How are we able to fix it so as to theoretically go over it, if the optimality and efficiency of the orbital angular momentum (OAM) multiplexing technique in a multi-input multi-output (MIMO) system fails in practice -- something that technically causes a considerable reduction in the maximum achievable value of the transceiver's degree-of-freedom? Is the aforementioned challenging issue theoretically interpretable? This paper considers the above mentioned challenge in the reconfigurable intelligent surface based systems -- as an exemplified system model -- and consequently proposes a Mendelain randomaisation (MR) technique through which we can further overcome the aforementioned challenge. Towards such end and in this paper, we take into account a Markov chain $\mathcal{G}-\circ-\mathcal{X}-\circ-\mathcal{Y}-\circ-\mathcal{V}$ while $\mathcal{G}$, $\mathcal{X}$, $\mathcal{Y}$ and $\mathcal{V}$ respectively denote transmit design parameters, quality of goods, error floor in data detection and non-orthogonality set in the OAM implementation -- arisen from other non-optimalities, disturbances and inefficiencies such as the atmospheric Kolmogorov turbulence. Furthermore, we consider a not-necessarily-Gaussian randomisation method according to which the resultant probability distribution function (PDF) is 2-D in relation to a definable outage probability in the design problem in relation to $\mathcal{Y}$ -- that is, in terms of $\beta_{\mathcal{X}}\theta$ while the randomisation is applied to $\theta$ as the control input which should be optimally derived as the graph-theoretically associating parameter between $\mathcal{X}$ and $\mathcal{Y}$, and $\beta_{\mathcal{X}}$ is the the associating parameter between $\mathcal{G}$ and the exposure $\mathcal{X}$.