Mean Time Between Failures

Mean Time Between Failures

As a Financial and Business Analyst I am often asked if it is possible, with the right information, to predict what will happen in any business.

The limitation of analysis is that there can always be a new factor or variable that you did not take into account or that had never happened before.  Covid is a good example of that.

I have also noticed that first attempts at analyzing anything are almost always wrong.  This is usually because we failed to take ALL variables into account, we forgot a factor or two that drastically changes what the analysis is telling us.

But good analysis, once refined, CAN predict the likelihood of any outcome and better prepare ourselves for those likely events.  AND this is why we always encourage using metrics, and often say “if you treasure it, you should measure it”.

These measurements are what allow you to analyze and predict the likelihood of both desired and undesired events.

But analysis doesn’t end with Financials!  Sometimes Operational Analysis can be just as effective and usually more fun.  Think in terms of units and labor hours.

Think about the power of how many units produced over how many labor hours measured daily/weekly/monthly.  This would allow you to understand costs, set goals, know when something went wrong, and begin the journey to process improvements.

We can analyze our operation to better find, manage, and ultimately eliminate constraints within the processes that we use and often find more efficient ways of doing things as well as eliminating waste.

One of my favorite operational analyses is MTBF or Mean Time Between Failures.

Mean Time Between Failure (MTBF) is a reliability metric that estimates the average time between two failures of a repairable system during normal operation.

1. It is calculated by dividing the total time of operation by the number of failures that occur during that time.
2. MTBF is used to assess a system’s reliability and can be used to estimate the expected service life of the system or component.
3. MTBF is often used in failure analysis to identify the root cause of failures and to improve system design and maintenance.
4. It can also be used to compare the reliability of different systems or components, and to identify areas for improvement.

Wouldn’t you like to know when your systems are about to break down so that you can prevent downtime and keep your operation moving?

Downtime can be expensive, and often the cost of fixing pales in comparison to cost of idle labor, and lost sales.  MTBF analysis, based on YOUR history of usage can tell us when a piece of equipment or a particular component of that critical equipment is likely to fail.  Thereby allowing you to schedule replacement parts to arrive BEFORE the failure, and schedule convenient time to affect the repair.

Whether it is transportation vehicles, manufacturing equipment, building maintenance systems, or computer and networking systems, your business is likely out of business during the failure of something that can… and will breakdown.

Once you have identified what those items are, careful logging of those the failures begins to create a history on which we can predict breakdowns and failures.  The more history we have, the more accurate our predictions become, and over time we can eliminate most of our downtime.

What are the critical pieces of equipment that you use in delivering your goods or services to your customers?

Whether you are measuring the how often to replace the brakes on your delivery truck, or when the motor will need to be replaced or repaired on a critical piece of manufacturing equipment, knowing the Time Between Failures could save you considerable time, cash, and sales, because, as they say “time is money”, especially in business.

One last note is that MTBF is not applicable for non-repairable systems, for which Mean Time To Failure (MTTF) is the term used instead.  MTTF is the expected time to failure for a non-repairable system.  However, it is the exact same concept and measured the same way.

So, you can see from this MTBF example your analysis doesn’t always have to be about counting the “beans” it can be about counting time, units, occupancy, frequency, sales, covers, square footage, utilization, capacity, and many more.

When you divide one of these factors by another related factor it usually tells us something important.  And then if you add revenue, COGS, expenses, or profit as additional factors the value of your analysis goes up exponentially.

A good example of this is typical hotel analysis.  It started by measuring occupancy to determine if the hotel was doing well.  But anybody can fill rooms if the price is low enough, so then they started measuring average room rate.

However, neither of these numbers told them if they were making money, so now many hotels now take the total number of their rooms available and divide that into how much revenue they made that day and end up with one number RevPAR, or Revenue Per Available Room.  A simple, yet effective tool for daily measurement of “how well are we doing”.

In conclusion, Analysis can be fun when we create measurements that tell us critical things about our business.  Things that are important to know sooner rather than later.  And when done properly can give us a fairly reliable way of predicting the future.

If you leave all of that “Math Stuff” to the accountants, the only one that learns something valuable about your company is the IRS.

Skip Williams

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