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When does improving OEE become a waste?

OEE, overall equipment effectiveness, remains one of my favorite key performance indicators and is commonly used throughout manufacturing industries. You may be in the infant stages of calculating it yourself as you sort your delay reasons into the applicable Availability buckets. You may have transitioned to a professional level when you synchronized multiple production units’ OEE through a web of process steps to optimize the product flow of your sold widgets. You may have been at the OEE connoisseur because you have succeeded at connecting the routes and product mix to maximize your contributions per hour for migratory bottleneck units. Regardless of your level of OEE maturity, it remains one of the most valid indicators to validate progress, reduce waste, and measure the impact of what you did not do. But when does improving OEE revert to waste?

It is difficult to cheat at OEE so that your numbers look good. You may have an organization that tries to improve its OEE by deleting its delays (Winkenhoffer effect) to improve its Availability. But these shortlived strategies get deflected when the culprits have to explain their sudden increase in Performance losses. You may have seen team members doing a shuffle game that moves delays into invalid Availability buckets. However, establishing business plan targets that include calendar time can counter this shell game.

While these are sneaky strategies to improve OEE, they are typically short-lived due to OEE's ability to unearth the shenanigans that are going on. Instead, it is the areas that are improving accurately OEE that leaders and lean manufacturers need to be aware of. There are generally six reasons why improving OEE could become counterproductive to the organization's mission.

  1. Misalignment to demand - At the end of the day, any production unit or process is supposed to provide a return on investment. You could see scenarios where you have a non-bottleneck unit that incurs investments that returns little value in return. Consider a scene where you are buying every imaginable IoT or industrial 4.0 widget to reduce unplanned delays. With each investment, you are experiencing value as you see your Unscheduled Downtime significantly reduce. All great things if the incremental value of the investment enhances the economic benefit and mitigation of risk for the enterprise. If not, it becomes a waste.

  2. Producing the easy stuff - Some have called it the shift-handover phenomena or siloed strategies. These are the types of organization types that can hand-select the product mix to inflate the Performance leg of the OEE calculations for a momentary increase in OEE. This strategy is in organizations that allow their shifts to pick the easy stuff to run or routinely defer planned downtime. These examples momentarily inflate an OEE from a micro point of view to the demise of the macro OEE. It may even severely disgruntle the next shift that walks in to see a mountain of downtime deferred that they must now manage.

  3. Myoptic strategies - Consider the scenario where a production unit needed additional production for the next six months. The original plan had a two-week outage in the fall for critical repairs, but selfishly, producing became the priority. This may look wise for a few months, as the scheduled downtime gets replaced by a production schedule. As if synchronized like a cuckoo coming out at noon on an antique clock, the wheels fall off three months into this strategy.

  4. The operating unit inevitably compounded extended downtime that was more than what the planned downtime would have been over the six months. The team accepted a momentary increase in OEE for the first three months to create the desired output, but they failed to realize that an increase in OEE momentarily deteriorates the long-term OEE.

  5. Shipping defects - Rooted in his philosophical cornerstones, Edward Demming’s philosophy remains that you should not process incoming quality defects. But if a unit chooses to process the incoming defective product, it can unfavorably impact its Availability, Performance, and Quality versus the unit that delivered the defective product. The upstream unit’s OEE remains unblemished because the downstream unit elected to process the defective material.

  6. Blind to the value of the delay buckets - Availability is typically broken down into Idle Downtime, Transitional Downtime, Scheduled Downtime, and Unscheduled Downtime. Combine these downtime categories with losses in Performance and Quality and you have six buckets to categorize a loss. However, the losses have different values associated with them. For example, the value of an hour of Idle Downtime is tremendously cheaper than an hour of Quality defects.

Overall, OEE continues to be one of the most useful metrics for organizations. Used in conjunction with other metrics, interpreting efficiencies and unlocking opportunities for improvement become evident. However, monitor your OEE improvements with caution so that they remain warranted by the organization and not creating waste.



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