If you think OEE has no secrets to you and that your plant maintains the highest OEE results… think again. The harsh truth is that most manufacturing plants’ OEE lands somewhere between 35 and 43%. They just don’t know about it.
OEE (Overall Equipment Effectiveness) is the undisputedly most important metric in estimating manufacturing efficiency. It’s simple, easy to understand (the percentage is self-explanatory), results allow us to compare even seemingly incomparable. That’s why it was widely adopted as the main KPI to compare the productivity of production assets like machines, work cells, lines, or even entire sites and plants. The premise of the ultimate magic formula to guide the plant to the highest efficiency enchanted engineers and managers across the industrial world. Can it really be the ultimate reliable answer to all problems with losses and wasted production potential?
In an era of “Made in Japan” domination over Western world markets, Professor Seichii Nakajima set out his goals to find a simple, yet insightful formula. An algorithm that will support companies like Toyota or Sony in optimizing production and assuring their market position for years to come. He came to the conclusion that key roles in final production market strength lie in three KPIs:
Availability – a metric to describe how reliable and well… available to produce equipment is.
Performance – the amount of products that leave the machine in the time it is available for production.
Quality – the number of produced goods good enough to be released to the market among all of the produced ones.
But how to compare the availability of a vast variety of machines like, e.g., full automatic monoblocks and manual assembly workstations? And how to compare the performance of cutting-edge CNC with a hydraulic press built in the ’60s? And what about the quality of industrial robots and PVC injection molding machines? How to evaluate and compare directly such different and relative parameters?
Percentage to the rescue! No, not the ones you were thinking of… despite not knowing for sure how the creative process of Professor Nakajima looked like. His pivotal idea was to translate the results into a percentage of each asset’s fulfilled potential. It’s true that a milling CNC can spend tens of hours on one detail while an injection molding machine will release countless thousands. However, it still does not translate to the advantage of one over the other. With percentages, it was finally possible to evaluate if the asset works at full of ITS OWN achievable production potential.
Assets also mean people…
OEE creation marked a milestone on the Lean Manufacturing and TPM development map. The KPI brought about easy-to-comprehend simplification of complex manufacturing processes results. With it, management staff became able to spot bottlenecks dragging whole plant efficiency down. It unlocked ways to improve tasks like maintenance, doubling crucial stations, or replacing weak links in the production chain. But no production plants are autonomous (yet) so OEE became indirectly applicable to personnel efficiency as well, e.g. maintenance, operators, intra-logistics… Soon companies turned their punishing eyes armed with OEE lenses on managers as well, and thus began the era of pressure on the score.
But where there is pressure, human ingenuity comes to the rescue.
There are companies or even whole industries (e.g. automotive), where each subcontractor is OBLIGED to present an OEE above a certain limit, or he may lose the contract.
There are plenty, where whole crew bonuses depend on it.
There are managers whose entire careers hinge on the reported OEE of their plant.
No wonder that in many cases, the result is known first, and then the calculation method is adjusted.
In case of all those MES production controlling companies where MES stands for Manual Excel Systems tampering with the results is the easiest, so I will omit it in this article. Companies with such digital maturity of production monitoring have bigger problems than “creative” OEE.
More interesting is how to trick the systems which main purpose is reliable data collection and trustworthy calculations?
Let’s recall how the OEE formula looks, and it will be easier to spot where „tweaking” results opportunities lays:
Calculated as a percentage of time when an asset is ready to produce within the scope of the full planned production time. And it does not matter whether the production actually took place and what the final result was.
The most common ways to tamper Availability:
Unexpected unplanned downtime? Isn’t it a great opportunity to immediately schedule a crew meeting, „planned” maintenance, or just a fire evacuation drill? With one magic trick, you’ll turn unplanned downtime into planned one, avoid Availability decline, and move losses from OEE to OOE… which is not reported (usually).
Calculated as the number of products accepted as „good enough” to the total amount of produced goods in a given time.
The most common ways to tamper Quality:
The easiest method is to control product quality without any relation to the machine/line. Let’s set the QC station close to the warehouse, or make it in a dedicated laboratory. Separate division – separate KPIs. Scrap or rework in separate reports do not affect your line’s OEE – the line itself works flawlessly.
Calculated as the amount of produced goods in comparison to the maximum volume possible to be produced in a given time (of availability).
The most common ways to tamper Performance:
The easiest way to „raise” Performance while „improving” Quality is to count reworked goods as new ones. If the system does not track serial numbers, each good that passes through the machine is counted, despite in some cases being reworked due to quality issues. So, even if the same product passed through the same machine multiple times, it will be counted as a newly produced good. Maybe there will be some discrepancies in the ready goods warehouse since they received 100 goods and apparently 120 were produced but… the plan was to produce two details per minute and that’s what the machine achieved.
