Common "cooling off period" of CWs
between assignment limits.

Projected shortfall of high-skilled
workers in China by 2020.

What Norwegians earn on average
per hour per day.

Increase of robot sales globally,
the highest level ever.

% of today's occupations
that may cease to exist by the
end of the 21st century.

Our approach not only gauges workforce needs at a job title level but determines the ideal mix between internal and external resources. Besides providing cost efficiencies in hiring, increasing leverage with outside source suppliers, and reducing expensive staffing cycles, the right workforce mix synchs perfectly with the overall strategy of your organization- a blend of seasoned loyalists and leaders with on-demand staff supplementation and specialized talent. What's more, the array of talent being sourced via modern channels of contingent workforce services offers unprecedented agility to human capital management.

To help turn thinking into action, we don't generate reams of redundant information, we dive into the data you have in your HR/IT systems; such as workforce budgets, historical headcounts, worker classifications and assignments and corporate growth estimates. We also ask a few targeted questions (and we mean a few, not lengthy questionnaires or interviews) relating to your recruiting and sourcing models, contingent workforce programs and business practices. Next, we look at the availability of and demand for qualified candidates in specific, relevant markets to ensure the suggested workforce mix is both realistic and viable. Finally, we incorporate macro-economic indicators that further refine your optimal workforce composition. Because the means to an end doesn't really matter if the end doesn't help you grow.

Workforce demands are not only changing at a job level but an industry level; necessitating a change in models to the betterment of business. Read more about the new CW collaboration at the Talent Data Exchange.


Better input, better output.

Brightfield doesn't just incorporate data into workforce mix optimization models, we factor in the dynamics of the data, how quickly it changes and with what velocity. While in many cases more complex than the shown example, we find the added detail of the input leads to a more reliable output and a greater chance of success.