A visual framework for resolving divergent priorities
A visual framework for resolving divergent priorities
A visual framework for resolving divergent priorities
A visual framework for resolving divergent priorities

We believe that decision intelligence can lead to two types of outcomes: one that is dynamic and constantly changes as circumstances progress, and one where the goal is to make the single best decision.

We believe that decision intelligence can lead to two types of outcomes: one that is dynamic and constantly changes as circumstances progress, and one where the goal is to make the single best decision.

We believe that decision intelligence can lead to two types of outcomes: one that is dynamic and constantly changes as circumstances progress, and one where the goal is to make the single best decision.

We believe that decision intelligence can lead to two types of outcomes: one that is dynamic and constantly changes as circumstances progress, and one where the goal is to make the single best decision.

In partnership with Decision Labs, Parallel has created a tool to optimise the process of making the single best decision. This decision is dependent on multi-factorial analysis across a wide spectrum of decision-makers and subject matter experts. However, mediating multiple factors that often have contrary agendas and needs can be a challenging task.


THE APPROACH

A common challenge we see is that complex decision-making processes rely on tools that we believe are outdated for the increasingly complex world of decision-making. Working with decision theory and multi-criteria decision analysis, we designed and built a tool that allows facilitators to identify and prioritise the most important factors.



We allow facilitators to add up the criteria carefully, as not all criteria are created equally. There are two types of criteria: Constraints - to minimise negative impacts, and Opportunities - to maximise possible benefits.



The solution

We have developed an interface that enables cross-filtering to comprehend dependencies across multi-dimensional measures. The ability to dynamically reorder items and apply different weights permits important stakeholders to fine-tune the model that ultimately calculates the best decision.

Transparency enables individuals to understand priorities between different stakeholders, facilitating discussion and potentially aiding conversations around compromises.


Above: Understanding who is aligned on which specific criterion is a powerful mediator


We apply the principles of the swing weight matrix, which permits every objective to be measured independently and converted into a unified scale, both quantitatively and qualitatively.


Above: Visualising every locations score across multiple criteria 


Above: Quantitative and qualitative scales


Above: Reordering criteria


Providing a dynamic interface allows key stakeholders to understand the size of differences for each criterion, which in turn enables them to apply an overall importance score. This includes functionality to build different scenarios allows for what-if situations, where each stakeholder can instantly see the best and worst options.



Got a project in mind?

To collaborate with us, find out more about our work, or talk to us about a project you have in mind, get in touch.