Optimizing multi-project resource allocation with the combination of project performance metrics and AI techniques

In our recent webinar with my colleague Stephen Devaux, an expert in project management with 36+ years of experience, we discussed one of the effective project management techniques, the DeVaux Index of Project Performance (DIPP), which Steve developed in the 90s. It has significantly influenced project management by improving resource allocation and letting companies gain maximum profitability.

Inspired by the decision-making processes of chess players, Steve identified parallels with project management, specifically the timing of project termination or continuation. This epiphany led to his first major contribution: a paper on the DeVaux Index of Project Performance (DIPP), which was published in the Project Management Journal in 1992. The DIPP provided a new way to evaluate project performance by integrating various metrics, and it marked the beginning of Steve's journey into developing sophisticated project management tools.

So, let’s briefly overview the technique and learn the ways it contributes to the optimization of resource allocation in multi-project environments.?

Original DIPP

The original DeVaux Index of Project Performance (DIPP) is a comprehensive metric that considers the total project contribution margin, opportunity cost, and cannibalization worth, all divided by the cost estimate to complete. This metric was designed to help project managers make informed decisions about whether to continue or terminate projects based on their potential profitability, i.e., it helps evaluate the potential value of a project.?

Simple DIPP

As the "opportunity cost", "cannibalization worth", and "project termination costs" indicators are required when we plan to abort a project only, we don’t need them in our daily project management routine. All we need to know is the value of the project and the cost estimate to complete it. Therefore, recognizing the need for a more streamlined approach, Steve introduced the Simple DIPP. This version simplifies the original metric by focusing on the expected monetary value of the project divided by the cost estimate to complete. This simplification makes it easier for project managers to use the metric on an ongoing basis to track project performance and make adjustments as needed.

Project Tracking DIPP

The Project Tracking DIPP integrates actual DIPP values with planned DIPP values to provide a clear picture of project progress. This approach helps project teams and AI systems track and optimize project performance in real-time. By comparing the actual performance against the planned one, teams can identify issues early and make necessary adjustments to ensure project success.

Application of DIPP with AI

One of the most exciting aspects of Steve's work is the integration of DIPP metrics with artificial intelligence. AI can analyze vast amounts of data to optimize multi-project resource allocation, ensuring that resources are used where they will provide the greatest value. By evaluating different resource allocation scenarios, AI can help project managers make data-driven decisions that maximize portfolio profitability.

For example, when you add a new project to the portfolio, AI can predict the impact on overall project performance. It can identify resource bottlenecks and suggest optimal resource allocation strategies to minimize delays and maximize project value. This capability is particularly valuable in complex multi-project environments, where manual analysis would be impractical.

Therefore, using metrics like DIPP in combination with AI, project managers can better understand the value of their projects, make informed decisions, and optimize resource allocation for maximum profitability.?

References?

Jan Willem Tromp, Steve Devaux (2024). Webinar “Optimising Allocation of Resources in a Multi-Project Environment”.?