Case study

Using software to improve decision making on asset investment and deployment

Frazer-Nash and Dstl worked collaboratively to implement a software solution to improve decision making on asset investment and deployment.

How do we undertake optimisation in an uncertain parameter space with competing definitions of ‘success’?

The challenge

Military bridging operations are complex, and the time for a convoy to complete a route depends on the gaps encountered, as well as the number, type, and location in the convoy of the available bridges. There are finite resources for investment, meaning there is a finite pool of assets. Different assets have different attributes and therefore different impacts on route completion time. What is the ‘best’ mix of assets that the UK can invest in, bearing in mind there may be competing definitions of best, most resilient on average, most resilient for a specific route, faster on average…, etc.?

The solution

Blending UX with scientific output to enable clear decision making.

Frazer-Nash and Dstl have worked collaboratively to implement a software solution to improve decision making on asset investment and deployment, by using software to encode complex logic to reduce simulation time from one run per day to hundreds per hour to enable meaningful ‘what-if’ calculations.

Desktop and cloud based software systems provide insight and value, enabling users to make decisions in an efficient manner to further the sustainability, energy, security and transport sectors.

What was involved?

  • Requirements capture and an Agile development approach: answers the right question, at the right time. We spent six-week cycles with Dstl analysts, deploying and responding to change.
  • Background in fast and efficient algorithms to be coded in many languages: reduced turnaround time for simulations. Hundreds of runs per hour were possible with the new tool, compared to one run a day previously.
  • Logging and auditing, including ISO9001 and TickIT plus: this is repeatable and auditable. Encapsulated decisions as code and automatically wrote detailed log files.
  • Expertise in understanding Monte Carlo and its application across a range of sectors: this informed risk decisions. It offered the option to set bounding cases and run different ‘what if’ scenarios
  • Bespoke software to link to other tools and resources, including pipeline integration: this informed investment decisions. This allowed automatic aggregation of cost metrics for the impact of decisions.
  • Customisable output and visualisation: enabled sharing of knowledge to internal and external stakeholders. Novel visualisation tools and deploying our systems thinking highlighted the key answers.

Examples of implementation

Using software to improve decision making on asset investment and deployment

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