Navigating The Maze Of SMS Jargon

Do you sometimes find yourself lost in the maze of jargon that surrounds SMS?


A recent story in the national press and television laughed about the family of four who became lost at dusk in a corn maze. They called 911 and the local fire department and television camera crews came to everyone’s rescue. Yes, the story ended well but for the embarrassment of the parents.

Do you sometimes find yourself lost in the maze of jargon that surrounds safety management systems (SMS)? Let’s try to clarify a few terms that can be confusing. Also, let’s show that SMS is often a matter of formalizing your current safety program.

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. 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
  • Predictive

FAA aligns guidance with ICAO. Different data need different methods to collect, analyze and use the information to promote safety. Let’s look at the three data types in more detail:

REACTIVE DATA

The event has already occurred. The damage is done. Depending on the severity of an event, you hear about it 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. They are prepared to launch an investigation team and establish fact-based contributing factors. There are very good processes to interview those involved or witnesses to an event. 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 at another carrier. We learn from one another.

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. The industry has relied on such information for a long time in the successful effort to ensure continuing safety.

PROACTIVE DATA

Collecting, analyzing and applying proactive data is not new. Only the emphasis on the term “proactive” 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 the International Air Transport Association (IATA), the Coordinating Agency for Supplier Evaluation (C.A.S.E.) and the National Aviation Authority are nothing new. In SMS language, these audits represent your proactive data.

PREDICTIVE DATA

We are a very safe industry. The positive data from 2011 broke all safety records. The United States has gone nearly three years without an airliner accident fatality and the accident rate is considerably down worldwide. Today’s aviation news is more likely to describe a long wait in a TSA line than an airliner safety issue. We must continue on that increasing safety path.

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” vs. 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.

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