In today's highly competitive and rapidly changing marketplace, project managers attach great importance to dealing with the uncertainty in order to avoid schedule disruptions. The critical chain scheduling and buffer management (CC/BM) technique has shown to provide an effective approach for coping with schedule variability. Yet, the current buffer monitoring mechanism faces a problem of neglecting the related activity uncertainty information and the dynamic feature of the project execution. In order to overcome these deficiencies, this paper develops a new two-stage buffer monitoring approach(SBMA) based on a statistical process control procedure. In the first phase, our approach allows the user to set the duration variation that is acceptable on the activity level, which will result in tolerance limits of the buffer consumption at the project level in the form of a statistical buffer control chart(SBCC) by using Monte Carlo simulation. In the second phase, the observations of schedule deviations measured by the buffer consumption can be plotted against the tolerance limits on the SBCC, and the corresponding action decisions can be derived by using the hypothesis testing theory. A computational experiment has been set up to compare the capability of the proposed SBMA versus the existing traditional/relative/dynamic buffer monitoring approach in the literature. The results demonstrate that the SBMA can report warning signals more accurately and hence trigger control actions more timely, while maintaining a comparatively high probability of successful detections. Besides, the SBMA is more capable of differentiating between variations that are either acceptable or unacceptable in the duration of the individual activities.