Survivorship Bias

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Jan 21

Ever wondered why we tend to hear only about the success stories of entrepreneurs, companies, or even athletes? It's because of a sneaky little cognitive trap called survivorship bias. This phenomenon leads us to focus on the winners, ignoring the countless failures that never make it into the spotlight. In business and manufacturing, this bias can distort decision-making, leading to costly missteps.

A Brief History of Survivorship Bias

The most famous example of survivorship bias comes from World War II, when the U.S. military analyzed the damage on returning aircraft to determine where to add armor. The initial plan was to reinforce the heavily damaged areas, such as the wings and tail. But mathematician Abraham Wald flipped the script. He pointed out that the data came only from planes that survived, meaning the planes that were shot down likely had damage in areas that weren’t accounted for—like the engine. His recommendation? Armor the spots that showed little damage on returning planes, as those were likely the fatal weak points.

This example underscores how failing to consider the "invisible failures" can lead to misguided conclusions.

How Survivorship Bias Affects Decision-Making

Survivorship bias influences a wide range of areas, from finance to health to manufacturing. It manifests in subtle but impactful ways, leading organizations to draw overly optimistic conclusions based on incomplete data. The most common trap is assuming that the visible successes are representative of all possible outcomes, which leads to flawed strategies and unrealistic expectations. Here’s how it manifests:

Business Strategy: Many businesses look at the strategies of successful competitors and assume that replicating them will guarantee success. They fail to consider the numerous failed companies that followed the same strategies but didn’t survive. For example, adopting a market expansion strategy just because it worked for an industry leader ignores factors like timing, resource allocation, and unique competitive advantages that may have played a crucial role in success.
Hiring Practices: Organizations often focus on the traits of successful employees without considering those who failed in similar roles. This results in hiring biases that overlook key attributes like adaptability and resilience, which may not always be visible in the most successful employees but are critical for long-term performance. Companies may invest in certain skill sets, only to find out later that those same traits didn’t guarantee success in different contexts.
Technology Adoption: Companies may implement technologies based on success stories without analyzing the challenges faced by those who failed in similar endeavors. This can lead to costly misadventures where organizations invest in digital transformation tools without addressing the operational or cultural barriers that caused others to fail. Understanding the pitfalls and lessons from those who struggled with implementation could lead to more informed technology investments.

Survivorship Bias in Manufacturing

In manufacturing, survivorship bias can creep in and lead to flawed operational and strategic decisions, often masking critical vulnerabilities and missed opportunities for improvement. Because manufacturing processes are complex and involve many interdependent factors, overlooking failures can have significant consequences, such as reduced efficiency, compromised quality, and increased costs.

Quality Control: When manufacturers focus solely on products that passed inspections, they may overlook critical flaws in the production process that caused failures. This can lead to recurring defects that go undetected until they cause significant damage, such as warranty claims or regulatory non-compliance. By only analyzing successful outputs, manufacturers miss out on the opportunity to improve production standards and eliminate hidden inefficiencies.
Process Improvements: If only successful process tweaks are analyzed, the learnings from failed experiments are lost. Manufacturing facilities might optimize workflows based on best-performing production lines, ignoring the valuable insights from lines that failed to meet targets. This creates a false sense of improvement, potentially leading to the replication of inefficiencies instead of their resolution.
Supplier Selection: Choosing suppliers based solely on those who have long-standing reputations might overlook potential risks that others faced and didn’t survive. A supplier with a flawless record may not have faced the same challenges as competitors who failed, meaning their resilience and adaptability remain untested. Evaluating suppliers that have overcome significant hurdles can provide better insights into their true capabilities and long-term viability.
Maintenance Strategies: Many manufacturers focus on the equipment that has run smoothly without considering the maintenance approaches that failed. This can lead to an over-reliance on preventive maintenance without exploring predictive or condition-based strategies that might prevent failures before they occur.
Product Development: In manufacturing, it’s common to celebrate successful product launches without examining the failures of previous designs or iterations. Ignoring unsuccessful prototypes or market feedback from failed products can result in repeated design mistakes and missed opportunities for innovation.

Example: A manufacturing plant may analyze data from high-performing production lines and assume those are the best practices to follow. However, ignoring failed lines could miss critical insights about machine maintenance, workforce training, or supply chain dependencies. By analyzing both successful and failed initiatives, manufacturers can develop a more comprehensive understanding of what truly drives operational excellence.

How to Avoid Survivorship Bias

To mitigate survivorship bias, consider the following actionable steps:

Seek Out Failures: Actively analyze data from both successful and failed projects to get a holistic view.
Conduct Root Cause Analyses: Look beyond the surface-level successes to identify underlying factors that contribute to both success and failure.
Diversify Data Sources: Use data from multiple sources, including failed initiatives, to make well-rounded decisions.
Challenge Assumptions: Always question if the data you're relying on includes all perspectives or just the survivors.
Use Controlled Experiments: In manufacturing, conduct trials with different variables to identify what truly works rather than what just appears to work.

Survivorship bias is an invisible but powerful force that can skew our perception of reality, leading us to draw incorrect conclusions and make misguided decisions. Whether you’re leading a manufacturing operation, investing in new technologies, or making strategic business decisions, always remember: the failures that didn’t make it to the final report can teach you just as much, if not more, than the success stories.

By taking a balanced, data-driven approach and acknowledging the hidden failures, businesses can build more resilient and informed strategies that stand the test of time.

So next time you’re inspired by a success story, ask yourself—what are you not seeing?