Structured Problem Analysis
Generate plausible explanations for a problem, group them into categories, and test them against the evidence. In the bakery example, human, mechanical, material, and interacting causes organize the investigation.
THE IDEA
Structured problem analysis is a systematic approach to diagnosing complex issues. It involves two main phases: diverging and clustering.
Diverging
First, brainstorm as many potential explanations for the problem as possible, without judgment. For example, in the Bakery case, explanations for overbaked bread included sabotage, carelessness, fatigue, drunkenness, flaws in work plan, malfunctioning oven gauge, rack, blower, gas pressure, and spoiled ingredients.
Clustering
Next, group these explanations into meaningful categories. In the Bakery analysis, four categories emerged:
- Human: Intentional acts (sabotage) or errors (carelessness, fatigue, etc.)
- Mechanical: Equipment failures (thermostat, racks, blowers, gas pressure)
- Materials: Faulty ingredients (e.g., new flour from a different supplier)
- Combinations: Interactions between the above
Each category is then analyzed separately, using evidence from the situation to support or rule out explanations. This can help challenge an early assumption, such as Willis’s focus on sabotage, and identify what evidence to gather next.
The categories organize possible causes; they do not guarantee that every explanation has been considered. Revise them if new evidence does not fit.
When to use it
Diagnosing why a production line is producing defective items
Investigating the cause of a sudden drop in sales
Analyzing the reasons behind team conflict in the workplace
Solving a complex technical issue by systematically ruling out categories of causes
FROM THEORY TO EVERYDAY LIFE
In practice
The Bakery Overbaking Problem
A bakery experiences three batches of overbaked bread during the busy Thanksgiving period. The production manager suspects sabotage or mechanical issues. Using structured analysis, the problem was explored by generating plausible explanations (sabotage, careless error, thermostat malfunction, bad flour, etc.) and clustering them into human, mechanical, materials, and combinations. This systematic approach helps avoid bias and focuses on evidence, such as the arrival of flour from a new supplier and the timing of the burns.
A MOMENT TO REFLECT
Put it to work.
When faced with a complex problem, how can you generate a wide range of possible explanations before jumping to conclusions?
In what ways can categorizing explanations into groups help you avoid bias in problem analysis?
Think of a recent problem you encountered. How could you apply the structured analysis method to it?
Often confused with
CONTINUE THE CONVERSATION
Source texts
- The Thinkers Toolkit by Morgan D Jones