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Predictive Analytics for Talent Management?

Posted By Jeff Moad, February 22, 2013 at 12:51 PM, in Category: Next-Generation Leadership and the Changing Workforce

The talent management game has changed, particularly for manufacturers in industries such as aerospace and defense who are facing a wave of retirements among current technical employees and a shortage of qualified new engineering talent. Recent reports identify the lack of STEM (science, technology, engineering, and math) trained workers as a major threat to A&D manufacturers. And, in a recent survey conducted by Deloitte and Aviation Week, manufacturers called development of a highly skilled workforce the greatest competitive issue they face.

What hasn't changed is the way many manufacturers go about recruiting and retaining technical talent. Many still rely on traditional sources of new engineering talent, key word searches on resumes, and interviews of large numbers of candidates in which the intuition of the interviewer is the primary tool applied.

Some experts, however, say manufacturers should begin to apply new tools such as predictive analytics to their STEM workforce management efforts. In a webcast this week, David Rizzo, a principal at Deloitte, said the use of predictive analytics models and tools can help manufacturers identify more qualified STEM candidates quicker, reduce recruitment costs, and boost retention rates of engineers and other technical talent already on the payroll.

Essentially Rizzo is recommending that manufacturers take a page from the playbook of retailers and insurance companies who already make extensive use of predictive analytics to understand and profit from consumer behavior. Retailers, for example, are increasingly creating detailed models, based on purchasing patterns and other data, that, combined with techniques such as multivariate regression analysis and trend forecasting, can predict the behavior of their customers. Using those models, retailers are fine-tuning product and promotion offers for individual consumers.

Rizzo suggests that manufacturers should do the same type of thing to boost STEM recruitment and retention. By building a model of the type of candidate that is likely to be successful in a given role, for example, manufacturers can use resume information and external data to effectively isolate and rank job candidates.

Similarly, manufacturers can use predictive analytics to improve STEM employee retention, Rizzo says. By building a model that includes factors most associated with employee resignations, manufacturers can spot valued employees who are likely to leave before they do so. Employers can then make efforts to keep them around by offering flexible work arrangements, or a transfer to another business unit, for example.

There's one big caveat to all of this: Before applying predictive analytics techniques to workfoce management processes, manufacturers need to establish and follow principles of sensitive, legal, and ethical use of the information these techniques use and produce, says Rizzo.

Do you see a role for predictive analytics in workforce develoment? Can this be an effective approach? Or is it a little too Big Brother?



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Written by Jeff Moad

Jeff Moad is Research Director and Executive Editor with the Manufacturing Leadership Community. He also directs the Manufacturing Leadership Awards Program. Follow our LinkedIn Groups: Manufacturing Leadership Council and Manufacturing Leadership Summit



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