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Author's profile photo Thomas Pohl

Do you have the tools to predict when your aircraft landing gear will fail next?

OEM maintenance service providers and operators in the A&D industry need to ensure that the maintenance of mission-critical assets is done with maximum efficiency and effectiveness. Because of the critical nature of the industry, reliability and maintainability are top priorities to ensure safety of flight, high operational availability, and low total lifecycle costs. To achieve this, organizations need capabilities to predict failures that avoid catastrophic losses and unplanned downtime, as well as the ability to run optimization and prediction scenarios enabling them to optimize maintenance programs and reduce total costs of ownership.

Predictive analysis can help turn huge amounts of maintenance-relevant data (whether machine data from sensors or logistic information from ERP systems) into actionable information, helping ensure that maintenance technicians execute the right work steps at the right time and with the right tools. Predictive analysis can help drive strategic improvements and provide better-quality output at lower operating cost and improved return on investment. With the introduction of in-memory technology, the time and cost of analyzing massive quantities of data has been reduced dramatically, and makes it possible to perform predictive analysis against vast volumes of data in real time.

SAP Predictive Analysis software allows organizations to mine and analyze their data, anticipate business changes, and drive smarter, more strategic decision making. This helps them stay agile and gain a competitive edge with efficient forecasting and planning, and also solve problems before they happen by exposing hidden risks and hazards.

Now, imagine, you are a maintenance company who recognizes that landing gear failures have increased noticeably over the last few months for an operator.

SAP Predictive Analysis software can be used to understand why the failures are happening and mitigate risk by preventing future failures:

  • From a long-term perspective, the operator must identify the root cause of the problem in order to determine the corrective action – deficient design, technician training issues, operational policies, and so forth. Discovering and visualizing correlations, trends, and associations across multiple variables will help uncover the root cause of the issue. Understanding the root cause will then help reliability engineers implement the proper solution.
  • The operator also needs to deal with the problem in the short term. Using predictive software, maintenance planners can analyze the current installed base of aircraft and make a prediction on when future failures might occur. This prediction enables planners to take proactive steps to avoid unplanned failures and impacts to operational schedules.

The reliability engineer loads the historical failure data into the software. This failure data includes variables such as tail number; location; climate region (weather has a huge impact on performance); type of failure (such as leak in hydraulic system or crack in piston); part number; and so on.

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Historical failure data in the SAP Predictive Analysis application

To help identify the root cause, the reliability engineer can begin a series of adhoc queries. The root cause of a problem typically consists of a combination of variables. The software simplifies the analysis across multiple variables, helping the reliability engineer to find the combination of variables that are at the source of the issue. The results of the analysis are displayed visually as different chart types. This is especially useful when working with large data sets.

As product engineering changes and new training programs often take considerable time to implement, the reliability engineer is also interested to mitigate risk in the short term by predicting installed base failures. The SAP Predictive Analysis software can be used to make predictions about the current installed base. This enables organizations to proactively repair or replace landing gear assemblies or components before they fail so as not to impact flight operations.

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Visualization of historical failure data in the SAP Predictive Analysis application

Using statistical algorithms, the reliability engineer designs a predictive model based on the historical failures. The algorithm is “trained” based on the historical data and then applied to the current install base to predict future failures.


SAP enables users in organizations to combine the power of predictive processing using the SAP HANA platform with intuitive modeling and advanced data visualization provided by SAP Predictive Analysis software. In addition to supporting statisticians and professional data analysts, predictive insight can be leveraged by business users and key decision makers in the organization by extending predictive functionality into business applications, business intelligence, and collaboration environments, as well as mobile devices.

SAP Predictive Analysis combined with SAP HANA can enable organizations to:

  • Gain real-time insight and visibility into operations, allowing them to operate in preventive and predictive mode
  • Intuitively design complex predictive models
  • Visualize, discover, and share hidden insights
  • Develop a maintenance strategy that is supportive of continuous improvement

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