Uncertainties have a negative impact on the duration of project activities. When an activity faces the higher uncertainty, it is likely to experience larger fluctuations in its duration. This increases the risk of delays. However, classical buffer monitoring methods usually adopt the setting mode of uniform and fixed monitoring time points for different activities, failing to account for differences in uncertainty levels between them, which reduces the effectiveness of project schedule control. Therefore, we propose a dynamic buffer monitoring method combining buffer monitoring and forecasting. Firstly, a duration prediction model based on support vector machine is established to predict the duration of the subsequent activity relying on the duration data of completed activities. Secondly, the buffer consumption rate is calculated according to the predicted activity duration and the corresponding monitoring frequency is obtained. Matlab is finally utilized to verify the method proposed in this paper. The results show that compared with classical buffer monitoring methods, the proposed method achieves the dual optimization of project duration and cost.

PurposeThe activities using drum resources restrict the operation of multi-project systems. However, existing monitoring methods are not suitable for the characteristics of drum activities in the multi-project system. The authors therefore propose an adaptive capacity constraint buffer monitoring model based on the attributes of drum activities, aiming to build a high-efficiency progress control framework for multiple projects.Design/methodology/approachConsidering the attributes and the interrelationship of drum activities, the monitoring reference points are determined on the basis of decentralized buffers. The authors next set action thresholds according to the relationship between the drum activities' interval margin and buffer consumption, and then the corresponding monitoring measures are taken.FindingsThe empirical results show that, compared to the classic methods, the proposed approach can effectively monitor the progress of the drum plan and realize the dual optimization of multi-project duration and cost.Research limitations/implicationsThe buffer consumption at the follow-up monitoring time point is neglected when determining the action thresholds. Prediction methods can be introduced to present more all-sided monitoring.Practical implicationsThis paper fulfils the dual optimization of multi-project duration and cost. It provides a reference guide for project managers.Originality/valueA capacity constraint buffer monitoring method suitable for a multi-project environment is produced.