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Waste. We all know it. We all have seen it. We are all responsible for it. Just how big of a deal is waste? How does it prevent us from achieving excellence in all that we do? A wise man once said that sin is a measure of the gap between what we are and what we could be – a measure of our potential. Now, we could get into a hypothetical discussion about what exactly that means – about who we are, or what we could be … but we don’t have that kind of time. So, let’s add some focus, scope and definition. Should any form of waste really be considered sinful?
There are many variations of waste. How we rank in comparison to our “ideal self”, wherever we are lacking, therein lies sin. More importantly, in that gap between the “ideal self” and our “current state” lies opportunity: opportunity for reflection, opportunity for clarification, and opportunity for improvement. In an organizational context, improvement means money – either saved or earned – and everyone likes the sound of that.
Money and sin … at this point you may be wondering if this article is appropriate for daytime reading. More pointedly, you may be wondering what either has to do with loss elimination. Keep reading. How have we defined waste – as a sin; as anything less than theoretical; as loss? The mere identification of waste is an important part of loss elimination. It is a place to start. Many organizations do not have systems in place for identifying waste – they are not even aware of the losses around them. But identifying loss does little to reduce it. Awareness does not immediately bring forth action, and action is what yields results. It is the act of eliminating loss that creates opportunity, not only the awareness of it. Awareness is merely the beginning.
Many organizations attempt to quantify OEE (overall equipment effectiveness) as a means of defining and measuring loss. Most stop there. As a review, OEE is defined on a percentage basis as Rate x Quality x Uptime. It can be defined for an asset, for a production line, for a department, for an entire plant. The idea is to define the system in question, and quantify it in terms of comparison to some ideal state. In essence, it is capturing that gap – that measure of potential.
Potential turns to sin only when it is not realized. Rate is calculated by comparing your current rate to some ideal – either nameplate or best demonstrated. Quality, in a similar fashion, is calculated by comparing current quality to some ideal; i.e. no scrap. Lastly, uptime relates the amount of time the asset/line/department is available for production to the amount of time it is scheduled. It is the inverse of downtime.
Taken together, the calculated values of Rate x Quality x Uptime give OEE. If your facility produced 900 widgets per month (ideal: 1,000 widgets per month) with no scrap and 30 percent downtime, your OEE for that month is 63 percent (90 percent x 100 percent x 70 percent = 63 percent).
So, what does an OEE of 63 percent tell us? In truth, it tells us very little. It is just a number. We know in some ideal state we could achieve an OEE of 100 percent. So, there’s that. But how good is that 63 percent? It doesn’t sound very good, but context is everything. Ted Williams, one of the greatest hitters in baseball, was the last major-leaguer to hit .400 in a single season and had a lifetime batting average of .344. On a percentage basis, that’s 34 percent. In comparison, how does our OEE number look now? If for the past 12 months, we’ve consistently had an OEE between 50 and 55 percent, that 63 percent looks great! It is comparison to an established baseline that is important.
Every percentage point in OEE can and should have a certain dollar value assigned to it. And although a 10-point swing in OEE could be worth millions to your facility, to consistently improve over time – to work on closing that gap – we need to understand why the numbers are the way they are. Why isn’t that 63 percent closer to 100 percent? Why is my equipment down 30 percent of the time? Why can’t I make as many widgets as I have in the past? More importantly, if and when you determine the causes for any of those “less than ideal” performances, what are you going to do about it? Where are you going to focus your time?
Every organization I have encountered is resource-limited. Whether lack of material or manpower or money, we have all had to make do with less. So, how do you begin to attack your downtime losses? Where do you start? There are only so many hours in the day. If I want to close a gap and attack a 30 percent loss, I need to have an understanding of what makes up that loss. Is it two 15 percent losses, or 15 2 percent losses? What if it was one 24 percent loss and six 1 percent losses? I could spend all month working on the small losses, perform multiple root cause analyses, put together and execute an effective action plan that would make any PMP proud. Even if executed to perfection, at best I could hope for a 6 percent improvement in downtime – raising my OEE to 68 percent, a 5 percent increase (even less if I wouldn’t have had such great quality numbers). If, by contrast, I would have ignored the six 1 percent losses, focused on the one 24 percent loss, performed a careful investigation, put in place appropriate corrective actions, and was able to reduce that loss to only 10 percent (a 60 percent reduction), I would have improved the OEE by 13 percent to 76 percent (90% x 100% x 84%).
What’s the lesson in all of this? Prioritize. Go after the big stuff first. The use of Pareto charts is widely accepted as the weapon of choice for logically organizing loss data. First-, second- and third-level Pareto charts can be used to take 30 percent departmental downtime losses and break them down into the bad actors responsible (that gearbox coupling failure from this production line). Developing a list of bad actors and prioritizing them gives a logical grouping of what to go after. Once developed, bad actors can be analyzed, action plans developed, assumed loss reduction calculations put forth, ROIs calculated, and projects undertaken – all on a scale appropriate to mitigate the amount of loss incurred. Oh, and don’t forget about the follow-up activities to ensure that your actions have yielded the desired results. After all, in business, there is nothing more important than execution and results.
There are a few more things to consider. Often the most difficult task in closing that gap, in seizing that opportunity, lies in the ability to collect the necessary data to determine where losses are coming from in the first place. Unfortunately, third-level Pareto charts neither grow on trees nor do they float down from some lofty place on high. Accurate, consistent and valuable data is the key to any successful loss elimination process. All decisions are based on it. Ideally, loss data should be captured electronically – for great discipline is needed to establish a culture where reasons for rate, quality and uptime losses are captured consistently across different shifts by different people.
The goal in any environment is to determine what is possible theoretically, establish performance baselines and targets, and then figure out why you aren’t meeting those targets consistently. One more thing: loss elimination is more than OEE – it is the elimination of any and all losses. It does not just involve equipment. What if 40 percent of my rate loss was due to a raw material issue? Am I to still concentrate on the 5 percent rate loss due to the slipping of a conveyor belt? Where is the logic in that? To be truly effective, a loss elimination program leaves no stone unturned when it comes to defining and capturing loss data. For this to occur, multi-department participation and strong upper echelon endorsement is needed to facilitate site wide-cooperation and participation.
So, how do you begin to close that gap; how do you drive sin out of your business? Diagnose where you are within your organization and determine what level of analysis is needed in order to get yourself moving on the right track. While it is important to understand loss elimination best practices and keep such methodologies in mind as a future state target, if you are struggling at your facility to determine and enforce any practices at all, best practice may not be the most logical place to start. Don’t be afraid to start small, and continue to work on closing that gap. Realize your opportunity.
About the author:
Josh Rothenberg is a graduate of the University of Texas at Austin and has been a reliability engineer in various industries for the last four years. A Life Cycle Engineering reliability subject matter