Research tools for analysing design margins in complex engineered systems
Three integrated modules — parametric margin valuation, top-down margin allocation, and probabilistic margin risk — built around the Margin Value Method.
Map components onto a Design Structure Matrix, capture the likelihood and impact of change propagation between them, and find the couplings most likely to consume your margins.
Margins do not fail in isolation. A change introduced anywhere in a system — a new requirement, a different supplier, a redesigned subassembly — tends to propagate along the couplings between components, and what eventually consumes a margin is rarely the change that started the chain.
This module captures couplings as a Design Structure Matrix (DSM) and runs Clarkson's Change Prediction Method (CPM) on it. The result is a matrix of combined risk values that show, for any pair of components, how likely a change in one is to reach the other through the network — and how big that impact is expected to be.
The module follows Clarkson's CPM — the de-facto reference method for change propagation analysis — with a margin-focused interpretation.
For each ordered pair of components, capture the probability that a change in the source will require a change in the target.
For each pair, capture the average severity of the propagated change — the size of the redesign that would be triggered.
The CPM algorithm composes direct probabilities along all paths between two components, yielding combined likelihood, impact, and risk matrices.
What the module gives you on top of a paper-and-spreadsheet DSM.
Add components, group them into subsystems, and edit direct likelihood and impact in a clean matrix view with keyboard navigation.
Visualise CPM results as a heatmap on the same DSM, so you can spot risk hotspots and dominant propagation paths at a glance.
Drill into any component to see its incoming and outgoing risk profile, ranked by likelihood, impact, or combined risk.
Edit a direct value and see the combined-risk matrix update live, so you can test the effect of decoupling, redesign, or interface standardisation before you do it.
This module addresses the question that Margin Value Analysis deliberately leaves to one side: which margins are likely to actually be needed?
Once Margin Value Analysis has identified which margins are valuable in principle, this module weights those margins by the expected change activity in their part of the system — the components most exposed to propagating change are the ones whose margins most need to be preserved.