Omega 365 - Qualitative and Quantative Risk Management

Effective risk management starts with structure, visibility, and follow-up. In Omega 365, this has long been a core strength. Many organizations use Omega 365 as their primary platform for qualitative risk management, supported by clear processes, strong visualizations, and solid follow-up of mitigation actions. Building on this foundation, Omega 365 has recently expanded its capabilities to also include quantitative, plan-based risk analysis.
Johnny Vik
Johnny Vik

Strong and mature qualitative risk management

Based on feedback from customers and extensive use across projects and assets, Omega 365 is recognized for its strong qualitative risk management capabilities.

The solution supports structured risk and opportunity management with configurable scoring models, clear workflows, and role-based responsibilities. Risks are easy to assess, communicate, and follow up, and mitigation actions are tracked and managed as an integrated part of project execution.

Omega 365 also provides well-established visualizations and reporting, including risk matrices, trend analysis, and dashboards that give both project teams and management a clear overview of risk exposure and development over time.

AI support further strengthens this area by assisting with risk descriptions, assessments, and follow-up, helping teams work more consistently and efficiently.

Quantitative risk management based on the project plan

In addition to these established qualitative capabilities, Omega 365 now provides built-in support for quantitative risk management based on Monte Carlo simulation of the project plan.

Note that the plan may have been created directly in Omega 365, or it can also be imported from other scheduling software, such as PrimaVera og MS Project. You can read more about Link Plans in our user documentation.

This capability has been introduced recently and is available as part of the platform. The analysis is anchored directly in activities, durations, and dependencies, making it possible to model schedule uncertainty in a realistic and structured way.

Rather than relying on single-point forecasts, projects can now analyze probability distributions for completion dates and key milestones, giving decision-makers a stronger and more transparent basis for planning and prioritization.

Results of the Monte Carlo simulation

Also, the gantt chart itself indicates the risk ... indicating the span for potential finish dates of the activities:


Linking risks directly to activities

Omega 365 also supports linking risks directly to activities in the schedule.

This creates a clear connection between qualitative risk assessments and quantitative analysis. When risks are tied to specific activities, their impact is reflected directly in the Monte Carlo simulation, making it easier to understand which risks contribute most to uncertainty in the project plan.

This helps teams focus mitigation efforts where they matter most and improves communication of risk exposure.

From insight to practical project control

Quantitative risk analysis in Omega 365 is designed to complement existing qualitative practices, not replace them.

By integrating plan-based simulation with established risk registers, assessments, reporting, and follow-up of actions, risk management becomes a natural and practical part of day-to-day project control. The result is better insight that supports decisions throughout the project lifecycle.

Bow-tie analysis evolving into standard support

Bow-tie analysis is another area where Omega 365 has seen increasing interest from customers.

While this has previously been handled mainly through customer-specific configurations, Omega 365 is now evolving this into more standardized support using scope items as a foundation. This work builds on practical experience from customer projects and will further strengthen the link between qualitative understanding and quantitative analysis. Our goal is that this is included in our standard SaaS product within the next few months.

Illustration of Bow-Tie Analysis concept