We live in a world where problems aren’t just growing—they’re evolving into ever-more complex challenges. During the 20th century, we pushed the boundaries of innovation, creating complicated systems that demanded structured problem-solving approaches. Techniques like 5 Whys and the Ishikawa fishbone diagrams emerged, helping individuals and teams tackle the technical hurdles of that era. These methods, grounded in classic logic, empowered us to master the complicated.

But today, we face a new kind of challenge. Problems once neatly defined are now deeply intertwined, influenced by countless variables and cascading effects. In this landscape, the structured methods of the past are no longer sufficient on their own.

To stay ahead, we must adapt. The 21st century demands a shift from mastering the merely complicated to navigating the truly complex. This means merging once-discrete problem-solving methods into unified, resilient approaches, and embracing innovation. This isn’t an option but a necessity.

One example of such a methodology merger would be a combination of the human and organizational performance (HOP) approach and root cause analysis (RCA) for a greater overall understanding.

Grounding the challenge in quality management

Although problems span multiple dimensions, let’s ground this discussion in the world of quality management with a fictional example.

Consider a jet engine manufacturer that discovered a critical issue during a routine audit: A batch of nuts that should have undergone heat treatment had not been properly processed. These nuts were supplied by Supplier A, which outsourced heat treatment to Supplier B. Due to a misstep during production, part of the batch never went through the heat-treatment process. A technician on the day shift at Supplier B partially completed the batch, but when the night shift technician took over, they assumed the work was finished. Without verifying, the night shift signed off on the paperwork, and the batch was packaged and sent back to Supplier A.

Supplier A, relying on Supplier B’s documentation, did not perform hardness testing and shipped the nuts directly to the engine manufacturer, which had certified Supplier A as a “dock-to-stock” supplier. This certification allowed shipments to bypass regular incoming inspections. The issue went unnoticed until a periodic audit revealed that some nuts failed hardness testing. Upon further investigation, a mix of treated and untreated nuts was found in the batch, prompting an urgent traceability exercise to determine the extent of the condition.

Thanks to effective traceability protocols, the manufacturer could confidently confirm that no untreated nuts had been installed on engines or shipped to customers.

A reflexive response: Blame the guilty

A reflexive response to this problem might be to “find and blame the guilty.” In manufacturing, this approach often triggers a cascading chain of accountability: The regulator penalizes the prime manufacturer, the manufacturer pressures Supplier A, Supplier A chastises Supplier B, and Supplier B ultimately disciplines the technician. Problem solved, right? Not quite.

Classic root cause analysis techniques, like 5 Whys, tend to “prune” off the most important parts of the problem—the contextual contributors. They reduce complex problems to oversimplified chains of events. While this oversimplification isn’t necessarily wrong, it’s not complete. Methods such as 5 Whys or Ishikawa fishbone diagrams often lead us to identify humans as the so-called “root causes” of problems. This perspective naturally results in human-centric solutions like retrain, remind, reinforce—or even disciplinary action.

By focusing too narrowly on human error, organizations risk ignoring the broader systemic factors that allow such errors to occur.

By focusing too narrowly on human error, organizations risk ignoring the broader systemic factors that allow such errors to occur. The real danger lies in believing the problem has been solved while the underlying contributors remain untouched.

Evolving to meet the challenge

It’s out of this need to innovate that companies like Sologic are born. Leading providers of root cause analysis—including Sologic, Kepner-Tregoe, System Improvements (TapRooT), ThinkReliability, Apollo, Reliability Center Inc. (RCI), and others—largely emerged to address the shortcomings of original RCA methods like 5 Whys and the fishbone diagrams. These newer approaches still have their roots in classic logic, but they offer critical enhancements.

For one, they recognize that problems are rarely the result of a single cause but instead arise from multiple, interrelated contributors. This shift away from reductionist thinking allows for a more comprehensive understanding of complex events. Additionally, these methods challenge the notion of human error as the ultimate root cause. By delving deeper into the causes and conditions that lead to errors, they move beyond blame to uncover systemic contributors and resilient solutions.

Modern software tools, such as Sologic’s Causelink, amplify these advancements. Causelink combines structured methodologies with the power of data analytics and visualization. They enable teams to map complex webs of causation and identify solutions. When integrated with AI, these tools take problem-solving to the next level. AI serves as a “digital participant” with a unique perspective, capable of processing vast amounts of data, identifying patterns, and suggesting novel solutions that might elude even the most experienced teams.

Applying 5 Whys to the heat-treated nuts incident

Let’s revisit the case of the mixed batch of heat-treated nuts to see how traditional methods like 5 Whys might approach the problem.

Problem: Nonheat-treated nuts available at final assembly

• Cause 1: Nonheat-treated nuts delivered to the engine manufacturer by Supplier A
• Cause 2: Nonheat-treated nuts delivered to Supplier A by Supplier B
• Cause 3: Supplier B error—creation of a mixed batch of heat-treated and nonheat-treated nuts
• Cause 4: Batch mixed up at shift turnover
• Cause 5: Human error—technician mistake

This sequence may seem logical and, to an extent, it is. However, by stopping at “human error,” the analysis fails to explore deeper systemic contributors. Why did the technician assume the batch was complete? What conditions or gaps in the process allowed this mistake to occur? Why was there no verification process in place to catch the error before it escalated?

By focusing narrowly on individual actions, the 5 Whys approach misses critical contextual contributors, such as process design, communication protocols, and cultural reliance on paperwork over verification. This oversimplification leads to human-centric solutions like retraining or discipline, which, while perhaps necessary, are unlikely to prevent recurrence without addressing the systemic factors at play.

The effects of time pressure

Another dimension of this problem is the ever-present time pressure to deliver answers when issues arise. Investigating, analyzing, and solving problems is often constrained to a very narrow window.

In this environment, the goal is to demonstrate that ‘action has been taken,’ often at the expense of deeper learning and systemic improvement.

This urgency shifts the focus from learning to compliance. The immediate concern is no longer the fact that noncompliant parts were nearly installed on an airplane engine. Instead, the problem becomes how quickly the organization can satisfy the regulator or reassure the customer. In this environment, the goal is to demonstrate that “action has been taken,” often at the expense of deeper learning and systemic improvement.

The result? Investigations may focus on finding a single cause or responsible party, and solutions often aim to appease external pressures rather than prevent recurrence. This reactive approach perpetuates a cycle where the same issues are likely to resurface under slightly different circumstances.

Integrating human and organizational performance

The next step in the evolution of organizational learning is to explore the advancements made in the realm of human and organizational performance (HOP). Although these principles have historically been championed in the field of safety, they are increasingly finding applications in quality, reliability, and operations. Thought leaders like Sydney Dekker, Todd Conklin, Erik Hollnagel, Tony Muschara, and others have synthesized these concepts into practical frameworks, helping organizations better understand and manage complexity.

Conklin’s HOP principles, for example, serve as a foundational guide:
• Error is normal.
• Blame solves nothing.
• Context drives behavior.
• Learning is mandatory.
• How we respond matters.

While HOP has brought critical insights to light, its early proponents often criticized traditional structured problem-solving methods as inherently flawed. This criticism (fair, in many cases) stemmed from the tendency of classic root cause analysis techniques to blame individuals and ignore the role of systemic and contextual contributors.

Fortunately, forward-thinking leaders across both domains (HOP and RCA) are moving beyond this false dichotomy. By recognizing the complementary strengths of these approaches, organizations are leveraging HOP principles as a crucial layer within structured problem-solving.

By recognizing the complementary strengths of these approaches, organizations are leveraging HOP principles as a crucial layer within structured problem-solving.

At its core, HOP creates a fertile substrate for learning. It enables teams to harness diverse perspectives, knowledge, and experiences under a set of shared principles. This collaborative approach amplifies the impact of structured methods, blending process with the human-centric insights needed to navigate complex systems. Together, these methodologies offer a powerful tool kit for addressing the multifaceted challenges of the 21st century.

Applying HOP principles to the mixed nuts example

Revisiting the supplier quality problem, HOP principles guide us to ask a fundamental question: What does work “normally” look like at each stage of the process? By exploring the context in which work occurs, we can learn from the people closest to it—those who manage risks, adapt to challenges, and ensure the system functions. This marks a crucial paradigm shift: Workers are not the source of our problems—they are the source of our successes.

Humans can be inherently variable and unpredictable, a lesson learned the hard way by the fictitious machine overlords in the Matrix movies. But this variability is, in many ways, humanity’s greatest strength. Humans are highly adaptable, and in a world of increasing complexity, we must rely on our employees to recognize, adapt to, and overcome problems as they arise. This means anticipating human error and designing resilient systems that can fail safely without causing harm to people, processes, or products.

For instance, consider the night shift technician who unknowingly packaged mixed parts. What can we learn about their experience working under high load, with limited training, inconsistent processes, and minimal oversight? Or consider the quality manager tasked with running a just-in-time inventory system at a jet engine manufacturer. What insights can they offer about the brittleness of processes under production pressure, and the trade-offs inherent in lean manufacturing?

By engaging these perspectives, HOP principles allow us to uncover contextual contributors—those less obvious factors that shape outcomes in complex environments. This approach not only deepens our understanding of specific incidents but also reveals the realities of life in the world of heat-treated parts production amid escalating complexity.

This evolution in organizational learning enables us to systematically embrace complexity. By fostering a collaborative learning culture, we can leverage existing tools and processes while adapting to new challenges, creating resilient systems that thrive in a dynamic world.

Conclusion

The challenges of the 21st century demand that we evolve beyond the tools and approaches of the past. By recognizing the complementary strengths of structured problem-solving methodologies, human-centric HOP principles, and the capabilities of modern software and AI, we can address complexity with greater agility and resilience.

The integration of these approaches represents not merely a refinement of problem-solving but a transformation—one that empowers organizations to learn, adapt, and thrive in the face of ever-evolving challenges.