Move suppression coefficients serve a dual purpose in the model predictive controller (MPC) architecture. These include suppressing aggressive control action and conditioning the system matrix prior to inversion. The work presented here exploits this dual effect in deriving an analytical expression that computes appropriate move suppression coefficients as a function of process model parameters, other MPC design parameters, and partitioned block condition numbers of the system matrix. The development is based upon an approximate mosaic Hankel matrix structure of the multivariable system matrix. The primary contribution of this work is the derivation of the analytical expression for computing move suppression coefficients and its demonstration in an overall MPC tuning strategy (Table 1). The examples presented show that the move suppression coefficient remains properly scaled as the other MPC design parameters and process characteristics change to produce a consistent closed loop performance. This tuning method is applicable to unconstrained multivariable processes, including non-square systems.