Classic methodologies of DOE are widely applied in design, manufacture, qualitymanagement and related fields. The resulting data can be analysed with linear modelingmethods such as multiple regression which generates a set of equations, Y = F(X), thatenable us to understand how varying the mean of one or more inputs changes the mean of oneof more responses. To develop, scale-up and transfer robust processes to manufacturing wealso need to set the control tolerances of each critical X and understand the extentto which variation in the critical X’s propagate through to variation in theY’s and howthis may impact performance relative to requirements (or specifications). Visual toleranceanalysis provides a simple way to understand and reduce propagation of variation fromX’s toY’s usingmodels developed from DOE’s or historical data. This paper briefly introduces the conceptof tolerance analysis and extents this to visual tolerance analysis through defectprofiles and defect parametric profiles. With the help of visual tolerance analysis,engineering and statistical analysts can work together to find the key factors responsiblefor propagating undesired variation into responses and how to reduce these effects todeliver a robust and cost effective process. A case study approach is used to aidexplanation and understanding.