An aircraft’s job: to take off and land reliably, safely, and efficiently. As we look at today's technologies and tomorrow's advanced predictive tools, we believe that fleet health will increasingly be measured and analyzed in real-time, while fleet maintenance will be scheduled based on digitally predicted needs. Some of the factors that created challenges in the past, like unexpected downtime, inventory tracking, and environmental compliance, are now compounded by the COVID-19 impact on fleet age. As we track the digital transformation and workflow processes of the last decade, we want to spotlight where we believe fleet management will be in the future.
In the past, fleet management was as much art as science. It is, however, getting more challenging to manage an aviation fleet from a spreadsheet or whiteboard. Now, more than ever, intelligent software and data tools are needed to support decision making. The industry is evolving as some MROs leveraged options in value-based technology to streamline processes and reduce cost. This step toward digital transformation enables fleet managers to make informed decisions around condition-based maintenance and readiness based on real-time data analytics.
Some maintenance & repair operators (MROs) and airlines are leveraging the grounded time created by COVID-19 to accelerate their digital transformation by integrating robotic process automation (RPA) to complete repetitive manual processing tasks. By implementing RPA, an airline or MRO 'hires' a digital workforce that can accelerate a workflow and improve compliance. Think about a material requirement planning (MRP) provider and its customer who is using two different MRO software systems. Let's assume that the MRO provider's consumable stock is only available in their own MRO software system. The maintenance planning is performed in the customer's MRO software system and is dependent on the available parts supply. Previously, only inadequate and timely interfacing options were available to update independently linked data systems. RPA can solve the problem by creating a software robot that automatically makes the up-to-date inventory transactions available in all required MRO software systems and ensures synchronization without any human intervention. This unlocks time for the maintenance team to work on higher priority tasks that are impacting down time. In addition, studies indicate that RPA delivers higher accuracy on process execution and will thus ensure compliance to regulatory requirements. This is a significant benefit when managing the requirements of multiple compliance agencies.
A significant impact on down time that is expected to continue, is fleet condition. In today's paradigm, manufacturers design and produce aircraft based on a specific set of requirements and conditions. The aircraft in the field needs maintenance. In some instances, to reduce down time, parts are swapped or modified to restore operation. This is customary in the Defense industry when down-time could impact the success of a mission. Aircraft condition is also affected by use and environmental factors. The same model of aircraft will present different maintenance requirements based on the number of hours flown, number of landing cycles, impact from landings, even environmental factors like saltwater or desert sand. Aircraft manufactures are taking on a more system-design approach to maintenance by integrating predictive data models much like those found in our cars, which tell us to schedule an oil change or check tire air pressure. Condition based maintenance has improved costs and efficiency across the industry, but there is still a significant cost associated with unscheduled maintenance events. As we look to the future of condition-based maintenance, the conversation has expanded to expect intuitive aircraft structural-system health monitoring. Commercially, real time aircraft health monitoring is used minimally, with extensive opportunity to allow more data flow to improve maintenance turnaround time and diagnostic oversight.
The sensors and data processing capabilities in today's automobiles are powering everything from comfort controls and self-parking, to alerting the owner well in advance of a maintenance needs. Several commercial aircraft are now deployed with sensors that monitor the health of many of the on-board systems and track routine maintenance lifecycle parameters. The ability to transmit aircraft data to the ground during flight, will allow maintenance managers the opportunity to be proactive with maintenance activities as soon as the aircraft lands, reducing downtime. Operational decisions such as equipment swaps, flight delays, or flight cancelations can be more proactively managed. The cost to create dynamic models of multi-disciplinary fleet management systems has declined considerably over the past few years and is well within the budget of most manufacturing-machine design projects.
As we look to the future of aviation, we believe that formal requirement management, advances in wireless communication capabilities, and the development of high-fidelity dynamic models used in simulations will result in structural health management systems based on simulated digital twinning. A digital twin provides a virtual representation or simulation of a physical asset and monitors its performance in real-time. These tools can simulate performance, predict potential maintenance needs, and ultimately optimize the asset for peak performance. Businesses will embed their physical assets with sensors to produce the data necessary to inform a digital twin. By transforming the physical assets into IoT-enabled devices, they can track and monitor the physical piece of machinery. All of these innovations will lend themselves to help aircraft operators benefit from lower downtime in both the commercial and defense spaces.
This post is part of a series on the future of fleet management. Today’s post discusses the current and future innovations that are shaping how aviators reduce down time. The next item in the series will highlight parts inventory management.
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