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16 March 2009 8:11am

Employers that gather and accurately interpret workforce data before resorting to massive job cuts can avert unnecessary workplace upheaval and save big bucks in future recruitment costs, according to Qantas workforce analytics manager, Nathan Carbone.

“For instance, an analysis of average employee resignation rates can tell employers how many people are likely to resign within the course of a year,” Carbone says.

“If this matches the number of people the organisation intends to exit, it puts forward a solid case for employers to consider letting natural attrition rates take their course without the upheaval of mass redundancies.”

Employers that act rashly and let too many workers go, he says, could find themselves recruiting within the “pay-back period” (before the cost-benefits of layoffs are realised) and battling for the very talent they’ve retrenched.

Conversely, companies that resort to “cost-cutting” alternatives to lay-offs, without first conducting a detailed analysis, could be inadvertently adding to their outlay.

For example, full-time employees offered a part-time role – as an alternative to redundancy – often end up working full-time hours anyway and, due to penalty rates, cost the company more in remuneration spend than before, he says.

Analytics versus “gut feel”
At Qantas, data is gathered and compiled into four key workforce areas, says Carbone, who will be discussing the topic at the upcoming Australasian Talent Conference.

These are:
attraction – including the number of applicants per advertised role; the number that meet the Qantas criteria; that are interviewed; that are offered positions; and that accept positions;

development and motivation – average training and development hours and spend per worker; and individual performance gains post-training;

productivity – profit relative to remuneration; expenses and profitability per individual employee and labour group; and cost of all internal elements, including in-flight meals and fuel; and

retention – exit survey results; and demographics including age, gender, labour group, work pattern and tenure.
An employee’s “entire lifecycle” – from entry to exit – is covered, assisting Qantas to make prudent decisions and accurate projections regarding its workforce, Carbone says.

“Being able to accurately interpret workforce data is crucial in the current environment,” he says.

“Workforce analytics is… helping businesses to make decisions with the backing of data rather than intuition and gut feel.”

Qantas senior managers have “seen the effect of analytics”, he says, and have learnt to “rely on the stories the data is telling them”.

The company can predict future shortages of certain skill-sets and can plan to recruit or train in-house accordingly. It can also strike a more practical balance between a part- and full-time team, and accurately determine the best times to either enforce lay-offs or hold on to staff.

Analytics drive re-focus
The power of HR analytics over “gut feel”, Carbone says, can be illustrated by an experience he had with a previous employer.

HR had hypothesised, he says, that new managers would counter their lack of experience in resource management by allowing a high rate of overtime among the staff in their departments until they established the right mix of employees per shift.

Therefore, fast-tracking the staff-resourcing learning curve of new managers would dramatically reduce the company’s overtime spend, it was argued.

“What the data analytics showed us, however, was that more experienced managers were allocating overtime as a reward for employees and scheduling overtime based on employee needs, rather than the organisation’s requirement,” Carbone says.

“Their overtime spend was actually higher than new managers’.”

This led to a change in focus, he says, away from strategies to fast-track training for new managers and instead to direct experienced managers to be more responsible in their use of overtime.


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