Helicopters Magazine

Features Procedures Safety & Training
So Much Data, So Little Information

Bill de Decker discusses techniques to put your data to good use


October 17, 2007
By Bill deDecker

Topics

chartsThey say we live in the information age. But most of the time, all we get is reams of data that do not tell us much of anything. In fact data is pretty useless, from a management point of view, until someone has tabulated, organized and analyzed it. Because that’s when data becomes information you can use to better manage your operation.

The maintenance of aircraft is an excellent example of this. Consider the drawers full of work orders, log book pages, part tags, purchase orders, inventory records, etc. that reside in any maintenance department.  Usually, this data is carefully filed and never looked at again unless there is some specific maintenance problem or question. And so the data sits in a filing cabinet, until moved to long-term storage or transferred to the new owner when the aircraft is sold. Obviously, it is very important to have all of this data available in case there is a problem, but it does not help one bit in managing the maintenance department. And yet … that same data contains a treasure trove of information, waiting for the smart manager to mine and put to use. 

One way good information can make you a smarter manager is by focusing attention on the things where extra effort will make a big difference. We’ve all suffered from examples where that focus was on the wrong thing. For example, one large company I worked for had a policy, and I’m not making this up, that you couldn’t get a new pencil from the supply room unless you turned in a pencil stub less than 2.5 inches long! The policy didn’t last very long, but while it was in effect, I’m sure it saved a bunch of money on pencils. However, there was also a complete mismatch between the effort required and the impact on the company’s bottom line.

There are a number of techniques used to match effort and impact. One frequently used technique divides a task into its sub-tasks and then focuses on the five most expensive ones. Typically, these five most expensive sub-tasks will account for 60 to 90% of the total cost of the task. In short, this technique focuses attention on the high-impact items.

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A second technique to match effort and impact is to list the cost for every occurrence of a particular maintenance action for a specific component or task, such as the overhaul of a gear box. This focuses attention on the variation in cost that often occurs from one overhaul to the next. The goal, of course, is to understand what causes these variations.

A third technique is to benchmark the various cost factors experienced by your operation against data from other operators, databases and manufacturers’ published data. The idea here is to see how well you are doing when compared with others, and to understand what is causing the differences.
Our company has analyzed work orders for tens of thousands of flight hours covering various fixed- and rotary-wing aircraft. Some examples of the potential impact of this type of analysis follow:
One analysis we did of work order data for 14,000+ flight hours in the life of three light single-engine helicopters focused on the cost of maintenance by ATA chapter. Refer to Table 1.

This highlights the fact that almost 90% of the total maintenance cost is caused by the engine and the drive train. This probably does not come as a surprise to anyone involved in helicopter maintenance. But it does emphasize the importance of focusing attention on these cost drivers.

But there is more to be learned from this type of analysis that can lead to further savings. For example, analyzing overhaul costs for a particular component will often show that each individual overhaul costs either considerably less than the average or considerably more than the average, as shown in Table 2.

In this case, the average cost of overhaul was about $16,500. However, the actual cost was either $5,000 to $10,000 or it was about $35,000 to $40,000! This cost data leads to the obvious question: what do we have to do to get low-cost overhauls every time?  A further detailed analysis showed that the variation was always associated with a particular part. If it did not meet tolerances, it needed to be replaced at a cost of over $30,000, since there was no repair procedure. Development of a repair procedure lowered the average repair costs and eliminated almost all $35,000 to $40,000 overhauls.
And sometimes this type of analysis contains real surprises, as shown in the data in Table 3 for about 5,000 hours of flight time for a mid-size business jet:

The surprise is the maintenance cost for the navigation system. Further investigation showed a large part of the problem was the fact that the operator had installed 12 vertical gyros over the 4,900 flight hours, instead of the one the factory data would predict. Further discussions with the manufacturer of this component and subsequent troubleshooting revealed that a collapsed cooling duct had caused the premature failures. Interestingly, the manufacturer really didn’t believe there was a problem until they were presented with the hard data.  

Benchmarking is a powerful way to measure the performance of your maintenance department from year to year and against others. For example, one analysis we did of a small fleet of twin-engine helicopters showed that over a period of five years this operator had averaged 1.65 labour hours per flight hour. This compared with a benchmark (from a published database) of 1.79 labour hours per flight hour – good confirmation that this company’s efforts to control maintenance labour were headed in the right direction.

How do you get the data to accomplish this kind of analysis? Assuming you have the work orders, there are two ways. The first, and by far the best, is to use integrated maintenance management software that records work order data and provides this type of in-depth analysis capability. The second is to develop your own database and then enter the work order data into it. In either case, you’ll take all that data and turn it into powerful information you can use to effect real savings. 


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