Flying Lessons: How stories from Aviation can make us better Managers

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In this six part series, we introduce five activities critical to business success and examine each through the lens of lessons learned from the field of aviation. These activities are Observe, Communicate, Organize, Deliver and Improve. Each one plays a vital role in allowing a business to be successful in serving their customers.

In just over one hundred years, the aviation industry has moved flight from being a novelty for the adventuresome to an integral component of our modern world. What stories from aviation can assist us as managers to improve our businesses and support our customers better? How can we leverage insights from over a century of trial and error, analysis and lessons learned to help our team become more productive? In what follows we will engage in a journey by picking examples of excellence in each of the critical activities and find out how we can to apply them to our business.

The picture above is of one of Boeing’s iconic 737 airliners representing a culmination of aviation lessons for business managers. The 737 started out as a project to support a Lufthansa airlines requirement in mid-1960’s. Almost 60 years later, over 9000 737s have been delivered. It is estimated that around the world at any one time 1000 737s are in flight and that a 737 either takes off or lands four times every minute. In microcosm, the 737 represents the result of five activities all organizations must do to be viable. Boeing observed their environment and communicated, organized and delivered a product that it has improved over time. The ability to Observe, Communicate, Organize, Deliver and Improve are critical to business success. Moreover, the business that does these five activities well is on its way beyond viability and towards achieving enterprise excellence.

Almost 60 years later, over 9000 737s have been delivered. It is estimated that around the world at any one time 1000 737s are in flight and that a 737 either takes off or lands four times every minute. In microcosm, the 737 represents the result of five activities all organizations must do to be viable.

Observation is important for a business because it provides basic data about internal and external environments. In the context of the business, this data becomes valuable in the form of information when it is organized and applied towards the accomplishment of purposeful tasks, activities and goals. We all know that there is a difference between observing and observing accurately. We can also agree in general that the more accurate the observation the more likely that actions taken as a result will provide the intended outcome. To put it simply, accurate observations yield higher quality information and as a result, improved outcomes.

Our lesson begins with Abraham Wald who was a mathematician and professor in Vienna in the 1930’s. As a refuge, he fled from Austria to the United States before WWII, after the National Socialists annexed Austria into the Third Reich. While on the faculty of Columbia University, Wald worked for the US Navy’s Statistical Research Group applying statistical analysis to solve problems associated with America’s war effort. Wald’s lesson in observation for us begins in the dark days of WWII, when the outcome of the war was still up in the air, so to speak.

The US Army Air Forces in Europe had been collecting information on aircraft battle damage. The bombers in question flew dangerous missions, some with a staggering 41% loss rate. Battle damage data was a count of both the type of damage, typically from anti-aircraft artillery and fighter aircraft attacks, and its location on the aircraft. The idea was to use the data to make decisions about aircraft modifications. Aircraft are weight sensitive machines, and for every pound added here is a corresponding decrease in either fuel capacity (which decreased operational range) or ordnance load (which decreased mission effectiveness). The intent was to increase the survivability of both crew and aircraft by deciding where to add improvements, like armor.

When doing statistical analysis, the typical approach is to identify and gather data about a population. In this case, the data reflected bombers returning from missions over Europe. Initial observations based upon the data indicated that additional protection of wings and fuselage would improve survivability (see B-17 Bomber: Aircraft top view and with battle damage plot).

When Wald scrutinized the data, he discovered that the ‘population’ in the data was limited to only those bombers that returned from missions. The accumulated data related to areas of damage on aircraft that returned from missions. His observations and questioning of the intent of the research lead him to question what damage had occurred to aircraft that had not returned. In effect, the population should have included all bombers that departed for missions, not just those that returned. This expanded analysis brought up additional questions. The primary question being: What do we know, or what can we understand, about those aircraft that did not return based upon those that did?

Wald’s observations and insights called into question earlier ideas about intended improvements. The assumption can be made that the distribution of battle damage is even across all aircraft. That being said, a statement can be made that aircraft that did not return most likely had damage in those areas that are not represented explicitly in the data. Look again at the diagram. By careful observation and the application of analysis, Wald was able to show that the real areas of concern are those areas on the aircraft that the data showed without battle damage, because aircraft that sustained damage in those areas did not make it back.

The “damage free” areas in the diagram are the cockpit/nose, engines and tail areas. These are areas susceptible to critical battle damage, to the extent that hits dramatically decrease aircraft survivability. The initial suggestion of reinforcing those areas based upon the data collected as opposed to the entire population of aircraft reflects is what is now called “Survivorship Bias”. This is where sample data does not match the actual population in question.

When making decisions based upon observations, strive for accuracy, not only in terms of the data, but also in terms of the subject under scrutiny.

The Lesson for Managers: The intended outcome from observation is accuracy. Accuracy is the degree to which actions result in the intended outcome. In this case, making improvements to areas besides the cockpit/nose, engines and tail would have been a poor decision and most likely would not have dramatically increased survivability. An awareness of Survivorship Bias can help you look beyond the obvious to ask, “What are my intended outcomes?” Taking a seemingly obvious course and drawing inaccurate conclusions results in poor decisions. When we broaden our observational view, and expand our understanding of intended results, we increase the potential to accumulate accurate information about both what we want to do and what the data is telling us. As managers we have a responsibility to use due diligence in making accurate observations which helps us develop more accurate information and as a result, improve the quality of our decisions.

Battle Damage Diagram:

B-17 Bomber: Aircraft top view and with battle damage plot.

Further Reading:

1. Center for Naval Analyses: Wald, A Method of Estimating Plane Vulnerability Based on Damage of Survivors ADA091073 (at https://

2. The Legend of Abraham Wald (at © 2017 Praxis Analytics, Inc. All Rights Reserved.

Attribution: Jim Myers is the principal and founder of Praxis Analytics, Incorporated. Jim serves as a trusted advisor to business leaders in their quest to operate efficiently, improve continuously and prosper. His background includes two decades working in manufacturing, supply chain, customer service and maintenance management roles within markets that range from capital equipment to aerospace and defense. Jim balanced his practical operations experience with theory and served as the Associate Dean of the Atkinson Graduate School of Management (AGSM) at Willamette University where he led projects to improve school operations and taught graduate courses in Operations and Information Management, Strategy Alignment and Project Management.

Jim can be reached at
Praxis Analytics web site:

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