Inclusive and sustainable economic growth supports the transformation of society towards a better quality of life and well-being. Therefore, it is crucial to diagnose the binding factors to growth. However, existing frameworks of growth diagnostics and inclusive growth analytics, which are widely used to analyse growth problems, mainly focused on a snapshot of growth constraints of a selected location at a particular point of time. Applications of these frameworks are not fully capable of addressing the dynamic nature of growth. Moreover, most existing applications of growth frameworks are mainly predictive studies, thus they do not fully support the effort of identifying the root cause of growth problem. This calls for a more comprehensive approach to diagnosing growth problems. Building upon the existing growth diagnostics and inclusive growth analytics frameworks, as well as on cybernetics and systems-based approach, we develop a cybernetics approach to analysing inclusive growth constraints. This approach enables analysis of the dynamic nature of growth, which supports the identification of growth constraint variables and mapping of their relationships, based on data gathered about the location.