Turns workflows, data, causes, and improvements into a tangible result by describing the deviation, choosing a comparison case, and deriving causes and actions.
Which differences between should and is explain the observed deviation, and which of those differences are causal, contributing, or random?
The method helps clarify workflows, data, causes, and improvements in a concrete way. It looks for patterns, causes, weak points, or bottlenecks and derives measures from them. The result is captured as a change matrix, cause hypotheses, and validation questions.
The team follows the steps: describe the deviation, choose a comparison case, collect differences systematically, check which differences matter, and derive causes and actions. Each step is captured visibly. At the end, a change matrix, cause hypotheses, and validation questions are available so decisions, tests, or actions can follow directly.
Method sketch for a quick mental model.
The runsheet guides execution with 5 phases, timeboxes, 5 pitfalls, and clear stop criteria.
Open runsheetChange Analysis uses comparison as the main move: what is different from before or from a working case. The method does not search broadly for every possible cause but focuses on relevant changes around the time of the deviation. Differences become hypotheses that can be validated with data or observation. That makes hidden process, material, or context changes visible.
Prepare a good comparison case and collect data on time, place, people, material, and process. Keep the discussion strictly on what changed and separate relevant from random differences. Every change should turn into a testable cause hypothesis.
Compact working template for Change Analysis with context, input, output artifacts, and next step.
| Element | Description | Rating | Evidence | Owner | Next step |
|---|---|---|---|---|---|
| 1 | |||||
| 2 | |||||
| 3 |
What consequence follows from the matrix?
Short decision aid for existing alternatives.
Statt Change Analysis, wenn keine klare Veränderung als Auslöser vorliegt und ihr eine einfache Ursachekette sucht.
Statt Change Analysis, wenn du Ursachen in klaren Kategorien sammeln und Diskussionen strukturiert halten willst.
Turns workflows, data, causes, and improvements into a tangible result by collecting event data, reconstructing the timeline, and deriving root causes and measures.
Turns workflows, data, causes, and improvements into a tangible result by forming the team, describing the problem, and verifying correction and prevention.
Turns workflows, data, causes, and improvements into a tangible result by defining the top event, collecting direct causes, and deriving critical paths and measures.
Paired data points reveal whether two variables move together and where a suspected relationship needs deeper proof.
Turns technical problems, hypotheses, and next steps into a tangible result by clarifying the symptom and system boundaries, breaking the fault space into areas, and narrowing the affected area.
Turns technical problems, hypotheses, and next steps into a tangible result by collecting symptoms, formulating hypotheses, and refining them through testing.