Workforce analytics in support of the Australian Defence Force

Whilst data analytics is no longer the ‘new kid on the block’ for public organisations, it is still at the core of many initiatives to improve workforce performance and ensure workforce planning is aligned with commercial objectives.

Some organisations are more advanced in using analytics to deliver value, but that can depend on many variables – be they scope of objectives, size of organisation or nature of priorities.

For the Australian Defence Force (ADF), which constitutes the uniformed component of the Department of Defence (DoD), it happens to be all three. With more than 80,000 uniformed employees both full-time and part-time, it is vital for workforce planning to remain effective and reliable at all times.

The tri-Service Workforce Modelling directorate comprises a 15-person team with representatives from Navy, Army, Air Force and Defence civilians. This team reports to the head of People Capability within the Defence Force, and is largely responsible for the provision of data and recommendations to inform strategic HR decision-making.
However, it is a two-pronged responsibility, with an upward focus to inform decisions made by the ADF Executive; and a downward focus to inform stakeholders as well as various HR departments.

Major James Ford, Workforce Modeller within the Australian Army, has been a member of this team for the past two years. He says the supporting nature of the directorate is an important foundation to the way they deliver analytical insights.

“Influence is drawn principally by gaining stakeholder credibility and trust, so the better information we can provide to our stakeholders in a timely fashion, the more they’re willing to implicitly trust our advice when shaping key Australian Defence Force HR decisions.”

Challenges & priorities

Budget and training decisions are two key factors that determine the directorate’s outreach. For example, the uniformed component of the workforce has a salary-related budget based on forward estimates provided by Government, and the team has to monitor this budget through provision of a monthly ‘average funded strength’ report.

This reporting function occurs throughout the fiscal year, in which the directorate must track not only its current state, but also rely on predictive analysis techniques. They need to ensure the portion of the salary-related budget paid to ADF members is as close as possible yet does not exceed parameters allocated by Government.

“Average funded strength is not directly relatable to headcount. Instead, it’s more closely aligned with the number of salaries paid every fortnight averaged across a fiscal year, which includes pro-rata amounts for staff employed through irregular workplace arrangements such as part-time leave without pay,” Major Ford explains.

Several forms of predictive modelling are applied to inform HR recommendations, and these models are fed by historic workforce behaviours.

It is the directorate’s role to back-cast, by reviewing historical workforce behaviours extracted from various job families and apply these metrics to generate appropriate assumptions for future behaviours. These workforce behaviours could include separation rates, promotion rates, transfer rates, time spent at each rank level, as well as the capacity of the labour market to support hiring rates in each discrete job family.

The Workforce Modelling directorate’s reach also extends to support ADF recruiting and training functions. “Any organisation that recruits human capital must also commit to an extensive training program to yield the best results from their staff,” Major Ford comments.

Training for the majority of new staff is principally completed in-house by TAFE-like training institutions known as ADF Schools. Examples of these institutions might include the ADF School of Catering, the Army School of Transport or Basic Flight Training School.

The Workforce Modelling team supports this function by mapping out recruiting and training requirements by job families, to ensure there are minimal gaps between when a staff member is recruited, when they undergo training and when they are considered job-ready to yield capability to the ADF.

Reporting structure & metrics

The DoD uses multiple IT support tools to manage its staff. The most pervasive is a universal HR database known as PMKeyS (Personal Management Key Solutions). This database captures key employee data for all staff, whether they be ADF, APS or Defence contractors, and is available at all DoD establishments.

The Workforce Modelling directorate relies heavily on Cognos BI, which is an IBM business intelligence (BI) solution that extracts data from the master HR database. It allows the directorate to analyse past and present workforce metrics and make future predictions about likely workforce behaviours.

The directorate is also reliant on traditional Microsoft products, such as Excel to apply Markov chain methodologies to support recommendations regarding future job family structures and expected future workforce demographics.

“By employing a combination of these three key IT systems, we’re able to examine the full spectrum of workforce analytics, from past and present trends right through to predicted analytics – down to the individual job family level and often well below it,” Major Ford notes.

This spectrum is by no means fully automated; it requires a lot of human interaction and a lot of workforce modeller judgment.

“Anyone can extract raw figures from an HR database, however, the true art performed by a workforce modeller is the application of skills, experience and judgment to interpret data trends and rely on these to provide recommendations on future workforces,” he adds.

Whilst staying below the average funded strength is a priority for the ADF Workforce Modelling directorate, focus is also directed towards separation behaviors, and the depth of data granularity required extends below job family level.

At this micro level, individual skillsets are modelled to build a picture of future workforce structures in an effort to match them with predicted future asset levels. This opens an interesting discussion regarding the analysis of loss rates. There is often a perception high loss rates are negative and equate to workforce unhappiness, and conversely, low loss rates are always positive. However, the Workforce Modelling team takes a slightly different perspective.

“Each job family has an ideal loss rate, but this rate differs across the spectrum of families. Very high loss rates can be indicative of workplace dissatisfaction, compounded by the fact that the recruiting and training functions are being heavily overused or even overworked. This could influence staff members’ decisions to separate, resulting in significant workforce churn.

“But at the same time, if you have virtually no loss rates, the workforce stagnates as no one’s getting promoted, staff become disgruntled because there’s no progression and this can be just as unhealthy as high loss rates,” Major Ford remarks.

With that in mind, although his team does not directly influence these loss rates, they do have access to the data and can give recommendations to decision-makers to influence HR policies.

Monitoring and reporting upon the state of job families is a significant part of the workforce planning responsibilities for the directorate. To support the Army, the Workforce Modelling team generates an annual ‘Job Family Status Report’. This report is a non-technical Microsoft Excel based document widely published and available to all ADF members.

The report reflects upon six key areas including recruiting, training, current and future asset gaps, and provides a traffic-light classification system across all job families to reveal overall job family health, as well as identifies key areas that represent risk to generating capability.

The non-technical nature of the report echoes an important consideration to the diverse maturity of stakeholders, especially when it comes to interpreting workforce analytics. So long as such reports are structured in a way that meets their needs, engagement will remain steadfast and ensure the insights are useful.

“We can develop a snapshot of job family health with real-time data and then provide both short-term and long-term forecasts, using models on each individual category and where we believe they will head in the future,” Major Ford states.


The term ‘sustainability’ has a particular technical reference for the ADF Workforce Modelling directorate, which centres on having an effective structure upon which to build a strong current and future workforce. The ADF’s technical definition of structural sustainability refers to having a sufficient amount of lower-rank staff which, over time, can grow and promote into senior roles.

“We have to design the demand setup strategically for the longer term, as the majority of the ADF workforce must be internally grown,” Major Ford says.

The progressive nature of the modelling strategy is an exciting indicator of how effective workforce analytics can be in the context of influence. Even though it is not an executive ADF function, it has the capacity to drive decision-making based on empirical evidence, and cement the Workforce Planning directorate’s role as pivotal to long term HR success in support of ADF operations.

“What sets our directorate apart from other HR organisations is our ability to operate on a time-spectrum, ranging from historical reporting, the provision of current snap-shots right through to short and long term workforce predictions.”

I hope you found this article interesting. Major James Ford's insights are part of a new eBook put together ahead of the Workforce Planning for the Public Sector conference in September. 

Major Ford will present an in-depth case study on the Workforce Modelling strategy at the conference in September. Please download the brochure or visit to know more. 

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