The assembling of components coming from manufacturing processes characterized by high dimensional variation has been traditionally approached from two different perspectives: selective and adaptive assembling. Most recent techniques, however, usually adopt a combination of these approaches. A disadvantage of selective techniques is the need of inspecting and classifying all the component items that have to be assembled. Adaptive assembling techniques usually do not use all the information available and only take into account the last inspected sample to estimate the necessary adjustments. None of these approaches consider the nature of the variation as a superposition of different variation forms that evolve independently over time.This thesis embraces the challenge of developing an assembling technique to deal with the problem of producing assemblies that have low dimensional variation by means of mating components that have high dimensional variation. The main objectives of the proposed model are the reduction of the resulting variation, the reduction of the scrap levels and the improvement of the process capability indices. The innovative approach proposes the dynamic management of specifications, target and tolerance, using the Statistical Dynamic Specifications Method (SDSM) and the assembling of complementing groups using the Statistical Feed-Forward Control Model (SFFCM). While SDSM can be seen as a statistical tool that helps determine the right specification adjustments using reduced samples taken from groups of items produced consecutively during a short-time interval; SFFCM can be seen as a monitoring tool that helps counter the effect of an eventual "detectable" long-term component present in the variation.With the help of proprietary software, the production of lots of one thousand assemblies made of two components coming from non-capable processes (c p1.33) was obtained by means of combining two non-capable subprocesses.