Friday, January 27, 2012

Focus and Leverage Part 80

As promised, in the next several postings, Bruce Nelson will be discussing metrics.  Here is his first of a 3 part series.

Efficiency, Productivity, and Utilization (EPU) ©

Usable Metrics or the Evil Trio
Bruce Nelson
Many businesses, seemingly across all industries, are prone to develop and use some type of metrics in their decision making process.  Many of these organizations are focused on Efficiency, Productivity and Utilization, or EPU.  Using these metrics as guidelines, many organizations try to forecast there business activity and make a judgment call concerning their current status.  And, in many respects this is probably not all bad.  However, what is bad is the seemingly common nonsensical way these metric are used.
Many business leaders understand that in order to make good business decisions, they must have good data on which to base their decisions.  If the data is incorrect or interpreted incorrectly, it is highly probable that bad decisions will be the outcome.  And, if bad decisions are implemented it can spell the death of an otherwise good company.
If good data is required to make good decisions then, collecting, interpreting, and using the data would seem to be of paramount importance.  Useful data collection is a way of using past performance to help predict future performance.  Accurate data can provide the user with the ability to make the necessary course corrections and get the organization back on track and headed in the right direction.  This brings up another important point.  It’s imperative to know where you are going in order to set a course of actions on how to get there.  If you don’t know where you are going, then it doesn’t matter what actions you take to get there, which seems to be the way that many organizations make crucial decisions!  Many organizations make decisions not based on what they need or understand, but instead based on what everyone else is doing.  This decision making process is sometimes referred to as “Bench Marking.”  This type of decision thinking only works if you can validate the assumption that what the competitor is doing is correct!  It is possible that what the competitor is doing might make sense for them, but not necessarily for you.  So, wishfully following what the competitor is doing in hopes of having the same effect for your organization is fantasy leadership.
Many organizations use some kind of metric on a daily basis, albeit in a roundabout way.  Consider the instrumentation in your car.  There are measuring devises (gages and read-outs) designed to keep the driver informed as to the operational stability of the vehicle they are driving.  The gas gage tracks the consumption of raw material inventory required to keep the system operational.  The tachometer tracks the speed at which the system is working (RPM’s) and the speedometer monitors the speed of the system through time.  The temperature gage, oil pressure gage, and battery charging system all provide vital data about the status of the system, but only if you understand how to interpret the data and react to it.  If you analyze and interpret the data incorrectly, the system could operate sub optimally or, worst case the system could fail. That is not what we want to have happen.  These same measurement principles hold true for analyzing production systems and business data within an organization.  So, what is important to measure and why is it important?

Somewhere along the way efficiency became “king” of the metrics mountain.  Many organizations are measuring efficiency, not because it was really important to them, but rather, because somebody else was measuring efficiency (bench marking.)  The assumption being that if the competitor is doing it, then we should be doing it also.  Question: “Is that a valid assumption?”
Efficiency is a metric used by many industries and it is a metric that is used incorrectly most of the time.  When used incorrectly, efficiency will give the false impression of “If we look good, then we are doing good.”  There is hardly a day that goes by that you don’t read about efficiency in the paper, or hear it used on the news.  The new battle cry is; “We must become more efficient at what we do.” Or, “We must improve our efficiency to stay competitive.”  There is a downside to these mottos if, in fact, you measure efficiency incorrectly.
The concept of “efficiency” is often times confused with the term “effectiveness.”  Many believe that a high efficiency is synonymous with being highly effective.  Not true!  Efficiency is measurable and therefore quantitative.  Effectiveness is non-quantitative and therefore a rather vague concept associated mostly with achieving a goal or objective.
Efficiency also has many models for application including Physics, Economics and other sciences.  It is expressed in terms of a ratio, i.e., the ratio in terms of something produced (units) divided by the resources consumed to produce it (hours), or r = P/C.  However, there are some concerns with trying to use the efficiency model in a production setting.  The most obvious is that the efficiency model doesn’t fit well within a typical production system.  The best measure of the production system is the productivity measure.  In essence, the productivity measure tells you the efficiency of the system, but more on that later.
The mathematical limitation of efficiency is that it can never exceed 100%, and yet there are companies who proclaim much higher efficiency metrics than 100%!  How do they do that?  One simple trick they employ is to measure efficiency as a ratio of standard hours given divided by actual hours used. As an example; suppose for a period of work time there were 1000 standard hours issued to do the work, but the actual time to do the work was tracked at 500 hours, then 1000/500 = 200% efficiency.  The first hint that this is incorrect comes from the definition of efficiency - there is no variable of what is actually produced!  Measuring efficiency this way only gives you a ratio of the hours given (standard hours) compared to hours used (actual hours). If efficiency goes up (which it probably will), it translates to the standard hours being incorrect (which they probably are.)   In fact, when using this method (and many do) of calculating efficiency it is very probable that ALL the standard hours could be consumed and not a single unit of product produced!  The metric would tell you that the system is operating at 100% efficiency, and yet not a single unit was produced.  What the measure really says is this: “The more you improve the further away from the goal you get!”  Is this an accurate measure of the system performance?
In this case, the system looks really good (the efficiency), but the system isn’t performing well at all (the output.)  So, how useful is a metric that paints this picture?  The efficiency metric says you are doing fine and, yet reality says you are missing the goal/objective!  Suppose this was the data output from your system – what would you do?
For obvious reasons the efficiency metric is not reliable, and certainly not an accurate measure to get a clear picture of what is going on.  Even when efficiency is calculated correctly it can still have a devastating effect on a system.  As an example; suppose in your organization the metric was to maintain high efficiency levels all the time.  In this scenario efficiency can be literally interpreted to mean “keep everyone busy all the time.”  The assumption with this thinking is that “busy people” equate to high efficiency – which is true!  But keeping people busy all the time also prompts an organization to buy and release more and more raw materials to achieve its goal.  Buying, and releasing, more raw materials only serves to increase the work-in-process (WIP) in the system.  Higher WIP levels will also have a negative effect with on-time-delivery (OTD) and will cause it to drop as the WIP levels go higher.  A system can actually become so polluted with WIP that it might produce nothing at all.  So, you achieved a very high efficiency, but at what expense?  Understanding these different scenarios begs the question; “Does high efficiency also equal higher levels of productivity?”  Efficiency, when used this way could prevent good decision making for production systems.  The efficiency metric would better serve its user for calculating the gas mileage of a car, or for the efficiency of a gas furnace, but not so much in a production system.  So, if efficiency might not be the best metric for a system, then what is?  Let’s take a look at the merits of the productivity metric and see how it might apply.
In my next posting, we’ll take a look at the merits of Productivity as a metric.
Bruce Nelson

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