The design and certification process of composite structures relies on the building-block approach, which starts from the mechanical characterization of the material at coupon-level. The certification of composite laminates is thus the first challenge and requires the definition of the design allowables, which are statistically defined as established by the Composite Material Handbook. These quantities are strength or strain values indicated as A- or B-basis and an accurate estimation is needed in order to guarantee the integrity of the structure. Given the statistical nature of material allowables, a high number of experimental tests have to be performed for the full mechanical characterization of a material. Notched and unnotched allowables are determined under different testing conditions and in accordance to the standards specified by American Society for Testing and Materials. Different methodologies are presented in literature and explore the combination of simulation-based solutions supported by test-evidence of a reduced number of coupons. In the recent years, several approaches have been explored in the field of uncertainty quantification and Machine Learning demonstrating to be powerful alternatives to full testing-based or simulation-based solutions. A comprehensive literature review is presented in this contribution among the main solutions for the definition of material allowables. This review demonstrates that simulations supported by a reduced number of tests can reach accurate results and the number of simulations can be further reduced by combining the numerical models with predicting theories.

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