Managing aircraft life-limited parts is an imprecise science because so much of the critical data is comprised of estimates and proxies. Thankfully, the Internet of Things (IoT) is changing that. Parts planners can now pull data from sensored aircraft to predict life-limited parts demand.

Introducing Connected Service Parts Management (CSPM)

Tracking life limited parts by serial number is complicated enough. Layering on the different airworthiness criteria that applies to different parts creates a very complicated material planning challenge.

As a result, parts planners focus on the next required removal, and material relies on maintenance scheduling to trigger part demand. This is a constraint for time-phased material plans that strive to comprehend a much longer horizon. When applied across a fleet of aircraft, the challenge expands.

Causal forecasting models have been used for decades to represent the future into models that have calculated failure rates. In aviation, those failure rates are represented as the mean time between unscheduled removals (MBTUR). While this breaks the reliance on historical forecasts, the date used to represent the future is a forecast itself, and one that applies broadly across a fleet.

Leveraging connected assets allows planning to obtain actual operational profiles (hours, cycles) by tail number, and use that data to create predictions of future activity for each aircraft. This is a practice associated with connected service parts management (connected SPM).

This becomes the foundation for a range of material planning activities, including that of life-limited parts. When the tail number-level activity forecasts are merged with an as-flown configuration of serialized parts and the LLP criteria, maintenance can generate a long-range profile of scheduled removals. This is not intended to replace detailed maintenance planning and scheduling, but rather to provide materials with anticipated part demand over a longer planning horizon.

Corollary Benefit: Greater Precision in Causal Forecasting

Connected SPM applications analyze install base and utilization data to predict future utilization rates (flight cycles, flight hours, etc.), and then optimize parts inventory plans based on expected activity. Causal forecasting applies failure rates scaled by those activity metrics to predict failures. The precision of the failure rate becomes the limiting constraint of forecast accuracy.

Unfortunately, when planners lack the ability to track the activity profiles of individual aircraft, they must apply a single failure rate across an entire fleet of aircraft. This fleet can be comprised of different aircraft types that use a common part – old and new aircraft, long haul and short haul routes, different environmental considerations etc. Each of these factors could be represented in different fail rates but in practice are all averaged and smoothed out to one universally applied MTBUR.

By observing activity and failures by tail number, and capturing the considerations enumerated above via connected assets, an organization can begin to compile an extremely valuable basis for computing different MTBURs for the same part, but based upon its use.

Connected SPM empowers planners to maximize every part’s useful life. A part planner could recognize that part A2-79 has 500 flight-hours left on its lifecycle, and then use a predictive analytics algorithm to predict when that part will need to be replaced within a window of days. This is a hallmark of data-driven aviation supply chain management.

Running every part toward the end of its life-limit (or close to it) minimizes the amount of stock you need to carry overall because you have a much more accurate idea as to how many units of each part you’ll need across your supply chain. 

More coordinated maintenance operation

An effective way to minimize maintenance costs is to expand the economies of scale associated with each maintenance event. Instead of grounding an aircraft to replace one part, you would ideally like to service whatever other parts that need attention.

In the past, the only visibility MRO organizations had into the status of an aircraft’s health was its maintenance history and BOM information. A manager may see that part X was replaced in October 2017, but he or she has no idea how long that part has left on its FAA-designated life limit.

Connected SPM provides the level of visibility maintenance needs to:

• Track all the on-wing life-limited parts associated with a particular aircraft.
• Forecast when each life-limited part is most likely going to need to be replaced.
• Arrange maintenance events to ensure the greatest number of life-limited parts can be serviced at the same time.

What this does is minimize the number of total maintenance events needed to service an aircraft over its service lifecycle. Instead of grounding an aircraft every time you need to service one part, you can service multiple parts per one grounding event, based on their life-limits.

The tip of the iceberg

Right now, most connected SPM solutions can only track asset populations and utilization rates. However, a number of organizations are racing to create value out of the ability to connect directly to the aircraft. When you capture a tremendous amount of data, the challenge is turning it into useful information.

The Holy Grail is, of course, predictive maintenance, where real time sensor-based data is used to predict removals before the part fails. Practices such as connected SPM will be stepping stones towards that goal.

PTC has authored an eBook providing an overview of what connected SPM is. Download it via the link below, and please don’t hesitate to connect with me on LinkedIn if you have any questions regarding the practice: