From failure possibilities to proven prevention.

Super Advanced Anticipatory Failure Determination is a structured approach to understanding the conditions that make failure possible—and interrupting them in a way that lasts.

The central idea

Make the failure pathway visible.

A failure is rarely explained by one isolated event. It emerges when triggers, conditions, decisions, weaknesses, and gaps in protection come together. SAAFD makes those connections the object of investigation.

The goal is not to invent an impressive list of potential causes. It is to build a pathway that can be tested, challenged, and improved.

The sequence

Six connected phases

The phases are a thinking pathway, not a substitute for safe engineering judgment, established testing protocols, or verified data.

Frame the Failure Target

State the specific undesirable result, the process or system at issue, its operating boundaries, and how success or failure can be recognized. Separate observable facts from interpretation before investigating causes.

Key question: Exactly what outcome are we trying to prevent—and under what conditions could it occur?

Output: a bounded, evidence-ready failure statement.

Invert the Pathway

Reverse the usual question. Instead of only asking what caused the failure, ask what would have to happen to produce it reliably. Explore mechanisms, enabling conditions, interactions, and missing safeguards.

Key question: If this failure had to occur, what sequence of conditions would make it possible?

Output: candidate pathways and mechanisms to investigate.

Structure the Failure Recipe

Organize the candidate pathway into a coherent recipe: triggers, resources, sequence, timing, system weaknesses, detection gaps, and control failures. Highlight assumptions and missing evidence.

Key question: Which ingredients are necessary, which are only possible contributors, and how do they interact?

Output: a structured, testable failure-recipe model.

Prove the Path

Use available data, observations, comparisons, and safe experiments to evaluate the proposed mechanism. Disconfirm attractive explanations where possible, document uncertainty, and avoid treating correlation as proof.

Key question: What evidence would convince us the pathway is real—and what would disprove it?

Output: an evidence-supported path with confidence and gaps made explicit.

Break the Recipe

Design controls that remove, separate, block, or detect the critical failure-enabling conditions. Prioritize reliable system changes over instructions that depend entirely on memory or vigilance.

Key question: Where can we break the necessary chain with the strongest practical control?

Output: prevention actions tied directly to supported failure mechanisms.

Lock the Learning

Verify that the changes work in actual conditions. Update standards, controls, training, ownership, and review cycles so the organization learns beyond a single incident and can detect drift early.

Key question: How will we know the prevention keeps working after the project closes?

Output: sustained controls, verification, and shared learning.

How it works with other tools

Build on what teams already know.

SAAFD is intended to strengthen established quality methods. It can guide when to use problem statements, process mapping, fishbone analysis, FMEA, measurement studies, safe testing, control plans, and corrective action systems.

Human judgment remains essential.

AI may help generate questions, compare patterns, organize findings, or challenge an investigation. It cannot independently verify a mechanism or approve a prevention control. People remain responsible for evidence, safety, and decisions.

Discuss the methodology

Go deeper

Read the thinking. Learn the practice.

The SAAFD book and upcoming learning programs expand each phase into practical methods and exercises.

Discover the Book