Case study

Using software to enable ‘what-if’ calculations of both obstacle crossing and barrier design

Frazer-Nash and Dstl worked collaboratively to improve decision making on asset deployment by using software to enable meaningful ‘what-if’ calculations of both obstacle crossing and barrier design.

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

The challenge

Obstacle crossing and barrier design are complex operations. The time and risk associated with an obstacle crossing is dependent on many factors, such as platforms, countermeasures, field layout and breaching strategy. Commanders need to understand and minimise risk during obstacle crossings. Since the field properties may be unknown the ‘correct’ solution can’t be determined a priori and a stochastic risk based approach must be taken. How can commanders and engineers robustly answer questions like ‘what is the best use of the countermeasures I have?’, ‘what is the probability of a successful breach?’, and ‘how best to approach an unknown field?’

The solution

Frazer-Nash and Dstl have worked collaboratively to implement a software solution to improve decision making on asset deployment, by using software to encode complex logic to increase flexibility to enable meaningful ‘what-if’ calculations of both obstacle crossing and barrier design using the same tool.

 

Our approach

Collaborate

  • Frazer-Nash and Dstl worked collaboratively to understand user requirements.
  • Weekly technical catchups and monthly releases to get regular test feedback.
  • ‘Reach back’ within Frazer-Nash’s defence heritage and expertise.

Develop

  • Software developed in C# to provide a rich user experience.
  • Modular build for rapid deployment, testing, and expansion to new features.
  • Frazer-Nash build in a wide range of languages to suit the specific problem.

Analyse

  • Test cases and ‘real runs’ to get a deep understanding of the problem.
  • Understand model improvements and recommendations to Dstl.

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

NOCAM Case Study

Download Filesize: 416.3KB