SMS Jargon and Collecting Predictive Data

For most airline and airline-related service organizations, an SMS can be a formalization of the many safety programs already in place


The International Civil Aviation Organization (ICAO) guides the direction of required safety programs. Its safety management manual (Google the term “ICAO SMM”) describes the multitude of suggested requirements for a safety management system (SMS). The requirements apply to airlines, charter companies, MROs, airports, air traffic organizations, and others. Each organization must have a formal means to collect, analyze, and apply results from three different types of data: reactive; proactive; and predictive.

FAA aligns guidance with ICAO; different data requires different methods to collect, analyze, and use the information to promote safety.

SMS Data Types

When looking at reactive data, the event has already occurred and the damage done. Depending on the severity of an event, it can be heard about on the news. However, many other events, such as a runway incursion, high value ramp damage, or worker injury, may remain known only to the airline or company personnel involved.

Companies usually have accident/event procedures in place and are prepared to launch an investigation team and establish fact-based contributing factors. There are very good processes to interview those involved; companies usually have the means to determine the cause and take actions to ensure that the event never happens again.

Afterward, the FAA and any number of industry organizations and publications help disseminate accident/event data. This helps reduce the chance that the event may be repeated by another company.

The event investigation process, the data collected, and the final report is merely a reaction to the event — hence, the term “reactive” data. There is high value in learning from the reactive data from big events.

Collecting, analyzing, and applying proactive data is not new, only the emphasis on the term is new. Aviation organizations have auditing, quality, and safety departments that apply a multitude of operational measures to assess current performance and safety.

The FAA Continuing Analysis and Surveillance System (CASS) exemplifies a robust data system. For CASS, the company is responsible for collecting data and assessing the opportunities for improvement. Traditional audits with IATA, the Coordinating Agency for Supplier Evaluation (C.A.S.E.), and the National Aviation Authority are nothing new.

Getting Predictive

SMS regulations encourage us to take our data collection to the next level. Predictive data systems are a means to use daily/normal operations to help identify a company’s strengths and weaknesses.

Sometimes there is confusion between the terms used in “system safety” versus the language used in “threat and error management.” Using the language of system safety, predictive data helps identify the small hazards in advance of assigning a risk level. Using the language of threat and error management, predictive data helps identify the threats so that they can be managed before they become errors. In any language, it is a matter of identifying and addressing challenges as early as possible.

How can one establish systems to gather and apply predictive data? Most likely, frontline employees see the hazards before management does. Predictive data, therefore, must be worker-centered. Companies must make it easy for employees to speak up. Voluntary reporting systems are likely the best means to achieve this goal.

FAA cooperates with the industry through programs such as the Aviation Safety Action Program (ASAP). It protects workers from FAA action when they report mistakes early. Progressive companies use ASAP as a means to report not only events but also to report the conditions (threats) that may lead to events.

Another new program created by FAA with the Airlines for America (A4A), formerly the Air Transport Association, is the Maintenance and Ramp Line Operations Safety Assessment system (MRLOSA). MRLOSA is a formal means for workers to conduct peer-to-peer assessments during normal operations. Such data may be the very best means to gather and use predictive data.

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