Mature project management is critical for innovations. At the same time, innovative technologies could also be applied for project delivery. This would speed up technology development and implementation even further. Next-Gen technology platform for Project Risk Management could completely change our view of Project Management in the near future. Are you ready for this change?

In episode N S3E55 of Project Chatter podcast two experts discussed “How Technology is driving Next-Gen Project Risk Management”.  

Both presenters have perfectly described various challenges in taking existing systems for project risk management to the next level. I don’t think they have mentioned though that such “intellectual project management systems” already exist and even widely used in some parts of the world, especially for major infrastructure projects. I am also very passionate about this topic and would like to discuss this a bit further.

There are the key challenges and ideas that I’ve captured from the podcast episode:

  • Standardisation of project management data is the most critical constraint now. We have a lot of historical data, but it is hard to reuse.
  • There is no feasible progress in risk management functionality in Primavera and, even, there are no expectations of having any valuable improvement soon.
  • Construction, comparing to the Oil & Gas industry, is not mature in risk management.
  • New methods to manage project risks expected from AI systems.
  • Risk management has to be integrated with a scheduling platform to embed results of the schedule risk analysis back into the schedule.
  •  It is hard to educate students and PMs to apply risk methods correctly as Risk platforms are too expensive.
  • Next-Gen systems are going to support us with Risk identification process (based on historical data).
  • Role of schedulers could be significantly different after such systems implemented.

Let’s review how the above challenges addressed in the “Next Gen” project management tools and where this could take us in the future. I am going to use Spider Project, project management tool, as an example. This tool has the most mature schedule risk management and very sophisticated authorisms, but there are other tools on the market, which could offer similar solutions.


  • Majority of project schedules are duration or effort driven. It makes data standardisation process very difficult.
  • Duration in many project activities depends on assigned resources, their current availability, practical quantity and productivity.
  • Activities in different projects may have the same type of work but have different volume of work which also has an impact on activity durations.

Data standardisation has to be volume-based

Corporate norms (or standards) usually include all aspects of project management:

• Standard WBS(es);
• Types of work;
• Type of resources (people & equipment);
• Skills;
• Resource productivity;
• Material norms;
• Cost per unit;

Activity durations in different projects are calculated based on a volume of work and available resources. 

It is a dynamic characteristic, not a fixed type of data.

If the volume of work or resources demand/supply is changed, the system could automatically re-adjust durations, offer the best sequence of work and recalculate resource critical path.

Risk Database

Additionally, corporate norms should include “historical risks” based on the lesson learned from previous projects. Enterprise portfolio corporate standards include not only estimates of the typical process, activity, resource, and assignment parameters but also project templates.  Risks are converted into “scheduling fragments” with a list of mitigation and/or action plan activities. 

Probability of each risk estimated initially for each project with historical data taking into the account.   

This library of schedule templates is not just used for risk identification but also allows schedulers to develop schedules for new projects much quicker.

Risk analysis methods

Existing challenges in project risk simulation addressed by high-technology systems.

Project risks and uncertainties usually managed to determine:

  • Project risks that require maximum attention
  • Achievable project targets with sufficient probabilities
  • Contingency reserves to meet the project targets
  • Current probabilities of meeting these targets
  • Resources required for the reliable achievement of project targets
  • Project activities that require maximum attention

Project risk optimisation systems need to support: 

  • Activity ranging (to manage uncertainties);
  • Advanced resource assignments;
  • Schedule and risks integration;
  • Risk evaluation methods;
  • Schedule optimisation methods;

The common risk simulation method is the Monte Carlo technique that simulates project execution many times, each time with new, randomly selected initial data, based on the expected probability distributions. In my view, Monte Carlo analysis, as a good mathematical method. It gives a reliable result but has two disadvantages:


  • It is very sensitive to the quality of risk data;
  • Monte Carlo simulation has high accuracy but low precision if the number of iterations is not sufficiently large.

Alternative schedule risk analysis methods which are less sensitive to the quality of risk data and size of schedule are more practical and are easier to use. I believe in the future we still could use MC method for some type of projects but mostly as an exception, not the rule.

 If you interesting more in Risk Analysis Challenges, there is very good presentation to read:


Spider project has fully functional demo version with some limitation on a number of tasks. This version is free and is used by many education providers and not just to show the tool’s functionality but, what is more important, how to apply advanced risk methods and techniques correctly.


The Next-Gen risk management systems are also going to change portfolio and project management processes.


  • A project prioritisation process is going to be build based on organisational bottlenecks (resources, skills, processes), not just based on strategic themes and available funds.
  • Integration of different type of projects (Waterfall, Agile & Hybrid) in a single dynamic model allows includes portfolio level risks into the analyse.
  • Ability to present information from different points of views will change project analysis process.

Scheduling Role

Presenters in the podcast have also discussed whether a scheduling role will be redundant soon and how new technology could change schedulers role and responsibility.

We actually could look into the future!!! The data standardisation helps to simplify schedule development & maintenance processes. Organisations that have already implemented advanced systems in project risk management have reduced a number of schedulers but require highly analytical skills. Schedule optimisation algorithms offer the most optimal solutions to schedule analysts. Armed with such powerful systems, the analysts now play the role of “analytical centre” in the project portfolio. Their recommendations support portfolio managers with critical portfolio decisions.

Industry Standards

While many of us are expecting dramatic changes in Project Delivery in the near future, such changes are impossible without mentality shift. We are able to learn much more from the previously delivered projects but only if we start using structural project delivery approach. The good news is that we do not need to be pioneers anymore. We could learn from industries and countries which already adopted their project delivery to the new standards.

Spider Project is the most popular tool for Project Management Delivery in Construction and Oil & Gas industries in some European, Asian and South American countries. Corporate norms, volume-driven and risk-driven scheduling are absolute standards for companies in these industries now. Thousands of projects, from small renovation projects to Olympic Games portfolio applied corporate norms and risk-driven methods. This generates a lot of structural project management data, which is used for project analytics and helps to improve schedule optimisation algorithms even further.

Alex Lyaschenko

PMO | Portfolio Planning & Delivery | PMP | P3O Practitioner | AgilePM Practitioner | Six Sigma