Collaborative shipping typically leads to cost savings. However, it's not always easy to determine each partner's contribution to the gains and to share them accordingly. An industry-oriented method has been tested in a set of pilots and promises to be fair, transparent and not overly complex.

Timely completion of projects is an important factor for project success. However, projects often exceed their predefined deadline, which results in a late project delivery and an increase in the total project cost. In order to increase the probability of timely completion, a project buffer can be planned at the end of a project. During project execution, an assessment of the total buffer consumption at the project completion date can be made in order to periodically monitor the project progress. When the expected buffer consumption is higher than 100%, the project deadline is expected to be exceeded and the project manager should take corrective actions to get the project back on track. In this paper, a new buffer monitoring approach is introduced, which sets tolerance limits for Earned Value Management/Earned Schedule (EVM/ES) schedule performance metrics by allocating the project buffer over the different project phases. The purpose of these tolerance limits is to provide the project manager with accurate and reliable information on the expected project outcome during the project execution. A computational study is carried out to assess the performance of the proposed approach and to compare its performance with traditional buffer consumption monitoring procedures. Additionally, existing performance metrics for tolerance limits have been put into a hypothesis testing framework, and new metrics have been developed in order to fill the detected gaps in performance measurement. Results have shown that the proposed tolerance limits improve the performance of the monitoring phase, especially for parallel projects. Consequently, the underperformance of EVM/ES for parallel projects is mitigated by these limits.

This paper presents five Artificial Intelligence (AI) methods to predict the final duration of a project. A methodology that involves Monte Carlo simulation, Principal Component Analysis and cross-validation is proposed and can be applied by academics and practitioners. The performance of the AI methods is assessed by means of a large and topologically diverse dataset and is benchmarked against the best performing Earned Value Management/Earned Schedule (EVM/ES) methods. The results show that the AI methods outperform the EVM/ES methods if the training and test sets are at least similar to one another. Additionally, the AI methods report excellent early and mid-stage forecasting results. A robustness experiment gradually increases the discrepancy between the training and test sets and demonstrates the limitations of the newly proposed AI methods.