it’s not considered a “main loss” for no reason. Frequent maintenance may indicate that our machines aren’t up to snuff anymore and that maintaining them for extended periods may not be worth the effort. 

This is where modern technology comes into play. Nowadays, with the advent of IIoT, many businesses are turning to prescriptive maintenance rather than predictive or reactive. 

The main advantage of prescriptive maintenance lies within IIoT. IIoT (Industrial Internet of Things) is a concept that describes a facility as a singular operating system, where all the machines “talk” to each other and their operators through a multitude of sensors, all tied into a single network.

As such, prescriptive maintenance not only provides the advantages of predictive maintenance but is also able to address the core issues by analyzing past data and making accurate predictions for the future. This type of maintenance ensures maximum efficiency out of our facilities, bringing us that much closer to a positive answer to the question we posed in the headline. 

Performance

Moving on to the second concept within OEE, Performance indicates how close our equipment comes to the manufacturer-determined MDR (maximum demonstrated rate). Essentially, it’s a KPI of how close our assets are to their maximum potential. 

The big losses tied to Performance are micro stops and slow cycles. Micro stops are usually frequent, short stoppages (usually a minute or less) that operators generally handle on the spot. 

Micro stops can appear in a variety of flavors – misfeeds, poor lubrication, poor environmental conditions, old machinery, or simply the need to clean out the machine or the working surface.

These micro stops aren’t terribly damaging to efficiency on their own, but if they persist, they can mount up quickly and be much more damaging in the long run. 

Slow cycles indicate times when our assets are performing below the MDR. Again, these can be caused by poor maintenance, outdated and worn equipment, or human error. 

In the end, all these performance issues can be solved with proper maintenance at a global level. Fixing micro stops at a base level is simply removing the symptoms without treating the cause. Slow cycles are even worse, as they’re an indicator that maintaining a certain piece of equipment is no longer feasible and that dumping money and time into it will hurt us in the long run. 

Quality

Finally, we must address the concept of Quality. Quality indicates the actual percentage of products produced that meet customer specifications. Problems associated with quality are product rejects, and start-up rejects. 

Product rejects are products made during the normal operation of the production line that has an issue with conforming to customer standards. Start-up rejects are faulty products made at the initial phase of production.

Both of these issues can easily be solved by implementing IIoT and prescriptive maintenance. As both types of rejects indicate a fundamental problem within the production line, the necessity of identifying and rectifying the core problem becomes even more urgent.

Conclusion

Thus, prescriptive maintenance enables us to not only identify core problems but address them before they can cause major harm. So, can it really allow us to reach that mythical 100% OEE?

Well, no, at least not yet. However, through the implementation of IIoT, we are coming one step closer. Machines wear down, and human error is a major factor in unplanned downtimes. But, through IIoT, we’re able to slowly and systematically eliminate errors until, who knows, somewhere down the line, perhaps within the next decade, we can finally achieve maximum efficiency. 

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