The Sociotechnical and Operations Research (STOR) team is within the Theatre Operations and Analysis (TOA) group in the Joint Operations and Analysis Division (JOAD).

STOR’s mission is to enhance the performance of an operational headquarters by improving warfighting decision-making through the application of sociotechnical and operations research to achieve superior command-and-control structures and processes.

In particular they provide Science & Technology (S&T) support to Headquarters Joint Operations Command (HQJOC).

Problem

The TOA Group required an experienced operations analyst to assist with the design and implementation of analytical studies that contribute towards Australian Defence Force (ADF) operations. The analyst applied Operations Analysis (OA) qualitative and quantitative methods and approaches to address ADF operational problems and assist ADF personnel with planning, situational awareness and decision-making.

The initial primary task was to capture and implement lessons from observations made by DSTG staff embedded on support to operations tasking (for example from Operation Bushfire Assist and Operation COVID-19 Assist).

The secondary major task was to explore the potential of automation advantages in an operational headquarters through the implementation of Machine Learning (ML) to expedite processes within HQJOC.

Approach

Shoal developed and managed a Lessons Learned Framework (LLF) that enabled embedded DSTG analysts to submit observations from their tasking. The LLF consists of a central repository, observation capture tools, guidance documents, briefing materials including a pack for embedded staff, email templates, and well-defined processes. This allows the team to process simple observations which develops insights and leads to lessons being identified and learned / internalised through implementing improvements.

Shoal also conducted a market research study into ML technologies to determine what applications are available in the current timeframe and understand their potential applications for supporting the ADF. The aim was to determine the suitability of various techniques, and suggest how ML could be used, for example by HQJOC, to assist analysts in performing their work. This report was the foundation for an ongoing activity to bring together DSTG ML researchers, typically looking at the 2 to 5 year horizon, and HQJOC operators who have needs for process improvements now.

Additional assignments during this contract have involved performing thematic analysis on a variety of tasks. One example is the support to Joint Task Force (JTF) 629. A survey was designed to understand how ADF support was received by emergency services, state government departments, and other supported agencies during Operation Bushfire Assist and Operation COVID-19 Assist. Thematic analysis of the survey responses assisted the JTF 629 commander to understand how effective ADF support was, including the process to request support.

Another task involved providing analytical support to a special forces symposium. The work included thematic coding of participants’ comments and analysis of the impact of future disruptive technology capability.

Results

The LLF is available to all embedded DSTG staff to record observations from operational tasking. It is an easy to manage system to track the progress of observations through to implemented lessons.

The ML report and discussion paper has provided a sound basis to underpin the development and application of ML by DSTG researchers and Shoal, working collaboratively to improve processes for analysts and operators at HQJOC.