Performance assessment of model predictive control (MPC) systems has been focusing on evaluation of the variability with, for example, minimum variance or LQG/MPC tradeoff curve as benchmarks. These previous studies are mainly concerned with the dynamic performance of MPC. However, the benefit of MPC is largely attributed to its capability for economic optimization. The economic performance, on the other hand, is also dependent on the variability reduction achieved through dynamic control. There is a need to assess MPC performance by considering economic performance, variability reduction, and their relationships. One of the good indications of this relation is the constraint tuning. In practical MPC applications, the constraint setups are important whenever an MPC is commissioned, and constraint tunings are not uncommon, even when the MPC is already on-line. Thus, the questions to ask are which constraints should be adjusted, and what is the benefit to do so? By investigating the relationship between variability and constraints, problems of interest are solved under the Bayesian inference framework. (namely, through the Bayesian approach for decision evaluation and decision-making). The decisions that are referenced are whether to tune the constraints to achieve the optimal economic MPC performance and which constraints should be tuned. A detailed case study for a distillation column MPC application is provided to illustrate the proposed performance assessment methods.