Multifactorial Failure Analysis (Root Cause Analysis)
Multifactorial failure analysis investigates failures or quality deviations by examining how several contributing factors interact. It systematically links process, material and test data to trace how these factors jointly led to a failure.
The term is frequently used in connection with root cause analysis. Rather than focusing on a single underlying cause, multifactorial failure analysis examines how several, partly independent factors can combine to cause a failure.
Why failures often have multiple causes
In materials development and industrial manufacturing, failures can arise through the interaction of several factors. For example, a component can fail because of the way the manufacturing process, environmental conditions and material structure interact. Anyone considering only one factor risks overlooking significant relationships.
An example from battery research illustrates this. A review study on dendrite formation in solid-state batteries shows that lithium metal anodes fail because several degradation processes interact. The researchers emphasise that the problem cannot be solved by addressing a single factor, since void formation, grain boundary effects, electronic conductivity and the diffusion rate of lithium all influence one another. Only by considering these factors together can we understand how the failure occurs.
The same principle applies to failure analysis in laboratories and production: process parameters, material properties and test results must be considered together to draw sound conclusions about the cause of failure.
How multifactorial failure analysis works
At its core, multifactorial failure analysis links three types of data:
- Process data: parameters from manufacturing or an experimental set-up, such as temperature, pressure or number of cycles.
- Material data: material properties and composition, such as purity, structure or formulation.
- Test data: results from tests and measurements that document deviations or failures.
Only when these types of data are considered in relation to one another can we identify patterns that often remain hidden when we examine a single data source. If process, material and test data sit in separate systems or documents, linking them becomes harder in practice.
Related methods
Multifactorial failure analysis is a methodological approach that can be combined with various analytical tools such as cause-and-effect diagrams (also known as fishbone diagrams) or structured failure mode and effects analyses (FMEA). The key is to include multiple data sources consistently.
Why this is relevant for R&D and quality assurance
If a failure is incorrectly attributed to a single factor, corrective measures often do not achieve the desired result: the actual problem persists and recurs later. Considering multiple factors reduces this risk, but requires structured data that allow process, material and test data to be linked.