All factors of the OEE formula can also be shaped by time qualification tricks. If some amount of goods is produced during planned downtime (e.g. startup after a weekend outage or retooling between orders), those goods can be used to fill loses during planned work time or replace low-quality goods. Or conversely, low-quality goods can be officially assigned as post-resetup trial production, which is usually condemned to become scrap anyway. The trick is to flexibly define production and planned downtime time/production.
Since OEE is composed of the subKPIs described above – Availability, Quality and Performance, where each one of them affects the final result as a cumulative percentage. It’s quite counterintuitive, but half of a half of a half is only 12,5%! And the only way to reach 100% of a result is to maximize each one of the three. Due to this, if it is known that one of the subKPIs is hard to measure and if measured can significantly decrease the final result… why not remove it completely from the formula? For instance, counting produced goods on any machine is not standard, and Performance is known of being usually below “perfect” (microstops and others…) then why not omit it and calculate OEE as Availability x Quality? It’s astonishing how many companies operate in this manner. “Our OEE is above 93%…” – yes, but without Quality, or Performance, or Availability…
The tampering methods listed above are just the tip of the iceberg of human creativity fueled by the fear of not reaching unrealistic goals. Usually, organizations themselves are not even able to see through the long-standing practices of tweaking the results, so the best way is to reach to an external auditor, partner or consultant.
Tampering, tweaking, improving… all come with some risk but promise a perfect result – “on paper”. That leads to a long-lasting pathology practice, which is the only way to reach unrealistic goalsm which are unrealistic due to the manipulation in the first place. In the end, it becomes a never-ending spiral, that traps manufacturing plants in illusory comfort by hiding the real sources of losses. Those factories that fall victim to the manipulation act blindly and despite their best efforts, cannot compete with more aware entities in the global market.
Without hard work to break the circle of manipulation, there is no chance to get rid of the real problems and increase margins, stability and competitiveness. Without financial stability, it’s hard to attract and maintain talents in the employee market. Even the smallest changes made with data-driven awareness of the real situation in the long run can determine who thrives and who withers on the challenging global market.
Unfortunately, it is not enough to just buy and implement any OEE solution from the myriad of offered off-the-shelf systems. Reporting, and monitoring real-time data directly from equipment with alerts and notifications, although an important step to avoid data manipulation, still does not make plants bullet-proof against tampering with the results (as proven above).
Truly beneficial OEE implementation is a marathon, not a sprint. It takes time relative to all the neglect that arose over the years. It’s not just about the technology but about change on an operational and mindset level. It brings rewards to those who dare to break through the veil of comforting lies and put real effort into the change. In this journey, it is smart to move with a companion that can support the change with an external perspective and experience. Be aware of salesmen offering easy answers and fast rewards under the “one size fit all” off-the-shelf solutions banner.
Only custom-made applications dedicated to processes’ specific, contextualizing all data from silos and adding layers of understanding like OOE or TEEP over OEE (that’s a whole other story) can bring true benefits. Translating OEE percentage results into „production time losses”, „losses in goods” or directly – „money” can make some organizations more empowered and willing to do well-prioritized optimizations of their manufacturing operations. Tight cooperation with technological partners is the best way to develop the ultimate solution that addresses long-standing hidden pain points and unlocks productivity bottlenecks.
It is estimated that 60% of market-leading companies currently relying on out-of-the-box OEE and MES solutions already plan to switch to custom-made ones with chosen partners by 2025. Other companies… have this realization still ahead of them.**
** Source: Magic Quadrant for Manufacturing Execution Systems
Don’t be like “other companies”. Let us help you discover if your OEE is adulterated, where and how you are wasting money, and how to start your journey towards manufacturing excellence. Together, let’s prepare a roadmap and define the most important aspects specific to your plant of the OEE solution so the one you will get will really help your business flourish in years to come. Visit our OEE production monitoring page.
OEE (Overall Equipment Effectiveness), also known as the machine efficiency index, is a measure of the results of production processes. Monitoring OEE makes it possible to assess to what extent machines are being used and how efficiently production is going on. Using OEE allows you to improve the manufacturing process, which has an impact on the profitability of production.
We calculate OEE by multiplying three factors together:
Therefore, the OEE formula looks as follows:
OEE = Availability x Performance x Quality
The formula for OEE consists of three components:
OEE is affected by all of the OEE (Availability, Performance, Quality) factors listed, so define what lowers OEE. Examples of how to realistically improve OEE in manufacturing processes:
Knowledge of OEE is central to the Total Productive Maintenance (TPM) approach and is critical to the efficiency of fleet utilization through:
OEE value can help companies increase productivity, reduce costs, and improve competitiveness.
Remember:
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